From 7aab1f69ddd275a883c7226b7568cfa00faabcc2 Mon Sep 17 00:00:00 2001 From: Aniello Anzevino Date: Sat, 30 May 2026 13:05:39 +0200 Subject: [PATCH] Revisione codice per risoluzione problemi --- .gitignore | 3 +- app.py | 43 +- data/raw/pubmed_20260530_111908.xml | 21919 +++++++++++++++ data/raw/pubmed_20260530_112127.xml | 21919 +++++++++++++++ data/raw/pubmed_20260530_124620.xml | 22257 ++++++++++++++++ sources/samples/standardized_scopus_test.xlsx | Bin 0 -> 127113 bytes www/services/file_extractor.py | 182 +- www/services/format_functions.py | 1 + www/services/standardizer.py | 558 +- www/services/validation.py | 78 + 10 files changed, 66670 insertions(+), 290 deletions(-) create mode 100644 data/raw/pubmed_20260530_111908.xml create mode 100644 data/raw/pubmed_20260530_112127.xml create mode 100644 data/raw/pubmed_20260530_124620.xml create mode 100644 sources/samples/standardized_scopus_test.xlsx diff --git a/.gitignore b/.gitignore index 23b99e0..a62a19a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ __pycache__/ bibliovenv/ Bibenv/ -.idea/ \ No newline at end of file +.idea/ +.shiny-app*.log \ No newline at end of file diff --git a/app.py b/app.py index a254180..5b7f6c1 100644 --- a/app.py +++ b/app.py @@ -108,7 +108,7 @@ @functools.lru_cache(maxsize=1) def get_latest_cran_version(): try: - resp = requests.get("https://crandb.r-pkg.org/bibliometrix") + resp = requests.get("https://crandb.r-pkg.org/bibliometrix", timeout=3) if resp.status_code == 200: data = resp.json() return data.get("Version", None) @@ -587,6 +587,14 @@ def reset_all_analyses(): # Instead of complex introspection, use a simple direct approach # Only reset if variables exist to avoid unnecessary operations pass # Analysis results will be reset naturally when new data is loaded + + def prepare_dataframe_for_app(dataframe: pd.DataFrame) -> pd.DataFrame: + """Prepare standardized ETL output for Shiny analysis widgets.""" + prepared = dataframe.copy() + prepared["PY"] = pd.to_numeric(prepared["PY"], errors="coerce") + prepared = prepared.dropna(subset=["PY"]) + prepared["PY"] = prepared["PY"].astype(int) + return prepared report_choices = reactive.Value({}) report_excel = reactive.Value(io.BytesIO()) @@ -799,10 +807,7 @@ def mostra(): # Combine all standard dataframes into one standardized_df = pd.concat(all_standardized_dfs, ignore_index=True) - # Converte l'anno in numerico e rimuove le righe senza anno valido - standardized_df["PY"] = pd.to_numeric(standardized_df["PY"], errors="coerce") - standardized_df = standardized_df.dropna(subset=["PY"]) - standardized_df["PY"] = standardized_df["PY"].astype(int) + standardized_df = prepare_dataframe_for_app(standardized_df) # --- FASE LOAD: Salvataggio nel reattivo Shiny --- df.set(standardized_df) @@ -811,6 +816,29 @@ def mostra(): except Exception as e: ui.notification_show(f"❌ Errore durante l'aggregazione dei dati: {e}", type="error", duration=10) + else: + ui.notification_show("Nessun record valido trovato nei file caricati.", type="warning", duration=8) + + elif selected_action == "1B": # File gia' standardizzati + files = input.Dataset() + if files: + loaded_dfs = [] + for file_info in files: + try: + loaded = pd.read_excel(file_info["datapath"]) + loaded_dfs.append(convert2df(loaded.to_dict(orient="records"), source="WEB_OF_SCIENCE", validate=True)) + except Exception as e: + ui.notification_show(f"Errore nel caricamento di ({file_info['name']}): {e}", type="error", duration=8) + + if loaded_dfs: + try: + standardized_df = pd.concat(loaded_dfs, ignore_index=True) + standardized_df = prepare_dataframe_for_app(standardized_df) + df.set(standardized_df) + reset_all_analyses() + ui.notification_show(f"File Bibliometrix caricato: {len(standardized_df)} record.", duration=5) + except Exception as e: + ui.notification_show(f"Errore durante il caricamento: {e}", type="error", duration=10) # -------- ADVICE BUTTON -------- @render.ui @reactive.event(input.advice_modal_completeness) @@ -976,10 +1004,7 @@ def esegui_pipeline_api(): # Pass "api" as file_type since these aren't traditional physical files standardized_df = convert2df(raw_records, source=source_mapped, file_type="api", validate=True) - # Prepara il dato per i calcoli temporali della UI - standardized_df["PY"] = pd.to_numeric(standardized_df["PY"], errors="coerce") - standardized_df = standardized_df.dropna(subset=["PY"]) - standardized_df["PY"] = standardized_df["PY"].astype(int) + standardized_df = prepare_dataframe_for_app(standardized_df) # Assegna i dati al DataFrame reattivo df.set(standardized_df) diff --git a/data/raw/pubmed_20260530_111908.xml b/data/raw/pubmed_20260530_111908.xml new file mode 100644 index 0000000..24dcff4 --- /dev/null +++ b/data/raw/pubmed_20260530_111908.xml @@ -0,0 +1,21919 @@ + + + + + 42216193 + + 2026 + 05 + 29 + +
+ + 1742-4755 + + + 2026 + May + 29 + + + Reproductive health + Reprod Health + + Predicting short birth intervals in Bangladesh using stacked machine learning and SHAP explainability: evidence from BDHS 2022. + 10.1186/s12978-026-02367-0 + + Short birth interval (SBI) is a major public health concern, associated with adverse outcomes such as preterm birth, low birth weight, and maternal depletion. Identifying its influential predictors is essential for improving family planning and maternal health interventions. The study aimed to predict SBI and identify its influential predictors using stacked machine learning and SHAP explainability. + This study used data, obtained from the Bangladesh Demographic and Health Survey (BDHS), 2022. The dataset comprised 11,872 respondents, of whom 2,137 experienced SBI (18.0%). Class imbalance problem was addressed by applying class weighting during model training. Boruta and least absolute shrinkage and selection operator-based methods were applied to identify the most important predictors of SBI. Subsequently, multiple machine learning models (including logistic regression, random forest, extreme gradient boosting, categorical boosting, artificial neural network, and stacking ensemble) were used to predict SBI and evaluated their model performances using accuracy, precision, sensitivity, F1-score, and the area under the curve (AUC). Finally, SHAP explainability were employed to identify the most influential predictors of SBI. + The stacking ensemble model achieved the highest predictive performance compared to the individual models, with an accuracy of 65.8%, precision of 42.6%, sensitivity of 72.3%, F1-score of 53.6%, and modest AUC of approximately 0.667. The SHAP analysis showed that no educated respondents, higher parity, and do not intend to use contraceptive method were the most influential predictors of SBI. + Interventions could therefore focus on improving female education, expanding access to contraceptives, and promoting awareness of optimal birth spacing. Policymakers may incorporate SHAP explainability to support data-driven reproductive health strategies to reduce SBI in Bangladesh. + © 2026. The Author(s). + + + + Mia + Mostakim + M + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Akter + Shanti + S + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Sraboni + Takyeatun Nesa + TN + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Islam + Md Merajul + MM + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Tawabunnahar + Mst + M + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Maniruzamman + Md + M + + Statistics Discipline, Khulna University, Khulna, 9208, Bangladesh. + + + + Hossain + Md Golam + MG + + Health Research Group, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh. hossain95@yahoo.com. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Reprod Health + 101224380 + 1742-4755 + + IM + + Birth Spacing + Family Planning + Maternal and Child health + Model Interpretability + Predictive Modeling + + Declarations. Ethics approval and consent to participate: The survey was conducted in accordance with the Declaration of Helsinki, and the protocol for the 2022 BDHS received ethical clearance from both the ICF Institutional Review Board ethics committee and the Bangladesh Medical Research Council (BMRC). Informed written consent was obtained from each participant before commencement of the interview. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Conflict of interest: The authors declare that they have no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 10 + 14 + + + 2026 + 5 + 19 + + + 2026 + 5 + 29 + 23 + 45 + + + aheadofprint + + 42216193 + 10.1186/s12978-026-02367-0 + 10.1186/s12978-026-02367-0 + + + + + + 42216185 + + 2026 + 05 + 29 + +
+ + 1758-2946 + + + 2026 + May + 29 + + + Journal of cheminformatics + J Cheminform + + Towards the design of artificial sensing materials via quantum-informed explainable AI. + 10.1186/s13321-026-01232-3 + + Computational design of sensing materials remains fundamentally challenging due to the vast configurational landscape and the absence of robust property correlations that enable efficient molecular representation. These challenges become particularly critical in healthcare applications, where reliable and interpretable molecular recognition is essential for non-invasive diagnostics. To address these challenges, we developed MORE-ML, a quantum-informed AI framework that combines electronic-structure-derived properties of e-nose molecular building blocks with machine learning (ML) methods to uncover sensing mechanisms and guide the design of new systems. Within this framework, we expanded our previous dataset, MORE-Q, to MORE-QX by sampling a larger conformational space of interactions between body odor volatilomes (BOV) molecules and mucin-derived receptors, both in the gas phase and when deposited on graphene. MORE-QX provides extensive electronic binding features (BFs) computed upon BOV adsorption. Analysis of the property space revealed weak correlations between quantum-mechanical (QM) properties of building blocks and resulting BFs. Leveraging this observation, we defined electronic descriptors of building blocks as inputs for tree-based ML models to predict BFs. Benchmarking showed CatBoost models outperform alternatives, especially in transferability to unseen compounds. Through explainable AI, we reduced the high-dimensional QM property space to a compact and physically interpretable set of descriptors, revealing the properties that most influence BF predictions. Collectively, MORE-ML combines QM insights with ML to provide mechanistic understanding and rational design principles for artificial sensing materials in BOV sensing. This approach establishes a foundation for advancing materials capable of analyzing complex odor mixtures, bridging the gap between molecular-level computations and practical e-nose applications. + © 2026. The Author(s). + + + + Chen + Li + L + + Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany. + + + + Medrano Sandonas + Leonardo + L + + Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany. leonardo.medrano@tu-dresden.de. + + + + Huang + Shirong + S + + Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany. + + + + Croy + Alexander + A + + Institute of Physical Chemistry, Friedrich Schiller University Jena, 07737, Jena, Germany. + + + + Cuniberti + Gianaurelio + G + + Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany. gianaurelio.cuniberti@tu-dresden.de. + + + Dresden Center for Computational Materials Science (DCMS), TUD Dresden University of Technology, 01062, Dresden, Germany. gianaurelio.cuniberti@tu-dresden.de. + + + Cluster of Excellence CARE, TUD Dresden University of Technology and RWTH Aachen, Dresden, Germany. gianaurelio.cuniberti@tu-dresden.de. + + + Cluster of Excellence CeTI, TUD Dresden University of Technology, Dresden, Germany. gianaurelio.cuniberti@tu-dresden.de. + + + + eng + + + 101046369 + HORIZON EUROPE European Innovation Council + + + + RTG2767 + Deutsche Forschungsgemeinschaft + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + J Cheminform + 101516718 + 1758-2946 + + + Explainable AI + Healthcare + Molecular sensing + Quantum-informed descriptors + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 17 + + + 2026 + 5 + 21 + + + 2026 + 5 + 29 + 23 + 45 + + + aheadofprint + + 42216185 + 10.1186/s13321-026-01232-3 + 10.1186/s13321-026-01232-3 + + + + + + 42216176 + + 2026 + 05 + 29 + +
+ + 1472-6947 + + 26 + 1 + + 2026 + May + 29 + + + BMC medical informatics and decision making + BMC Med Inform Decis Mak + + Correction to: Simultaneous prediction of early and delayed mortality in burn patients: a comparative machine learning analysis of feature importance in a single-center retrospective study. + 194 + 10.1186/s12911-026-03563-5 + + + Motamedi + Mehran + M + + Nanotechnology Engineering, Babol Noshirvani University of Technology, Shariati Avenue, Babol, Mazandaran Province, 47148-71167, Iran. + + + + Moallemkolaei + Najibeh Mohseni + NM + + Health Information Management, Health Information Management Research Center, Kashan University of Medical Sciences, Ravandi Boulevard, Kashan, Isfahan Province, 87159-73449, Iran. + + + + Hesamirostami + Mohammadhossein + M + + Plastic and Reconstructive Surgery, Mazandaran University of Medical Sciences, Daneshgah Boulevard, Sari, Mazandaran Province, 48157-33971, Iran. hesami_plas@yahoo.com. + + + + Ghorbani + Mojtaba + M + + Plastic and Reconstructive Surgery, Mazandaran University of Medical Sciences, Daneshgah Boulevard, Sari, Mazandaran Province, 48157-33971, Iran. + + + + Arani + Leila Shokrizadeh + LS + + Health Information Management, Health Information Management Research Center, Kashan University of Medical Sciences, Ravandi Boulevard, Kashan, Isfahan Province, 87159-73449, Iran. + + + + eng + + Published Erratum + + + 2026 + 05 + 29 + +
+ + England + BMC Med Inform Decis Mak + 101088682 + 1472-6947 + + IM + + + BMC Med Inform Decis Mak. 2025 Dec 5;26(1):7. doi: 10.1186/s12911-025-03311-1. + 41350855 + + + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 44 + + + epublish + + 42216176 + 10.1186/s12911-026-03563-5 + 10.1186/s12911-026-03563-5 + + + + + + 42216170 + + 2026 + 05 + 29 + +
+ + 1475-9276 + + + 2026 + May + 29 + + + International journal for equity in health + Int J Equity Health + + Intersection of gender and class in the assessment of self-rated health in the Spanish population: a classification tree study. + 10.1186/s12939-026-02897-w + + This study assesses the influence of various specific health conditions as activity limitations and groups of chronic diseases, in estimating poor self-rated health for the Spanish population aged 30-60, stratified by the intersections of gender and social class. Moving beyond unidimensional social gradients, the research utilises decision tree analysis to identify specific configurations of SRH responses that align with the epidemiological profiles of distinct intersectional strata. + Data were drawn from the 2023 Spanish Health Survey (ESdE), focusing on a sample of 10,210 adults aged 30-60. This age bracket was selected to capture health inequality dynamics during the working life-course. Specific machine learning decision trees (J48 algorithm) were employed to assess the pathways for classifying individuals with poor SRH across six distinct intersectional groups. + Resulting decision trees differ significantly in composition, shape, and length across strata. A clear trend emerged: less skilled groups exhibited higher tree complexity, reflecting a more rapid accumulation of chronic illnesses at earlier ages. + This research contributes to the emerging field of study regarding what truly underlies individuals' general assessment of their health. The resulting decision trees illustrate the social gradient in health in a novel way that enriches the understanding of health inequalities. Instead of simply showing a linear trend across a one-dimensional variable, these trees reveal that as disadvantages accumulate through the intersection between gender and social class, the combination of relevant diseases do not only become more complex, but also distinct, in specific and unique ways for each group, as the intersectional framework predicts. + © 2026. The Author(s). + + + + Gumà-Lao + Jordi + J + + Centre for Demographic Studies, Barcelona, Spain. jguma@ced.uab.es. + + + + Barnils + Núria Pedrós + NP + + Institute for Public Health and Nursing Research, University of Bremen, Bremen, Germany. + + + Department of Epidemiology and Global Health, Umeå University, Umeå, Sweden. + + + + Parra-Casado + Daniel La + D + + Department of Sociology II, University of Alicante, Alicante, Spain. + + + + eng + + + Ramón y Cajal program (RYC2022-037781-I) + Agencia Estatal de Investigación + + + + "Generación de Conocimiento" Project [PID2022-141543OB-I00] + Agencia Estatal de Investigación + + + + 535865195 + Deutsche Forschungsgemenischaft + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Int J Equity Health + 101147692 + 1475-9276 + + IM + + Health inequity + Intersectional framework + Machine learning + Self-rated health + + Declarations. Human ethics and consent to participate: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 3 + + + 2026 + 5 + 14 + + + 2026 + 5 + 29 + 23 + 44 + + + aheadofprint + + 42216170 + 10.1186/s12939-026-02897-w + 10.1186/s12939-026-02897-w + + + + + + 42216166 + + 2026 + 05 + 29 + +
+ + 1471-2474 + + + 2026 + May + 29 + + + BMC musculoskeletal disorders + BMC Musculoskelet Disord + + Optimizing preoperative planning for total hip arthroplasty using random forest models to predict stem size and compatibility. + 10.1186/s12891-026-10022-9 + + Preoperative planning is essential for optimal outcomes post-total hip arthroplasty (THA). Appropriate stem size and compatibility with femoral dimensions are essential in THA to prevent distal fixation. Previous studies have proposed deep learning-based models with image recognition for preoperative planning to determine the best-fit stem; however, whether size and compatibility are adequately optimized remains unclear. This study developed methods that may assist surgeons in preoperative planning to estimate the stem size and compatibility using supervised machine learning models. + Two Random Forest (RF) models were developed to estimate the best-fit stem size and stem compatibility with the femoral geometry. Femoral size information measured at 10 locations from computed tomography images of 320 hips was used to train the models. The training data included the sizes and compatibility information as the target variables for supervised learning provided by a simulation software currently used in clinical practice to determine the optimal stem. As part of feature engineering, ratios derived from combinations of the 10 measured values were used as learning data. The size estimation model was designed as a seven-class classification model, whereas the compatibility estimation model was a binary classification model, predicting whether the stem will exclusively fit distally or not. Both models were tested using data from 109 hips of patients who underwent THA. The model performances were assessed using accuracy, F1 score, precision, and recall. Each parameter's impact was determined using feature importance analysis. + The size and compatibility estimation models trained without ratio information showed accuracies of 89.0% (exact-match: 46.8%) and 85.3%, respectively. When it was included, the models' performance improved, with the size and compatibility estimation accuracies increasing to 92.7% (exact-match: 39.4%) and 87.2%, respectively. Feature importance analysis highlighted that distal medullary cavity diameter is key in size estimation, while overall femoral dimensions are vital for compatibility estimation. + This study developed models that estimate the two critical factors in stem selection: size and compatibility. These models may support preoperative planning for THA using the Accolade II stem, although further validation is required to establish applicability to other implant systems. + © 2026. The Author(s). + + + + Kaneoka + Takehiro + T + 0000-0002-2742-4183 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Imagama + Takashi + T + 0000-0001-8509-1850 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Okazaki + Tomoya + T + 0000-0002-6635-3541 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Matsuki + Yuta + Y + 0000-0002-0664-4136 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Kawakami + Takehiro + T + 0000-0001-6363-560X + + Department of Orthopaedic surgery, Yamaguchi Prefectural Grand Medical Centre, Hofu, Yamaguchi, JP, Japan. + + + + Yamazaki + Kazuhiro + K + 0000-0002-1627-7368 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Sasaki + Kei + K + 0009-0003-8535-1589 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Asai + Yoshiyuki + Y + 0000-0001-5519-4306 + + Department of Systems Bioinformatics, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan. + + + + Sakai + Takashi + T + 0000-0001-6367-1299 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. cozy@yamaguchi-u.ac.jp. + + + + eng + + + JP22he0122010 + Japan Agency for Medical Research and Development (AMED) + + + + JP 23K10426 + JSPS KAKENHI + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Musculoskelet Disord + 100968565 + 1471-2474 + + IM + + femoral dimensions + machine learning + preoperative planning + random forest + total hip arthroplasty + + Declarations. Ethics approval and consent to participate: This retrospective study’s protocol was approved by the by the Ethics Committee of Yamaguchi University Graduate School of Medicine (approval number: H2021-166-2), and informed consent was obtained from all participants. The ethical standards from the 1964 Helsinki declaration and its later amendments were upheld. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2024 + 12 + 4 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 44 + + + aheadofprint + + 42216166 + 10.1186/s12891-026-10022-9 + 10.1186/s12891-026-10022-9 + + + + + + 42216115 + + 2026 + 05 + 29 + +
+ + 1471-2393 + + + 2026 + May + 30 + + + BMC pregnancy and childbirth + BMC Pregnancy Childbirth + + Predicting facility delivery and its determinants among reproductive-age women in Ethiopia using machine learning algorithm: evidence from performance monitoring for action Ethiopia 2019 dataset. + 10.1186/s12884-026-09354-0 + + Facility delivery attended by skilled health professionals is a critical strategy for reducing maternal and neonatal mortality. Despite substantial efforts to expand maternal health services in Ethiopia, a large proportion of births still occur at home. This study aimed to predict facility delivery and identify its associated factors among reproductive-age women in Ethiopia using machine learning approaches. + This study used secondary data from the 2019 Performance Monitoring for Action (PMA) Ethiopia cross-sectional household and female survey. A weighted sample of 5,413 women aged 15-49 years who had a recent birth was included in the analysis. Data extraction and preprocessing were conducted using STATA version 17, while machine learning models were implemented using Python version 3.11.5. Seven supervised machine learning algorithms were developed to predict facility delivery. Model performance was evaluated using accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC). Data preparation included feature engineering, data splitting, handling missing values, addressing class imbalance, and outlier detection. SHapley Additive exPlanations (SHAP) were applied to interpret the contribution of important predictors to model predictions. + Among the evaluated models, the Random Forest algorithm demonstrated the best predictive performance, achieving 74.15% accuracy, 72.58% recall, 76.27% F1-score, and 80.36% precision on test data. The most influential predictors of facility delivery included higher household wealth status, secondary or higher maternal education, television ownership, discussion of delivery location with a partner, partner encouragement for antenatal care attendance, family planning use, maternal age between 20 and 34 years, age at first sexual intercourse of 19 years or older, awareness of emergency contacts, and completion of primary education. + Interpretable machine learning approaches can provide useful insights for identifying factors associated with facility delivery. The findings highlight key socioeconomic and behavioral characteristics that may inform targeted strategies to improve utilization of facility-based delivery services and support efforts to enhance maternal health outcomes in Ethiopia. + © 2026. The Author(s). + + + + Degefa + Siraj Muhidin + SM + + Department of Health Informatics, College of Health Science, Mattu University, Mettu, Ethiopia. sirajmuhidin6@gmail.com. + + + + Hailu + Beriso Alemu + BA + + Department of Health Informatics, College of Health Science, Mattu University, Mettu, Ethiopia. + + + + Serbessa + Naol Gonfa + NG + + Department of Health Informatics, College of Health Science, Mattu University, Mettu, Ethiopia. + + + + Tsehay + Asmamaw Ketemaw + AK + + School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia. + + + + Sedi + Tigist Tollessa + TT + + Department of Health Informatics, College of Medicine and Health Sciences, Arbaminch University, Arbaminch, Ethiopia. + + + + Woldekidan + Eden Ketema + EK + + Department of Health Informatics, College of Medicine and Health Sciences, Arbaminch University, Arbaminch, Ethiopia. + + + + Ahmed + Mohammedjud Hassen + MH + + School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia. + + + + Adem + Jibril Bashir + JB + + Department of Public Health, College of medicine and Health sciences, Arsi University, Asella, Ethiopia. + + + + Wale + Agmasie Damtew + AD + + Department of Health Informatics, School of Public Health, Asrat Woldeyes Health Science Campus, Debre Berhan University, Debre Berhan, Ethiopia. + + + + Guadie + Habtamu Alganeh + HA + + School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia. + + + + Asemahagn + Mulusew Andualem + MA + + School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia. + + + + eng + + Journal Article + + + 2026 + 05 + 30 + +
+ + England + BMC Pregnancy Childbirth + 100967799 + 1471-2393 + + IM + + Determinants + Ethiopia + Facility delivery + Women + + Declarations. Ethics approval and consent to participate: For this study, secondary anonymized data were used, and permission to access the dataset was obtained from the Performance Monitoring for Action (PMA) data repository through the official PMA data access portal: PMA Dataset Access Portal. Ethical clearance for this secondary data analysis was obtained from the Institutional Review Board (IRB) of Bahir Dar University, College of Medicine and Health Sciences. The study was conducted in accordance with the Declaration of Helsinki. Since the dataset was fully anonymized and publicly available for research purposes, informed consent from participants was not required for this secondary analysis. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2024 + 11 + 7 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 42 + + + aheadofprint + + 42216115 + 10.1186/s12884-026-09354-0 + 10.1186/s12884-026-09354-0 + + + + + + 42216109 + + 2026 + 05 + 29 + +
+ + 2045-3701 + + + 2026 + May + 29 + + + Cell & bioscience + Cell Biosci + + Decoding the role of H19 in cholestatic liver injury using snRNA-seq, spatial transcriptomics, and machine learning-based disease prediction. + 10.1186/s13578-026-01590-3 + + Despite recent advances, Primary Sclerosing Cholangitis (PSC)-a chronic obstructive biliary disease-still lacks effective therapies to prevent disease progression or the need for liver transplantation. Moreover, up to 30% of transplant recipients experience recurrence. Long non-coding RNA H19 (H19) has been implicated in promoting PSC progression, yet the cellular and molecular mechanisms underlying its pathogenic role remain incompletely understood. + Liver tissues from age- and sex-matched wild type (WT), H19 knockout (H19KO), Mdr2 knockout (Mdr2KO), and double-knockout (DKO; Mdr2KO/H19KO) mice were analyzed using single-nucleus RNA sequencing (snRNAseq) and GeoMx spatial transcriptomics to define the cell type and spatially specific effects of H19 deletion in cholestatic liver injury. Machine learning models were built to develop cell-type-specific gene prediction signatures and cross-validated using the human dataset GSE243981. A disease-associated cholangiocyte subcluster that increased in Mdr2KO but was markedly reduced in DKO mice was identified. SPP1 signaling was significantly dysregulated in cholestatic liver injury and mitigated following H19 deletion. Translationally conserved healthy (Clu, Spp1) and diseased (Csmd1, Slco3a1, Cftr) cholangiocyte markers were identified. When validated in a human patient dataset (GSE243981), our machine-learning prediction models achieved AUC values > 0.869. Finally, spatial analyses demonstrated that the mitigation of disease-associated gene expression following H19 deletion was specifically restricted to hepatocytes within the bile duct region. + H19 deletion mitigates cholestatic injury by suppressing pathogenic cholangiocyte states, normalizing Spp1-mediated signaling, and shifting transcriptional programs specifically within the periductal niche. Furthermore, our machine-learning signatures demonstrate robust cross-species translation and may benefit future post-transplant analytics and disease recurrence predictions. + © 2026. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. + + + + Way + Grayson W + GW + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Jiang + Xixian + X + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Lu + Hongkun + H + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Wu + Nan + N + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Zhao + Derrick + D + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Tai + Yun-Ling + YL + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Bayatpour + Sareh + S + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Wang + Xuan + X + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Zhou + Huiping + H + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. huiping.zhou@vcuhealth.org. + + + Richmond Veterans Affairs Medical Center, Richmond, VA, 23249, USA. huiping.zhou@vcuhealth.org. + + + + eng + + + 5T32AA029975 + GF + NIH HHS + United States + + + DK115377, 2R56DK115377-05A1, 5R01AA030180, R01DK139587, NIH-NCI P01CA275740 + GF + NIH HHS + United States + + + 5I01BX005730,1 IS1 BX004777-01 and 1IS1BX005517-01, IK6BX004477 + U.S. Department of Veterans Affairs + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Cell Biosci + 101561195 + 2045-3701 + + + Cholestasis + H19 + Long non-coding RNA + Machine learning + Primary sclerosing cholangitis + SPP1 + + Declarations. Conflict of interest: Huiping Zhou is the corresponding author and is also an Editor for the journal. Ethics approval and consent to participate: All animal experiments were performed following institutional guidelines for ethical animal studies and approved by the Virginia Commonwealth University and Richmond VA Medical Center Institutional Animal Care and Use Committee, Virginia, USA. All patient samples used followed Richmond VA Medical Center Institutional Review Board guidelines for de-identified consenting patients. Consent for publication: All authors have given their consent for publication. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 12 + 11 + + + 2026 + 5 + 7 + + + 2026 + 5 + 29 + 23 + 40 + + + aheadofprint + + 42216109 + 10.1186/s13578-026-01590-3 + 10.1186/s13578-026-01590-3 + + + + + + 42216097 + + 2026 + 05 + 29 + +
+ + 1758-9193 + + + 2026 + May + 29 + + + Alzheimer's research & therapy + Alzheimers Res Ther + + EEG biomarkers can predict early-stage Alzheimer's disease and correlate with intracerebral pathology: a multimodal machine learning study. + 10.1186/s13195-026-02096-3 + + Early recognition of Alzheimer's disease (AD) is crucial for timely intervention and delaying disease progression. Electroencephalogram (EEG) technology provides a direct reflection of the brain's dynamic activity. However, the relationship between potential EEG features and cognitive function in early-stage AD patients, as well as cerebrospinal fluid (CSF) pathological biomarkers, remains unclear. + This study included 101 patients with mild cognitive impairment (MCI) and mild AD, alongside 69 healthy controls (HC) matched for gender, age, and educational attainment. Extracting EEG power spectral density (PSD) and microstates analysis features as training features for machine learning (ML), we employed five ML algorithms-Support Vector Machines (SVM), Logistic Regression (LR), Random Forests (RF), XGBoost, and LightGBM-for training and testing. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). SHapley Additive exPlanations (SHAP) plots were employed to elucidate variable importance within the model, and sequential forward selection (SFS) was utilised to identify potential features. Correlation analysis and mediation analysis were conducted to investigate the relationships between EEG features, CSF pathological biomarkers, and cognitive function. + The LR model demonstrated the highest average predictive performance in the training set (mean AUC = 0.859 ± 0.059). The model incorporating PSD and microstates features demonstrated optimal predictive performance in the test set (AUC = 0.949, 95% CI: 0.877-1.000), outperforming any single-feature model. Based on SHAP and SFS analyses, six potential EEG indicators were identified: central region delta frequency band, central region theta frequency band, temporal region beta frequency band, microstate mean duration, microstate C duration, and the transition probability from microstate C to A. Mediation analysis revealed a significant negative correlation between central region theta frequency band and CSF Aβ₁₋₄₂ levels (r = - 0.31, p = 0.015), and the central region theta frequency band mediated the relationship between Aβ₁₋₄₂ levels and Mini-Mental State Examination (MMSE) scores (indirect effect = 0.0007, 95% CI: 0.0001-0.0013). + The combined application of EEG and ML enables efficient classification diagnosis of early-stage AD, and EEG is correlated with intracerebral pathological biomarkers and cognitive impairment. + © 2026. The Author(s). + + + + Geng + Zhi + Z + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Song + Wenqian + W + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Gao + Chen + C + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Zhu + Yun + Y + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Wu + Yue + Y + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + Department of Sleep Psychology, The Second Hospital of Anhui Medical University, Anhui Medical University, Hefei, China. + + + + Song + Bo + B + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Guo + Haihua + H + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Wu + Jiayun + J + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Fang + Miao + M + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Yan + Yibing + Y + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Zhou + Shanshan + S + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Hu + Panpan + P + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Tian + Yanghua + Y + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Wu + Xingqi + X + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. wuxq@fy.ahmu.edu.cn. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. wuxq@fy.ahmu.edu.cn. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. wuxq@fy.ahmu.edu.cn. + + + + Wang + Kai + K + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. kwang@ahmu.edu.cn. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. kwang@ahmu.edu.cn. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. kwang@ahmu.edu.cn. + + + Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, China. kwang@ahmu.edu.cn. + + + + Wei + Ling + L + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. weil@fy.ahmu.edu.cn. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. weil@fy.ahmu.edu.cn. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. weil@fy.ahmu.edu.cn. + + + + eng + + + 82101498 + National Natural Science Foundation of China + + + + 82371201 + National Natural Science Foundation of China + + + + 82371201 + National Natural Science Foundation of China + + + + 2021kj19 + The 2021 Youth Foundation training program of the First Affliated Hospital of Anhui Medical University + + + + 202204295107020006 + The Anhui Province Clinical Medical ResearchTransformation Special Project + + + + XZZR202402048 + The Natural Science Foundation of theXizang Autonomous Region Group Medical Aid Project to Xizang + + + + 2022zhyx-B11 + Research Fund of Anhui Institute of Translational Medicine + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Alzheimers Res Ther + 101511643 + + IM + + Alzheimer’s disease + CSF biomarkers + EEG microstates + EEG power spectral density + Machine learning + + Declarations. Ethics approval and consent to participate: The studies involving human participants were reviewed and approved by the Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University. The patients/participants provided their written informed consent to participate in this study. Consent for publication: All participants provided consent for the publication of this manuscript. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 12 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 23 + 40 + + + aheadofprint + + 42216097 + 10.1186/s13195-026-02096-3 + 10.1186/s13195-026-02096-3 + + + + + + 42216034 + + 2026 + 05 + 29 + +
+ + 1475-2867 + + + 2026 + May + 29 + + + Cancer cell international + Cancer Cell Int + + DNA damage response signature-based prognostic genes for intrahepatic cholangiocarcinoma: a combined analysis of machine learning and biological experiments. + 10.1186/s12935-026-04355-7 + + Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive subtype of primary liver cancer with insidious onset, early metastasis, and poor prognosis. DNA damage response (DDR) dysfunction is linked to ICC tumorigenesis, progression, and drug resistance. However, the detailed mechanisms remain underexplored. + DDR activity was quantified by ssGSEA across 6 independent ICC cohorts. DDR risk model was analyzed using 10 machine learning algorithms to construct 101 combined models, and the optimal Lasso + RSF model was adopted by c-index. The core gene SFN was selected for experimental validation. Single‑cell RNA sequencing and spatial transcriptomics were used to explore cellular localization and tissue distribution of signature genes. In vitro assays were performed in ICC cell lines after SFN knockdown to assess cell proliferation and chemosensitivity. + The DDR risk model showed stable prognostic performance in training and external validation cohorts. High‑risk patients exhibited poor prognosis, immunosuppressive microenvironment and reduced sensitivity to multiple chemotherapeutic agents. SFN was specifically enriched in malignant cells. Knockdown of SFN significantly suppressed cell proliferation and colony formation, enhanced sensitivity to cisplatin and gemcitabine, and increased γ‑H2AX expression indicating elevated DNA double‑strand breaks. + We constructed a robust DDR‑based prognostic model for ICC that enables accurate risk stratification and prediction of therapeutic response. SFN serves as a critical driver of chemoresistance and a potential therapeutic target. These results offer novel biomarkers and mechanistic insights to facilitate precision medicine for ICC patients. + © 2026. The Author(s). + + + + Lu + Kai + K + + Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Li + Hanqi + H + + Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Wu + Yinying + Y + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Dong + Xuyuan + X + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Dong + Danfeng + D + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Fan + Yangwei + Y + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Li + Enxiao + E + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Sun + Liankang + L + + Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. sunliankang@xjtu.edu.cn. + + + + Shi + Yu + Y + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. shiyu@xjtu.edu.cn. + + + + eng + + + 2022JQ-846 + Natural Science Basic Research Program of Shaanxi + + + + 2021QN-01,2022QN-15 + Research Foundation of the First Affiliated Hospital of Xi'an Jiaotong University + + + + xzy012022103 + Fundamental Research Funds for the Central Universities + + + + 82303663 + National Natural Science Foundation of China + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Cancer Cell Int + 101139795 + 1475-2867 + + + DNA damage response + Experimental validation + Intrahepatic cholangiocarcinoma + Machine learning + Prognostic markers + + Declarations. Ethics approval and consent to participate: All data utilized in this study were retrieved from publicly available online databases, as specified in the manuscript. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 8 + 3 + + + 2026 + 5 + 23 + + + 2026 + 5 + 29 + 23 + 38 + + + aheadofprint + + 42216034 + 10.1186/s12935-026-04355-7 + 10.1186/s12935-026-04355-7 + + + + + + 42216026 + + 2026 + 05 + 29 + +
+ + 1757-6512 + + + 2026 + May + 29 + + + Stem cell research & therapy + Stem Cell Res Ther + + KRT6A derived from mesenchymal stem cells as a potential biomarker and therapeutic target for alopecia areata: insights from multi-omics analysis and experimental evidence. + 10.1186/s13287-026-05077-3 + + Mesenchymal stem cells (MSCs) secretome have shown promise in the treatment of alopecia areata (AA). However, the key therapeutic genes remain unclear. This study aimed to identify potential critical therapeutic molecules using multi-omics approaches. + MSCs extracellular vesicles related genes was retrieved from ExoCarta database, and which integrated with transcriptomic and proteomic sequencing data from AA for cross-analysis to identify AA-related therapeutic genes. Mfuzz clustering, PPI networks, and machine learning algorithms were employed to potential key therapeutic factors. Functional mechanisms were explored through gene expression analysis, immune infiltration, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics. AA mice were used for experimental validation. + We identified 146 MSCs-derived genes with potential therapeutic relevance for AA. Enrichment analysis suggests that they are involved in skin development and differentiation. Through Mfuzz clustering, 13 machine learning algorithms, and combined with SHAP interpretability analysis, the KRT6A was we further identified as the most critical candidate gene. Validation analyses demonstrated that KRT6A serves as a robust diagnostic marker for AA (AUC = 0.989), a predictor of disease severity (AUC = 0.769), and a protective factor (HR = 0.52, 95%CI: 0.34-0.79). Immune infiltration analysis revealed severe immune dysregulation in AA, and KRT6A was significantly negative correlated with dysregulated immune cells. By exploring the expression source of KRT6A, we found that it was mainly enriched in skin hair follicle tissues, and single-cell subset analysis and spatial transcriptomics further mapped KRT6A to hair follicle stem-like cells from the cellular level, yet KRT6A was significantly downregulated in AA models. Primary human hair follicle mesenchymal stem cells (HFMSCs) further confirmed KRT6A localization. Furthermore, KRT6A-overexpressing HFMSCs exhibited enhanced migration, whereas KRT6A knockdown reduced migration and suppressed WNT pathway activation. Conditioned medium from KRT6A-overexpressing HFMSCs effectively alleviated alopecia and reduced apoptosis in AA mice, whereas KRT6A knockdown reversed these effects. + MSCs-derived KRT6A may serve as a potentia biomarker and therapeutic target for AA, which provide novel insights into AA pathogenesis and may help the development of targeted therapeutic strategies. + © 2026. The Author(s). + + + + Peng + Shixiong + S + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. + + + + Deng + Jia + J + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. + + + + Meng + Xiuming + X + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. + + + + Deng + Shuangjie + S + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. + + + + Yan + Wenjie + W + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. ywj716@glmu.edu.cn. + + + + Huang + Xi + X + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. professorhuangxi@163.com. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Stem Cell Res Ther + 101527581 + 1757-6512 + + IM + + Alopecia areata + KRT6A + Machine learning + Mesenchymal stem cells + Multi-omics + + Declarations. Ethics approval and consent to participate: The animal research, entitled “KRT6A from Mesenchymal Stem Cells Exosomes as a Potential Biomarker and Therapeutic Target for Alopecia Areata: Insights from Multi-omics Analysis and Experimental Evidence”, was approved by the Animal Ethics Committee of Guilin Medical University (GLMU-IACUC-20251084) on August 13, 2025, and was performed in accordance with the ARRIVE 2.0 guidelines. The human-related research in this study, which was part of the project entitled “Exosome Multimodal Analysis Reveals the Mechanism by Which Mesenchymal Stem Cell Exosomes Regulate Hair Follicle Regeneration”, was approved by the Ethics Committee of the Affiliated Hospital of Guilin Medical University on February 18, 2025 (No. 2025QTLL-10), and was conducted in compliance with the principles of the Declaration of Helsinki. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 10 + 11 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 37 + + + aheadofprint + + 42216026 + 10.1186/s13287-026-05077-3 + 10.1186/s13287-026-05077-3 + + + + + + 42216024 + + 2026 + 05 + 29 + +
+ + 2050-6511 + + + 2026 + May + 29 + + + BMC pharmacology & toxicology + BMC Pharmacol Toxicol + + Network toxicology and bioinformatics reveal potential molecular links between cadmium exposure and pancreatic cancer. + 10.1186/s40360-026-01156-6 + + Environmental cadmium (Cd) pollution poses a severe threat to human health due to its strong bioaccumulation and high carcinogenicity. Although Cd exposure has been linked to various cancers, its specific role in pancreatic cancer (PC) remains unclear. This study aimed to explore the molecular mechanisms underlying Cd-associated PC and identify potential diagnostic and therapeutic targets. + Transcriptomic data of PC were obtained from the GEO database to screen differentially expressed genes (DEGs), which were then intersected with Cd-related genes retrieved from the Comparative Toxicogenomics Database (CTD). Functional enrichment analyses, including GO and KEGG, were conducted to identify key biological processes and pathways. Multiple machine learning algorithms were used to construct a diagnostic model and identify hub genes. Furthermore, molecular docking and molecular dynamics simulations were performed to evaluate drug-target interactions, and in vitro experiments were used to validate the effects of Cd exposure on PC cell proliferation and invasiveness. + A total of five key genes-FN1, COL5A1, AHR, COL3A1, and TIMP1-were identified as core regulators of Cd-associated PC. Functional analyses indicated that Cd primarily affects the extracellular matrix (ECM) metabolic and collagen remodeling pathways. Experimental results confirmed that Cd exposure significantly enhanced the proliferation and invasiveness of PC cells. Molecular docking and dynamics simulations revealed that the EGFR inhibitor dacomitinib exhibited favorable predicted binding affinity to FN1, suggesting its potential as a therapeutic agent. + This study reveals that Cd may promote the malignant progression of PC by regulating ECM remodeling through key genes such as FN1. Dacomitinib shows promise as an FN1-associated therapy, offering new insights for the precise prevention and treatment of Cd-associated PC. + © 2026. The Author(s). + + + + Liu + Shuyuan + S + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. + + + + Lu + Xinyan + X + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + + Li + Desheng + D + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + + Ma + Jingyuan + J + + The First Clinical Medical College, Liaoning University of Traditional Chinese Medicine, Shenyang, China. + + + + Zhang + Yunshu + Y + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. + + + + Li + Junchen + J + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. + + + + Wan + Xing + X + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. wanx03@dmu.edu.cn. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. wanx03@dmu.edu.cn. + + + + Shang + Dong + D + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. shangdong@dmu.edu.cn. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. shangdong@dmu.edu.cn. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. shangdong@dmu.edu.cn. + + + + Zhang + Qingkai + Q + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. zhangqk@dmu.edu.cn. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. zhangqk@dmu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Pharmacol Toxicol + 101590449 + 2050-6511 + + IM + + Cadmium + Machine learning + Molecular dynamic simulation + Network toxicology + Pancreatic cancer + + Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 11 + 11 + + + 2026 + 5 + 20 + + + 2026 + 5 + 29 + 23 + 37 + + + aheadofprint + + 42216024 + 10.1186/s40360-026-01156-6 + 10.1186/s40360-026-01156-6 + + + + + + 42216013 + + 2026 + 05 + 29 + +
+ + 1749-7922 + + + 2026 + May + 29 + + + World journal of emergency surgery : WJES + World J Emerg Surg + + Machine learning models for early mortality prediction in trauma patients using public data: a nationwide retrospective study. + 10.1186/s13017-026-00706-x + + Trauma is a leading cause of morbidity and mortality worldwide, particularly in younger populations. Early identification of high-risk trauma patients is critical for timely interventions and improved outcomes. Although artificial intelligence and machine learning have demonstrated promise in healthcare, their application in trauma mortality prediction has been limited. + This study developed and validated machine learning models to predict mortality in trauma patients using a large public dataset from the National Community-Based Critical Injury Survey (South Korea, 2016-2020). Overall, 207,012 cases were analyzed. Six machine learning algorithms, including logistic regression, k-nearest neighbor, decision tree, random forest (RF), extreme gradient boosting (XGB), and multi-layer perceptron, were trained and evaluated. Their performance was assessed using the areas under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC), and other metrics. Shapley additive explanations (SHAP) scores were used to interpret feature importance. + The XGB model demonstrated the highest performance (AUROC 0.985; AUPRC 0.957), followed closely by the RF model (AUROC 0.984; AUPRC 0.956). Performance remained stable during the COVID-19 period, supporting the model's temporal robustness under systemic disruption. SHAP analysis identified clinically actionable features such as out-of-hospital cardiac arrest, injury severity score, age, and time to transfusion. Unlike many prior studies based on small or single-center datasets, our model was developed using a nationally representative cohort and prioritized interpretability, scalability, and generalizability. + This study presents a high-performing, interpretable machine learning framework for early mortality risk stratification in trauma patients using nationwide registry data. The strong discrimination and temporal robustness of the model support its value as a system-level prediction tool; however, further calibration analyses, external validation, and prospective implementation studies are required before integration into clinical workflows. + © 2026. The Author(s). + + + + Baik + Seung Min + SM + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Lee + Jae Gil + JG + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Shim + Hongjin + H + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Kim + Sujung + S + + Division of Acute Care Surgery, Department of Surgery, Korea University Anam Hospital, Korea University College of Medicine, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Republic of Korea. + + + + Hong + Kyung Sook + KS + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Yi + Heejung + H + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Lee + Kyung Hyun + KH + + Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. + + + + Lee + Jae-Myeong + JM + + Division of Acute Care Surgery, Department of Surgery, Korea University Anam Hospital, Korea University College of Medicine, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Republic of Korea. ljm3225@hanmail.net. + + + + eng + + + RS-2023-00240766 + National Research Foundation of Korea + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + World J Emerg Surg + 101266603 + 1749-7922 + + IM + + Artificial intelligence + Clinical decision-making + Emergency medical services + Injury severity score + + Declarations. Ethics approval and consent to participate: The Ewha Womans University Mokdong Hospital Institutional Review Board deemed this study exempt from ethics review and waived the informed consent requirement. Consent for publication: Not applicable. Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 10 + 20 + + + 2026 + 5 + 23 + + + 2026 + 5 + 29 + 23 + 37 + + + aheadofprint + + 42216013 + 10.1186/s13017-026-00706-x + 10.1186/s13017-026-00706-x + + + + + + 42215973 + + 2026 + 05 + 29 + +
+ + 1472-6963 + + + 2026 + May + 29 + + + BMC health services research + BMC Health Serv Res + + Why are rural hospitals closing in the U.S.? Predictors identified using explainable machine learning. + 10.1186/s12913-026-14810-w + + Rural hospital closures in the U.S. reduce access to essential healthcare services and worsen health and economic outcomes in rural communities. This study uses national longitudinal data and explainable machine learning (XML) to identify and interpret risk factors of rural hospital closures. + We conducted a retrospective longitudinal study of U.S. rural hospitals using national datasets from 2011 to 2022, integrating hospital-level financial, operational, policy, population, and community data. Longitudinal feature engineering and statistical summarization were performed, class imbalance and multicollinearity were addressed using random undersampling and variance inflation factor (VIF) analysis, and an XGBoost classifier was trained using stratified three-fold cross-validation and Optuna for hyperparameter tuning. Model explainability was assessed using SHapley Additive exPlanations (SHAP). + Among 2,683 rural hospitals, 139 (5.2%) closed during the study period. The model achieved a recall of 86%, accuracy of 75%, and an AUC of 89%. The key variables linked to closure risk are sustained financial weakness and volatility (e.g., total margin, variability in return on equity, and current ratio), operational instability (low patient volume and variability in bed-days available), policy context (e.g., years since Medicaid expansion), and community context, including sociodemographic characteristics of the service area (e.g., proportion of Black or African American residents), which may reflect underlying structural and socioeconomic conditions rather than direct predictors or causal drivers of closure. + These findings indicate that the risk of closure of rural hospitals reflects accumulated long-term financial and operational instability, as well as policy and community context, rather than short-term distress, highlighting the value of longitudinal and explainable modeling approaches. + Longitudinal data integrated into XML provides a practical approach for early warning systems that enable proactive monitoring and timely interventions, particularly for hospitals serving historically underserved communities. + Not applicable. + © 2026. The Author(s). + + + + Balakrishnan + Kiruthika + K + + National Center for Rural Health Professions, University of Illinois College of Medicine Rockford, 1601 Parkview Avenue, Rockford, IL, 61107, USA. bkiruthi@uic.edu. + + + AI.Health4All Center, University of Illinois Chicago, 1747 West Roosevelt Road, Chicago, IL, 60608, USA. bkiruthi@uic.edu. + + + + Abuhay + Tesfamariam M + TM + + Department of Medicine, University of Illinois Chicago, 840 S. Wood St., Chicago, IL, 60612, USA. + + + AI.Health4All Center, University of Illinois Chicago, 1747 West Roosevelt Road, Chicago, IL, 60608, USA. + + + + Hinkle + Hana E + HE + + National Center for Rural Health Professions, University of Illinois College of Medicine Rockford, 1601 Parkview Avenue, Rockford, IL, 61107, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Health Serv Res + 101088677 + 1472-6963 + + IM + + Early warning systems + Explainable machine learning + Hospital closure risk factors + Longitudinally derived features + Rural hospital closures + + Declarations. Ethics consent to participate: This study is a retrospective analysis of publicly available data and published literature and did not involve human participants, identifiable private information, or the collection of new data requiring ethical approval or informed consent. Consent for publication: Not applicable Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 10 + + + 2026 + 5 + 15 + + + 2026 + 5 + 29 + 23 + 36 + + + aheadofprint + + 42215973 + 10.1186/s12913-026-14810-w + 10.1186/s12913-026-14810-w + + + + + + 42215935 + + 2026 + 05 + 29 + +
+ + 1472-6947 + + + 2026 + May + 29 + + + BMC medical informatics and decision making + BMC Med Inform Decis Mak + + Explainable machine learning for the prediction of Alzheimer's disease-related cognitive impairment: a consensus feature selection approach. + 10.1186/s12911-026-03585-z + + Early identification of Alzheimer's disease-related cognitive impairment remains challenging, and existing machine learning (ML) models often suffer from feature instability and limited interpretability. This study developed robust and explainable ML models using cerebrospinal fluid (CSF) biomarkers by systematically comparing sparsity-based (LASSO), importance-based (Boruta), and consensus feature selection strategies. + A publicly available cohort of 333 individuals (91 cognitively impaired, 242 cognitively normal) was analyzed. Data were split into training (70%) and independent test (30%) sets. Multiple classifiers, including Elastic Net-regularized logistic regression (LR), support vector machine (SVM), random forest, XGBoost, and Naive Bayes (NB), were trained using repeated 5-fold cross-validation (10 repetitions; 10 × 5-fold cross-validation) with class weighting. Model performance was evaluated using discrimination, calibration, and clinical utility metrics, and interpretability was assessed using SHAP. + All models demonstrated strong discriminative performance on the test set (AUROC 0.861-0.958). LASSO-based models showed high specificity, Boruta-based models achieved higher sensitivity, and consensus-based models provided the most balanced performance. The consensus-LR and -SVM models achieved AUROC values of 0.954 and 0.951, respectively. Beyond discrimination, the consensus-LR model demonstrated good calibration and consistent net clinical benefit in decision curve analysis, analyses that remain relatively underreported in the Alzheimer's disease machine learning literature. SHAP analyses highlighted biologically plausible contributions from key biomarkers, including tau, Aβ42, NT-proBNP, pancreatic polypeptide, and IL-7. + In summary, stable and interpretable ML models for Alzheimer's disease-related cognitive impairment can be developed using CSF-derived biomarkers obtained through lumbar puncture. The proposed consensus-based feature selection framework improves feature stability and model transparency, facilitating the discrimination between cognitively normal and impaired individuals and providing a foundation for future external validation studies. + © 2026. The Author(s). + + + + Cantaş Türkiş + Fulden + F + 0000-0002-7018-7187 + + Division of Biostatistics, Faculty of Medicine, Muğla Sıtkı Koçman University, Muğla, 48000, Turkey. fuldencantas@mu.edu.tr. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Med Inform Decis Mak + 101088682 + 1472-6947 + + IM + + Calibration + Clinical utility + Explainability + Feature selection + Machine learning + + Declarations. Ethics approval and consent to participate: Not applicable. This study is a secondary analysis of publicly available, anonymized data obtained from an open-access repository. Ethical approval and informed consent were obtained in the original study, and no additional ethical approval was required for the present analysis. The study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication: Not applicable. The study does not include any individual-level identifiable data. Authors’ information: Fulden Cantaş Türkiş is an Assistant Professor at Muğla Sıtkı Koçman University, Faculty of Medicine, Department of Biostatistics. Her research interests include biostatistics, time-to-event data analysis, machine learning, artificial intelligence in healthcare, explainable artificial intelligence, predictive modeling, and statistical learning using real-world clinical data. She collaborates with multiple interdisciplinary research groups as the lead biostatistician and provides statistical consultancy for clinical and translational research projects. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 22 + + + 2026 + 5 + 20 + + + 2026 + 5 + 29 + 23 + 35 + + + aheadofprint + + 42215935 + 10.1186/s12911-026-03585-z + 10.1186/s12911-026-03585-z + + + + + + 42215930 + + 2026 + 05 + 29 + +
+ + 1472-6947 + + + 2026 + May + 29 + + + BMC medical informatics and decision making + BMC Med Inform Decis Mak + + Beyond prediction: explainable machine learning for ferritin estimation from complete blood count. + 10.1186/s12911-026-03601-2 + + Serum ferritin is a primary marker of body iron stores; however, its diagnostic utility is limited by cost and non-specific elevation during inflammatory states. This study aimed to develop and validate explainable machine learning (ML) models to estimate ferritin status using only widely available and cost-effective complete blood count (CBC) and demographic parameters. + This prospective study analyzed 12,450 adult laboratory records. To ensure ferritin reflected true iron status, cases with inflammation (elevated white blood cell count or erythrocyte sedimentation rate) were excluded. A balanced dataset of 7,200 samples was used to train five supervised ML algorithms, including Logistic Regression, XGBoost, and Artificial Neural Network (ANN), for both binary classification and regression tasks. Model interpretability was assessed using SHapley Additive Explanations (SHAP) to evaluate global feature importance. + The ANN model achieved the best classification performance, with an area under the curve of 0.837 and an F1-score of 0.760 in discriminating low ferritin levels (< 30 ng/mL) from normal values. Although the regression model demonstrated a moderate global R² of 0.32, it showed higher predictive accuracy in lower ferritin ranges critical for diagnosing iron deficiency. SHAP analysis revealed Sex, Mean Corpuscular Hemoglobin, and Red Cell Distribution Width as the most influential predictors, consistent with iron-restricted erythropoiesis. + ML models based solely on routine hemogram data provide a reliable, zero-cost screening approach for iron deficiency. The integration of explainable AI enhances clinical credibility and supports implementation as an accessible decision support tool. + © 2026. The Author(s). + + + + Us + Ramazan Berk + RB + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. berkus62@gmail.com. + + + + Panatli + Oguzhan + O + + Department of Biomedical Engineering, Faculty of Engineering, Ankara University, Gölbaşı, Ankara, 06830, Türkiye. + + + Department of Computer Engineering, Faculty of Engineering, Ankara University, Gölbaşı, Ankara, 06830, Türkiye. + + + + Bozkürk + Mine Büşra + MB + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Öztürk + Atakan + A + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Sağlam + Suadiye + S + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Kösem + Arzu + A + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Öztürk + Alpaslan + A + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Bostanci + Gazi Erkan + GE + + Department of Computer Engineering, Faculty of Engineering, Ankara University, Gölbaşı, Ankara, 06830, Türkiye. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Med Inform Decis Mak + 101088682 + 1472-6947 + + IM + + Clinical decision support systems + Complete blood count + Explainable artificial intelligence + Ferritin + Iron deficiency + SHAP + + Declarations. Ethics approval and consent to participate: The study protocol was approved by the Ankara Etlik City Hospital Ethics Committee (Approval No: AEŞH-BADEK1-2025-032, Date: 02/07/2025) and was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all participants prior to inclusion in the study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 6 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 23 + 35 + + + aheadofprint + + 42215930 + 10.1186/s12911-026-03601-2 + 10.1186/s12911-026-03601-2 + + + + + + 42215875 + + 2026 + 05 + 29 + +
+ + 1480-9222 + + + 2026 + May + 29 + + + Biological procedures online + Biol Proced Online + + A novel prognostic model based on MVIRGs identifies ANGPT2 as a key target driving the malignant progression of HCC. + 10.1186/s12575-026-00347-z + + Hepatocellular carcinoma (HCC) is a highly lethal malignancy with high recurrence and limited therapies. Microvascular invasion (MVI) is a critical prognostic factor, but its molecular mechanisms and precise predictive tools are lacking. This study aimed to identify key MVI-associated genes (MVIRGs), develop a prognostic model, and elucidate the role of ANGPT2 in HCC progression and therapeutic responses. + Machine learning on multi-cohort data (TCGA, ICGC) identified MVIRGs and constructed a prognostic model. ANGPT2, the core oncogenic MVIRG, was validated clinically (IHC) and functionally (in vitro and in vivo). We analyzed its association with the tumor immune microenvironment (TIME), immune checkpoint inhibitors (ICIs), and drug sensitivity. + A powerful six-MVIRG prognostic model was developed, with ANGPT2 identified as the sole oncogenic gene. Clinically, ANGPT2 was significantly overexpressed, correlating strongly with aggressive features like MVI, vascular invasion, and advanced stages, serving as a robust poor prognostic biomarker for OS and PFS. Functionally, ANGPT2 knockdown significantly inhibited HCC cell growth and migration in vitro, and inhibited tumor growth in vivo. Bioinformatics revealed ANGPT2 fosters an immunosuppressive TIME, upregulating immune checkpoints (e.g., PD-L1) to mediate resistance to ICIs. High ANGPT2 expression showed a differential drug response: resistance to targeted therapies (e.g., EGFR-TKIs) but paradoxically increased sensitivity to chemotherapies (e.g., docetaxel, paclitaxel). + This study constructed a powerful MVIRG model and identified ANGPT2 as a core oncogenic molecule. ANGPT2 drives HCC progression, mediates immune resistance, and differentially regulates drug sensitivity, making it a key biomarker for prognosis and treatment guidance. + © 2026. The Author(s). + + + + Li + Xian + X + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Tan + Zhouli + Z + + The Fifth School of Clinical Medicine, Anhui Medical University, Hefei, China. + + + Navy Clinical College, Anhui Medical University, Beijing, China. + + + + Feng + Shanru + S + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Yu + Qiang + Q + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Zhou + Jian + J + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Fan + Jia + J + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Xu + Yang + Y + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. xuyangdoctor@163.com. + + + + eng + + + 82073272 + National Natural Science Foundation of China + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Biol Proced Online + 100963717 + 1480-9222 + + + ANGPT2 + Hepatocellular carcinoma + MVIRGs + Microvascular invasion + Prognostic model + + Declarations. Ethics approval and consent to participate: Ethical approval was provided by the Ethics Committee of Zhongshan Hospital, and written informed consent was obtained from each patient (Approval number: No. Y2020-374). All animal experiments involved in this study were reviewed and approved by the Ethics Committee of Zhongshan Hospital, Fudan University, Xiamen Hospital (Approval number: No. Y2024-021). We confirm that all experiments were performed in accordance with the approved protocols and all relevant guidelines and regulations for the care and use of laboratory animals. Regarding the tumor burden, the maximum tumor size/burden permitted by our Ethics Committee/IRB is a tumor volume not exceeding 2000 mm3 or a longest tumor diameter not exceeding 2.0 cm. We confirm that this maximum permitted tumor size/burden was not exceeded at any point during the study. Clinical trial number: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 1 + 31 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 23 + 33 + + + aheadofprint + + 42215875 + 10.1186/s12575-026-00347-z + 10.1186/s12575-026-00347-z + + + + + + 42215857 + + 2026 + 05 + 29 + +
+ + 1471-2164 + + + 2026 + May + 30 + + + BMC genomics + BMC Genomics + + PRIME: An evaluation framework for protein representation inference and generalization in viral mutation space. + 10.1186/s12864-026-12976-5 + + Protein language models (PLMs) have revolutionized protein fitness prediction, yet their application to rapidly evolving viral pathogens is often confounded by extreme sequence homology. This homology leads to "data leakage" in standard random validation splits, yielding inflated performance metrics that fail to translate into real-world biosurveillance utility. + We present Protein Representation Inference for Mutation Evaluation (PRIME), a framework that integrates domain-specific fine-tuning with a rigorous position-stratified validation protocol to evaluate viral threats. Using a dataset of 347,432 SARS-CoV-2 receptor binding domain (RBD) sequences, we demonstrate that while random training data split yields deceptive R2 values (> 0.90), they fail to generalize to novel mutational sites. By benchmarking models up to 650 M parameters, we show that domain-specific fine-tuning of the ESM-C 600 M model with correctly stratified data provides an initial demonstration of predictive signal for binding affinity and expression at unseen mutational sites of binding affinity and expression on unseen sites (R2 ~0.23), a significant advancement over base foundation models which exhibit no predictive power (R2 <0). PRIME's embedding-based clustering identified 3.03% of bat coronavirus sequences as candidates for further experimental prioritization based on their functional similarity to human-infective strains in embedding space, offering a perspective complementary to traditional phylogenetic methods. + PRIME establishes a new benchmark for the application of PLMs in pathogen surveillance. Our findings demonstrate that state-of-the-art models and fine-tuning, when paired with stratified validation, provide biologically meaningful insights into pathogen evolution and zoonotic risk. + © 2026. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. + + + + Gibson + Kaetlyn + K + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Li + Po-E + PE + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Li + Valerie + V + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Dix + Martha + M + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Hung + Li-Wei + LW + + Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Stelle + George Widgery + GW + + Applied Computer Science Group, Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Babinski + Michal + M + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Chain + Patrick + P + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Hu + Bin + B + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. bhu@lanl.gov. + + + + eng + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + + Journal Article + + + 2026 + 05 + 30 + +
+ + England + BMC Genomics + 100965258 + 1471-2164 + + IM + + Biosurveillance + Gomology leakage + Host tropism prediction + Machine learning efficiency + Model fine-tuning + Phenotype prediction + Position-stratified validation + Protein language models + + Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 12 + + + 2026 + 5 + 18 + + + 2026 + 5 + 29 + 23 + 32 + + + aheadofprint + + 42215857 + 10.1186/s12864-026-12976-5 + 10.1186/s12864-026-12976-5 + + + + + + 42215832 + + 2026 + 05 + 29 + +
+ + 1573-6822 + + + 2026 + May + 29 + + + Cell biology and toxicology + Cell Biol Toxicol + + Integrative multi-omics, machine learning, and experimental validation reveal that TPM1 suppresses M2 macrophage polarization and enhances chemosensitivity in acute myeloid leukemia. + 10.1007/s10565-026-10201-3 + + Acute myeloid leukemia (AML) remains a therapeutic challenge due to chemoresistance and immunosuppression. This study aimed to discover novel regulatory mechanisms in AML pathogenesis. + Integrative transcriptomic (GSE30029) and proteomic (PXD008378) analyses with machine learning (LASSO, SVM-RFE, random forest) were performed to identify core AML regulators. Functional validation was conducted in AML cell lines (HL-60, NB4), including cell proliferation and apoptosis assays, cytarabine (Ara-C) sensitivity testing, and a subcutaneous xenograft model. Macrophage polarization was evaluated in co-culture systems. Molecular mechanisms were explored via MeRIP, RIP, and mRNA stability assays. + Tropomyosin 1 (TPM1) was identified as a potential key factor in AML. TPM1 was downregulated in AML tissues and cells. TPM1 overexpression inhibited AML cell proliferation, promoted apoptosis and oxidative stress, and enhanced sensitivity to Ara-C. Furthermore, TPM1 upregulation attenuated M2 macrophage polarization. Mechanistically, the m6A eraser fat mass and obesity-associated protein (FTO) destabilized TPM1 mRNA in an m6A-dependent manner. Downregulation of TPM1 reversed the effects of FTO depletion on AML cell proliferation, Ara-C sensitivity, and M2 macrophage polarization. + This study unveils a novel FTO/m6A/TPM1 axis that coordinately governs AML cell growth, chemosensitivity, and macrophage polarization, highlighting it as a promising therapeutic target. + © 2026. The Author(s). + + + + Li + Yue + Y + + Department of Clinical Laboratory, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 222, West Huanhu 3Rd Road, Lingang New City 201306, Pudong New Area, Shanghai, China. + + + + Wei + Baosheng + B + + Department of Clinical Laboratory, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 222, West Huanhu 3Rd Road, Lingang New City 201306, Pudong New Area, Shanghai, China. + + + + Hu + Liuping + L + + Department of Clinical Laboratory, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 222, West Huanhu 3Rd Road, Lingang New City 201306, Pudong New Area, Shanghai, China. huhuliuping@163.com. + + + + Du + Yuzhen + Y + + Department of Clinical Laboratory, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 222, West Huanhu 3Rd Road, Lingang New City 201306, Pudong New Area, Shanghai, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Switzerland + Cell Biol Toxicol + 8506639 + 0742-2091 + + IM + + Acute myeloid leukemia + Chemosensitivity + M6A eraser + Macrophage polarization + Tropomyosin 1 + + Declarations. Ethics approval: The present study was approved by the ethical review committee of Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine with approval No.20250222. Written informed consent was obtained from all enrolled patients. Human study: This study was carried out in strict accordance with the relevant regulations and requirements of the Declaration of Helsinki. Written informed consents were obtained from all participants and this study was permitted by the Ethics Committee of Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. Animal study: The procedures performed on all animals complied with the standards formulated by the Experimental Animal Ethics Committee of Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and obtained the approval of the said committee. Conflicts of interest: The authors declare no competing interests. Clinical trial number: Not applicable + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 31 + + + 2026 + 4 + 23 + + + 2026 + 5 + 29 + 23 + 26 + + + aheadofprint + + 42215832 + 10.1007/s10565-026-10201-3 + 10.1007/s10565-026-10201-3 + + + + + + 42215825 + + 2026 + 05 + 29 + +
+ + 1618-1905 + + + 2026 + May + 30 + + + International microbiology : the official journal of the Spanish Society for Microbiology + Int Microbiol + + Gut microbial alterations and functional shifts in patients with hypertriglyceridemia: insights from a northwestern Chinese metagenomic study. + 10.1007/s10123-026-00845-w + + Although hypertriglyceridemia (HTG) is a significant contributor to lipid-associated pathologies such as atherosclerotic cardiovascular disease, its regulation by host‒microbiome interactions remain insufficiently characterized. While the gut microbiota (GM) is known to influence cholesterol metabolism, its specific role in systemic triglyceride (TG) homeostasis, particularly in non-Western populations, is poorly defined. This study aimed to identify preliminary robust GM signatures associated with HTG and to assess their translational potential using integrated multiomics and explainable machine learning approaches. In a cross-sectional investigation of 50 well-phenotyped adults from Northwest China, we combined 16S rRNA sequencing, shotgun metagenomics, and ensemble machine learning (LightGBM/XGBoost) to elucidate the associations between the GM and TGs. Microbial features were rigorously linked to serum lipid profiles through dual-algorithm validation and SHAP interpretability analysis, while functional potential was assessed via KEGG pathway mapping. Subjects with HTG exhibited a distinct gut microbial configuration, marked by consistent enrichment of Faecalibacterium and Bacteroides coprocola (positively correlated with serum TG levels) and depletion of Bifidobacterium pseudocatenulatum and Lactobacillus salivarius (inversely correlated). Machine learning converged on five exploratory consensus biomarker taxa, three of which were independently confirmed by LEfSe analysis (Faecalibacterium). Functional profiling further revealed the upregulation of microbial starch and sucrose metabolism pathways in the HTG cohort. Our findings establish a preliminary gut microbial signature for HTG patients and suggest context‑dependent associations of butyrate-producing taxa such as Faecalibacterium. By integrating multiomics with explainable artificial intelligence, this work addresses key challenges in reproducibility and mechanistic inference in microbiome research. These results pave the way for novel microbiota-targeted therapeutic strategies, including precision probiotics and dietary interventions, to modulate lipid metabolism, pending further validation in expanded cohorts and functional studies. + © 2026. The Author(s). + + + + Lv + Jing + J + + Department of Clinical Laboratory, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Wang + Ji-Han + JH + + Yan'an Medical College of Yan'an University, Yan'an, Shaanxi, 716000, China. + + + + Wang + Yang-Yang + YY + + School of Physics and Electronic Information, Yan'an University, Yan'an, Shaanxi, 716000, China. + + + + Huang + Jing + J + + Department of Clinical Pharmacy, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Chen + Fen-Rong + FR + + Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710000, China. + + + + Fang + Sa + S + + Department of Gastroenterology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Wang + Xiao-Juan + XJ + + Department of Gastroenterology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Li + Zi-Tong + ZT + + Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Shi + Yu-Peng + YP + + Department of Gastroenterology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. xjshiyupeng@163.com. + + + + Guo + Lei + L + + Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. guolei0711@163.com. + + + + eng + + + 2025JC-YBMS-916 + Shaanxi Natural Science Foundation of China + + + + No. 81702067 and 82560411 + National Natural Science Foundation of China + + + + + Journal Article + + + 2026 + 05 + 30 + +
+ + Switzerland + Int Microbiol + 9816585 + 1139-6709 + + IM + + Biomarker + Gut microbiota + Hypertriglyceridemia + Machine learning + Triglyceride + + Declarations. Competing interests: The authors declare no competing interests. Consent to participate: Informed consent was provided by all recruited subjects prior to the study. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 18 + + + 2026 + 5 + 22 + + + 2026 + 5 + 11 + + + 2026 + 5 + 29 + 23 + 26 + + + aheadofprint + + 42215825 + 10.1007/s10123-026-00845-w + 10.1007/s10123-026-00845-w + + + + The Writing Committee Of The Report On Cardiovascular Health And Diseases In China (2022) Key points of Report on Cardiovascular Health and Diseases in China 2021. Chin J Cardiovasc Res, 20(7): 577–596 + + + An HM et al (2011) Antiobesity and lipid-lowering effects of Bifidobacterium spp. in high fat diet-induced obese rats. 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+ + 1550-7416 + + 28 + 4 + + 2026 + May + 29 + + + The AAPS journal + AAPS J + + New Frontiers of Drug Development Through the Use of New Approach Methodologies. + 112 + 10.1208/s12248-026-01263-6 + + On November 2025, AAPS PharmSci 360 convened a symposium that included experts in the application of various types of New Approach Methodologies (NAMs). They shared their experiences and insights on topics that included microphysiological systems (MPS), the use of 3D organoids, and in silico tools. MPS systems were explored from the perspective of their testing and qualification through an academia-industry-government tissue chip testing consortium whose mission is to perform context-of-use-based testing of MPS and conduct comparative evaluations to support the use of MPS systems in safety assessments. 3D organoids were described and insights shared on how and when they may provide an appropriate tool for evaluating drug safety and effectiveness, and their use in supporting personalized medicine. Regarding in silico tools, examples were provided to describe their growing utility in predicting drug toxicity, population variability, and its potential benefits over traditional evidence-based toxicology. These tools include in silico models (e.g., physiologically based pharmacokinetic models), Artificial Intelligence (AI), and Machine Learning (ML) which, unlike other aspects of model informed drug development, reduce reliance on predefined models and allows for the integration of diverse sources of data. Computationally, these tools can generate predictions in hours where it would otherwise have taken weeks or months (and extensive experimentation). Within this meeting report, we highlight the key issues discussed during that symposium and share additional aspects to consider when developing a NAMs-based roadmap for specific contexts of use. + © 2026. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. + + + + Martinez + M N + MN + 0000-0002-4512-853X + + Food and Drug Administration, Center for Veterinary Medicine, Office of Generic Animal Drugs, CPK1 5001 Campus Drive, College Park, MD, 20740-3835, USA. marilyn.martinez@fda.hhs.gov. + + + + Mochel + J P + JP + 0000-0002-0997-3111 + + Precision One Health Initiative and Biomedical & Translational Sciences Institute (One Health), Diane L. Wynocker Endowed Professor, Athens, GA, USA. + + + + Allenspach + K + K + 0000-0002-2168-5809 + + Precision One Health and Department of Pathology, College of Veterinary Medicine, The University of Georgia, 501 D. W. Brooks Drive, Athens, GA, 30602, USA. + + + + Zdyrski + C + C + 0000-0002-7556-5298 + + Precision One Health and College of Veterinary Medicine, The University of Georgia, 501 D. W. Brooks Drive, Athens, GA, 30602, USA. + + + + Krishna + S + S + 0000-0002-4960-2753 + + Physicians Committee for Responsible Medicine, 5100 Wisconsin Ave., NW, Suite 400, Washington, DC, 20016, USA. + + + + Rusyn + I + I + 0000-0001-9340-7384 + + Department of Veterinary Physiology and Pharmacology, Texas A&M College of Veterinary Medicine & Biomedical Sciences, 4466 TAMU, College Station, TX, 77843, USA. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 29 + +
+ + United States + AAPS J + 101223209 + 1550-7416 + + IM + + + Drug Development + methods + trends + + + Humans + + + Microphysiological Systems + + + Computer Simulation + + + Organoids + + + Animals + + + Machine Learning + + + Artificial Intelligence + + + + AI + NAMs + PBPK models + organ-on-a-chip + organoid + + Declarations. Disclaimer: The findings and conclusions in this research have not been formally disseminated by the Food and Drug Administration and should not be construed to represent any Agency endorsement, determination, or policy. The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the FDA. Animal Use and Assurances: No animals were used in the writing of this Commentary. A.I. Statement: "No, but part of the graphical abstract had assist from Google AI. This was indicated in the figure.". Conflict of interest: Ivan Rusyn has no conflicts of interest to declare. Jonathan Mochel is a co-founder of 3D Health Solutions Inc., a small biotechnology company specializing in the development of animal-derived organoids for drug testing. Karin Allenspach co-founder of 3D Health Solutions. She serves as a consultant for Ceva Animal Health, Antech Diagnostics, hills, Royal Canin, Purina, and Heel GmbH. Christopher Zdyrski is the Director of Research and Product Development at 3D Health Solutions. Shagun Krishna has no conflicts of interest to declare. Marilyn N. Martinez has no conflicts of interest to declare. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 3 + 12 + + + 2026 + 5 + 18 + + + 2026 + 5 + 29 + 23 + 25 + + + epublish + + 42215821 + 10.1208/s12248-026-01263-6 + 10.1208/s12248-026-01263-6 + + + + Avila AM, Bebenek I, Mendrick DL, Peretz J, Yao J, Brown PC. 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+ + 2730-6011 + + + 2026 + May + 29 + + + Discover oncology + Discov Oncol + + Exercise-related genes predicts overall survival and tumor immune microenvironment, and identifies the biological role of SLC52A2 in breast cancer. + 10.1007/s12672-026-05321-5 + + Breast cancer (BRCA) is the most common malignant tumor in women, with prognoses differing markedly by molecular subtype. Exercise is known to be related to tumor occurrence and progression, yet its role in BRCA remains understudied. This research seeks to explore the prognostic and immunomodulatory functions of exercise-related genes (ERGs) in BRCA, and their links to molecular subtypes, so as to inform targeted and precision treatment. + We obtained BRCA gene expression profiles and clinical data from the TCGA-BRCA project. Using LASSO, random forest, gradient boosting machine (GBM), stepwise Cox regression (Stepcox), and extreme gradient boosting (XGBOOST) algorithms, we screened prognostic signature genes from the intersecting ERGs, determined by differential expression analysis and weighted gene co-expression network analysis (WGCNA), established and validated a novel risk prediction model. We further analyzed biological functions, mutation profiles, immune traits, and molecular subtype associations across different risk groups. Additionally, the prognostic, biological and immunological roles of the core risk biomarker in the exercise-related signature was evaluated. + WGCNA identified 345 differentially expressed ERGs in BRCA, and five machine learning algorithms further screened 6 prognostic genes (SNX22, SLC52A2, TNFRSF18, NFE2, EZR, and EMP1). The low-risk group had significantly higher overall survival rate than the high-risk group, a difference driven by the functional roles of the two groups' differentially expressed genes, which regulated the cell cycle and epithelial-mesenchymal transition (EMT). Additionally, the high- and low-risk groups showed distinct mutation and immunomodulatory features. Consensus clustering of prognosis between the groups linked these differences to BRCA molecular subtypes. As a core candidate factor, SLC52A2 contributed to BRCA progression and poor prognosis in patients by participating in multiple signaling pathways and regulating immune cells. + ERGs-based prognostic signatures could distinguish clinical outcomes of BRCA patients and were correlated with immune regulation and mutation patterns. The ERGs-constructed risk scoring system and molecular subtypes have the potential to serve as preclinical biological and immunological markers, paving the way for clinical management and treatment of BRCA. + © 2026. The Author(s). + + + + Chen + Caiping + C + + Department of Breast Surgery, The Affiliated Hospital of Jiaxing University (The First Hospital of Jiaxing), Jiaxing, China. + + + + Han + Chao + C + + Department of Breast Surgery, The Affiliated Hospital of Jiaxing University (The First Hospital of Jiaxing), Jiaxing, China. + + + + Xue + Li + L + + Department of Breast Surgery, The Affiliated Hospital of Jiaxing University (The First Hospital of Jiaxing), Jiaxing, China. + + + + Lu + Xiang + X + + Department of Breast Surgery, The Affiliated Hospital of Jiaxing University (The First Hospital of Jiaxing), Jiaxing, China. 00187649@zjxu.edu.cn. + + + + eng + + + 2023-FC-001 + Jiaxing Key Discipiline of Medcine - -Surgery Med (Mastopathy) + + + + 2023-PYXK-001 + Jiaxing Provinces and Cities Jointly Cultivate Discipline - -General Surgery (Minimally Invasive) + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + Discov Oncol + 101775142 + 2730-6011 + + + Breast cancer + Exercise + Genetic mutation + Immune response + Molecular cluster + Prognosis + + Declarations. Ethics approval and consent to participate: The cohort data used in this article were all from online databases and therefore did not involve ethics. Consent to participate is not applicable to this study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 11 + 28 + + + 2026 + 5 + 26 + + + 2026 + 5 + 29 + 23 + 25 + + + aheadofprint + + 42215809 + 10.1007/s12672-026-05321-5 + 10.1007/s12672-026-05321-5 + + + + + + 42215746 + + 2026 + 05 + 29 + +
+ + 1435-2451 + + + 2026 + May + 30 + + + Langenbeck's archives of surgery + Langenbecks Arch Surg + + Relationship between ileocolic arterial stump length and lymph node yield - a retrospective study using machine learning. + 10.1007/s00423-026-04048-9 + + In right-sided colon cancer surgery, ileocolic artery stump length may reflect the extent of mesenteric resection and lymph node harvest. This study evaluated the association between postoperative stump length measured on CT, lymph node yield, and recurrence or progression within 24 months. + This single-center retrospective study included 64 patients who underwent right or extended right hemicolectomy for right-sided colon adenocarcinoma between 2016 and 2024. Right-sided tumors were defined as those located in the cecum, ascending colon, hepatic flexure, or proximal transverse colon. Only primary surgical cases were included. Ileocolic artery stump length was measured on contrast-enhanced CT scans performed approximately 12 months postoperatively (± 1 month) by two independent radiologists. A Random Forest regression model using stump length as the only predictor variable was developed to estimate lymph node yield. Model performance was assessed using 10-fold cross-validation with R² and mean squared error (MSE). Exploratory recurrence/progression analysis used logistic regression with natural cubic splines. + Mean stump length was 2.03 cm (range 1.0-4.5 cm), and median lymph node yield was 17 (range 10-55). Adequate nodal evaluation (≥ 12 nodes) was achieved in 85.9% of patients. The model demonstrated a consistent inverse relationship between stump length and lymph node yield (R² = 0.78; MSE = 0.16). Exploratory analysis suggested a non-significant trend toward higher recurrence or progression with longer stumps. + Longer ileocolic artery stumps were associated with reduced lymph node yield, suggesting stump length may reflect the extent of mesenteric resection. External validation is required before clinical application. + © 2026. The Author(s). + + + + Zayas-Borquez + Rogelio + R + 0000-0003-1751-4211 + + Universidad Nacional Autónoma de México, Mexico City, Mexico. rogeliozb94@unam.edu. + + + + Arizaga-Ramírez + María Sofía + MS + 0009-0004-2981-0530 + + Department of Radiology and Imaging, National Institute of Medical Sciences and Nutrition "Salvador Zubirán", Mexico City, Mexico. + + + + Canto-Losa + Jorge + J + 0000-0003-3768-3688 + + Universidad Nacional Autónoma de México, Mexico City, Mexico. jorge.canto@incmnsz.mx. + + + + Gándara-Lois + Ana C + AC + 0009-0001-0265-1726 + + Department of Radiology and Imaging, National Institute of Medical Sciences and Nutrition "Salvador Zubirán", Mexico City, Mexico. + + + + Salgado-Nesme + Noel + N + 0000-0002-3559-5070 + + Department of Colorectal Surgery, National Institute of Medical Sciences and Nutrition "Salvador Zubirán", Vasco de Quiroga 15, Belisario Domínguez Seccion 16, Tlalpan, Mexico City, PC 14080, Mexico. + + + + Alvarez-Bautista + Francisco + F + 0000-0003-2645-832X + + Department of General and Endoscopic Surgery, "Dr. Manuel GEA González" General Hospital, Mexico City, Mexico. + + + + Santes + Oscar + O + 0000-0002-6393-4607 + + Department of Colorectal Surgery, National Institute of Medical Sciences and Nutrition "Salvador Zubirán", Vasco de Quiroga 15, Belisario Domínguez Seccion 16, Tlalpan, Mexico City, PC 14080, Mexico. + + + + eng + + Journal Article + + + 2026 + 05 + 30 + +
+ + Germany + Langenbecks Arch Surg + 9808285 + 1435-2443 + + IM + + Central vascular ligation + Complete mesocolic excision + Lymph node retrieval + Machine learning + Right hemicolectomy + + Declarations. Ethics approval: This study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board of the corresponding institution. Given the retrospective nature of the study and the use of anonymized data, the requirement for informed consent was waived. Informed consent: Informed consent was waived due to the retrospective design of the study and the use of anonymized patient data. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 16 + + + 2026 + 4 + 11 + + + 2026 + 5 + 29 + 23 + 23 + + + aheadofprint + + 42215746 + 10.1007/s00423-026-04048-9 + 10.1007/s00423-026-04048-9 + + + + + + 42215700 + + 2026 + 05 + 29 + +
+ + 1437-1596 + + + 2026 + May + 30 + + + International journal of legal medicine + Int J Legal Med + + Proteomic profiling of bone for the estimation of post-mortem interval and post-mortem submersion interval: a systematic review. + 10.1007/s00414-026-03844-8 + + Accurate estimation of the Post-Mortem Interval (PMI) and Post-Mortem Submersion Interval (PMSI) remains a persistent challenge in forensic science, especially when traditional morphological and entomological methods fail due to advanced decomposition or in aquatic environments. Proteomic profiling of bone tissues has recently emerged as a promising approach, leveraging the predictable degradation patterns of bone proteins to estimate time since death more reliably. This systematic review, conducted in accordance with PRISMA guidelines, analyzed 24 peer-reviewed studies focusing on the application of proteomic techniques to bone tissue for PMI and PMSI estimation. The included studies were evaluated based on sample type, analytical techniques used, identified biomarkers, environmental conditions assessed, and the overall reliability and reproducibility of the findings. The review found that specific bone proteins, particularly collagen, osteocalcin, fetuin-A, etc. exhibited consistent degradation patterns that correlated strongly with elapsed post-mortem time. Cortical bone was identified as a more stable and informative matrix compared to trabecular bone. Mass spectrometry, especially LC-MS/MS, emerged as the predominant analytical technique due to its high sensitivity and accuracy in detecting low-abundance proteins over extended PMIs and PMSIs. However, protein degradation rates were significantly influenced by environmental variables such as temperature, humidity, soil pH, and microbial activity. This review also emphasizes the transformative role of bone proteomics in advancing forensic science while identifying key gaps that must be addressed to achieve global standardization and practical implementation in diverse forensic contexts. The integration of proteomics with other emerging technologies, such as machine learning algorithms and computational modeling, may further enhance the precision of PMI and PMSI estimation in future applications. + © 2026. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. + + + + Dalal + Jyoti + J + 0000-0002-4690-6452 + + School of Forensic Sciences, Amity University Punjab, Mohali, Punjab, India. jyotidalal417@gmail.com. + + + + Kumari + Pallavi + P + + School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India. + + + Department of Forensic Science, University Institute Allied Health Sciences, Chandigarh University, Mohali, Punjab, India. + + + + Gupta + Vinayak + V + + School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India. + + + + Chhikara + Anjali + A + + School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 30 + +
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+ + 1546-170X + + + 2026 + May + 29 + + + Nature medicine + Nat Med + + From donor lungs to digital twins. + 10.1038/d41591-026-00029-z + + + O'Leary + Karen + K + + + eng + + News + + + 2026 + 05 + 29 + +
+ + United States + Nat Med + 9502015 + 1078-8956 + + IM + + Machine Learning + Technology + Translational research + + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 22 + + + aheadofprint + + 42215696 + 10.1038/d41591-026-00029-z + 10.1038/d41591-026-00029-z + + + + + + 42215691 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + O²RDL-net for joint risk classification and delay forecasting in logistics systems using interaction amplified deep. + 10.1038/s41598-026-55703-6 + + Transportation and logistics systems face frequent delays and operational risks due to congestion, human factors, and rapidly changing conditions. Predicting delays and identifying risk levels in advance is therefore essential for reliable logistics planning. Most existing methods handle delay forecasting and risk classification separately and often fail to capture complex temporal and interaction-driven patterns. To overcome these limitations, this study proposes O²RDL-Net, a load-aware and interaction-amplified deep learning framework for joint risk classification and delay forecasting. The proposed model integrates higher-order operational indicators, including the Risk Accumulation Index, Delay Sensitivity Indicator, Human-Operational Interaction Score, and Logistics System Representation, to capture cumulative risk, short-term sensitivity, and system-level interactions. A spatio-temporal attention encoder with risk regime modulation is used to improve learning stability and generalization. Experimental results show that O²RDL-Net achieves superior performance compared to baseline models, with a mean absolute error of 2.05, RMSE of 3.01, and R² of 0.91 for delay forecasting. For risk classification, the model reaches an accuracy of 92%, precision of 91%, recall of 92%, and F1-score of 91%. Interpretability analysis using SHAP and local explanations confirms that the model relies on meaningful and domain-consistent features. Overall, O²RDL-Net provides an accurate, stable, and interpretable solution for real-world logistics risk and delay management. + © 2026. The Author(s). + + + + Yang + Xiao + X + + Low-altitude Management and Digital Economy College, XIHANG UNIVERSITY, Xi'an, 710077, China. jonah-y@xaau.edu.cn. + + + + eng + + + Grant No. 2025YB0214 + Research on the Coupled and Coordinated Development of Transportation and Modern Industrial System in Shaanxi Province + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Artificial intelligence + Deep Learning + Feature Construction + Forecasting + Logistics Network + Machine Learning + Risk Prediction + Supply Chain + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 9 + + + 2026 + 5 + 26 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215691 + 10.1038/s41598-026-55703-6 + 10.1038/s41598-026-55703-6 + + + + + + 42215678 + + 2026 + 05 + 29 + +
+ + 2398-6352 + + + 2026 + May + 29 + + + NPJ digital medicine + NPJ Digit Med + + Detecting dataset bias in medical AI using a generalized and modality agnostic auditing approach. + 10.1038/s41746-026-02807-y + + Despite many success stories along the path of Artificial Intelligence's (AI) rise in healthcare, there are comparably many reports of significant shortcomings and unexpected behavior of AI in deployment. A major risk is AI's reliance on association-based learning, where non-representative machine learning datasets can amplify latent bias during training and hide it during testing. To unlock new tools capable of detecting such AI bias issues, we present Generalized Attribute Utility and Detectability-Induced bias Testing (G-AUDIT). G-AUDIT is a data modality-agnostic dataset auditing approach that automatically quantifies shortcut learning risks by examining the interplay between task-level annotations, sensor-level measurements, and patient, environmental, and acquisition characteristics. We demonstrate the broad applicability of this method by analyzing a range of medical datasets across three distinct modalities (images, text, and tabular data) and machine learning tasks, successfully identifying potential shortcuts commonly overlooked by traditional qualitative methods. + © 2026. The Author(s). + + + + Drenkow + Nathan + N + + The Johns Hopkins University, Baltimore, MD, USA. nathan.drenkow@jhuapl.edu. + + + The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA. nathan.drenkow@jhuapl.edu. + + + + Pavlak + Mitchell + M + + The Johns Hopkins University, Baltimore, MD, USA. + + + + Harrigian + Keith + K + + The Johns Hopkins University, Baltimore, MD, USA. + + + + Zirikly + Ayah + A + + The Johns Hopkins University, Baltimore, MD, USA. + + + Department of Computer Science, School of Engineering and Applied Science, George Washington University, Washington, DC, USA. + + + + Subbaswamy + Adarsh + A + + Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA. + + + + Farhangi + Mohammad Mehdi + MM + + Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA. + + + + Petrick + Nicholas + N + + Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA. + + + + Unberath + Mathias + M + + The Johns Hopkins University, Baltimore, MD, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Digit Med + 101731738 + 2398-6352 + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 6 + 3 + + + 2026 + 5 + 18 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215678 + 10.1038/s41746-026-02807-y + 10.1038/s41746-026-02807-y + + + + + + 42215677 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Stacked multi-fusion CNN: an adaptive attention model for privacy preserving deepfake forensics. + 10.1038/s41598-026-55325-y + + The emergence of Generative Artificial Intelligence (Gen-AI) and Generative Adversarial Network (GAN)-based deepfakes poses significant security risks in sociocultural and sociopolitical domains. This necessitates the development of advanced and effective detection methods to prevent vulnerability in social networks. Classical Machine Learning (ML) algorithms have their limitations, especially in classifying the deepfakes. To address these issues, this paper suggests a privacy-preserving Stacked Multi-Fusion (SMF) Convolutional Neural Network (CNN) approach to classify deepfakes. An improved CNN model is proposed, integrating an adaptive multi-scale attention framework with enhanced residual blocks and a Squeeze-and-Excitation (SE) mechanism. The selection of these components is backed with an ablation study to report the individual contribution to the overall optimal architecture. A hybrid lossless multilayer cryptosystem based on a chaos-based approach, Deoxyribonucleic Acid (DNA)-based computing, etc., is developed to secure images in cloud storage. The efficacy of the proposed SMF model is validated using the 140K Real and Fake Faces (RFF) image dataset. The proposed approach was found to achieve stable performance with minimal variation in various tests. It achieved a test accuracy and ROC-AUC of 97.80 and 99.79, respectively. This paper provides comprehensive relevant factors for building effective synthetic media detection systems. + © 2026. The Author(s). + + + + Rout + Jayanti + J + + P. G. Department of Computer Science, Fakir Mohan University, Balasore, 756019, Odisha, India. + + + + Mishra + Minati + M + + P. G. Department of Computer Science, Fakir Mohan University, Balasore, 756019, Odisha, India. + + + + Barik + Ram Chandra + RC + + Department of CSE, C. V. Raman Global University, Bhubaneswar, 752054, Odisha, India. + + + + Yadav + Devendra Kumar + DK + + School of Computer Science and Engineering, XIM University, Bhubaneswar, 752050, Odisha, India. devendra@xim.edu.in. + + + + Prasad + Dilip K + DK + + Department of Computer Science, UiT The Arctic University of Norway, Tromsø, 9037, Norway. + + + + Sekh + Arif Ahmed + AA + + Department of Computer Science, UiT The Arctic University of Norway, Tromsø, 9037, Norway. arif.a.sekh@uit.no. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Convolutional neural network + Deepfake detection + Squeeze-and-excitation attention + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 10 + 25 + + + 2026 + 5 + 23 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215677 + 10.1038/s41598-026-55325-y + 10.1038/s41598-026-55325-y + + + + + + 42215670 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + A hybrid AI vision model for single-camera tracking of rehabilitation exercises: a proof of concept in clinical settings. + 10.1038/s41598-026-55431-x + + Accurate measurement of range of motion (ROM) during physical rehabilitation is traditionally achieved using goniometers or multi-camera, marker-based motion capture systems. The latter, while highly accurate, require specialised laboratory infrastructure, are costly, and are unsuitable for home-based use. There is growing interest in markerless, camera-based alternatives that leverage artificial intelligence (AI) for joint angle estimation. This study presents a preliminary proof-of-concept evaluation of a hybrid AI vision system, combining the MediaPipe Pose framework (version 0.8.9.1, Full model variant) with a Mamdani-type fuzzy inference system, for joint angle estimation and exercise repetition counting using a single consumer-grade camera. Fourteen healthy adult rehabilitation staff members performed three exercises (elbow flexion, knee extension, and hip external rotation) simultaneously recorded by the proposed AI system and by a reference optical motion capture system (C-Motion Visual3D™, 14 calibrated infrared cameras, 53 retro-reflective markers). Reliability was assessed using two-way mixed-effects intraclass correlation coefficients (ICC[Formula: see text]) with absolute agreement. Individual-measure ICC values ranged from poor to good across joints and conditions (range: 0.005-0.68). The highest reliability was observed for the right hip external rotation ROM (individual ICC [Formula: see text]; average ICC [Formula: see text], 95% CI: 0.63-0.89). Several measures showed poor reliability, including the right elbow flexion ROM (individual ICC [Formula: see text]) and the minimum angle of the left hip external rotation (individual ICC [Formula: see text]). Confidence intervals were wide throughout. This proof of concept demonstrates that a single-camera AI system can capture general trends in joint angles during simple rehabilitation exercises in healthy adults under controlled conditions. However, reliability is inconsistent and frequently poor, and the system has not been evaluated in patient populations with pathological movement patterns. These findings do not support clinical deployment at this stage. Substantial further development and validation in patient cohorts are required before clinical or home-based use can be considered. + © 2026. The Author(s). + + + + Das + Utpal Chandra + UC + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. + + + School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Kelvin Grove, Brisbane, QLD, 4059, Australia. + + + Centre for Biomedical Technologies (CBT), Queensland University of Technology, Brisbane, QLD, 4000, Australia. + + + + Le + Ngoc Thien + NT + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. + + + + Vitoonpong + Timporn + T + + Department of Rehabilitation Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand. timporn.v@chula.ac.th. + + + Department of Rehabilitation Medicine, King Chulalongkorn Memorial Hospital & The Thai Red Cross Society, Bangkok, 10330, Thailand. timporn.v@chula.ac.th. + + + + Pongmala + Chatkaew + C + + Department of Radiology, University of Michigan, Ann Arbor, 48104, MI, USA. + + + Department of Neurology, University of Michigan, Ann Arbor, 48104, MI, USA. + + + + Prapinpairoj + Chalermdej + C + + Department of Rehabilitation Medicine, King Chulalongkorn Memorial Hospital & The Thai Red Cross Society, Bangkok, 10330, Thailand. + + + + Anannub + Kawee + K + + Health Service Center, Chulalongkorn University, Bangkok, 10330, Thailand. + + + + Kaewplung + Pasu + P + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. + + + + Chaitusaney + Surachai + S + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. surachai.c@chula.ac.th. + + + + Benjapolakul + Watit + W + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. watit.b@chula.ac.th. + + + + eng + + + B04G640068 + National Science, Research and Innovation Fund (NSRF) via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + Competing interests: The authors declare no competing interests. Ethical approval: The research conducted with healthy human participants at Chulalongkorn University in Thailand complied with the Regulations Governing Life Science and Medical Research Involving Human Subjects. Approval was secured from the Institutional Review Board of the Faculty of Medicine, Chulalongkorn University, Thailand (COA No. 0495-2022; IRB No. 846-64), in accordance with the Declaration of Helsinki, The Belmont Report, CIOMS Guidelines, and ICH-GCP. Informed consent: All study participants provided written informed consent for participation and for the publication of identifiable information and images. Participants were fully informed of the study objectives, dissemination plans, and potential risks and benefits prior to enrolment. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 9 + 20 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215670 + 10.1038/s41598-026-55431-x + 10.1038/s41598-026-55431-x + + + + + + 42215668 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Autism spectrum disorder identification using machine learning models on MRI data. + 10.1038/s41598-026-55163-y + + Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects sensory processing, speech, behavior and identifying the condition at an early stage is necessary to provide the treatment. The existing diagnostic methods are mainly based on personalized subjective and time consuming. This study introduces an objective diagnostic method by analyzing technical noise signature producing the Quality Vector which is extracted from multimodal MRI data. This work leverages Machine Learning (ML) and Deep Learning (DL) techniques on this Quality vector for improving ASD detection. The methodology leveraged technical Quality Assessment Protocol (QAP) metrics from the ABIDE II repository, integrating structural (sMRI), functional (fMRI), and diffusion (DTI) modalities. The methodology utilized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for outlier removal and Principal Component Analysis (PCA) for dimensionality reduction. A classification pipeline was implemented using a 1D-Residual Network (1D-ResNet), Convolutional Neural Network(CNN), SVM, kNN and a proposed Voting Ensemble (SVM, KNN, and XGBoost), validated through stratified 10-fold cross-validation. In this work, the proposed ensemble emerged as the superior model, achieving a diagnostic accuracy of 95.84%. These findings indicate that technical biomarkers derived from imaging quality metrics are highly useful for ASD detection. This research demonstrates that technical quality metrics, often dismissed as noise, contain significant diagnostic value. The Quality Vector framework provides a computationally efficient and objective tool for ASD identification. The high classification performance suggests that the biomarkers obtained from MRI have a potential future scope in research on early ASD diagnosis and detection. + © 2026. The Author(s). + + + + V + Milner Paul + MP + + Department of Electrical Engineering, National Institute of Technology Manipur (NITM), Manipur, India. vithayathilmilner@gmail.com. + + + Department of Electrical and Electronics Engineering, Christ College of Engineering, Thrissur, Kerala, 680125, India. vithayathilmilner@gmail.com. + + + + Babu + Caren + C + + Department of Electronics and Communication Engineering, Christ College of Engineering, Thrissur, Kerala, 680125, India. + + + + Jose + Suni + S + + Department of Computer Science and Engineering, Christ College of Engineering, Thrissur, Kerala, 680125, India. + + + + Adhikari + Shuma + S + + Department of Electrical Engineering, National Institute of Technology Manipur (NITM), Manipur, India. + + + + Singh + Loitongbam Surajkumar + LS + + Department of Electronics and Communication Engineering, National Institute of Technology Manipur (NITM), Manipur, India. + + + + Francis + Jijo + J + + Department of Zoology, Christ College (Autonomous), 680125, Irinjalakuda, India. + + + + Parekkattil + Adarsh V + AV + + Department of Electronics and Communication, Indian Institute of Technology Roorkee (IITR), Roorkee, India. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Autism spectrum disorder + Convolutional neural network (CNN) + Early detection + Machine learning + Magnetic resonance imaging + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 7 + 8 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215668 + 10.1038/s41598-026-55163-y + 10.1038/s41598-026-55163-y + + + + + + 42215666 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Blockchain-driven machine learning-enabled intrusion-resilient authenticated key agreement protocol for edge-centric IoT systems. + 10.1038/s41598-026-55266-6 + + The edge computing-based Internet of Things (IoT) system minimizes latency by processing data locally, reducing the distance it needs to travel. Processing data in proximity to its source enables rapid decision-making and real-time reactions. The edge-based IoT has several possible uses, including smart cities, smart healthcare, industrial automation & processing, smart farming, and many more. In this paper, we propose a blockchain-driven machine learning-enabled intrusion-resilient authenticated key agreement scheme for edge-centric IoT systems (in short, BMAS-EIoT), which is equipped with the features of authentication, key management, and machine learning-based intrusion detection. In BMAS-EIoT, we provide the network and threat models to enhance comprehension of the organization and deployment of devices and systems, as well as the potential threats to the system. BMAS-EIoT has been observed to possess protection against a variety of potential attacks during the security investigation. Moreover, it has been observed that BMAS-EIoT outperforms other present schemes in terms of performance comparison. A practical implementation of BMAS-EIoT is provided to evaluate the effectiveness of its key components, including intrusion detection and blockchain implementation. Furthermore, BMAS-EIoT possesses supplementary noteworthy capabilities and enhanced security attributes. + © 2026. The Author(s). + + + + Karnatak + Vijay + V + + Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India. + + + + Tripathi + Neha + N + + Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India. + + + + Wazid + Mohammad + M + + Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India. + + + + Mittal + Saksham + S + + Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, 248002, Uttarakhand, India. + + + + Das + Ashok Kumar + AK + + Center for Security, Theory and Algorithmic Research, International Institute of Information Technology, Hyderabad, 500032, India. ashok.das@iiit.ac.in. + + + Department of Computer Science and Engineering, College of Informatics, Korea University, 145 Anam-ro, Seongbuk-gu, 02841, Seoul, South Korea. ashok.das@iiit.ac.in. + + + + K + Vivekananda Bhat + VB + + Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India. kv.bhat@manipal.edu. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Authentication + Blockchain + Edge computing + Internet of things + Intrusion detection + Security + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 3 + 23 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215666 + 10.1038/s41598-026-55266-6 + 10.1038/s41598-026-55266-6 + + + + + + 42215649 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Machine learning based approaches for structure activity relationship analysis of heparanase inhibitors. + 10.1038/s41598-026-55098-4 + + Human Heparanase (HPSE), the only mammalian endo-β-D-glucuronidase, plays an important role in extracellular matrix remodeling and the release of heparin-bound growth factors. Its overexpression is strongly correlated with increased tumor growth, angiogenesis, metastasis, and inflammation, highlighting HPSE as a compelling therapeutic target for oncology and inflammatory diseases. This study aimed to develop and validate a robust computational workflow for predicting the activity class of potential HPSE inhibitors using curated data from the ChEMBL database. Bioactivity data ([Formula: see text]) for known HPSE inhibitors were extracted and put through a meticulous data curation process, which included chemical structure standardization, molecular weight filtering, and final deduplication based on standardized isomeric SMILES to ensure structural uniqueness. Continuous [Formula: see text] values (nM) were converted to [Formula: see text] and subsequently categorized into three activity classes: A ([Formula: see text] and [Formula: see text]), B ([Formula: see text] and [Formula: see text]), and C ([Formula: see text] and [Formula: see text]) for multi-class classification. Molecular representations included two-dimensional physicochemical descriptors, Morgan fingerprints, and three-dimensional descriptors derived from optimized low-energy conformers generated using ETKDGv3 and MMFF94s. Multiple machine learning classifiers were evaluated using pipelines incorporating imputation, scaling, optional Principal Component Analysis (PCA) dimensionality reduction applied to the combined feature sets, and SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance. Models were trained and optimized using randomized search cross-validation on an 80% training split, maximizing balanced accuracy. The best-performing model pipeline (RF_B, a Random Forest with PCA on 2D+Morgan Fingerprints+3D features) achieved approximately 80% accuracy and 78.5% balanced accuracy on the held-out 20% test set. The final validated model was successfully utilized to predict the activity classes of new, unseen compounds. This comprehensive pipeline provides a validated tool for classifying HPSE inhibitors derived from ChEMBL data, potentially aiding virtual screening efforts and guiding hit prioritization in drug discovery campaigns targeting HPSE. + © 2026. The Author(s). + + + + Shanbhogue + Rachana V + RV + + Manipal Institute of Technology, Manipal Academy of Higher Education, 576104, Manipal, Karnataka, India. shanbhoguerachana@gmail.com. + + + + Gandhi + Neha S + NS + + Department of Biotechnology and Chemical Engineering, Manipal University Jaipur, 303007, Rajasthan, India. neha.gandhi@jaipur.manipal.edu. + + + School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, Queensland, Brisbane, Australia. neha.gandhi@jaipur.manipal.edu. + + + + B + Shanthi P + SP + + Manipal Institute of Technology, Manipal Academy of Higher Education, 576104, Manipal, Karnataka, India. shanthi.moorkoth@manipal.edu. + + + + Navada + Sandhyalaxmi G + SG + + Manipal Institute of Technology, Manipal Academy of Higher Education, 576104, Manipal, Karnataka, India. sandhya.girish@manipal.edu. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 11 + 10 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 20 + + + aheadofprint + + 42215649 + 10.1038/s41598-026-55098-4 + 10.1038/s41598-026-55098-4 + + + + + + 42215613 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + A temporal adaptive dictionary-constrained LDA and Bi-calibrated dual granularity DTM framework for dynamic topic evolution analysis in academic papers. + 10.1038/s41598-026-54348-9 + + With the explosive growth of academic achievements in artificial intelligence (AI), accurately capturing the dynamic evolution of research topics is crucial for research topic selection, policy-making, and industrial innovation. To address the limitation that traditional topic models struggle to balance static topic recognition accuracy and dynamic evolution tracking capability, this study proposes a unified framework: Temporal Adaptive Dictionary-Constrained Latent Dirichlet Allocation (LDA) combined with Bi-calibrated Dual Granularity Dynamic Topic Model (DTM). We conduct a systematic empirical analysis of dynamic topic evolution in the AI field using English AI papers retrieved from the Web of Science (WOS) Core Collection (2014-2023). Specifically, we first optimize the topic initialization process of LDA using a domain-specific dictionary to enhance the accuracy of static topic clustering. Second, we take the core topics identified by the optimized LDA as prior knowledge and input them into DTM, while optimizing the traditional single temporal granularity to an "annual + quarterly" dual granularity to accurately track the changes in topic strength, keyword composition, and cross-topic association relationships over time. Finally, we verify the performance of the proposed model using perplexity and topic coherence indicators, and reveal the coupling characteristics between different topics through correlation analysis. Results indicate that six core topic clusters have emerged in the AI field, with "generative AI", "large language models", and "multimodal learning" evolving into explosive topics after 2021, traditional machine learning shows a declining trend, with its core focus shifting toward "few-shot learning" and "edge computing adaptation". The top three topic couplings are "generative AI-large language models", "computer vision-multimodal learning" and "reinforcement learning-robotics". The proposed combined model outperforms single models, achieving a 17.3% reduction in perplexity and a 23.5% decrease in topic drift rate, thereby providing valuable decision support for researchers seeking to grasp field frontiers and management departments for optimizing resource allocation. + © 2026. The Author(s). + + + + Yin + Xueqi + X + + School of International Studies, Tianjin University of Commerce, Tianjin, 300134, China. yinxq@tjcu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Academic paper analysis + Artificial intelligence + Dynamic topic model + Latent dirichlet allocation + Text mining + Topic evolution + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 30 + + + 2026 + 5 + 18 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215613 + 10.1038/s41598-026-54348-9 + 10.1038/s41598-026-54348-9 + + + + + + 42215604 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Development and validation of a noninvasive machine learning model using urinary extracellular vesicle physical parameters for prostate cancer diagnosis. + 10.1038/s41598-026-54920-3 + + Urinary extracellular vesicles (uEVs) are promising biomarkers for prostate cancer (PCa). Although novel uEV-based biomarkers have advanced early diagnosis, their detection processes remain cumbersome and cost-prohibitive. The physical parameters of uEVs offer untapped predictive potential for PCa, providing a more convenient, rapid, and cost-effective diagnostic approach. This study aims to construct a predictive model integrating uEV physical parameters with machine learning (ML) algorithms to enhance the early diagnosis of PCa. Urine samples were collected from 222 eligible participants. uEVs were isolated using a commercial kit, and their concentration and size parameters were quantified via nanoparticle tracking analysis (NTA). Utilizing these physical parameters, five ML algorithms were trained and evaluated to identify the optimal diagnostic model for PCa. Model performance was systematically assessed using the area under the receiver operating characteristic curve (AUC), learning curves, calibration curves, and decision curve analysis (DCA). Additionally, SHapley Additive exPlanations (SHAP) were employed to visualize the contributions of key predictors. Analysis of uEV physical parameters revealed that, compared to benign controls, PCa patients exhibited a significantly higher proportion of 30-150 nm uEVs and a smaller overall particle size (p < 0.001). Among the evaluated algorithms, the eXtreme Gradient Boosting (XGBoost) model demonstrated superior performance. For discriminating PCa from benign prostatic hyperplasia (BPH), the XGBoost model achieved AUC values of 0.934 and 0.864 in the training and testing cohorts, respectively. DCA demonstrated that the XGBoost model yielded a higher clinical net benefit across a threshold probability range of 0-60%. Overall, the diagnostic efficacy and clinical utility of the XGBoost model significantly outperformed routine clinical parameters, including prostate-specific antigen (PSA) and prostate-specific antigen density (PSAD). This study successfully developed and validated a noninvasive ML-based diagnostic model utilizing the physical parameters of uEVs. This approach serves as a preliminary adjunctive tool to assist clinicians in accurately identifying prostate cancer, thereby potentially reducing the incidence of unnecessary prostate biopsies. + © 2026. The Author(s). + + + + Jiang + Kangxian + K + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + Department of Surgical Critical Care Medicine, Beijing Shijitan Hospital, Capital Medical University, No.10, Tieyi Road, Beijing, 100038, China. + + + + Pan + Dongwei + D + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Yu + Jiayin + J + + Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + + Cai + Wenbing + W + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Zhang + Bingfeng + B + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Wang + Zuheng + Z + + Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China. + + + Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + + Lu + Yuqing + Y + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + + Wang + Dianyu + D + + Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China. + + + + Li + Xiao + X + + Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China. + + + Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + + Zheng + Deyong + D + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Wang + Mingda + M + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Wang + Fubo + F + + Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China. wangfubo@gxmu.edu.cn. + + + Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, Guangxi, China. wangfubo@gxmu.edu.cn. + + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. wangfubo@gxmu.edu.cn. + + + + Chen + Junyi + J + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. chenjunyidoctor@163.com. + + + + eng + + + 82372828, + National Natural Science Foundation of China + + + + 2023GXNSFFA026003 + Science Foundation for Distinguished Young Scholars of Guangxi + + + + AA22096030 + Science and Technology Major Project of Guangxi + + + + 2021015 + Yongjiang Program of Nanning + + + + 2021GGA038 + the Science and Technology Plan Project of Fujian Provincial Health Commission + + + + 2022C035R + High-level Talents Innovation and Entrepreneurship Project of Quanzhou Science and Technology Plan + + + + 2022J01277 + Natural Science Foundation of Fujian Province + + + + 2025CXB023 + Fujian Provincial Health Commission + + + + 2026J001703 + Fujian Provincial Natural Science Foundation of China + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Diagnosis + Machine learning + NTA + Prostate cancer + Urinary extracellular vesicles + + Declarations. Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: The studies involving human participants were reviewed and approved by the Ethics Committee of Shanghai Changhai Hospital, the Ethics Committee of Taizhou People’s Hospital, and the Ethics Committee of Zhongda Hospital. (Approval No. CHEC2013-115).Clinical trial number: not applicable. This study strictly adhered to the ethical principles outlined in the Declaration of Helsinki, ensuring that all data were derived from actual clinical cases. Informed consent: Informed consent was obtained from each patient or their legal representative before sample collection. Consent for publication: Not applicable. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 10 + 14 + + + 2026 + 5 + 21 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215604 + 10.1038/s41598-026-54920-3 + 10.1038/s41598-026-54920-3 + + + + + + 42215600 + + 2026 + 05 + 29 + +
+ + 2399-3669 + + + 2026 + May + 29 + + + Communications chemistry + Commun Chem + + CholBindNet as an interpretable neural network for cholesterol-binding site classification. + 10.1038/s42004-026-02064-w + + Cholesterol is a key modulator of membrane protein structure and function, yet predicting cholesterol-binding sites remains challenging due to its non-druglike physicochemical properties. Here, we curated more than 800 high-resolution transmembrane protein structures containing cholesterol, and developed an interpretable, atom-based graph neural network, called CholBindNet. A positive-unlabeled (PU) training strategy was employed to address the scarcity of negative samples due to the promiscuous nature of cholesterol binding. We show that CholBindNet substantially outperforms existing machine learning models trained on general ligand-binding datasets, including AlphaFold3, P2Rank, and DiffDock. The performance and generalizability of the model on unseen membrane proteins were further demonstrated by rapidly assessing cholesterol-binding sites in the PIEZO2 ion channel against all-atom molecular dynamics (MD) simulations conducted on Anton3 supercomputer. Additionally, strong model interpretability was achieved for CholBindNet through atom-level feature encoding, Grad-CAM visualization, and attention-based scoring analysis. Overall, CholBindNet provides an efficient and scalable approach for classifying and ranking cholesterol-binding sites on membrane proteins, achieving performance comparable to computationally expensive MD simulations while offering rich biophysical insights into the atomic-level spatial patterns beyond amino-acid sequence. This work lays the foundation for future deep-learning models targeting membrane protein drug-binding sites and cholesterol-modulated therapeutics. + © 2026. The Author(s). + + + + Hernandez + Alexis + A + + Computer Science Department, California State Polytechnic University, Pomona, CA, USA. + + + Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA, USA. + + + + Bhatt + Aashish + A + + Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA, USA. + + + + Revilla + Ivan + I + + Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA, USA. + + + + Levine + Jacob Ede + JE + + Computer Science Department, California State Polytechnic University, Pomona, CA, USA. + + + + Kosaraju + Sai Chandra + SC + 0000-0002-4332-6217 + + Computer Science Department, California State Polytechnic University, Pomona, CA, USA. skosaraju@cpp.edu. + + + + Luo + Yun Lyna + YL + 0000-0003-3581-754X + + Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA, USA. luoy@westernu.edu. + + + + eng + + + GM130834 + U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Commun Chem + 101725670 + 2399-3669 + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 1 + 1 + + + 2026 + 5 + 4 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215600 + 10.1038/s42004-026-02064-w + 10.1038/s42004-026-02064-w + + + + + + 42215593 + + 2026 + 05 + 29 + +
+ + 2397-768X + + + 2026 + May + 29 + + + NPJ precision oncology + NPJ Precis Oncol + + Early detection of gastric cancer: a novel circulating microbiome DNA based liquid biopsy assay. + 10.1038/s41698-026-01532-9 + + Microorganisms play significant roles in gastric cancer (GC) progression. However, it is unknown whether circulating microbiome DNA (cmDNA) possesses GC specific features and could serve as diagnostic biomarker for GC detection. In this study, one cohort of 586 participants from Zhejiang Cancer Hospital were divided randomly into the training and testing datasets. Another cohort of 299 participants enrolled from three hospitals was used as an independent validation cohort. The cmDNA from plasma samples were analyzed by sequencing and various tools. The significant features of cmDNA were used as inputs to establish a machine learning diagnostic model (cmDNA-MLM). The cmDNA-MLM achieved the area under receiver operating characteristic curve (AUC) of 0.831 for GC across all stages in the testing cohort and 0.914 in the independent validation cohort. Notably, this model demonstrated strong performance in detecting early-stage GC, achieving an AUC of 0.792 for stage I GC in the validation cohort and exhibited favorable sensitivities across various molecular subtypes. The stage shift analysis showed a notable increase in the number of patients diagnosed at stage I. This cmDNA-MLM exhibited promising performance in early GC detection, which could be used as a clinical liquid biopsy methodology after more clinical validation studies. + © 2026. The Author(s). + + + + Chen + Yongyi + Y + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China. + + + + Han + Xinhong + X + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Luo + Miao + M + + Department of Clinical Laboratory, Chongqing Yubei District People's Hospital, Chongqing, China. + + + + Luo + Liulin + L + + Department of Clinical Laboratory, Shanghai Yangpu Hospital, Tongji University School of Medicine, Shanghai, China. + + + + Wang + Tingzhang + T + + Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou, China. + + + + Kong + Congcong + C + + Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou, China. + + + + Ye + Liuqing + L + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Jin + Jiangang + J + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Hou + Dingding + D + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Liao + Haiqi + H + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Wang + Zhonglin + Z + + OmixScience Laboratory, OmixScience Co. Ltd., Hangzhou, China. + + + + Xue + Wei + W + + OmixScience Laboratory, OmixScience Co. Ltd., Hangzhou, China. + + + + Lin + Ziao + Z + + OmixScience Research Institute, OmixScience Co. Ltd., Shenzhen, China. + + + Liangzhu Laboratory, Zhejiang University, Hangzhou, China. + + + + Xu + Songxiao + S + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. xusx@zjcc.org.cn. + + + School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China. xusx@zjcc.org.cn. + + + + eng + + + 82302622 + National Natural Science Foundation of China + + + + 2022KY675 + Zhejiang Province Medical and Health Technology Project + + + + 2024KY862 + Zhejiang Province Medical and Health Technology Project + + + + 2024C03050 + "Pioneer" and "Leading Goose" R&D Program of Zhejiang + + + + 2022C03002 + "Pioneer" and "Leading Goose" R&D Program of Zhejiang + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Precis Oncol + 101708166 + 2397-768X + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 9 + 16 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215593 + 10.1038/s41698-026-01532-9 + 10.1038/s41698-026-01532-9 + + + + + + 42215588 + + 2026 + 05 + 29 + +
+ + 2398-6352 + + + 2026 + May + 29 + + + NPJ digital medicine + NPJ Digit Med + + Development and validation of a machine learning-based diagnostic system for 22 pediatric respiratory pathogens: a large-scale multicenter study. + 10.1038/s41746-026-02818-9 + + Respiratory tract infections (RTIs) are a significant cause of morbidity in children, caused by a wide range of pathogens. As treatment strategies depend on the causative pathogen, early and accurate diagnosis is crucial. We developed and validated an interpretable Pathogen Diagnostic System for Pediatric Respiratory Infections (Pathog-PDx), conducting a multicenter study involving 134,500 hospitalized children across three clinical centers and two databases. The model integrated 42 clinical and laboratory features from electronic health records (EHR) to enable early pathogen identification. Prospective validation was carried out on an independent cohort of 1338 children to assess the real-world applicability of the model. Pathog-PDx accurately distinguished 22 pathogen subtypes and outperformed conventional models in identifying both single and mixed infections. The model achieved high classification performance for key pathogens, such as influenza virus (AUC = 0.95; Sn: 0.88; Sp: 0.86), with mean AUCs of 0.88 for various pathogens of RTIs. The model has been deployed as a web-based decision support system, which is freely accessible at https://pathogpdx.zzu.edu.cn. Altogether, Pathog-PDx represents a potential tool for the early identification of respiratory tract pathogens in pediatric patients, which can provide actionable predictions ahead of conventional test results to guide timely and targeted therapy. + © 2026. The Author(s). + + + + Su + Dubin + D + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China. + + + + Chen + Qun + Q + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Xiamen Cell Therapy Research Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China. + + + + Xu + Ruizhi + R + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China. + + + + Chen + Qihong + Q + + Department of Pediatrics, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Department of Pediatrics, Pediatric Key Laboratory of Xiamen, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China. + + + + Ma + Chiyuan + C + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China. + + + + Chen + Xi + X + + Department of Clinical Laboratory, Children's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, Fujian, China. + + + + Yang + Yunyun + Y + + Department of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + + Qin + Feibo + F + + Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China. + + + + Zhou + Ziwei + Z + + Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China. + + + + Li + Sixian + S + + Department of Functional Neurology, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China. + + + + Peng + Jie + J + + Department of Pediatrics, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Department of Pediatrics, Pediatric Key Laboratory of Xiamen, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China. + + + + Ge + Dandan + D + + Department of Pediatrics, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Department of Pediatrics, Pediatric Key Laboratory of Xiamen, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China. + + + + Fan + Jiajun + J + + Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, Champaign, IL, USA. + + + + Li + Xingyu + X + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + + Wang + Yanbo + Y + + Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China. + + + + Yang + Yungang + Y + + Department of Pediatrics, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. xmyyg@sina.com. + + + Department of Pediatrics, Pediatric Key Laboratory of Xiamen, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China. xmyyg@sina.com. + + + + Guo + Yaping + Y + + Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China. guoyaping@zzu.edu.cn. + + + + Yang + Jingjing + J + + Department of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. Jingjingyang_xmu@126.com. + + + + Ning + Wanshan + W + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. ningwanshan@xmu.edu.cn. + + + + eng + + + 3502Z20214001 + Project of Xiamen Cell Therapy Research Center + + + + 32570802 + National Natural Science Foundation of China + + + + 32400532 + National Natural Science Foundation of China + + + + 3502Z20254ZD1032 + the Guided Project of Medical and Health Care in Xiamen + + + + 3502Z20254ZD1004 + the Guided Project of Medical and Health Care in Xiamen + + + + 2021ZD0201300 + National Key R&D Program of China + + + + 2024GGB18 + Fujian Provincial Health Technology Project + + + + 2025D022 + Fujian Science and Technology Program Guiding Project + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Digit Med + 101731738 + 2398-6352 + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 8 + 19 + + + 2026 + 5 + 15 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215588 + 10.1038/s41746-026-02818-9 + 10.1038/s41746-026-02818-9 + + + + + + 42215586 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Ethnicity-specific molecular subtypes and a machine-learning risk model in Asian patients with non-muscle-invasive bladder cancer. + 10.1038/s41598-026-55438-4 + + Non-muscle-invasive bladder cancer (NMIBC) has high rates of recurrence and progression, yet its molecular landscape across different ethnicities remains poorly defined. We assembled a meta-cohort of 202 Asian NMIBC transcriptomes and identified three novel molecular subtypes (AC0, AC1, AC2) using non-negative matrix factorization clustering. These subtypes, each associated with distinct biological pathways and clinical outcomes, were externally validated across 22 datasets. Subtype-specific biomarkers (RABL6, MYBL2, RAD54L, and FAM64A) were confirmed by immunohistochemistry in an independent Asian cohort (n = 210), enabling the development of a machine-learning-based risk stratification model. AC1, characterized by aggressive features and enrichment of cell cycle and oncogenic signaling pathways, exhibited the poorest prognosis. Notably, after adjustment for T-stage and grade, the combined risk model was independently prognostic in Asian cohorts but not in European cohorts, underscoring ethnic differences in NMIBC biology. Our findings delineate distinct molecular subtypes and introduce a clinically applicable, ethnicity-specific prognostic model for Asian patients with NMIBC. This study emphasizes the importance of incorporating ethnic diversity into precision oncology frameworks and provides a foundation for personalized therapeutic and surveillance strategies in underrepresented populations. + © 2026. The Author(s). + + + + Jung + Minsun + M + + Department of Pathology, Yonsei University College of Medicine, Seoul, Republic of Korea. + + + + Park + Sangyong + S + + Department of Statistics, Seoul National University, Seoul, Republic of Korea. + + + + Jang + Insoon + I + + Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea. + + + Blood Transfusion Research Institute, Korean Red Cross, Wonju, Gangwon-do, Republic of Korea. + + + + Han + Dohyun + D + + Department of Transdisciplinary Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul, Republic of Korea. + + + Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea. + + + + Cho + Yong Mee + YM + + Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. + + + + Kim + Kwangsoo + K + + Department of Transdisciplinary Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul, Republic of Korea. kksoo716@gmail.com. + + + Department of Medicine, Seoul National University, Seoul, Republic of Korea. kksoo716@gmail.com. + + + + Moon + Kyung Chul + KC + + Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea. blue7270@snu.ac.kr. + + + Kidney Research Institute, Medical Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea. blue7270@snu.ac.kr. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Cluster Analysis + Ethnicity + Machine Learning + Non-Muscle Invasive Bladder Neoplasms + Tumor Biomarkers + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 5 + 13 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215586 + 10.1038/s41598-026-55438-4 + 10.1038/s41598-026-55438-4 + + + + + + 42215583 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Wireless charging ferrite core design with improved material efficiency for mobile devices by enhancing outer-region flux density. + 10.1038/s41598-026-55270-w + + Wireless power transfer (WPT) systems integrated with machine learning (ML) techniques have emerged as promising solutions for enhancing performance and efficiency. Material efficiency, which involves achieving high performance with minimal material use, is crucial for mobile devices to enhance user satisfaction. This paper proposes a core structure design methodology for WPT systems that enhances mutual inductance (MI) by segmenting and assembling the ferrite core, thereby improving material efficiency within limited space constraints. For genetic algorithm (GA)-based optimization, an ML estimator is implemented to replace iterative finite element method simulations during the GA optimization loop, reducing the average computation time from 203s to 86μs and significantly accelerating the GA optimization process. Furthermore, the designed core structure exhibits improved uniformity of magnetic flux density by enhancing the outer-region magnetic flux density between the coils, thereby contributing to higher MI. Experimental results show that the proposed core structure achieves a 17.44% higher normalized MI compared to conventional structures, using less material through an asymmetrical configuration, which also reduces weight and cost. Furthermore, the optimized asymmetrical structure demonstrates robustness under lateral misalignment conditions ([Formula: see text] mm), consistently maintaining higher material efficiency than the full-piece reference even under spatial offsets. These findings demonstrate the potential of ML-driven approaches for developing high-performance, computationally efficient, and cost-effective WPT systems. + © 2026. The Author(s). + + + + Park + Yohan + Y + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. + + + + Shah + Syed Ahson Ali + SAA + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. + + + + Cho + Woo-Young + WY + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. + + + + Park + Hyojun + H + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. + + + + Lee + Hyunwoong + H + + Oceansbio, Seoul, 04303, South Korea. + + + + Kim + Yun-Su + YS + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. yunsukim@gist.ac.kr. + + + Research Institute for Solar and Sustainable Energies, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. yunsukim@gist.ac.kr. + + + + eng + + + No. RS-2026-25533249 + Korea Institute of Energy Technology Evaluation and Planning + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Ferromagnetic cores design + Genetic algorithm + Inductive power transfer + Mutual inductance + Wireless power transfer + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 3 + + + 2026 + 5 + 23 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215583 + 10.1038/s41598-026-55270-w + 10.1038/s41598-026-55270-w + + + + + + 42215579 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Machine learning based detection of subacute ruminal acidosis in early lactation dairy cows using multi-sensor behavioral, physiological, and milk production data. + 10.1038/s41598-026-55162-z + + Subacute ruminal acidosis (SARA) is a common metabolic disorder in early lactation dairy cows that negatively affects rumen function, milk production, and animal welfare. Early identification remains challenging because clinical signs are often subtle and transient. The aim of this study was to evaluate whether multi sensor behavioral, physiological, and milk-production data could be used to identify cows experiencing SARA using machine-learning approaches. The study included early-lactation Holstein cows during the first 100 days in milk. The final dataset comprised 636 cow-day observations, including 134 SARA cases and 502 clinically healthy controls. Cow identification numbers were additionally used as grouping variables during cross-validation to ensure complete separation of individual animals between training and validation subsets. SARA was defined based on continuous ruminal pH measurements, where cows were classified as SARA when ruminal pH remained between 5.2 and 5.8 for at least 180 min per day. Sensor derived variables included rumination time, activity, water intake, reticulorumen temperature, milk yield, and milk composition obtained from intraruminal boluses and an in-line milk analyzer. Six supervised machine learning classifiers were developed to classify SARA status based on combined sensor data. Among the evaluated models, SVM demonstrated the highest discriminatory performance, which achieved an area under the curve (AUC) of 0.97, accuracy of 0.95, sensitivity of 0.86, and specificity of 0.98 under repeated cow-level grouped cross-validation. Random forest showed similar performance (AUC = 0.97; accuracy = 0.93 and 0.98, respectively). Across all models, specificity was consistently higher than sensitivity, indicating that healthy cows were classified more accurately than SARA cases. These results demonstrate that integrated behavioral, physiological, and milk production data obtained from automated sensor systems can support classification of cows experiencing SARA under commercial farm conditions. The findings support the potential of multi sensor monitoring systems combined with machine learning classifiers as a tool for automated detection of rumen health disturbances in precision dairy farming. + © 2026. The Author(s). + + + + Grigė + Samanta + S + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. Samanta.grige1@lsmu.lt. + + + + Girdauskaitė + Akvilė + A + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Grigas + Ovidijus + O + + Faculty of Informatics, Kaunas University of Technology, 50254, Kaunas, Lithuania. + + + + Japertas + Sigitas + S + + Practical Training and Research Center, Lithuanian University of Health Sciences, Topolių g. 6, 54310, Kaunas, Lithuania. + + + + Malašauskienė + Dovilė + D + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Televičius + Mindaugas + M + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Urbutis + Mingaudas + M + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Džermeikaitė + Karina + K + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Krištolaitytė + Justina + J + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Antanaitis + Ramūnas + R + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Biosensors + Dairy cattle + Innovations + Machine learning + + Declarations. Competing interests: The authors declare no competing interests. Ethics approval: All animal procedures were reviewed and approved by the Institutional Animal Care and Use Committee of the Lithuanian University of Health Sciences (Protocol No. G2-227, approved on 7 March 2025). + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 1 + 2 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 18 + + + aheadofprint + + 42215579 + 10.1038/s41598-026-55162-z + 10.1038/s41598-026-55162-z + + + + + + 42215538 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Transition of blue space in the urban area of khulna city corporation using geospatial and machine learning techniques. + 10.1038/s41598-026-52656-8 + + Urban blue-space loss and grey-space expansion have significantly altered the surface water systems of the Khulna City Corporation, increasing the environmental degradation and hydrological vulnerability of this climate-sensitive coastal city. This study used multi-temporal Landsat imagery, including Landsat 5 TM data from 1994 to 2004 and Landsat 8 OLI data from 2014 to 2024. The analysis integrated the Normalized Difference Vegetation Index, Normalized Difference Built-up Index, and Normalized Difference Water Index with Random Forest classification and post-classification change detection to quantify long-term land-use/land-cover transitions and urban blue-space transformation. Post-classification comparison generated directional transition matrices documenting conversion pathways among land-cover categories. The results show that surface water bodies have decreased from 4.20 km2 in 1994 to 2.27 km2 in 2024, while the built-up area has increased from 25.9 to 53.3% of the area studied. The accuracy assessment resulted in an overall accuracy of 84.5 to 89.3%, with Kappa coefficients ranging from 0.78 to 0.86. Water body classifications have remained reliable throughout all periods, which has supported the use of classifiable maps for long-term migration analysis. Temporal analysis revealed two main phases: an early phase characterized by mixed land reclamation and later phases dominated by conversion of water bodies into built-up areas. Spatial analysis identified critical hotspots of canal infill, wetland loss, and riverbank modification, mainly in central and southern municipal zones. These findings highlight the need to treat urban blue spaces as critical infrastructure and to promote integrated management, natural drainage solutions and regular remote sensing monitoring. The study frames the loss of blue space as a process of urban transformation and advocates better monitoring of urban water systems in coastal secondary coastal cities. + © 2026. The Author(s). + + + + Hossan + Md Refath + MR + + Institute of Disaster Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh. + + + + Hossain + Mohammad Ismail + MI + + Institute of Disaster Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh. ihossain@idm.kuet.ac.bd. + + + + Islam + Md Wahidul + MW + + Department of Chemistry, Gopalganj Science and Technology University, Gopalganj, 8100, Bangladesh. + + + + Shaon + Zahid Hassan + ZH + + Institute of Disaster Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + GIS analysis + Land cover change + Remote sensing + Surface water decline + Urban expansion + + Declarations. Competing interests: The authors declare no competing interests. Consent to participate: This study did not involve any human participants, animal subjects, or personally identifiable. data, and therefore did not require ethical approval. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 4 + 6 + + + 2026 + 5 + 6 + + + 2026 + 5 + 29 + 23 + 17 + + + aheadofprint + + 42215538 + 10.1038/s41598-026-52656-8 + 10.1038/s41598-026-52656-8 + + + + + + 42215534 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + An explainable spatio-temporal deep learning framework for crop yield prediction and recommendation. + 10.1038/s41598-026-54439-7 + + Agriculture plays a critical role in ensuring global food security, yet crop yield variability driven by climate change, soil heterogeneity, and environmental fluctuations poses persistent challenges. Accurate yield forecasting and intelligent crop recommendation are essential for sustainable and efficient farm management. This study proposes a novel AI-driven framework for precise crop yield prediction and data-driven crop selection. The framework integrates systematic data preprocessing, hybrid feature selection, deep learning, and attention-based modeling to capture complex nonlinear relationships within agricultural datasets. Raw data, including soil properties, topography, climatic variables, and historical yield records, are processed using median-based imputation, normalization, and Z-score outlier detection to enhance reliability. A multi-stage hybrid feature selection approach combining Minimum Redundancy Maximum Weight, Sequential Forward Subset Selection, and Recursive Fisher Score identifies the most informative features while reducing redundancy. Yield prediction is performed using an attention-enhanced hybrid kernel Extreme Learning Machine (ELM). Crop recommendation is achieved through a Spatio-Temporal Explainable Group-Enhanced Transformer Network (STX-GTNET) optimized with the PantheraCobra metaheuristic. Model interpretability is ensured using Grad-CAM and Integrated Gradients. Experimental results demonstrate strong performance, achieving an RMSE of 281.6 and R² of 0.94 for yield prediction, and 98.4% accuracy with a 0.991 ROC-AUC for crop recommendation. + © 2026. The Author(s). + + + + Piyar S + Nifasath + N + + Department of Computer Science and Engineering, Anna University, Chennai, 600 025, India. nifasathpiyar@gmail.com. + + + + S + Dr Baulkani + DB + + Department of Electronics and Communication Engineering, Government College of Engineering, Bodinayakkanur, India. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Crop recommendation system + Crop yield prediction + Explainable artificial intelligence and precision agriculture + Spatio-temporal deep learning + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 24 + + + 2026 + 5 + 19 + + + 2026 + 5 + 29 + 23 + 17 + + + aheadofprint + + 42215534 + 10.1038/s41598-026-54439-7 + 10.1038/s41598-026-54439-7 + + + + + + 42215508 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + The role of biopsychosocial factors in classifying pain intensity across various chronic pain conditions. + 10.1038/s41598-026-46612-9 + + Chronic pain is a multifaceted condition shaped by biological, psychological, and social factors, which challenges its diagnosis and treatment. While prior research largely focused on sensory profiles to distinguish pain mechanisms, this study adopted a biopsychosocial perspective with the key addition of experimental pain paradigms to identify factors explaining variance in chronic pain severity across diverse etiologies. The dataset comprised 101 individuals with chronic pain and 63 pain-free controls. Participants were classified into three groups (no pain, mild-to-moderate pain, and severe pain) using three models: a sensory-profile-based model including quantitative sensory testing (QST model), a biopsychosocial model incorporating QST alongside psychological and social variables (biopsychosocial model), and a model using the same biopsychosocial data but excluding QST measures (noQST model). The QST model achieved an accuracy of 0.51 and an F1-score of 0.48. In contrast, the biopsychosocial model performed best, with both accuracy and F1-score of 0.71, while the noQST model reached 0.60 for both metrics. Key predictors in the best-performing model included quality of life, loss of sensation, depressive mood, pain catastrophizing, anxiety, fatigue, and general health. Overall, biopsychosocial factors enhanced chronic pain severity classification beyond sensory profiles alone across heterogeneous pain etiologies. + © 2026. The Author(s). + + + + De Schoenmacker + Iara + I + + Biomedical Data Science Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich, 8092, Zurich, Switzerland. deschoeia@gmail.com. + + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. deschoeia@gmail.com. + + + Biomedical Data Science Lab, Swiss Federal Institute of Technology (ETH), Lengghalde 2, 8008, Zurich, Switzerland. deschoeia@gmail.com. + + + + Monzon + Maria + M + + Biomedical Data Science Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich, 8092, Zurich, Switzerland. + + + + Sirucek + Laura + L + + Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland. + + + Department of Chiropractic Medicine, Integrative Spinal Research Group, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark. + + + + Scheuren + Paulina S + PS + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada. + + + Department of Anesthesiology, Pharmacology and Therapeutics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada. + + + + Lütolf + Robin + R + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Gorrell + Lindsay M + LM + + Department of Chiropractic Medicine, Integrative Spinal Research Group, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Brunner + Florian + F + + Physical Medicine and Rheumatology, Balgrist University Hospital, Zurich, Switzerland. + + + + Curt + Armin + A + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Rosner + Jan + J + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + Danish Pain Research Center, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark. + + + + Schweinhardt + Petra + P + + Department of Chiropractic Medicine, Integrative Spinal Research Group, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Hubli + Michèle + M + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Jutzeler + Catherine R + CR + + Biomedical Data Science Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich, 8092, Zurich, Switzerland. + + + Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland. + + + + eng + + + PF198F + International Foundation for Research in Paraplegia + + + + P500PB_214416 + Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung + + + + PZ00P3_186101 + Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung + + + + RT-2023-3173 + Michael Smith Health Research BC + + + + R359-2020-2620 + Lundbeck Foundation + + + + 380 + Strategic Focus Area "Personalized Health and Related Technologies (PHRT)" of the Swiss Federal Institutes of Technology + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Biopsychosocial model + Chronic pain + Machine learning + Pain classification + Quantitative sensory testing + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 5 + 21 + + + 2026 + 3 + 26 + + + 2026 + 5 + 29 + 23 + 16 + + + aheadofprint + + 42215508 + 10.1038/s41598-026-46612-9 + 10.1038/s41598-026-46612-9 + + + + + + 42215501 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Predictive analysis on mechanical properties, material flow and thermal analysis of friction stir welded dissimilar AA2014/AA7075 Al-alloy joints using machine learning. + 10.1038/s41598-026-48688-9 + + Aluminum alloys are the most ideal materials for aerospace applications, and joining of a light weighted materials with an eco-friendly method is encouraging ongoing research into the green welding technique. In the current work solid-state joints of 10 mm thick AA2014/AA7075 Al-alloys dissimilar material joints with hybrid pin profiles were fabricated by using Friction Stir Welding (FSW) technique. The microstructure and mechanical properties of weld joints, temperature analysis of the welds processed under various conditions is investigated. An Artificial Neural Network (ANN) model is used to predict the mechanical properties of the welded joints fabricated at different conditions. The weld joints processed with hybrid pentagonal pin profile exhibits the highest UTS and YS measuring as 294 and 267 MPa respectively. The ANN showed strong prediction using a dataset of 27 experimentally obtained results providing a reliable data-driven method for forecasting UTS and optimizing FSW parameters. The weld joints processed at 900 rpm 31. 5 mm/min 2° tilt angle 0 mm tool offset and pentagonal pin profile revealed 62% of joint efficiency, which is suitable for the suggested AWS requirements for aerospace applications must be met by 60%. ANN model provided a good agreement with experimental results (R² = 0.93). However, due to the relatively small dataset (L27 design). + © 2026. The Author(s). + + + + Rao + R Venkateswara + RV + + Department of Mechanical Engineering, Vignana Bharathi Institute of Technology, Telangana, 501301, India. + + + + Kumar + M Senthil + MS + + School of Mechanical Engineering, Vellore Institute of Technology, Chennai, 600127, India. msv305@yahoo.co.in. + + + + Vanmathi + M + M + + Department of Electronics and Communication Engineering, B S Abdurrahman Crescent Institute of Science and Technology, Chennai, 60048, India. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Dissimilar Materials + FSW Process + Mechanical Properties + Process Parameters + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 10 + 10 + + + 2026 + 4 + 9 + + + 2026 + 5 + 29 + 23 + 16 + + + aheadofprint + + 42215501 + 10.1038/s41598-026-48688-9 + 10.1038/s41598-026-48688-9 + + + + + + 42215494 + + 2026 + 05 + 29 + +
+ + 2396-8370 + + + 2026 + May + 29 + + + NPJ science of food + NPJ Sci Food + + Novel insights into the binding mechanisms of selected aldehydes during heat-induced protein unfolding. + 10.1038/s41538-026-00874-9 + + This investigation aimed to clarify the binding mechanisms between six aldehydes and myofibrillar proteins (MPs), with a structural explanation in response to stage-heating treatments. The conformational intermediates of MPs, which form during heat processing, were systematically characterized to elucidate their role in aldehyde binding and flavor retention. Machine learning results suggested that high-temperature boiling promoted extensive protein denaturation and aggregation, while subsequent low-temperature stewing induced partial rearrangement. Thermodynamic parameters indicated that hexanal-MPs formation was primarily driven by hydrogen bonding, whereas other longer-chain and unsaturated aldehydes penetrated hydrophobic pockets. Proteomics revealed that saturated aldehydes predominantly formed Schiff bases with the lysine ε-amino group. Unsaturated aldehydes, especially (E, E)-2,4-decadienal, undergo both Schiff base reactions and Michael addition with cysteine, histidine, and tryptophan residues. The retention/release behavior of aldehydes during processing is determined by covalent and non-covalent interactions. These results provide a scientific basis for precisely controlling flavor quality in meat products. + © 2026. The Author(s). + + + + Wang + Jingfan + J + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. + + + Laboratory of Chemistry of Natural Molecules, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium. + + + Laboratory of Analytical Chemistry, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium. + + + + Wang + Tianze + T + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. + + + + Yang + Ping + P + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. + + + + Han + Dong + D + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. orange_1101@126.com. + + + + Zhang + Chunhui + C + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. dr_zch@163.com. + + + + Jia + Wei + W + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. + + + Jiangsu Chaoyue Agricultural Development Co. Ltd, Taizhou, China. + + + + Purcaro + Giorgia + G + + Laboratory of Analytical Chemistry, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium. + + + + Fauconnier + Marie-Laure + ML + + Laboratory of Chemistry of Natural Molecules, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium. + + + + eng + + + No. 32472392 + the National Natural Science Foundation of China + + + + ASTIP-Y2025QC29 + the Agricultural Science and Technology Innovation Program + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Sci Food + 101739627 + 2396-8370 + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 12 + 25 + + + 2026 + 4 + 28 + + + 2026 + 5 + 29 + 23 + 15 + + + aheadofprint + + 42215494 + 10.1038/s41538-026-00874-9 + 10.1038/s41538-026-00874-9 + + + + + + 42215477 + + 2026 + 05 + 29 + +
+ + 2731-6068 + + + 2026 + May + 29 + + + npj aging + NPJ Aging + + Latent biochemical phenotypes delineate divergent health trajectories in older adults. + 10.1038/s41514-026-00415-4 + + Ageing heterogeneity hampers prevention and care. We used routine biochemical panels and unsupervised learning to identify latent phenotypes in community-dwelling older adults. In 1491 participants from the Toledo Study for Healthy Ageing (TSHA) with ~10-11 years of follow-up, 39 blood biomarkers were dimension-reduced and clustered, yielding three phenotypes: Healthy, Metabolic (subclinical dysmetabolism), and Haematological (low erythroid/renal profile). Phenotypes differed in functional capacity, frailty, and independence at baseline (all p < 0.05 after age/sex adjustment) and predicted long-term mortality (Metabolic women HR = 1.49, p = 0.016). Sex-specific analyses revealed distinct disease-trajectory patterns (e.g., hypertension in Metabolic women HR = 1.30, p = 0.005; thrombosis in Haematological men HR = 7.20, p = 0.018; syncope in Haematological women HR = 1.88, p = 0.009). Findings are partially replicated in a cohort of physically active older adults (EXERNET), supporting the generalizability of the Metabolic phenotype. Standard laboratory data, integrated through machine learning, capture ageing-relevant biology and stratify future risk without specialised assays, enabling low-cost, scalable precision prevention. + © 2026. The Author(s). + + + + González-Martos + Raquel + R + + Centro de Tecnología Biomédica (CTB), Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Madrid, Spain. + + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. + + + Nageru S.L, Madrid, Spain. + + + + Rodríguez-Gómez + Irene + I + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. + + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. + + + Geriatrics Department, Hospital Universitario de Toledo, Toledo, Spain. + + + + Galeano + Javier + J + + Grupo de Sistemas Complejos, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Madrid, Spain. + + + Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain. + + + + Ara + Ignacio + I + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. + + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. + + + + Alegre + Luis M + LM + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. + + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. + + + + Rodríguez-Mañas + Leocadio + L + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Geriatrics Department, Getafe University Hospital, Getafe, Spain. + + + + Garcia-Garcia + Francisco J + FJ + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. + + + Geriatrics Department, Hospital Universitario de Toledo, Toledo, Spain. + + + + Ramírez-Castillejo + Carmen + C + + Centro de Tecnología Biomédica (CTB), Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Madrid, Spain. + + + Departamento de Oncología, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain. + + + + Guadalupe-Grau + Amelia + A + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. amelia.guadalupe@uclm.es. + + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. amelia.guadalupe@uclm.es. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. amelia.guadalupe@uclm.es. + + + + eng + + + IND2022/BMD-23595 + Comunidad de Madrid + + + + CD23/00236 + Instituto de Salud Carlos III + + + + PI10/01532, PI031558 + Instituto de Salud Carlos III + + + + PI2010/020 + Junta de Comunidades de Castilla-La Mancha + + + + 03031-00 + Consejería de Sanidad de Castilla-La Mancha + + + + CB16/10/00456 + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable + + + + CB16/10/00477 + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Aging + 9918402285106676 + 2731-6068 + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 11 + 25 + + + 2026 + 5 + 18 + + + 2026 + 5 + 29 + 23 + 15 + + + aheadofprint + + 42215477 + 10.1038/s41514-026-00415-4 + 10.1038/s41514-026-00415-4 + + + + + + 42215447 + + 2026 + 05 + 29 + +
+ + 2041-1723 + + + 2026 + May + 29 + + + Nature communications + Nat Commun + + Multifactorial sheltering in peristromal niches shapes in vivo responses of lung cancers to targeted therapies. + 10.1038/s41467-026-73454-w + + Targeted therapies induce strong clinical responses but fail to eliminate advanced cancers, as a subset of tumor cells survives within residual disease and eventually develops resistance. While numerous cell-intrinsic and microenvironmental mechanisms have been implicated in this survival, their relative contributions remain poorly defined. Using spatial histological inferences from ALK + NSCLC models, we show that peristromal niches protect tumor cells from elimination, enabling in vivo persistence. This spatially restricted sheltering provides an ecological rescue mechanism that sustains residual populations, enabling their eventual evolutionary escape. Mechanistically, this protective effect reflects an integrated action of multiple juxtacrine and paracrine signals. This complexity limits the utility of targeting individual mechanisms of protection, favoring a shift towards exploiting orthogonal collateral sensitivities of residual disease. We find that adaptive HER2 upregulation, associated with both cell-intrinsic and stroma-mediated persistence, can be exploited by the antibody-drug conjugate T-DXd to dramatically enhance therapeutic responses and suppress tumor relapse. + © 2026. The Author(s). + + + + Desai + Bina + B + 0000-0003-0454-7583 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + Cancer Biology Ph.D. Program, University of South Florida, Tampa, FL, USA. + + + + Miti + Tatiana + T + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Prabhakaran + Sandhya + S + 0000-0002-9537-507X + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Miroshnychenko + Daria + D + 0000-0001-6425-5000 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Kumar + Pragya + P + 0000-0002-5691-7016 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Henry + Menkara + M + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Marusyk + Viktoriya + V + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Siqueira + Natalia Souza Nunes + NSN + 0000-0002-7877-2655 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Gatenbee + Chandler + C + 0000-0002-9730-5964 + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Ozakinci + Hilal + H + + Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Bui + Marilyn + M + 0000-0003-4963-2255 + + Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + Department of Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Boyle + Theresa A + TA + 0000-0001-5950-3970 + + Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Scott + Jacob + J + 0000-0003-2971-7673 + + Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA. + + + + Altrock + Philipp M + PM + 0000-0001-7731-3345 + + Department of Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany. + + + + Pellini + Bruna + B + + Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + Department of Thoracic Oncology, Miami Cancer Institute, Baptist Health South Florida, Miami, FL, USA. + + + + Haura + Eric + E + + Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Anderson + Alexander R A + ARA + 0000-0002-2536-4383 + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Basanta + David + D + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Marusyk + Andriy + A + 0000-0002-0087-9575 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. andriy.marusyk@moffitt.org. + + + Department of Molecular Medicine, University of South Florida, Tampa, FL, USA. andriy.marusyk@moffitt.org. + + + + eng + + + NIH U54 CA274507 + U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Nat Commun + 101528555 + 2041-1723 + + IM + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 4 + 2 + + + 2026 + 5 + 8 + + + 2026 + 5 + 29 + 23 + 14 + + + aheadofprint + + 42215447 + 10.1038/s41467-026-73454-w + 10.1038/s41467-026-73454-w + + + + + + 42215401 + + 2026 + 05 + 29 + +
+ + 1879-2022 + + + 2026 + May + 13 + + + ISA transactions + ISA Trans + + Interpretable physical-informed machine learning for dynamic characteristics prediction of air spring with limited measurement information. + S0019-0578(26)00247-8 + 10.1016/j.isatra.2026.05.010 + + Pipe-type air spring with auxiliary airbag (ASAA) are important vibration isolation component due to its tunable volume and adjustable dynamic characteristics. However, its dynamic modeling is challenging because airbag expansion work, rubber bladder viscoelasticity, and turbulence-dominated throttle dynamics are strongly coupled under large-amplitude excitations. Traditional data-driven models often require large datasets and lack interpretability. To address this issue, this study proposes an interpretable physics-informed machine learning (PIML) framework for ASAA dynamic prediction. A reduced-order physical model is first developed to provide baseline predictions of stiffness and lag angle, and the discrepancies between physical predictions and experimental measurements are then used to construct residual datasets. An interpretable Kolmogorov-Arnold Network (KAN) is employed to learn these residuals through adaptive B-spline activation functions and derive explicit symbolic correction terms. Experimental validation shows that the proposed hybrid model improves prediction accuracy while retaining physical interpretability, outperforming pure data-driven, advanced physics-data and physical models. + Copyright © 2026 ISA. Published by Elsevier Ltd. All rights reserved. + + + + Qin + Wu + W + + School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China. Electronic address: qw@ecjtu.edu.cn. + + + + Liao + Xundong + X + + School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China. Electronic address: 2023038080200027@ecjtu.edu.cn. + + + + Qi + Junyu + J + + State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China; Reutlingen University, Alteburgstraße 150, Reutlingen 72762, Germany. Electronic address: junyu.qi@reutlingen-university.de. + + + + He + Yi + Y + + School of Mechanical Engineering, South China University of Technology, China. Electronic address: 202111080705@mail.scut.edu.cn. + + + + Liu + Feifei + F + + School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China. Electronic address: 2971@ecjtu.edu.cn. + + + + Chen + Zhuyun + Z + + State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: mezychen@gdut.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + United States + ISA Trans + 0374750 + 0019-0578 + + IM + + Air spring + Data-driven + Dynamic characteristic predictions + Physics-informed Machine Learning + + Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 9 + 12 + + + 2026 + 5 + 10 + + + 2026 + 5 + 10 + + + 2026 + 5 + 29 + 21 + 58 + + + aheadofprint + + 42215401 + 10.1016/j.isatra.2026.05.010 + S0019-0578(26)00247-8 + + + + + + 42215359 + + 2026 + 05 + 29 + +
+ + 1879-0828 + + + 2026 + May + 29 + + + European journal of internal medicine + Eur J Intern Med + + Artificial intelligence in cardiovascular care for internal medicine: from promising algorithms to useful clinical services. + + 106944 + 106944 + + 10.1016/j.ejim.2026.106944 + S0953-6205(26)00249-9 + + Artificial intelligence is expanding rapidly in cardiovascular medicine, but its value in internal medicine depends less on raw model performance than on whether it improves triage, risk targeting, and coordination of care within real-world workflows. Rather than cataloguing applications, this review focuses on three use cases with near-term relevance in internal medicine: AI-augmented electrocardiography, risk prediction for targeted prevention, and AI-enabled clinical decision support, including selected applications of large language models. Across these domains, evaluation should extend beyond discrimination to calibration, positive predictive value at action thresholds, net benefit, alert burden, override rates, and downstream testing. Cardiovascular AI should not be judged as an autonomous decision-maker, but as a supervised component of care delivery. Its usefulness depends on predefined confirmation pathways, integration into existing information systems, actionable outputs, user training, and post-deployment monitoring for drift, safety, and equity. We propose a practical implementation lens - Train, Explain, Integrate, Accompany (TEIA) - to structure deployment in routine care, and summarize governance issues relevant to European practice, including intended use, interoperability, traceability, cybersecurity, and lifecycle oversight. The central question is not whether an AI tool can classify or predict, but whether its use in routine care supports better decisions with acceptable workload, safer pathways, and measurable clinical value. + Copyright © 2026 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved. + + + + Desroche + Louis-Marie + LM + + Department of Cardiology, North Site, CHU de La Réunion, Allée des Topazes, Saint-Denis, La Réunion 97400, France; CIC-EC INSERM 1410, CHU de La Réunion, Saint-Denis, France. Electronic address: louis-marie.desroche@chu-reunion.fr. + + + + Trimaille + Antonin + A + + Department of Cardiology, Strasbourg University Hospital, Strasbourg, France; UR3074, Translational Cardiovascular Medicine, University of Strasbourg, Strasbourg, France; GERCA (Groupe pour l'Enseignement et la Recherche Cardiologique en Alsace), Strasbourg, France. + + + + Lequeux + Benoît + B + + Department of Cardiology, Poitiers University Hospital, Poitiers, France. + + + + Homehr + Nicolas + N + + Primary Care Practice, Lherm, France; CPTS du Sud Toulousain, Haute-Garonne, France. + + + + Girerd + Nicolas + N + + CHRU de Nancy, Centre d'Investigations Cliniques Plurithématique 1433, INSERM, Université de Lorraine, INSERM U1116 - DCAC, and F-CRIN INI-CRCT(Cardiovascular and Renal Clinical Trialists), Nancy, France. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 29 + +
+ + Netherlands + Eur J Intern Med + 9003220 + 0953-6205 + + IM + + Artificial intelligence + Cardiovascular disease + Clinical decision support + Electrocardiography + Governance + Implementation + Internal medicine + Machine learning + Risk prediction + + Declaration of competing interest AT has received a fellowship grant from Edwards Lifescience. NG declares honoraria from Alnylam, AstraZeneca, Bayer, Boehringer, Cardiostory, Lilly, Echosens, GSK, NP medical, Novartis, Novo Nordisk, Roche diagnostics. The other authors declare no conflict of interest. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 4 + 29 + + + 2026 + 5 + 12 + + + 2026 + 5 + 29 + 21 + 57 + + + aheadofprint + + 42215359 + 10.1016/j.ejim.2026.106944 + S0953-6205(26)00249-9 + + + + + + 42215334 + + 2026 + 05 + 29 + +
+ + 2588-9311 + + + 2026 + May + 29 + + + European urology oncology + Eur Urol Oncol + + AI-MIRACLE: Artificial Intelligence and MultIpaRAmetric MRI Predict CLinical OutcomEs to Neoadjuvant Immunotherapy in Patients with Muscle-invasive Bladder Cancer Undergoing Radical Cystectomy. + S2588-9311(26)00133-1 + 10.1016/j.euo.2026.05.006 + + Neoadjuvant immune-checkpoint inhibitors (ICIs) in muscle-invasive bladder cancer (MIBC) were tested in patient's ineligible for cisplatin-based chemotherapy. The PURE-01 trial (NCT02736266) evaluated three courses of pembrolizumab before radical cystectomy (RC). We developed AI-MIRACLE, an international study assessing artificial intelligence (AI) and multiparametric magnetic resonance imaging (mpMRI) for predicting treatment response. + This multi-institutional study included data acquisition in Italy, and centralized analysis in the United States. Among 112 PURE-01 patients, pre- and post-ICI MRIs were analyzed. T2-weighted signal intensities were standardized for radiomics (Image Biomarker Standardization Initiative-compatible Python-based Computational Environment for Radiological Research (pyCERR)) and deep feature extraction (AI-BLADE toolbox using VGG19). Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI data underwent model-based analysis. Supervised machine learning algorithms (elastic net, random forest) were trained and cross-validated to predict pathological major response (pMR:ypT<2N0 residual disease) and pathological complete response (pCR: ypT0) pathological response. + The predictive models using post-ICI mpMRI with either a combination of radiomics and DCE-derived features or radiomics alone achieved the same high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.96 for pMR. A shape-based radiomic model achieved an AUC of 0.86 for predicting pCR. These models outperformed benchmark models based on clinical predictors. + Shape-based radiomics, DCE-derived features, and deep features may serve as noninvasive imaging biomarkers for predicting response to neoadjuvant pembrolizumab in MIBC. This imaging-based approach provides a non-invasive assessment of treatment response following neoadjuvant immunotherapy, which may help inform bladder-preserving management decisions prior to definitive surgery. + Copyright © 2026 The Author(s). Published by Elsevier B.V. All rights reserved. + + + + Necchi + Andrea + A + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy; Department of Medical Oncology, Vita-Salute San Raffaele University, Milan, Italy. Electronic address: necchi.andrea@hsr.it. + + + + Brembilla + Giorgio + G + + Department of Medical Oncology, Vita-Salute San Raffaele University, Milan, Italy; Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Whiting + Karissa + K + + Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Arita + Yuki + Y + + Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Akin + Oguz + O + + Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Apte + Aditya + A + + Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Awais + Muhammad + M + + Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Lema-Dopico + Alfonso + A + + Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Paudyal + Ramesh + R + + Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Cosenza + Michele + M + + Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Maiorano + Brigida Anna + BA + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Tateo + Valentina + V + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Cigliola + Antonio + A + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Mercinelli + Chiara + C + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + De Cobelli + Francesco + F + + Department of Medical Oncology, Vita-Salute San Raffaele University, Milan, Italy; Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Capanu + Marinela + M + + Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Shukla-Dave + Amita + A + + Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. Electronic address: davea@mskcc.org. + + + + Schwartz + Lawrence H + LH + + Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Netherlands + Eur Urol Oncol + 101724904 + 2588-9311 + + IM + + Bladder cancer + Deep feature + MRI + Radiomics + + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 12 + 18 + + + 2026 + 4 + 28 + + + 2026 + 5 + 6 + + + 2026 + 5 + 29 + 21 + 56 + + + aheadofprint + + 42215334 + 10.1016/j.euo.2026.05.006 + S2588-9311(26)00133-1 + + + + + + 42215211 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1095-9998 + + 139 + + 2026 + Oct + + + Food microbiology + Food Microbiol + + Single-cell Raman spectroscopy-machine learning combination: Rapid and accurate strain-level identification of Bifidobacterium animalis subsp. lactis J12. + + 105131 + 105131 + + 10.1016/j.fm.2026.105131 + S0740-0020(26)00095-X + + Rapid and accurate strain-level identification of Bifidobacterium animalis is critical for probiotic quality control and intellectual property protection in the food industry. In this study, a novel identification method for Bifidobacterium animalis subsp. lactis J12 was established by integrating single-cell Raman spectroscopy (SCRS) with machine learning (ML) algorithms. A total of 500-1000 valid single-cell Raman spectra were collected for each of five Bifidobacterium animalis subsp. lactis strains and seven heterologous lactic acid bacterial strains. The SCRS data were processed and optimized using t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and support vector machine (SVM) algorithms. Results showed that stationary-phase cells exhibited the most distinctive chemical fingerprints, enabling the SVM model to identify J12 among the five target strains with an accuracy of over 99%. The established method displayed high robustness: classification accuracy and recall for lyophilized bacterial powders both exceeded 99.8%; the predicted proportions of strains in artificial mixed samples were highly consistent with theoretical values, with a mean absolute error (MAE) of <0.5% and a correlation coefficient (R2) of >0.998; and the identification accuracy for J12 isolates from commercial yogurt remained above 99%. Comparative genomics analysis confirmed that the unique Raman phenotypic characteristics of J12 were correlated with its specific genomic features, verifying the method's reliability at the genetic level. This label-free and pure culture-free SCRS-ML approach provides a rapid and high-precision tool for strain-specific identification in the probiotic industry, and fills the technical gap in rapid detection of highly homologous B. animalis strains. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Jiang + Yixuan + Y + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: 15245133725@163.com. + + + + Deng + Yu + Y + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: 18184400300@163.com. + + + + Feng + Bing + B + + Qingdao Singlecell Biotechnology Co., Ltd., Qingdao, 266000, China. Electronic address: fengbing@singlecellbiotech.com. + + + + Zhang + Hongxing + H + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: hxzhang511@163.com. + + + + Xie + Yuanhong + Y + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: xieyuanh@163.com. + + + + Gao + Jingyu + J + + Science and Technology Research Center of China Customs, Beijing, 100026, China. Electronic address: gjyhgxy@sina.com. + + + + Song + Xiaodong + X + + Inner Mongolia Mengniu Dairy (Group) Co., Ltd., Key Laboratory of Dairy Quality Intelligence Monitoring Technology, State Administration for Market Regulation, Hohhot, 011517, China. Electronic address: songxiaodong@mengniu.cn. + + + + Jin + Junhua + J + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: jinjunhua002008@163.com. + + + + eng + + Journal Article + Evaluation Study + + + 2026 + 04 + 22 + +
+ + England + Food Microbiol + 8601127 + 0740-0020 + + IM + + + Bifidobacterium animalis + classification + isolation & purification + chemistry + genetics + + + Spectrum Analysis, Raman + methods + + + Machine Learning + + + Support Vector Machine + + + Single-Cell Analysis + methods + + + Probiotics + + + Yogurt + microbiology + + + Discriminant Analysis + + + + Bifidobacterium animalis + Machine learning + Probiotics + Quality control + SCRS + Strain identification + + Declaration of competing interest The authors declare no conflicts of interest. + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 18 + + + 2026 + 4 + 20 + + + 2026 + 4 + 21 + + + 2026 + 5 + 29 + 20 + 55 + + + ppublish + + 42215211 + 10.1016/j.fm.2026.105131 + S0740-0020(26)00095-X + + + + + + 42215204 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1095-9998 + + 139 + + 2026 + Oct + + + Food microbiology + Food Microbiol + + Climatic factors and initial microbiota-driven prediction of the accumulated microbiota at the end of stacking fermentation for soy sauce aroma type baijiu. + + 105124 + 105124 + + 10.1016/j.fm.2026.105124 + S0740-0020(26)00088-2 + + Most traditional fermented foods are produced through spontaneous fermentation, which contain complex microbiota. However, it is still unclear what the key driving factors of the microbiota are and how to predict the microbiota at the end of fermentation. This study employed a typical spontaneous fermentation process, stacking fermentation for soy sauce aroma type baijiu, as a case to predict the microbiota at the end of fermentation based on key driving factors. The microbiota at the start of fermentation was mainly provided from the starter (daqu), air, and the ground of the fermentation room, contributing 45.22%, 10.05%, and 7.48% of the bacteria, and 41.83%, 10.77% and 1.21% of the fungi, respectively. Average air temperature and average photosynthetic photon flux density were identified as key climatic factors driving microbial succession. Finally, a Transformer-based machine learning model was constructed to predict the microbiota at the end of fermentation based on key climatic factors and the initial microbiota. 29 out of 34 dominant genera (average relative abundance ≥1%) could be accurately predicted (NRMSE <30%). Aitchison distance between true and predicted value using Transformer model was significantly lower than that using Random Forest model (P < 0.001). This work revealed the roles of climatic factors and the microbiota at the start of fermentation in shaping the microbiota at the end of fermentation. The constructed predictive model could facilitate the regulation of the microbiota in food fermentation. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + He + Binghao + B + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. + + + + Zheng + Yifu + Y + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. + + + + Ban + Shibo + S + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. + + + + Madadi + Meysam + M + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. + + + + Khaneghah + Amin Mousavi + AM + + Faculty of Biotechnologies (BioTech), ITMO University, 9 Lomonosova Street, Saint Petersburg, 191002, Russia. Electronic address: mousavi.amin@gmail.com. + + + + Aadil + Rana Muhammad + RM + + National Institute of Food Science and Technology, University of Agriculture, Faisalabad, 38000, Pakistan. Electronic address: muhammad.aadil@uaf.edu.pk. + + + + Wu + Qun + Q + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. Electronic address: wuq@jiangnan.edu.cn. + + + + eng + + Journal Article + + + 2026 + 04 + 16 + +
+ + England + Food Microbiol + 8601127 + 0740-0020 + + IM + + + Fermentation + + + Soy Foods + microbiology + analysis + + + Microbiota + + + Bacteria + classification + genetics + isolation & purification + metabolism + + + Climate + + + Odorants + analysis + + + Fungi + classification + genetics + metabolism + isolation & purification + + + Fermented Foods + microbiology + analysis + + + Food Microbiology + + + + Climate + Fermentation + Machine learning + Microbiota + Soy sauce aroma type baijiu + Succession distance + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 2 + + + 2026 + 4 + 12 + + + 2026 + 4 + 16 + + + 2026 + 5 + 29 + 20 + 55 + + + ppublish + + 42215204 + 10.1016/j.fm.2026.105124 + S0740-0020(26)00088-2 + + + + + + 42215160 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1876-4452 + + 66 + 3 + + 2026 + May + + + Science & justice : journal of the Forensic Science Society + Sci Justice + + On-site strip-type drug test kit enhanced by AI-based image analysis. + + 101408 + 101408 + + 10.1016/j.scijus.2026.101408 + S1355-0306(26)00014-6 + + The demand for technology that can rapidly and precisely detect drugs in the field is increasing because of the growing number of criminal incidents involving the tampering of beverages with drugs. This paper presents the development and validation of a strip-type drug test kit that is user-friendly and portable, reduces the consumption of reagents that are hazardous to humans, and produces less waste. A drawback of strip-type drug test kits is that they depend on visual interpretation of the results, which can vary depending on user skill, lighting, and slight color changes. To address this limitation, this study implemented an analytical method that minimizes uncertainty and facilitates quantitative evaluation by utilizing images of color reaction outcomes from the newly developed strip-type drug test kit. Specifically, we developed an automatic discrimination algorithm using artificial intelligence (AI) image analysis techniques. Using a collection of 7,295 kit images, we evaluated the efficacy of several deep-learning models, including Convolutional Neural Network (CNN), Vision Transformer (ViT), and CNN-GRU (a hybrid of CNN and Gated Recurrent Unit). The findings indicated that the CNN-GRU model showed superior performance across all parameters, including accuracy, precision, recall, F1-score, and Cohen's kappa, achieving a perfect score of 1.0000. The model achieved perfect accuracy, surpassing the average performance of 32 police officers in a comparative experiment. This study demonstrated that AI-based precision reading algorithms can significantly improve the automation and reliability of strip-type drug test kits, highlighting their potential applicability and effectiveness in investigative and forensic settings. + Copyright © 2026. Published by Elsevier B.V. + + + + An + Sora + S + + Department of Forensic Science, Sungkyunkwan University, Suwon 16419, the Republic of Korea. + + + + Lee + Joong + J + + Institute of AI and Big Data in Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, the Republic of Korea. + + + + Lee + Sangki + S + + Department of Forensic Science, Sungkyunkwan University, Suwon 16419, the Republic of Korea. + + + + Chung + Heesun + H + + Department of Forensic Science, Sungkyunkwan University, Suwon 16419, the Republic of Korea. Electronic address: hschung1024@gmail.com. + + + + eng + + Journal Article + + + 2026 + 02 + 18 + +
+ + England + Sci Justice + 9508563 + 1355-0306 + + + + 0 + Reagent Strips + + + IM + + + Humans + + + Image Processing, Computer-Assisted + + + Artificial Intelligence + + + Substance Abuse Detection + instrumentation + methods + + + Algorithms + + + Convolutional Neural Networks + + + Intelligent Systems + + + Deep Learning + + + Reagent Strips + + + + AI-based precision + Artificial Intelligence (AI) + Deep-learning models + Image analysis + Strip-type drug test kit + + Declaration of competing interest the authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Heesun Chung reports financial support was provided by Commercializations Promotion Agency for R&D Outcomes. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 9 + 22 + + + 2025 + 12 + 30 + + + 2026 + 2 + 11 + + + 2026 + 5 + 29 + 20 + 55 + + + ppublish + + 42215160 + 10.1016/j.scijus.2026.101408 + S1355-0306(26)00014-6 + + + + + + 42215144 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2162-9471 + + 133 + + 2026 + + + Advances in clinical chemistry + Adv Clin Chem + + Drug induced renal injury (DIRI) biomarkers. + + 1 + 44 + 1-44 + + 10.1016/bs.acc.2026.01.006 + S0065-2423(26)00020-X + + Drug-induced renal injury (DIRI) remains a substantial issue in clinical practice, contributing to high morbidity, mortality, socioeconomic and healthcare cost. Traditional markers, such as serum creatinine or blood urea nitrogen, have low specificity and sensitivity for early identification, underscoring the need for new reliable biomarkers. This chapter investigates biomarker improvements for DIRI, focusing on clinical uses, limitations, and future possibilities. The chapter examines biomarkers such as neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, cystatin C, β2-microglobulin and clusterin for diagnosing nephrotoxicity. In addition, we highlight the role of proteomics and metabolomics in facilitating the identification and development of novel biomarker candidates via high-throughput ultra-sensitive technology in investigating DIRI mechanisms. The chapter discusses validation frameworks, regulatory criteria for biomarker adoption in clinical settings, and how they are integrated into treatment monitoring and personalized medicine. Case examples demonstrate the real-world applicability in early diagnosis, risk classification, and therapy optimization. Standardization, cost, and translational gaps are discussed, as well as the potential for artificial intelligence and machine learning to improve biomarker processing and interpretation. Finally, this chapter emphasizes the transformative potential of DIRI biomarkers in providing a pathway to more accurate and timely identification of renal injury, improved patient outcomes, and reduced healthcare costs. Future research and global collaboration are required to fully realize the potential of these advances in clinical and regulatory settings. + Copyright © 2026. Published by Elsevier Inc. + + + + Sher + Emina Karahmet + EK + + School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom; International Society of Engineering Science and Technology, Nottingham, United Kingdom. Electronic address: emina.karahmet@gmail.com. + + + + Boras + Marijana Marković + MM + + Department of Laboratory Diagnostics, University Clinical Hospital Mostar, Mostar, Bosnia and Herzegovina; International Society of Engineering Science and Technology, Nottingham, United Kingdom. + + + + Fajkić + Almir + A + + Department of Pathophysiology, Faculty of Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina. + + + + Begović + Faris + F + + International Society of Engineering Science and Technology, Nottingham, United Kingdom. + + + + eng + + Journal Article + Review + + + 2026 + 02 + 23 + +
+ + United States + Adv Clin Chem + 2985173R + 0065-2423 + + + + 0 + Biomarkers + + + 0 + Hepatitis A Virus Cellular Receptor 1 + + + 0 + Cystatin C + + + IM + + + Humans + + + Biomarkers + analysis + blood + + + Acute Kidney Injury + diagnosis + chemically induced + + + Proteomics + + + Metabolomics + + + Hepatitis A Virus Cellular Receptor 1 + + + Cystatin C + + + + Clinical chemistry + Cystatin c + Drug-induced renal injury biomarkers + Early diagnosis + Kidney injury molecule-1 + Metabolomics + Nephrotoxicity + Neutrophil gelatinase-associated lipocalin + Personalized medicine + Proteomics + + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 20 + 55 + + + ppublish + + 42215144 + 10.1016/bs.acc.2026.01.006 + S0065-2423(26)00020-X + + + + + + 42215142 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + A hybrid chemometric and deep learning model for monitoring quality loss in thermally processed edible oils. + + 119497 + 119497 + + 10.1016/j.foodres.2026.119497 + S0963-9969(26)01180-4 + + Edible oils are routinely exposed to high temperatures during domestic and commercial cooking, leading to chemical transformations that degrade the quality of oils and pose potential health risks. This study investigated the thermal degradation of groundnut, sesame, and restaurant-collected oils, with particular emphasis on polymerization, oxidation, and hydrolysis processes. Fourier-transform infrared (FTIR) spectroscopy and dielectric measurements were employed to monitor the molecular changes induced by heating the oils, whereas quantitative assessments of oil deterioration were performed using key chemical parameters and dielectric indices. Advanced chemometric and deep learning models like CARS-PLS, RF-PLS, and 1D-CNN were used to find the most important spectral regions and key chemical and dielectric features linked to oil degradation. Among these, the 1D-CNN model exhibited the highest predictive accuracy for both acid value (AV) and dielectric constant (DC), achieving training R2values of 0.9984 and 0.9931, and an external test R2values of 0.9904 and 0.9898, respectively, with the lowest RMSE. Heating oil shows the results with significant alterations in the FTIR bands, an increased dielectric constant, and elevated AV, corresponding to the accumulation of oxidized triglycerides, free fatty acids, and high-molecular-weight polymeric residues. Furthermore, explainable artificial intelligence (XAI) using SHAP analysis revealed key spectral contributions corresponding to ester groups, carbonyl stretching of triglycerides, free fatty acids, and CH₂/CH₃ vibrations. This study demonstrates that integrating spectroscopic and dielectric analyses with predictive modeling provides a sensitive, non-destructive laboratory-based proof-of-concept approach for evaluating the oil degradation during thermal processing. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Varma + Shruti O + SO + + Department of Physics, The Institute of Science, Dr. Homi Bhabha State University, Mumbai 400032, Maharashtra, India. + + + + Vishwakarma + Ajay L + AL + + Department of Physics, The Institute of Science, Dr. Homi Bhabha State University, Mumbai 400032, Maharashtra, India. + + + + Sonawane + M R + MR + + Department of Physics, The Institute of Science, Dr. Homi Bhabha State University, Mumbai 400032, Maharashtra, India. Electronic address: smahadev123@gmail.com. + + + + Garad + Nitin P + NP + + School of Physical Sciences, Swami Ramanand Teerth Marathwada University, Nanded 431606, India. + + + + Kumbharkhane + A C + AC + + School of Physical Sciences, Swami Ramanand Teerth Marathwada University, Nanded 431606, India. + + + + eng + + Journal Article + + + 2026 + 05 + 20 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + + + 0 + Plant Oils + + + IM + + + Hot Temperature + + + Spectroscopy, Fourier Transform Infrared + + + Deep Learning + + + Cooking + methods + + + Plant Oils + chemistry + analysis + + + Food, Processed + + + Oxidation-Reduction + + + Hydrolysis + + + + Chemical properties + Chemometrics + Deep learning + Dielectric properties + Explainable artificial intelligence (XAI) + Thermal degradation + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 25 + + + 2026 + 5 + 17 + + + 2026 + 5 + 19 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215142 + 10.1016/j.foodres.2026.119497 + S0963-9969(26)01180-4 + + + + + + 42215140 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + Multimodal analysis of spontaneous speech for predicting food liking: Integrating linguistic and prosodic features with machine learning. + + 119483 + 119483 + + 10.1016/j.foodres.2026.119483 + S0963-9969(26)01166-X + + Spontaneous speech offers a promising yet underexplored window into consumers' emotional experiences during food evaluation. Unlike traditional self-reported measures, vocal expression captures both explicit linguistic content and implicit affective cues, potentially providing a richer account of hedonic perception. This study investigates whether linguistic and prosodic cues embedded in natural spoken responses can predict product liking. Ninety participants evaluated three chocolates and three plant-based beverages, providing hedonic ratings and open-ended spoken descriptions. Speech was analysed using psycholinguistic, prosodic, and representation-based features. Multiple machine learning algorithms were applied for both classification (low/medium vs high liking) and regression (continuous liking prediction). Feature-level comparisons showed that chocolates elicited richer emotional language, higher vocal intensity, and greater acoustic variability than plant-based drinks (p < 0.005). Across the full dataset, linguistic markers were the strongest predictors of liking (F1-score = 0.695; mean absolute error (MAE) = 1.530), while prosodic features provided complementary information related to arousal and engagement. Speech embeddings showed limited generalisation (area under the curve (AUC) < 0.518; MAE > 1.602). Product-specific analyses revealed higher predictive accuracy for chocolates (F1-score = 0.825; MAE = 1.213) than for plant-based beverages (F1-score = 0.559; MAE = 1.619), indicating that the strength of affective cues in speech depends on the emotional resonance of the product being evaluated. These findings demonstrate that spontaneous speech contains measurable emotional signals related to food liking. Interpretable linguistic and prosodic features can partially predict hedonic responses, highlighting the potential of multimodal, speech-based emotion analysis as a complementary tool in sensory and consumer research. + Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved. + + + + Sousa + Pedro M + PM + + GreenUPorto/Inov4Agro, DGAOT, Faculty of Sciences, University of Porto, Portugal. + + + + Lima + Rui C + RC + + Sense Test, Lda, Vila Nova de Gaia, Portugal. + + + + García-Segovia + Purificación + P + + i-Food, IUIA-FoodUPV, Universitat Politècnica de València, Spain. + + + + Alcañiz + Mariano + M + + LabLENI, HumanTech/LabLENI, Universitat Politècnica de València, València, Spain. + + + + Cunha + Luís M + LM + + GreenUPorto/Inov4Agro, DGAOT, Faculty of Sciences, University of Porto, Portugal. Electronic address: lmcunha@fc.up.pt. + + + + eng + + Journal Article + + + 2026 + 05 + 18 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + IM + + + Humans + + + Machine Learning + + + Speech + + + Food Preferences + psychology + + + Female + + + Emotions + + + Male + + + Young Adult + + + Cues + + + Adult + + + Linguistics + + + Consumer Behavior + + + + Acoustic features + Affective computing + Sensory analysis + Speech emotion + Supervised learning + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 1 + 4 + + + 2026 + 4 + 28 + + + 2026 + 5 + 16 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215140 + 10.1016/j.foodres.2026.119483 + S0963-9969(26)01166-X + + + + + + 42215133 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + Translating Food Biosensing into Practice: Smart Packaging, On-Site Analytics, and AI Integration. + + 119473 + 119473 + + 10.1016/j.foodres.2026.119473 + S0963-9969(26)01156-7 + + The global food system encounters increasing challenges from contamination threats, necessitating innovative monitoring technologies to guarantee safety and quality. This review investigates novel precision monitoring methodologies, emphasizing sophisticated biosensing technologies, intelligent packaging systems, molecular docking, and AI-integrated platforms. Biosensors utilize electrochemical, optical, and thermal transducers for the swift, on-site identification of contaminants, toxins, and allergens, surpassing conventional methods in both speed and accessibility. The incorporation of intelligent packaging improves real-time quality assurance, while molecular docking determines contaminant interactions to improve sensor design. Advancements in AI and machine learning enhance predictive analytics and traceability, meeting intricate supply chain requirements. Nonetheless, obstacles such as matrix interference, sensor stability, and regulatory deficiencies remain. Future directions highlight nanotechnology, biodegradable materials, and defined standards to improve scalability and sustainability. These breakthroughs will continue to transform food safety, minimize waste, and enhance global health equity, thereby revolutionizing the food industry for a sustainable future. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Seyedhosseini-Ghaheh + Hooria + H + + Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. Electronic address: h.hosseini@nutr.mui.ac.ir. + + + + Rafieian + Fatemeh + F + + Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. + + + + Rezaei + Atefe + A + + School of Life Sciences, Plant Proteins and Nutrition, Technical University of Munich, Freising 85354, Germany. + + + + Askari + Gholamreza + G + + Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. + + + + Zarrabi + Ali + A + + Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul 34396, Türkiye; Graduate School of Biotechnology and Bioengineering, Yuan Ze University, Taoyuan 320315, Taiwan. Electronic address: ali.zarrabi@istinye.edu.tr. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 16 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + IM + + + Biosensing Techniques + methods + + + Artificial Intelligence + + + Food Packaging + methods + + + Food Safety + methods + + + Food Contamination + analysis + + + Molecular Docking Simulation + + + Intelligent Systems + + + Humans + + + + Artificial Intelligence + Food biosensors + Molecular Docking + Smart Packaging + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 23 + + + 2026 + 4 + 24 + + + 2026 + 5 + 15 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215133 + 10.1016/j.foodres.2026.119473 + S0963-9969(26)01156-7 + + + + + + 42215117 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + Artificial intelligence integrated in non-destructive technologies for monitoring food freshness: A review of next generation approach. + + 119450 + 119450 + + 10.1016/j.foodres.2026.119450 + S0963-9969(26)01132-4 + + Food freshness is a significant quality and safety attribute, which contributes in consumers' health and their food choices. The adoption of rapid, accurate, and reliable food freshness detection novel approaches is of great significance. Non-destructive testing (NDT) offers precise, fast and efficient detection of food freshness, quality and safety but, still confronts multiple issues in data preprocessing, accuracy, reliability and adaptability. However, artificial intelligence (AI) integration in NDT upholds significant promise to overcome these issues. This review, spotlights the significance of integrating the AI in NDT (spectroscopy, imaging and other technologies) for boosting the performance of NDT. It further probes the merits, and demerits, and the principles of AI assisted NDT technologies. Moving forward, integrating AI in NDT technologies have significantly boosted the reliable and accurate freshness detection in complex food matrices, plant and animal derived foods. In spite of recent advancements in this field of research, there are still several existing challenges of compatibility, precision, and adaptability associated with the NDT approaches coupled with AI. Therefore, this review is comprehensively emphasized on the promising transformative prospects of AI integrated in NDT approaches for the non-destructive, rapid, reliable and accurate detection of food freshness based on its quality indicators accompanied with highlighting the future challenges, innovations and directions. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Waseem + Muhammad + M + + College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China; Department of Food Science and Technology, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, 63100, Pakistan. + + + + Javed + Muhammad Rizwan + MR + + Department of Food Science and Technology, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, 63100, Pakistan. + + + + Manzoor + Muhammad Faisal + MF + + Guangdong Provincial Key Laboratory of Intelligent Food Manufacturing, School of Food Science and Engineering, Foshan University, Foshan, China. + + + + Xia + Xiuqi + X + + College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China. + + + + Huang + Yilei + Y + + College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China. + + + + Chen + Xiaomei + X + + College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China. Electronic address: xmchen@jmu.edu.cn. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 14 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + IM + + + Artificial Intelligence + + + Food Analysis + methods + + + Humans + + + Food Quality + + + Animals + + + Intelligent Systems + + + + Artificial intelligence + Deep learning + Freshness + Machine learning + Rapid monitoring + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 12 + 22 + + + 2026 + 5 + 6 + + + 2026 + 5 + 11 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215117 + 10.1016/j.foodres.2026.119450 + S0963-9969(26)01132-4 + + + + + + 42215102 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + Towards sustainable serum-free media development from alternative sources for cultivated meat. + + 119434 + 119434 + + 10.1016/j.foodres.2026.119434 + S0963-9969(26)01116-6 + + The economic feasibility and scalability of cultivated meat (CM) is critically hindered by the high manufacturing costs, particularly the cost associated with cell culture media. Therefore, numerous studies have proposed the use of sustainable alternatives to replace the expensive cell culture media components, especially animal serum. In this review, we comprehensively surveyed such developments and interpreted the opportunities and limitations of animal serum-reduction or replacement methodologies in designing culture media for CM production. Among the various alternative sources investigated, plant-derived sources and recombinant proteins have been the most extensively studied. We also highlight the critical gaps in existing studies such as the lack of demonstration of scalability, cost-effectiveness and long-term applicability of proposed alternative sources. Subsequently, we provide a roadmap for future studies on how to demonstrate the proposed alternative does not have adverse effects on cellular long-term stability, as well as comply with food safety and regulatory standards while being cost-effective which are critical for large scale CM manufacturing. We further propose a two-pronged framework based on high-throughput screening, systems biology, and Machine Learning (ML) tools to optimize serum-free CM media formulations using alternative sources in a rapid and scalable manner. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Sivakumar + Sanjay + S + + Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India. + + + + Manayankath + Aparna + A + + Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India. + + + + Hong + Yi Fan + YF + + Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Department of Biomedical Engineering, National University of Singapore, Singapore. + + + + Thrivikraman + Greeshma + G + + Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India. + + + + Tekkatte + Chandana + C + + The Good Food Institute India, Bengaluru, India. + + + + Choudhury + Deepak + D + + Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Department of Food Science and Technology, National University of Singapore, Singapore; Bezos Centre for Sustainable Protein at the National University of Singapore, Singapore. Electronic address: Deepak_Choudhury@a-star.edu.sg. + + + + Pang + Kuin Tian + KT + + Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Department of Biomedical Engineering, National University of Singapore, Singapore; Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore; School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore. Electronic address: zach_pang@a-star.edu.sg. + + + + Lakshmanan + Meiyappan + M + + Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India; Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Wadhwani School of Data Science and AI (WSAI), Indian Institute of Technology Madras, Chennai, India; Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology Madras, Chennai, India. Electronic address: meiyappan@iitm.ac.in. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 13 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + + + 0 + Culture Media, Serum-Free + + + IM + + + In Vitro Meat + + + Animals + + + Culture Media, Serum-Free + chemistry + + + Meat Substitutes + + + + Alternative proteins + Animal serum alternatives + Cultivated meat + Serum-free media + Sustainability + Systems biology + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 12 + 11 + + + 2026 + 3 + 21 + + + 2026 + 5 + 9 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215102 + 10.1016/j.foodres.2026.119434 + S0963-9969(26)01116-6 + + + + + + 42214983 + + 2026 + 05 + 29 + +
+ + 1879-3363 + + 231 + + 2026 + May + 29 + + + Marine pollution bulletin + Mar Pollut Bull + + Deep learning-based prediction of trace silver concentrations in marine sediments from mid-infrared spectroscopy. + + 119930 + 119930 + + 10.1016/j.marpolbul.2026.119930 + S0025-326X(26)00717-4 + + Silver (Ag) is a biologically toxic element, and the rapid detection of its concentrations in environmental samples is thus of great importance. This study investigates mid-infrared (MIR) spectra of low-Ag sediments from the South China Sea (SCS), and successfully predicts Ag concentrations by three machine learning models, including Kennard Stone-competitive adaptive reweighted sampling-partial least squares regression (KS-CARS-PLSR), Kennard Stone-random forest (KS-RF), and random selecting-squeeze excitation attention-residual network (RS-SE-ResNet-1D). Among the three techniques, the RS-SE-ResNet-1D model performs best in terms of Ag prediction, achieving an R2 of 0.82, an RMSE of 0.03, and an RPD of 2.38. A common feature wavenumber band in the range of 1408.3-1408.7 cm-1, corresponding to the v3 symmetric stretching absorption band of carbonate ions (CO3 + 2-), is identified from all three models. This suggests that marine productivity is possibly the dominant factor controlling Ag levels in marine sediments of the SCS. The machine learning models based on MIR spectral data developed in this study provide a new analytical approach for determining Ag concentrations in low-Ag marine sediments, and offer new insights into the study of biogeochemical cycling of Ag. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Yu + Boyuan + B + + School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, Anhui, China. + + + + Xu + Liqiang + L + + School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, Anhui, China. Electronic address: xlq@hfut.edu.cn. + + + + Ning + Yu + Y + + Instrumental Analysis Center, Hefei University of Technology, Hefei, Anhui, China. + + + + Xu + Fang + F + + School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui, China. + + + + Han + Qingli + Q + + School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Mar Pollut Bull + 0260231 + 0025-326X + + IM + + Machine learning + Marine sediments + Mid-infrared spectra + Productivity + Silver + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 28 + + + 2026 + 5 + 23 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 19 + 52 + + + aheadofprint + + 42214983 + 10.1016/j.marpolbul.2026.119930 + S0025-326X(26)00717-4 + + + + + + 42214978 + + 2026 + 05 + 29 + +
+ + 1878-0539 + + 118 + + 2026 + May + 14 + + + Journal of plastic, reconstructive & aesthetic surgery : JPRAS + J Plast Reconstr Aesthet Surg + + Development and nested cross-validation of machine learning models for predicting transfusion requirements in pediatric craniosynostosis surgery. + + 209 + 220 + 209-220 + + 10.1016/j.bjps.2026.05.033 + S1748-6815(26)00288-3 + + Blood transfusion is common during pediatric craniosynostosis surgery; however, transfusion volumes and use of cell salvage systems can vary considerably. To support preoperative transfusion planning in patients undergoing craniectomy for craniosynostosis, we developed and internally validated machine learning (ML) models to predict perioperative transfusion risks and transfusion volumes in this patient population. Data from eligible patients ≤36 months of age from the NSQIP® Pediatric dataset were included for modeling. A classifier pipeline was developed to predict probabilities associated with transfusion risk, and a probabilistic/quantile regression pipeline was developed to predict the median transfusion volume (in mL/kg) with interquartile range. Performance of the modeling pipelines was evaluated using nested cross-validation with 5-fold outer loops. In total, 8045 patients were included with 4091 (50.9%) receiving perioperative transfusion. For the classification pipeline, the optimal model configuration was a LightGBM classifier. The modeling pipeline achieved a mean area under the receiver operating characteristic curve (AUC-ROC) of 0.77 (95% confidence interval [CI] 0.75-0.78) and mean Brier score of 0.20 (95% CI 0.19-0.20) across nested cross-validation. For the regression stage, the optimal model configuration was a probabilistic NGBoost regressor. The modeling pipeline achieved an average median absolute error of 6.86 mL/kg (95% CI 6.61-7.11), with a mean IQR coverage of 53% (95% CI 52-53%). These models may support individualized blood product preparation and selective use of cell salvage systems. External validation and further real-world testing are needed prior to the clinical implementation of this tool. + Copyright © 2026 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved. + + + + Deng + Jiawen + J + + Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada. + + + + Gharavi + Aidin + A + + Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, MN, USA. + + + + Heybati + Kiyan + K + + Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA. + + + + Haile + Dawit + D + + Department of Anesthesiology, Mayo Clinic, Rochester, MN, USA. + + + + Ahn + Edward S + ES + + Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA. + + + + Mardini + Samir + S + + Division of Plastic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN, USA. + + + + Gibreel + Waleed + W + + Division of Plastic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN, USA; Department of Pediatric and Adolescents Medicine, Mayo Clinic, Rochester, MN, USA. Electronic address: Gibreel.Waleed@mayo.edu. + + + + eng + + Journal Article + + + 2026 + 05 + 14 + +
+ + Netherlands + J Plast Reconstr Aesthet Surg + 101264239 + 1748-6815 + + IM + + Blood transfusion + Craniectomy + Craniosynostoses + Machine learning + Operative blood salvage + + Declaration of Competing Interest The authors declare no conflict of interest. The ACS-NSQIP® and participating hospitals are the source of this data; they have not verified nor are they responsible for the statistical validity of the data analysis or the conclusions derived by the authors. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 12 + 9 + + + 2026 + 4 + 20 + + + 2026 + 5 + 12 + + + 2026 + 5 + 29 + 19 + 52 + + + aheadofprint + + 42214978 + 10.1016/j.bjps.2026.05.033 + S1748-6815(26)00288-3 + + + + + + 42214919 + + 2026 + 05 + 29 + +
+ + 1090-2104 + + 827 + + 2026 + May + 25 + + + Biochemical and biophysical research communications + Biochem Biophys Res Commun + + Employing network toxicology, molecular docking, machine learning, and single-cell analysis to analyze BPA exposure-induced ccRCC. + + 154037 + 154037 + + 10.1016/j.bbrc.2026.154037 + S0006-291X(26)00801-6 + + This study investigates the molecular mechanisms underlying bisphenol A (BPA)-induced clear cell renal cell carcinoma (ccRCC). We integrated transcriptomic data from multiple GEO datasets and performed differential expression analysis and WGCNA to identify BPA-associated candidate genes. Enrichment analyses implicated pathways including cell adhesion, lipid metabolism, arachidonic acid signaling, and innate immune response. Using twelve machine learning algorithms, we identified four core genes (ITGB2, TBXAS1, LIPA, and CLEC7A), all upregulated in ccRCC. Molecular docking suggested stable BPA-protein interactions with favorable binding energies. Single-cell analysis showed predominant expression of these genes in monocytes and macrophages. LIPA was elevated in kidney cancer tissues and associated with clinical outcomes. In vitro experiments confirmed that BPA exposure promoted ccRCC cell progression by regulating core gene expression. This integrated approach offers new insights into the molecular mechanisms of BPA-induced ccRCC and identifies potential biomarkers for environmental risk assessment. + Copyright © 2026 Elsevier Inc. All rights reserved. + + + + Chen + Jianyu + J + + Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. + + + + Wu + Lianquan + L + + Department of Urology, Longgang People's Hospital, Wenzhou, 325000, China. + + + + Jin + Haoqi + H + + Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. + + + + Li + Yeping + Y + + Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China; Institute of Urology, Wenzhou Medical University, Wenzhou, 325000, China. Electronic address: liypwz@126.com. + + + + eng + + Journal Article + + + 2026 + 05 + 25 + +
+ + United States + Biochem Biophys Res Commun + 0372516 + 0006-291X + + IM + + Bisphenol A + Clear cell renal cell carcinoma + Machine learning + Molecular docking + Single-cell analysis + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 22 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 19 + 51 + + + aheadofprint + + 42214919 + 10.1016/j.bbrc.2026.154037 + S0006-291X(26)00801-6 + + + + + + 42214908 + + 2026 + 05 + 29 + +
+ + 1876-035X + + 19 + 7 + + 2026 + May + 25 + + + Journal of infection and public health + J Infect Public Health + + The Italian hand hygiene project: A joint-commission Italian network analysis. + + 103268 + 103268 + + 10.1016/j.jiph.2026.103268 + S1876-0341(26)00140-1 + + Hand hygiene (HH) is a straightforward yet highly effective preventive measure against healthcare-associated infections; however, global compliance remains suboptimal. This study analyzed a substantial Italian dataset, focusing on adherence to HH guidelines among healthcare professionals, and identified key factors influencing compliance. + Direct multi- wave cross-sectional observational data on hand hygiene from over 749,000 opportunities were collected between 2017 and 2024 across 30 Italian hospitals participating in the Joint Commission International Hand Hygiene Project. Traditional statistical analyses and supervised machine learning (ML) models were employed to evaluate compliance rates and to examine their association with the year of observation, clinical area, professional role, and patient interaction moment (opportunity). Additionally, ML models were implemented to support proactive, risk- based surveillance within hospital settings by identifying high- risk contexts and facilitating more targeted monitoring strategies. + Overall hand hygiene compliance increased from 73.5% in 2017-82% in 2024, surpassing the World Health Organization's (WHO) target of 80% from 2020 onwards. Nursing staff demonstrated the highest adherence. High-risk opportunities, such as pre-aseptic procedures and post-exposure to bodily fluids, exhibited higher compliance than other moments. Emergency departments recorded the lowest compliance rates (76.3%). ML models confirmed that the year, patient interaction moment, and professional role are the most significant predictors of adherence. The most effective ML model achieved an accuracy of 64.6% in classifying hand hygiene actions. + This analysis underscores an improving trend in hand hygiene practices within Italian hospitals and concurrently identifies specific contexts-such as emergency departments-that require targeted interventions. The integration of observational monitoring with predictive artificial intelligence models could facilitate proactive, risk-based surveillance and focused improvement strategies. Accreditation-driven, multi-modal initiatives like this project highlight the vital role of measurement in fostering a culture of hand hygiene and advancing patient safety. + Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved. + + + + Buja + A + A + + Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua I-35131, Italy. Electronic address: alessandra.buja@unipd.it. + + + + Damiani + G + G + + Center of Research in Precision Medicine and Chronic Inflammation, Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan 20122, Italy; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan 20122, Italy. + + + + Tangherloni + A + A + + Center of Research in Precision Medicine and Chronic Inflammation, Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan 20122, Italy; Department of Computing Sciences, Bocconi University, Milan 20136, Italy. + + + + Manfredi + M + M + + Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua I-35131, Italy. + + + + Lanza + G + G + + Network Joint Commission Italia, Italy. + + + + Palmeri + C + C + + Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua I-35131, Italy. + + + + eng + + Journal Article + + + 2026 + 05 + 25 + +
+ + England + J Infect Public Health + 101487384 + 1876-0341 + + IM + + Hand hygiene + Health Care Associated Infection + Hospital infection + Nosocomial infection + Prevention and control + + Declaration of Competing Interest The authors declare that there are no conflicts of interest regarding the publication of this manuscript. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 1 + 13 + + + 2026 + 5 + 11 + + + 2026 + 5 + 19 + + + 2026 + 5 + 29 + 19 + 50 + + + aheadofprint + + 42214908 + 10.1016/j.jiph.2026.103268 + S1876-0341(26)00140-1 + + + + + + 42214885 + + 2026 + 05 + 29 + +
+ + 1879-0771 + + 132 + + 2026 + May + 24 + + + Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society + Comput Med Imaging Graph + + An interpretable multimodal model integrating clinical, spectral CT imaging, and deep learning analysis of intra- and peritumoral regions for preoperative prediction of perineural invasion in gastric cancer: A prospective, multicenter study. + + 102776 + 102776 + + 10.1016/j.compmedimag.2026.102776 + S0895-6111(26)00079-0 + + Perineural invasion (PNI) is crucial for risk stratification and treatment planning. This study aims to develop a novel multimodal model for preoperative prediction of PNI in gastric cancer. + A total of 250 patients with pathologically confirmed gastric cancer from multiple centers were enrolled and divided into a training cohort (n = 138), an internal validation cohort (n = 59), and an external validation cohort (n = 53). Spectral CT parameters from both intra- and peritumoral regions were acquired using the GSI viewer software, while deep learning features were extracted via a ResNet-50 architecture. Clinical features were screened through univariate and multivariate regression analyses. Based on machine learning, we developed a spectral parameter model, a deep learning feature model, a clinical prediction model, and a multimodal fusion model (MFM). The predictive performance of the models was evaluated using calibration curves, receiver operating characteristic curves, and decision curve analysis (DCA). Model interpretability was achieved by employing Shapley Additive exPlanations (SHAP). + Compared with individual models, MFM demonstrated the best predictive performance, with area under the curve (AUC) values of 0.926, 0.885, and 0.871 in the training, internal validation, and external validation cohorts, respectively. DCA confirmed that MFM provided a higher net benefit over a wide range of threshold probability. Calibration curve showed MFM had better predictive consistency, and the Hosmer-Lemeshow test indicated good fit across all cohorts (all p-values > 0.05). SHAP effectively interpreted the decision-making process of the model. + The novel multimodal fusion model, which integrates clinical, spectral CT parameters, and deep learning features from intra- and peritumoral regions, demonstrated outstanding performance in preoperatively predicting PNI in gastric cancer. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Zhao + Yuncong + Y + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China. + + + + Chen + Lihong + L + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China; Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou 350001, China. + + + + Wang + Lili + L + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China; Clinical Research Center for Radiology and Radiotherapy of Fujian Province (Digestive, Hematological and Breast Malignancies), Fuzhou 350001, China. + + + + Yue + Wenjing + W + + The Fourth Clinical College, Henan Medical University, Xinxiang, 453003, China. + + + + Zeng + Deye + D + + Department of Pathology, Fujian Medical University Union Hospital, Fuzhou 350001, China. + + + + You + Yaru + Y + + Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou 450052, China; Henan Key Laboratory of CT Imaging, Zhengzhou 450052, China. + + + + Weng + Caiming + C + + Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou 350001, China. + + + + Li + Weixing + W + + Department of Radiology, Xinxiang Central Hospital, The Fourth Clinical College, Henan Medical University, Xinxiang, 453003, China. + + + + Chen + Yilin + Y + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China. + + + + Lin + Lin + L + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China; Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou 350001, China. Electronic address: linlin@fjmu.edu.cn. + + + + Xu + Haiping + H + + School of Computer and Data Science, Minjiang University, Fuzhou 350108, China. Electronic address: haiping@mju.edu.cn. + + + + Xue + Yunjing + Y + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China; Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou 350001, China. Electronic address: xueyunjing@fjmu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 24 + +
+ + United States + Comput Med Imaging Graph + 8806104 + 0895-6111 + + IM + + Deep learning + Gastric cancer + Peripheral nerve invasion + SHAP + Spectral CT + + Declaration of Competing Interest The authors have no relevant financial or non-financial interests to disclose. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 10 + 15 + + + 2026 + 4 + 27 + + + 2026 + 5 + 13 + + + 2026 + 5 + 29 + 19 + 50 + + + aheadofprint + + 42214885 + 10.1016/j.compmedimag.2026.102776 + S0895-6111(26)00079-0 + + + + + + 42214863 + + 2026 + 05 + 29 + +
+ + 1873-3336 + + 513 + + 2026 + May + 28 + + + Journal of hazardous materials + J Hazard Mater + + Advances in multispectral and hyperspectral inversion for soil heavy metal contamination: Mechanisms, machine learning algorithms, and future perspectives. + + 142542 + 142542 + + 10.1016/j.jhazmat.2026.142542 + S0304-3894(26)01520-7 + + Heavy metal (HM) contamination in soil exhibits insidious and cumulative effects, posing long-term risks to ecosystems and human health. Traditional field sampling and laboratory analysis are increasingly insufficient for large-scale continuous monitoring, driving the adoption of multispectral (MS) and hyperspectral (HS) remote sensing. Bibliometric analysis reveals clear research trends: target elements are primarily copper, lead, and zinc, while data acquisition has progressively shifted from laboratory spectroscopy to portable devices and satellite platforms, reflecting an expansion from local to regional scales. This has led to increasing data complexity and greater demands on model robustness and generalization. However, expanding the spatial scale and transitioning to satellite observations introduce fundamental challenges. Mixed pixels and moisture-induced spectral distortions reduce signal purity, while the indirect spectral response of HMs further complicates quantitative inversion. Sample scarcity and spatial heterogeneity also limit cross-regional generalization, constraining model robustness and stability. In response, models have evolved from traditional linear regression to ensemble learning methods such as Extreme Gradient Boosting (XGBoost), and further to deep learning frameworks, including Convolutional Neural Networks (CNN) and Transformers, enabling hierarchical feature extraction and task-oriented structural design. This paper reviews the key technical bottlenecks in soil HM spectral inversion, integrating bibliometric insights with methodological advances to provide a comprehensive framework for understanding current progress and guiding future developments in large-scale, high-precision inversion. + Copyright © 2026 Elsevier B.V. All rights reserved. + + + + Huang + Minghao + M + + School of Geographic Sciences, School of Resources and Environmental Science, Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China. + + + + Wu + Xiang + X + + School of Geographic Sciences, School of Resources and Environmental Science, Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China; Key Laboratory of Danjiangkou Reservoir Area's Aquatic Eco-Environment and Health, Shiyan City (Hanjiang Normal University), Shiyan 442000, China. + + + + Peng + Qian + Q + + School of Geographic Sciences, School of Resources and Environmental Science, Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China; Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources (Ministry of Education), Sichuan Normal University, Chengdu 610068, China. Electronic address: qianpeng@hubu.edu.cn. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 28 + +
+ + Netherlands + J Hazard Mater + 9422688 + 0304-3894 + + IM + + Hyperspectral + Indirect Inversion Mechanisms + Machine learning + Multispectral + Soil Heavy metals + Spectral Feature Selection + + Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 21 + + + 2026 + 5 + 25 + + + 2026 + 5 + 26 + + + 2026 + 5 + 29 + 19 + 49 + + + aheadofprint + + 42214863 + 10.1016/j.jhazmat.2026.142542 + S0304-3894(26)01520-7 + + + + + + 42214786 + + 2026 + 05 + 29 + +
+ + 1876-4754 + + + 2026 + May + 28 + + + Brain stimulation + Brain Stimul + + EEG Biomarkers of Accelerated Prolonged Intermittent Theta-Burst Stimulation in Depression: Machine Learning with a Minimal-Electrode Montage. + + 103135 + 103135 + + 10.1016/j.brs.2026.103135 + S1935-861X(26)00112-9 + + + Chang + Chun-Hung + CH + + An Nan Hospital, China Medical University, Tainan, Taiwan; Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan; Shu-Zen Junior College of Medicine and Management, Tainan, Taiwan. + + + + Sack + Alexander T + AT + + Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Brain+Nerve Centre, Maastricht University Medical Centre (MUMC), The Netherlands. + + + + Chu + Che-Sheng + CS + + Department of Psychiatry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Non-Invasive Neuromodulation Consortium for Mental Disorders, Society of Psychophysiology, Taipei, Taiwan; Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan. Electronic address: youngtzuchi@hotmail.com. + + + + Chang + Hsin-An + HA + + Non-Invasive Neuromodulation Consortium for Mental Disorders, Society of Psychophysiology, Taipei, Taiwan; Department of Psychiatry, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan. Electronic address: chang.ha@mail.ndmctsgh.edu.tw. + + + + eng + + Letter + + + 2026 + 05 + 28 + +
+ + United States + Brain Stimul + 101465726 + 1876-4754 + + IM + + EEG biomarkers + accelerated piTBS + brain stimulation + machine learning + major depressive disorder + resting-state electroencephalography + + Declaration of Competing Interest ☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: ATS is director of the Academy of Brain Stimulation (www.brainstimulation-academy.com) and the International Clinical TMS Certification Course (www.tmscourse.eu), receiving equipment support from MagVenture, MagStim, Deymed, Yingchi, Brainsway. He also serves as scientific advisor for Platoscince Medical and Alpha Brain Technologies. Other authors declare that they have no competing financial interests or personal relationships that could be perceived to have influenced the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 23 + + + 2026 + 5 + 25 + + + 2026 + 5 + 26 + + + 2026 + 5 + 29 + 19 + 35 + + + aheadofprint + + 42214786 + 10.1016/j.brs.2026.103135 + S1935-861X(26)00112-9 + + + + + + 42214688 + + 2026 + 05 + 29 + +
+ + 1878-1632 + + + 2026 + May + 28 + + + The spine journal : official journal of the North American Spine Society + Spine J + + From Three to Eight Clusters: A Machine Learning-Based Classification of Operated Adult Spinal Deformity Patients. + S1529-9430(26)00157-9 + 10.1016/j.spinee.2026.05.003 + + Adult spinal deformity (ASD) is a heterogeneous condition encompassing diverse etiologies, clinical presentations, and surgical challenges. While previous unsupervised learning models have stratified ASD patients into three broad phenotypes, these often lack clinical granularity for surgical decision-making. + To refine the classification of operated ASD patients using unsupervised machine learning and assess whether expanding from 3 to 8 clusters improves clinical discrimination and predictive robustness. + Retrospective multicenter study using machine learning clustering. + 471 adult patients who underwent surgery for ASD across six specialized spine centers. + ODI, SRS-22, spinal alignment metrics, surgical strategy, and postoperative complications. + A k-means clustering algorithm was applied to a selected set of 12 demographic, radiographic, and functional variables (C12). Clustering solutions with 3 and 8 groups were compared. Each cluster was analyzed for age, etiology, disability scores (ODI, SRS-22), spinal alignment, surgical strategy, and complication rates. Predictive models using LDA and KNN assessed classification accuracy for new patient assignment. + The 8-cluster model identified clinically distinct phenotypes, including subgroups of young patients with idiopathic scoliosis, structural hyperkyphosis, and early sagittal decompensation. In elderly patients, clusters differentiated pain profiles, alignment, and frailty. The most severe clusters distinguished coronal-dominant versus sagittal collapse deformities, with differing risks and outcomes. LDA maintained robust accuracy (91.4%) in predicting cluster assignment across 50 testing splits, outperforming KNN. + To our knowledge, this is among the most granular unsupervised ML-based phenotypic classifications of operated ASD patients reported to date. This refined 8-cluster model enhances clinical phenotyping in ASD surgery, offering more precise subgroup stratification than traditional 3-cluster models. It supports the integration of unsupervised learning into personalized surgical planning, risk stratification, and multicenter outcome trials. + Copyright © 2026 Elsevier Inc. All rights reserved. + + + + Roscop + Cecile + C + + ELSAN, Polyclinique Jean Villar, Bruges Cedex, France. Electronic address: cecile.roscop@gmail.com. + + + + Bourghli + Anouar + A + + Spine Surgery Department, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia. + + + + Baroncini + Alice + A + + Casa di Cura Humanitas San Pio X, Milano, Italy. + + + + Alanay + Ahmet + A + + Spine Center, Acibadem University School of Medicine, Istanbul, Turkey. + + + + Pellise + Ferran + F + + Spine Surgery Unit, Vall D'Hebron Hospital, Barcelona, Spain. + + + + Kleinstueck + Frank + F + + Schulthess Klinik, Zürich, Switzerland. + + + + Pizones + Javier + J + + Spine Surgery Unit, Hospital Universitario La Paz, Madrid, Spain. + + + + Charles + Yann-Philippe + YP + + Hôpitaux Universitaires de Strasbourg, France. + + + + Larrieu + Daniel + D + + ELSAN, Polyclinique Jean Villar, Bruges Cedex, France. + + + + Boissière + Louis + L + + ELSAN, Polyclinique Jean Villar, Bruges Cedex, France. + + + + Obeid + Ibrahim + I + + ELSAN, Polyclinique Jean Villar, Bruges Cedex, France. + + + + European Spine Study Group (ESSG) + + No institution. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Spine J + 101130732 + 1529-9430 + + IM + + Adult spinal deformity + Clustering + K-means algorithm + Machine learning + Phenotyping + Predictive modeling + Surgical planning + + Declaration of competing interest The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 12 + 11 + + + 2026 + 5 + 15 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 19 + 34 + + + aheadofprint + + 42214688 + 10.1016/j.spinee.2026.05.003 + S1529-9430(26)00157-9 + + + + + + 42214660 + + 2026 + 05 + 29 + +
+ + 1878-3511 + + + 2026 + May + 28 + + + International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases + Int J Infect Dis + + Investigating Infection and Clinical Data to Predict Effective Antibiotic Therapy and Monitor Resistance Trends. + + 108842 + 108842 + + 10.1016/j.ijid.2026.108842 + S1201-9712(26)00477-7 + + Data-driven approaches to effectively select antibiotics are crucial to improving patient outcomes and reducing antibiotic resistance. This study aimed to determine whether routinely collected clinical and microbiological data can be used to train machine learning (ML) models to predict antibiotic resistance in patients with bacterial infections. + We conducted a retrospective observational study on clinical data from two separate Italian hospitals, analyzing 15,581 bacterial isolates collected from 9,966 patients between 2018 and 2024 at a multi-department hospital located in Rome and secondary clinic located in Capranica, about 70 Km away from Rome. Multiple ML models were trained, using both unbalanced and SMOTE-balanced datasets. Performance was assessed using cross-validation and independent test sets, comprising 2023-2024 isolates. Performance was measured using the Area Under the Receiver Operating Characteristic curve (AUROC), F1 score, accuracy, precision, and recall. + XGBoost consistently outperformed other trained models, achieving AUROCs of 0.882 and 0.878 for Gram-positive and Gram-negative datasets, respectively. Species-specific models further improved discrimination, reaching AUROC scores up to 0.946 for P. aeruginosa, 0.941 for K. pneumoniae, 0.938 for E. faecalis, 0.919 for E. coli, 0.894 for P. mirabilis, and 0.891 for S. aureus. + These results demonstrate the utility of ML models in accurately predicting antibiotic susceptibility from routine clinical data, thus facilitating rapid initiation of targeted therapy. Such an approach can potentially reduce treatment delays by up to 48 hours compared to traditional diagnostic methods, presenting a useful tool to manage patients in critical conditions and combat antibiotic resistance in clinical practice. + Copyright © 2026. Published by Elsevier Ltd. + + + + Benedetto + Marco + M + + Department of Management and Innovation Systems, University of Salerno, Fisciano (SA) 84084, Italy; Kelyon S.r.l., via Benedetto Brin, 59 C5/C6, 80100 Naples, Italy. + + + + Piccinni + Giuseppe + G + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + De Leo + Cristina + C + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + Cirielli + Corrado + C + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + Facchiano + Francesco + F + + Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy. + + + + Facchiano + Angelo + A + + Laboratory of Bioinformatics and Computational Biology, Institute of Food Science, CNR, via Roma 52 A/C, 83100 Avellino, Italy. + + + + Panebianco + Annarita + A + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + Tagliaferri + Stefano + S + + Kelyon S.r.l., via Benedetto Brin, 59 C5/C6, 80100 Naples, Italy. + + + + La Rocca + Michele + M + + Department of Economics and Statistics, University of Salerno, Fisciano (SA) 84084, Italy. + + + + Marchetti + Paolo + P + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + Tagliaferri + Roberto + R + + Department of Management and Innovation Systems, University of Salerno, Fisciano (SA) 84084, Italy. + + + + Facchiano + Antonio + A + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy; Department of Life Sciences, Health and Health Professions, Link University, 00165 Rome, Italy. Electronic address: a.facchiano@idi.it. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + Canada + Int J Infect Dis + 9610933 + 1201-9712 + + IM + + Antimicrobial resistance + antibiotic susceptibility prediction + antimicrobial stewardship + machine learning + precision medicine + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 11 + 28 + + + 2026 + 4 + 17 + + + 2026 + 5 + 26 + + + 2026 + 5 + 29 + 19 + 33 + + + aheadofprint + + 42214660 + 10.1016/j.ijid.2026.108842 + S1201-9712(26)00477-7 + + + + + + 42214547 + + 2026 + 05 + 29 + +
+ + 1873-6424 + + + 2026 + May + 28 + + + Environmental pollution (Barking, Essex : 1987) + Environ Pollut + + Machine-learning-based estimation of ground-level NO2 concentrations across Southeastern Europe using multi-source satellite, reanalysis, and emission data. + + 128461 + 128461 + + 10.1016/j.envpol.2026.128461 + S0269-7491(26)00831-6 + + Reliable estimation of ground-level nitrogen dioxide (NO2) remains challenging due to limited monitoring coverage and complex interactions among emissions, meteorology, and land-use factors. This study develops a machine-learning framework to estimate surface NO2 concentrations across Southeastern Europe, covering thirteen countries with diverse emission sources and sparse monitoring networks. Daily NO2 observations from 317 monitoring stations were integrated with multiple satellite and ancillary datasets, including tropospheric NO2 from Sentinel-5P TROPOMI, vegetation indices from MODIS, and nighttime light data from VIIRS. Additional inputs included meteorological variables from ERA5, anthropogenic emissions from EDGAR v8.1, land-use data from Copernicus Corine Land Cover, elevation from the Copernicus Digital Elevation Model, and population density from the Global Human Settlement Layer. All datasets were processed and harmonized using Google Earth Engine. Three Gradient Boosting Decision Tree models (XGBoost, LGBM, and CatBoost) were trained and optimized with Optuna. Model performance was assessed using 10-fold cross-validation and independent test sets with R2, MAE, and RMSE metrics. All models performed consistently, with LGBM achieving the best results (R2 = 0.86; MAE = 5.34 μg/m3; RMSE = 8.32 μg/m3). SHapley Additive exPlanations (SHAP) identified key predictors and improved interpretability. Seasonal analysis showed higher accuracy in summer and autumn than in winter and spring. These results demonstrate that integrating multi-source data with advanced machine learning enables reliable surface NO2 estimation in regions with limited monitoring, supporting air quality assessment, exposure analysis, and evidence-based policymaking in Southeastern Europe. + Copyright © 2026. Published by Elsevier Ltd. + + + + Bilgiç + Efem + E + + Department of Environmental Engineering, Faculty of Engineering, Dokuz Eylul University, Izmir, Türkiye. Electronic address: efem.bilgic@deu.edu.tr. + + + + Elbir + Tolga + T + + Department of Environmental Engineering, Faculty of Engineering, Dokuz Eylul University, Izmir, Türkiye; Environmental Research and Application Center (CEVMER), Dokuz Eylul University, Izmir, Türkiye. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Environ Pollut + 8804476 + 0269-7491 + + IM + + Nitrogen dioxide + Southeastern Europe + air quality modeling + machine learning + satellite data integration + + Declaration of Competing Interest ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 26 + + + 2026 + 5 + 24 + + + 2026 + 5 + 28 + + + 2026 + 5 + 29 + 19 + 32 + + + aheadofprint + + 42214547 + 10.1016/j.envpol.2026.128461 + S0269-7491(26)00831-6 + + + + + + 42214540 + + 2026 + 05 + 29 + +
+ + 1873-6424 + + + 2026 + May + 28 + + + Environmental pollution (Barking, Essex : 1987) + Environ Pollut + + Nonlinear meteorological controls on coastal air quality along the Indian East Coast: Insights from a multi-city interpretable machine learning framework. + + 128448 + 128448 + + 10.1016/j.envpol.2026.128448 + S0269-7491(26)00818-3 + + This study investigates the role of meteorological variability in shaping ambient air quality across five coastal cities along the Indian East Coast (Haldia, Balasore, Visakhapatnam, Chennai, and Puducherry). Despite their shared coastal setting, the cities exhibit pronounced meteorological heterogeneity, leading to a clear latitudinal gradient in air pollution, with northern cities (Haldia and Balasore) experiencing substantially higher particulate matter concentrations and more frequent exceedances than southern cities (Chennai and Puducherry). Across all cities, PM10 is the dominant contributor to variability in Air Quality Index (AQI) variability. A Random Forest model reveals that meteorological parameters explain a substantial fraction of particulate matter variability (R2 up to ∼0.6), whereas gaseous pollutants show lower predictability (R2 < 0.35). Using Shapley Additive exPlanations (SHAP) analysis, we identify critical nonlinear thresholds and binary regime switches governing pollutant accumulation. A consistent regional specific humidity (SH) threshold of ∼15 ± 2 g/kg is identified as a key regime-switching threshold, with values below this level consistently associated with PM10 accumulation across all cities. In southern cities, a Sea Breeze Component (SBC) > 3 m/s is the threshold for coastal ventilation; below this level, pollutants remain trapped. Furthermore, we uncover a monsoon break thermal trap, in which temperatures exceeding 32 °C flip the expected seasonal dispersion into an accumulation regime. These findings demonstrate that coastal air quality is governed by discrete meteorological regime transitions rather than linear responses. The identified thresholds provide a physically interpretable framework for improving localized air quality forecasting and mitigation strategies in complex coastal environments. + Copyright © 2026. Published by Elsevier Ltd. + + + + Varaprasad + V + V + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. + + + + Madhavan + B L + BL + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. + + + + Jain + Chaithanya D + CD + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. + + + + Krishnaveni + A Sai + AS + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. + + + + Ratnam + M Venkat + MV + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. Electronic address: vratnam@narl.gov.in. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Environ Pollut + 8804476 + 0269-7491 + + IM + + Coastal air quality + RF model + SHAP + boundary layer dynamics + meteorological variability + nonlinear interactions + + Declaration of Competing Interest ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 8 + + + 2026 + 5 + 22 + + + 2026 + 5 + 27 + + + 2026 + 5 + 29 + 19 + 32 + + + aheadofprint + + 42214540 + 10.1016/j.envpol.2026.128448 + S0269-7491(26)00818-3 + + + + + + 42214280 + + 2026 + 05 + 29 + +
+ + 1532-8708 + + 53 + 4 + + 2026 + Apr + 07 + + + Seminars in oncology + Semin Oncol + + Physics-informed machine learning for tumor microenvironment-responsive nanomedicine: Recent updates. + + 152498 + 152498 + + 10.1016/j.seminoncol.2026.152498 + S0093-7754(26)00045-X + + Physics-informed machine learning (PIML) is rapidly emerging as a transformative paradigm for designing tumor microenvironment (TME)-responsive nanomedicines. While existing reviews have surveyed PIML in broader biomedical contexts, this work provides a focused, critical synthesis specifically at the intersection of PIML, multiscale TME biophysics, and clinically actionable nanomedicine design. We argue that the unique value of PIML lies not merely in combining physics with data, but in its capacity to resolve the "personalization paradox" in oncology: the tension between the need for patient-specific models and the scarcity of patient-specific data. By embedding governing physical laws-such as Darcy's flow, reaction-diffusion kinetics, and Navier-Stokes equations-as soft constraints, PIML models can generate physically plausible, patient-tailored predictions even with sparse clinical inputs. This review uniquely articulates a translational roadmap that systematically links fundamental PIML methodologies to concrete nanomedicine optimization tasks: predicting nanoparticle transport in heterogeneous TMEs, designing stimulus-responsive nanocarriers, and integrating multi-omics/imaging for personalized therapy. We further introduce a novel comparative framework evaluating PIML against purely physics-based and purely data-driven approaches, highlighting its superior data efficiency and interpretability for TME applications. However, significant challenges remain, including data standardization, computational scalability, and regulatory adaptation. Looking forward, we identify under-explored yet high-impact frontiers, such as quantum-informed PIML for molecular-scale nanocarrier design, real-time adaptive nanomedicine guided by patient digital twins, and the ethical-regulatory frameworks needed for clinical deployment. By synthesizing cross-disciplinary insights and proposing a clear path from bench to bedside, this review aims to not only summarize the state-of-the-art but also to catalyze the next generation of intelligent, patient-centric cancer nanotherapeutics. + Copyright © 2026 Elsevier Inc. All rights reserved. + + + + Hasannia + Maliheh + M + + Cancer Research Center, Semnan University of Medical Sciences, Semnan, Iran. + + + + Abounoori + Ali + A + + Department of Microbial Biotechnology, Faculty of Biotechnology, Amol University of Special Modern Technologies, Amol, Iran. + + + + Abounoori + Mahdi + M + + Cancer Research Center, Semnan University of Medical Sciences, Semnan, Iran. + + + + Shirzad + Maryam + M + + Nanotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran. + + + + Rahdar + Abbas + A + + Department of Physics, University of Zabol, Zabol, Iran. Electronic address: a.rahdar@uoz.ac.ir. + + + + Fathi-Karkan + Sonia + S + + Natural Products and Medicinal Plants Research Center, North Khorasan University of Medical Sciences, Bojnurd, Iran; Department of Advanced Sciences and Technologies in Medicine, School of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran; Food and Drug Research Center, Food and Drug Administration, Ministry of Health and Medical Education, Tehran, Iran. Electronic address: Soniafathi92@gmail.com. + + + + Romanholo Ferreira + Luiz Fernando + LF + + Graduate Program in Genomic Sciences and Biotechnology, Catholic University of Brasília, Brasília, 71966-700, Brazil. Electronic address: luiz.romanholo@p.ucb.br. + + + + eng + + Journal Article + Review + + + 2026 + 04 + 07 + +
+ + United States + Semin Oncol + 0420432 + 0093-7754 + + IM + + Multi-omics integration + Nanomedicine + Nanoparticle transport + Physics-informed machine learning + Tumor microenvironment + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 11 + 14 + + + 2026 + 2 + 12 + + + 2026 + 4 + 5 + + + 2026 + 5 + 29 + 18 + 10 + + + aheadofprint + + 42214280 + 10.1016/j.seminoncol.2026.152498 + S0093-7754(26)00045-X + + + + + + 42214151 + + 2026 + 05 + 29 + +
+ + 1873-3573 + + 309 + + 2026 + May + 28 + + + Talanta + Talanta + + Machine learning assisted transparent bending-insensitive SERS substrate for reliable quantitative analysis of thiram residues on fruit surfaces. + + 130059 + 130059 + + 10.1016/j.talanta.2026.130059 + S0039-9140(26)00715-0 + + Transparent and flexible surface-enhanced Raman scattering (SERS) substrates are highly suitable for rapid pesticide detection on fruit surfaces, owing to their capability for in situ detection on curved surfaces. However, achieving reproducible and stable SERS signals from flexible SERS substrates under bending conditions remains a major challenge. Additionally, integrating machine learning into Raman spectral analysis can further enhance the practicality of SERS technology. Herein, we develop a novel sensing platform that combines a transparent, bending-insensitive SERS substrate with machine learning for the rapid quantitative determination of thiram residues on fruit surfaces. The flexible SERS substrate was constructed by depositing silver nanoparticles (AgNPs) onto a polydimethylsiloxane (PDMS) twisted nanowrinkle array. Endowed with high SERS activity, exceptional transparency, and bending insensitivity, this substrate enabled reliable and consistent detection of thiram residues on apple and cherry tomato peels, with a sensitivity as low as 10-7 M. Furthermore, a back propagation neural network (BPNN)-based machine learning model was developed to predict thiram concentrations, achieving remarkable accuracy with a coefficient of determination (R2) of 0.9954 and a root mean square error (RMSE) of 0.0985. Thus, the proposed sensing platform provides a rapid, accurate, and reliable approach for the identification and quantification of thiram in fruits. + Copyright © 2026 Elsevier B.V. All rights reserved. + + + + Wang + Yue + Y + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Huang + Lili + L + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Wang + Jie + J + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Li + Zetong + Z + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Bian + Ziyang + Z + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Yang + Zhenshan + Z + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Li + Hefu + H + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. Electronic address: lihefu@lcu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + Netherlands + Talanta + 2984816R + 0039-9140 + + IM + + BPNN + Bending insensitivity + Quantitative analysis + SERS + Thiram + + Declaration of competing interest The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this study. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 9 + + + 2026 + 5 + 23 + + + 2026 + 5 + 27 + + + 2026 + 5 + 29 + 18 + 7 + + + aheadofprint + + 42214151 + 10.1016/j.talanta.2026.130059 + S0039-9140(26)00715-0 + + + + + + 42214146 + + 2026 + 05 + 29 + +
+ + 1879-0534 + + 212 + + 2026 + May + 29 + + + Computers in biology and medicine + Comput Biol Med + + A recurrent interferon, stress, and survival axis identifies a rare malignant programme across glioblastoma single-cell, spatial, and longitudinal cohorts. + + 111780 + 111780 + + 10.1016/j.compbiomed.2026.111780 + S0010-4825(26)00344-6 + + Glioblastoma exhibits profound intratumoral heterogeneity and rapid adaptation under stress, complicating durable therapeutic control. Here, a biologically grounded transcriptional axis is defined by three complementary modules capturing type I interferon signalling, immediate-early stress response, and pro-survival signalling enriched in unfolded protein response components. Using this equal-weighted axis, a rare axis-high programme was identified in a glioblastoma training cohort (GSE173278; 75,075 cells after filtering; 5.95% prevalence) and mapped to external cohorts through marker-anchored cohort-specific clusters. To enable reproducible detection, an interpretable 40-gene regularised logistic regression classifier was trained using fold-internal panel selection and Platt calibration fitted strictly on out-of-fold predictions. The conservative out-of-fold training evaluation showed informative discrimination and good probability calibration (AUC 0.767, AP 0.116, Brier 0.055, ECE 0.011). When the final model was applied to marker-anchored operational labels, ranking performance was high in an independent glioblastoma cohort (GSE131928; AUC 0.950, AP 0.351) and in an exploratory pan-cancer transfer setting (GSE203612; AUC 0.944, AP 0.343), although calibration slopes indicated dataset-dependent probability-scale shift. Longitudinal CARE glioblastoma samples showed that recalibration on T1 samples improved probability-scale reliability on later T2/T3 samples without changing rank discrimination. Spatial stratification based on available sample annotations linked higher detector probabilities to necrotic, bulk, and tumour-region samples. Irradiation feature-count contrasts supported positive shifts of the 40-gene panel and axis-related modules, while cell-level treated/control summaries were interpreted as context-specific perturbational evidence. Together, these results define a rare axis-high glioblastoma programme and provide a recalibratable gene-panel detector for probability-guided experimental triage and mechanistic follow-up. + Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved. + + + + Pastore + Emmanuel Pio + EP + + Department of Biology, Ecology and Earth Science, University of Calabria, 87036 Rende, Italy. + + + + De Rango + Francesco + F + + Department of Biology, Ecology and Earth Science, University of Calabria, 87036 Rende, Italy. Electronic address: francesco.derango@unical.it. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + Comput Biol Med + 1250250 + 0010-4825 + + IM + + Glioblastoma + Interferon signaling + Machine learning + Protein response + Reproducible bioinformatics + Single-cell RNA-seq + Stress response + Unfolded + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 4 + + + 2026 + 5 + 11 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 18 + 7 + + + aheadofprint + + 42214146 + 10.1016/j.compbiomed.2026.111780 + S0010-4825(26)00344-6 + + + + + + 42214095 + + 2026 + 05 + 29 + +
+ + 1933-0693 + + + 2026 + May + 29 + + + Journal of neurosurgery + J Neurosurg + + Assessment system for short-term lower cranial nerve dysfunction following medulla oblongata glioma surgery: risk stratification and optimal surgical strategy. + + 1 + 12 + 1-12 + + 10.3171/2025.12.JNS251593 + + Medullary gliomas pose significant surgical risks, particularly the risk of postoperative lower cranial nerve (LCN) dysfunction, which profoundly affects quality of life. The lack of standardized risk assessment hinders optimal surgical planning. The aim of this study was to develop and validate an individualized predictive model for short-term postoperative LCN impairment integrating clinical and imaging data and to estimate individual risk across a range of resection extent to optimize surgical planning. + A retrospective cohort (n = 111, January 2020-February 2023) was used for model development, with prospective validation (n = 45, February 2023-December 2024). The primary outcome was postoperative LCN dysfunction (inability to be extubated within 14 days or requiring tracheotomy with persistent ventilation). Predictive modeling was performed using logistic regression, incorporating multistage feature selection, hyperparameter optimization, and bootstrapped validation. Model performance was evaluated using metrics such as area under the curve (AUC), Brier score, calibration, and decision curve analysis (DCA) in the prospective validation set. Shapley Additive Explanations (SHAP) analysis was used to interpret feature contributions, and a nomogram was constructed for clinical implementation. Optimal extent of resection (EOR) thresholds were explored to balance functional preservation and tumor clearance. + Four independent predictors of LCN dysfunction were identified: EOR (OR 1.84, 95% CI 1.07-3.16), infiltrative growth (OR 10.46 [95% CI 2.70-40.52]), preoperative LCN impairment (OR 6.79 [95% CI 2.54-18.16]), and cervical cord involvement (OR 4.64 [95% CI 1.55-13.91]). The model demonstrated strong discrimination (training AUC 0.85 [95% CI 0.76-0.92], testing AUC 0.89 [95% CI 0.79-0.97]) and good calibration (Brier score 0.12). High-risk patients, defined as those with a model predicted risk probability > 0.471 based on Youden's index, had significantly higher rates of pneumonia, tracheostomy, and prolonged mechanical ventilation. Stratified resection plans showed that low-risk patients benefited from gross-total resection (EOR 88.4%, [95% CI 75.7%-100.0%]), while high-risk patients achieved optimal functional outcomes with limited resection (EOR 40.8%, [95% CI 38.2%-50.5%]). SHAP and nomogram analyses provided transparent, patient-specific information for risk consultations. + This study presents the first predictive model tailored to short-term postoperative LCN outcomes following medullary glioma surgery, proposing a dynamic resection paradigm based on individualized risk stratification. By guiding surgical planning and intraoperative decision-making, this model facilitates a balance between maximal tumor control and functional preservation. + + + + Zhang + Mingxin + M + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + 2Chinese Institutes for Medical Research, Beijing. + + + + Xiao + Xiong + X + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Jia + Heyuan + H + + 5School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing. + + + + Liu + Yinan + Y + + 6Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing; and. + + + + Jiang + Wenju + W + + 7Beijing Arion Cancer Center, Beijing, China. + + + + Xie + Luyang + L + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + 2Chinese Institutes for Medical Research, Beijing. + + + + Pan + Changcun + C + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Wu + Zhen + Z + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Zhang + Junting + J + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Zhang + Liwei + L + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + 2Chinese Institutes for Medical Research, Beijing. + + + 3Beijing Neurosurgical Institute, Capital Medical University, Beijing. + + + 4China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Zhang + Peng + P + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + J Neurosurg + 0253357 + 0022-3085 + + IM + + lower cranial nerve dysfunction + machine learning + medullary glioma + oncology + predictive model + risk stratification + skull base + surgical decision-making + tumor + + + + + + 2025 + 6 + 21 + + + 2025 + 12 + 19 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 17 + 52 + + + aheadofprint + + 42214095 + 10.3171/2025.12.JNS251593 + + + + + + 42214078 + + 2026 + 05 + 29 + +
+ + 1520-5215 + + + 2026 + May + 29 + + + The journal of physical chemistry. A + J Phys Chem A + + IPSBrain: A Unified Intelligent Data Analysis Platform for Multimodal Experimental Characterization at Advanced Photon Source. + 10.1021/acs.jpca.6c01154 + + Advanced photon sources deliver X-ray with unprecedented brilliance and coherence, facilitating sophisticated multimodal characterization of diverse materials while simultaneously generating data volumes that far exceed the capacity of traditional, expert-driven analytical workflows. This growing gap between data acquisition and interpretation has become a critical bottleneck for timely scientific discovery, which creates a critical need for high-throughput and automated data analysis tools. Recent advances in artificial intelligence (AI) offer a transformative potential to address this challenge, enabling the direct mapping of complex experimental observations into scientific insights. However, existing AI-driven tools are remain largely fragmented and task-specific, limiting their adoption of modern multimodal experiments at the facility scale. Here we present IPSBrain, a unified AI-powered platform that serves as Intelligent Photon Source Brain for automated analysis of multimodal synchrotron experimental data. The platform integrated a series of novel, self-developed machine-learning-based models that enable automated data analysis from multiple experimental techniques, including diffraction, scattering, X-ray absorption, and tomograph. The platform is accessible through an intuitive web interface, which significantly lowers the technical barrier to advanced scientific data interpretation. By enabling end-to-end intelligent workflows from data acquisition to quantitative physical insight, IPSBrain supports high-throughput, reproducible, and scalable analysis aligned with the operational demands of modern synchrotron radiation sources. IPSBrain not only demonstrates a practical and extensible framework for AI-driven synchrotron data analysis but also establishes a transferable paradigm for intelligent data analysis of other large-scale research infrastructures, such as neutron sources and free-electron lasers. + + + + Pang + Yihe + Y + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + Spallation Neutron Source Science Center, Dongguan 523800, China. + + + + Li + Lin + L + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China. + + + + Deng + Xiangwen + X + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + University of Chinese Academy of Sciences, Beijing 100049, China. + + + + Xi + Zuoxin + Z + 0000-0003-1237-7780 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Information Engineering, Minzu University of China, Beijing 100081, China. + + + + Li + Jing + J + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Information Engineering, Minzu University of China, Beijing 100081, China. + + + + Han + Mengdan + M + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Information Engineering, Minzu University of China, Beijing 100081, China. + + + + Sun + Yaping + Y + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Science, China University of Geosciences Beijing, Beijing 100083, China. + + + + Yao + Haodong + H + 0000-0001-5959-8594 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + University of Chinese Academy of Sciences, Beijing 100049, China. + + + + Wu + Liyuan + L + 0000-0002-4890-2770 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + + Yuan + Qin + Q + 0009-0001-7123-6155 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + + Zhao + Lina + L + 0000-0002-9796-0221 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + University of Chinese Academy of Sciences, Beijing 100049, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + J Phys Chem A + 9890903 + 1089-5639 + + IM + + + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 17 + 12 + + + aheadofprint + + 42214078 + 10.1021/acs.jpca.6c01154 + + + + + + 42214077 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2561-3278 + + 11 + + 2026 + May + 29 + + + JMIR biomedical engineering + JMIR Biomed Eng + + Psychosocial Stress in the Chinese Community: Speech Analytics Through Linguistic and Acoustic Fusion Using Machine Learning. + + e91138 + e91138 + + 10.2196/91138 + + Family caregivers experience significant stress due to intensive caregiving activities, making them highly susceptible to adverse psychosocial health conditions. Early detection of this stress is crucial for timely interventions to prevent disease progression and long-term disability. + This study aimed to develop and validate the Linguistic and Acoustic Speech Analytics Program, a novel machine learning approach capable of providing a fusion analysis of linguistic and acoustic speech features to enhance the effectiveness of psychosocial stress assessment. + This quantitative study analyzed speech data collected from 100 Chinese family caregivers. Participants responded to 12 open-ended questions, and their voices were recorded for linguistic and acoustic feature extraction. Various machine learning classifiers, including support vector machine, were developed to process speech data. A key methodological step was the application of an orthogonalization procedure to decorrelate acoustic features from linguistic features before fusion analysis. The classifiers were then trained to evaluate psychosocial stress levels based on the processed and fused linguistic and acoustic speech features. Model performance was measured using receiver operating characteristic-area under the curve, F1-score, and accuracy. + The linear support vector machine model emerged as the top performer, achieving a receiver operating characteristic-area under the curve of 78.28%, an F1-score of 75.27%, and an accuracy of 73%. These results demonstrate the model's strong capability in identifying stressed participants based on their speech. Critically, the fusion of linguistic and acoustic features significantly outperformed models using either feature type alone. Furthermore, the orthogonalization procedure proved essential, as decorrelating features before fusion markedly enhanced classification accuracy compared to using non-orthogonalized features. + This study demonstrates that fusion analysis of linguistic and acoustic features effectively identifies psychosocial stress among family caregivers. It also emphasizes the importance of proper feature processing when combining multiple features extracted from the same audio sample. These findings provide valuable insights for developing machine learning models for psychosocial stress assessment and addressing various psychosocial conditions in different contexts, supporting population mental health management. + © Amanda M Y Chu, Benson S Y Lam, Jenny T Y Tsang, Agnes Tiwari, Jacky N L Chan, Mike K P So. Originally published in JMIR Biomedical Engineering (http://biomsedeng.jmir.org). + + + + Chu + Amanda M Y + AMY + 0000-0002-9543-747X + + Department of Social Sciences and Policy Studies, The Education University of Hong Kong, Tai Po, Hong Kong, China. + + + + Lam + Benson S Y + BSY + 0000-0002-0836-4162 + + Department of Mathematics, Statistics and Insurance, The Hang Seng University of Hong Kong, Shatin, Hong Kong, China. + + + + Tsang + Jenny T Y + JTY + 0000-0003-4558-9384 + + School of Nursing, Tung Wah College, Homantin, Hong Kong, China. + + + + Tiwari + Agnes + A + 0000-0002-3993-8552 + + School of Nursing, Hong Kong Sanatorium and Hospital, Hong Kong, China. + + + + Chan + Jacky N L + JNL + 0000-0003-1704-4221 + + Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China, 852 2358 7726. + + + + So + Mike K P + MKP + 0000-0003-0781-8166 + + Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China, 852 2358 7726. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Canada + JMIR Biomed Eng + 101771351 + 2561-3278 + + + caregivers + digital health + psychosocial health + speech analytics + text analytics + + + + + + 2026 + 1 + 11 + + + 2026 + 3 + 23 + + + 2026 + 3 + 24 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 17 + 12 + + + epublish + + 42214077 + 10.2196/91138 + v11i1e91138 + + + + + + 42214073 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1929-0748 + + 15 + + 2026 + May + 29 + + + JMIR research protocols + JMIR Res Protoc + + Creation of an mHealth Infrastructure to Support the Development and Delivery of mHealth Interventions: Protocol for Demonstration Projects Addressing Smoking Cessation in Cancer Care. + + e92288 + e92288 + + 10.2196/92288 + + Cancer remains a leading cause of morbidity worldwide. To reduce this burden, scalable, effective approaches are needed to address modifiable risk factors for cancer and support behavioral self-management. With smartphone ownership now nearly ubiquitous, mobile health (mHealth) interventions offer a powerful means to extend the reach, accessibility, and sustainability of evidence-based treatments for a variety of modifiable risk factors (eg, excessive alcohol use, physical inactivity, poor diet, and smoking). Moreover, the flexibility of mHealth platforms enables efficient delivery of novel interventions, supports innovative study designs, and facilitates real-time data collection to advance public health research. + Despite the great potential of mHealth interventions, developing high-quality mHealth tools is complex, time-consuming, and resource-intensive. To address these challenges, we are developing a coordinated, accessible, research-grade infrastructure for mHealth app development, testing, and dissemination. + The mHealth Florida infrastructure (mFLi) will provide a comprehensive, low-code software platform that enables researchers to build apps compatible with major mobile operating systems, namely, Apple iOS and Google Android. Through a modular interface, users will select from a menu of prebuilt features to tailor functionality to specific study needs. The platform will include 3 integrated environments (development, testing, and production), allowing researchers to prototype, evaluate, and deploy mHealth interventions. This infrastructure will be developed and maintained by a multidisciplinary team, ensuring that the platform is technically robust and usable and adheres to institutional and regulatory standards. To demonstrate the platform's functionality, utility, and adaptability, a multisite study comprising three initial projects focused on smoking cessation among patients with cancer is being conducted: (1) participant screening and enrollment, (2) randomization and treatment delivery, and (3) data processing using machine learning methods with on-device and cloud-based approaches. + This study was funded in May 2023, and ethics approval was obtained from all involved sites' institutional review boards between February 2024 and October 2025. Recruitment began in March 2025 and enrollment is ongoing. As of January 2026, 41% (37/90) of the target sample have been enrolled and 21% (19/90) have completed their 6-month assessment. Data collection will be completed once the final participant completes their 6-month assessment (expected May 2027), with analyses commencing thereafter. Study findings are anticipated to be published in a peer-reviewed journal in 2027. + Collectively, these projects will illustrate how mFLi can streamline app development, facilitate rapid translation of research into practice, and reduce barriers for researchers and developers. Ultimately, mFLi is designed to accelerate innovation in mHealth research, enhance access to behavioral interventions, and improve health outcomes among diverse populations. + ClinicalTrials.gov NCT06909357; https://clinicaltrials.gov/study/NCT06909357. + PRR1-10.2196/92288. + ©Lindsey S Sparrock, Jennifer I Vidrine, Christine E Vinci, Issam M El Naqa, Steven K Sutton, Ramzi G Salloum, Jesse Dallery, Tracy E Crane, Frank J Penedo, Samuel J Brockway, Sarah R Jones, Charles E Hoogland, Richard R Reich, Guillermo Gonzalez-Calderon, Vani N Simmons, Damon J Vidrine. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 29.05.2026. + + + + Sparrock + Lindsey S + LS + 0000-0003-3230-7886 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + + Vidrine + Jennifer I + JI + 0000-0001-7159-7252 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Psychology, College of Arts and Sciences, University of South Florida, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + + Vinci + Christine E + CE + 0000-0001-8883-0989 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Psychology, College of Arts and Sciences, University of South Florida, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + + El Naqa + Issam M + IM + 0000-0001-6023-1132 + + Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, FL, United States. + + + + Sutton + Steven K + SK + 0000-0002-2504-7601 + + Department of Psychology, College of Arts and Sciences, University of South Florida, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, United States. + + + + Salloum + Ramzi G + RG + 0000-0002-8139-2418 + + Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States. + + + Health Cancer Center, University of Florida, Gainesville, FL, United States. + + + + Dallery + Jesse + J + 0000-0002-2882-1105 + + Department of Psychology, University of Florida, Gainesville, FL, United States. + + + + Crane + Tracy E + TE + 0000-0002-4288-134X + + Department of Medicine, Miller School of Medicine, University of Miami, Miami, FL, United States. + + + + Penedo + Frank J + FJ + 0000-0002-2780-0417 + + Department of Medicine, Miller School of Medicine, University of Miami, Miami, FL, United States. + + + Department of Psychology, University of Miami, Coral Gables, FL, United States. + + + + Brockway + Samuel J + SJ + 0009-0000-4761-768X + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + + Jones + Sarah R + SR + 0000-0001-5513-2527 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + + Hoogland + Charles E + CE + 0000-0001-7735-1931 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + + Reich + Richard R + RR + 0009-0009-9559-6858 + + Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, United States. + + + + Gonzalez-Calderon + Guillermo + G + 0000-0002-4075-8946 + + Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, United States. + + + + Simmons + Vani N + VN + 0000-0002-4381-5336 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Psychology, College of Arts and Sciences, University of South Florida, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + + Vidrine + Damon J + DJ + 0000-0002-8711-796X + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + + eng + + + ClinicalTrials.gov + + NCT06909357 + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Canada + JMIR Res Protoc + 101599504 + 1929-0748 + + IM + + + Humans + + + Smoking Cessation + methods + + + Telemedicine + + + Neoplasms + therapy + + + Mobile Applications + + + Digital Health + + + + cancer + feasibility + mHealth + randomized controlled trial + smoking + tobacco cessation + + + + + + 2026 + 2 + 9 + + + 2026 + 3 + 18 + + + 2026 + 3 + 17 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 16 + 53 + + + epublish + + 42214073 + 10.2196/92288 + v15i1e92288 + + + + + + 42214056 + + 2026 + 05 + 29 + +
+ + 2688-1535 + + + 2026 + May + 29 + + + JCO oncology practice + JCO Oncol Pract + + Development and External Validation of a Machine Learning-Based Risk Classification Score for Prostate Cancer-Specific Cardiovascular Risk. + + OP2600131 + OP2600131 + + 10.1200/OP-26-00131 + + Patients with prostate cancer (PC) face elevated cardiovascular (CV) risk. Conventional CV risk scores are often poorly calibrated for PC and require laboratory tests that are not routinely available in PC clinics, limiting oncology use. + The development cohort (DC) included men with PC (age ≥18 years) diagnosed in 2010-2014. Validation cohort 1 (V1) included men age 45 years and older within 1 year of PC diagnosis or near initiation of androgen deprivation therapy. Validation cohort 2 (V2) included men with PC diagnosed in 2006-2019. Outcomes were cardiovascular disease (CVD), atherosclerotic CVD (ASCVD), and heart failure (HF). Predictors were selected with XGBoost and LASSO, modeled with Fine-Gray and penalized Cox regression, and scaled to additive point scores (low/intermediate/high risk). Performance was assessed using time-dependent AUC (TDAUC), which quantifies the model's ability to correctly rank patients by risk at each time point, and compared with conventional scores. + The DC included 1,815 patients, and V1 and V2 included 4,022 and 1,729 patients, respectively (median follow-up 10.3 years). At 10 years, cumulative incidence was 15.0% for composite CVD, 9.4% for ASCVD, and 10.1% for HF. The composite CVD score (age, race, smoking history, high-risk Gleason score, and household members) achieved a 10-year TDAUC of 0.71 (95% CI, 0.64 to 0.78) in the DC and 0.59 (95% CI, 0.52 to 0.65) in V2, with a 2-year TDAUC of 0.66 (95% CI, 0.56 to 0.75) in V1. In the DC, ASCVD and HF scores achieved 10-year TDAUCs of 0.66 (95% CI, 0.58 to 0.75) and 0.70 (95% CI, 0.62 to 0.79), respectively, and retained discrimination in validation. + To our knowledge, these are the first PC-specific CV risk scores derived from oncology-available variables, supporting pragmatic CV risk stratification in PC clinics and motivating further validation and implementation studies. + + + + Stabellini + Nickolas + N + 0000-0002-3135-7980 + + Case Western Reserve University, Cleveland, OH. + + + Department of Hematology-Oncology, University Hospitals Seidman Cancer Center, Cleveland, OH. + + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Nahle + Tarek + T + 0009-0004-4584-3308 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Shah + Viraj + V + 0009-0003-5850-9862 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Kollapaneni + Sai S + SS + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Makram + Omar M + OM + 0000-0001-8361-9068 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Cullen + Jennifer + J + + Cancer Prevention and Control Program, Houston Methodist Neal Cancer Center, Houston, TX. + + + + Weintraub + Neal L + NL + 0000-0002-5138-3705 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + Vascular Biology Center, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Kunhiraman + Harikrishnan H + HH + 0000-0002-6395-7956 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Swami + Umang + U + 0000-0003-3518-0411 + + Division of Oncology, Department of Internal Medicine, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT. + + + + Mahmoudi + Morteza + M + 0000-0002-2575-9684 + + Department of Radiology and Precision Health Program, Michigan State University, East Lansing, MI. + + + + Fillmore + Nathanael + N + 0000-0002-8058-3423 + + Department of Medicine, Harvard Medical School, Boston, MA. + + + + Garraway + Isla P + IP + + Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, CA. + + + Department of Surgical and Perioperative Care, VA Greater Los Angeles, Los Angeles, CA. + + + + Leong + Darryl + D + 0000-0002-1968-169X + + Population Health Research Institute and Department of Medicine, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada. + + + + Pinthus + Jehonathan + J + + Division of Urology, Department of Surgery, McMaster University, Hamilton, ON, Canada. + + + + Shanahan + John + J + + Cancer Informatics, Seidman Cancer Center at University Hospitals of Cleveland, Cleveland, OH. + + + + Montero + Alberto J + AJ + 0000-0002-8221-8120 + + Case Western Reserve University, Cleveland, OH. + + + Department of Hematology-Oncology, University Hospitals Seidman Cancer Center, Cleveland, OH. + + + + Agarwal + Neeraj + N + 0000-0003-1076-0428 + + Huntsman Cancer Institute, University of Utah, Salt Lake City, UT. + + + + Guha + Avirup + A + 0000-0003-0253-1174 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + Cardio-Oncology Program, Medical College of Georgia at Augusta University, Augusta, GA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + JCO Oncol Pract + 101758685 + 2688-1527 + + IM + + + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 16 + 12 + + + aheadofprint + + 42214056 + 10.1200/OP-26-00131 + + + + + + 42213920 + + 2026 + 05 + 29 + +
+ + 1079-7114 + + 136 + 19 + + 2026 + May + 15 + + + Physical review letters + Phys Rev Lett + + Hierarchical and Ultrametric Barriers in the Energy Landscape of Jammed Granular Matter. + + 198202 + 198202 + + 10.1103/mtn5-s26m + + According to the mean-field glass theory, the (free) energy landscape of disordered systems is hierarchical and ultrametric if they belong to the full-replica-symmetry-breaking universality class. However, examining this theoretical picture in three-dimensional systems remains challenging, where the energy barriers become finite. Here, we numerically explore the energy landscape of granular models near the jamming transition using a saddle dynamics algorithm to locate both local energy minima and saddles. The multiscale distances and energy barriers between minima are characterized by two metrics, both of which exhibit signatures of an ultrametric space. The scale-free distributions of energy barriers confirm the landscape's hierarchical organization and align with the distribution of elastic avalanches under athermal quasistatic shear. + + + + Wu + Shuonan + S + + Peking University, School of Mathematical Sciences, Beijing 100871, China. + https://ror.org/02v51f717 + + + + Xie + Yuchen + Y + + Peking University, School of Mathematical Sciences, Beijing 100871, China. + https://ror.org/02v51f717 + + + + Pan + Deng + D + + Chinese Academy of Sciences, Institute of Theoretical Physics, Beijing 100190, China. + https://ror.org/034t30j35 + + + + Zhang + Lei + L + + Peking University, School of Mathematical Sciences, Beijing 100871, China. + https://ror.org/02v51f717 + + + Peking University, Beijing International Center for Mathematical Research, Beijing, 100871, China. + https://ror.org/02v51f717 + + + Peking University, Center for Quantitative Biology, Beijing, 100871, China. + https://ror.org/02v51f717 + + + Peking University, Center for Machine Learning Research, Beijing, 100871, China. + https://ror.org/02v51f717 + + + + Jin + Yuliang + Y + + Chinese Academy of Sciences, Institute of Theoretical Physics, Beijing 100190, China. + https://ror.org/034t30j35 + + + University of Chinese Academy of Sciences, School of Physical Sciences, Beijing 100049, China. + https://ror.org/05qbk4x57 + + + University of Chinese Academy of Sciences, Center for Theoretical Interdisciplinary Sciences, Wenzhou Institute, Wenzhou, Zhejiang 325001, China. + https://ror.org/05qbk4x57 + + + + eng + + Journal Article + +
+ + United States + Phys Rev Lett + 0401141 + 0031-9007 + + IM + + + + + 2025 + 12 + 22 + + + 2026 + 2 + 10 + + + 2026 + 4 + 21 + + + 2026 + 5 + 29 + 18 + 35 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 15 + 12 + + + ppublish + + 42213920 + 10.1103/mtn5-s26m + + + + + + 42213819 + + 2026 + 05 + 29 + +
+ + 1535-1386 + + + 2026 + May + 29 + + + The Journal of bone and joint surgery. American volume + J Bone Joint Surg Am + + Compliance with the CONSORT AI Extension in Orthopaedic Randomized Controlled Trials. + 10.2106/JBJS.25.01439 + + The utilization of artificial intelligence (AI), such as machine learning, in health care has risen exponentially, underscoring the necessity of transparency and traceability in the reporting of AI in randomized controlled trials (RCTs) that involve AI. In 2020, a CONSORT (Consolidated Standards of Reporting Trials) AI extension was published that included 14 additional items that should be reported when AI is used in RCTs. This study aimed to evaluate compliance with these additional guidelines. + A systematic approach was used to identify all RCTs in orthopaedics that involved AI since the extension was published. Three databases were searched (PubMed, Embase, Cochrane) using the search terms ([orthopaedic] OR [orthopedic]) AND ([artificial intelligence] OR [AI] OR [machine learning] OR [deep learning]). + One hundred and seventy-four articles were identified, and 11 articles satisfied the inclusion and exclusion criteria and were assessed for compliance. The average number of CONSORT AI-extension items met per study was 11.4 ± 1.6 (81.2% ± 12.1%). Eight items (1a [i], 1b [ii], 2a [i], 4a [i], 5 [ii], 5 [v], 5 [vi], 19) that were related to standard reporting of AI utilization were reported in all articles. The items with the lowest reporting rates were related to data transparency and included 4a (ii), 5 (iii), and 25, with only 9.1%, 36.4%, and 45.5% of all studies complying, respectively. + Orthopaedic RCTs involving AI showed high compliance with respect to standard trial reporting and broad AI descriptors but low compliance with respect to detailed data transparency, data-quality handling, and code accessibility. Addressing these weaker areas is essential to improve reproducibility, reduce bias, and strengthen trust in AI-driven clinical research. + Improved compliance with CONSORT-AI reporting standards may help orthopaedic clinicians to critically evaluate AI-based randomized trials, reproduce findings, and determine whether AI interventions are reliable and safe for clinical use. + Copyright © 2026 by The Journal of Bone and Joint Surgery, Incorporated. + + + + Strasser + Thomas + T + 0000-0003-4456-8178 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + + Baker + Christopher + C + 0009-0009-9822-6633 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + + Cheminant + Jenna + J + 0009-0001-2747-5263 + + College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, California. + + + + Han + Blake + B + 0009-0002-9570-1944 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, California. + + + + Canumay + Sallie + S + 0009-0009-1627-8828 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + + Schlechter + John + J + 0000-0001-8389-8407 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + Rady Children's Health Orange County, Orange, California, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + J Bone Joint Surg Am + 0014030 + 0021-9355 + + IM + Disclosure: No external funding was received for this work. The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJS/J301). + + + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 14 + 2 + + + aheadofprint + + 42213819 + 10.2106/JBJS.25.01439 + 00004623-990000000-01850 + + + + Schulz KF, Altman DG, Moher D; CONSORT Group. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010 Mar 23;340:c332. + + + Moher D, Hopewell S, Schulz KF, Montori V, Gøtzsche PC, Devereaux PJ, Elbourne D, Egger M, Altman DG. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ. 2010 Mar 23;340:c869. + + + Ioannidis JP. How to make more published research true. PLoS Med. 2014 Oct 21;11(10):e1001747. + + + Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020 Sep;26(9):1364-74. + + + Fan X, Qiao X, Wang Z, Jiang L, Liu Y, Sun Q. Artificial Intelligence-Based CT Imaging on Diagnosis of Patients with Lumbar Disc Herniation by Scalpel Treatment. Comput Intell Neurosci. 2022 May 27;2022:3688630. + + + Anderson PG, Baum GL, Keathley N, Sicular S, Venkatesh S, Sharma A, Daluiski A, Potter H, Hotchkiss R, Lindsey RV, Jones RM. Deep learning assistance closes the accuracy gap in fracture detection across clinician types. Clin Orthop Relat Res. 2023 Mar 1;481(3):580-8. + + + Gauci MO, Olmos M, Cointat C, Chammas PE, Urvoy M, Murienne A, Bronsard N, Gonzalez JF. Validation of the shoulder range of motion software for measurement of shoulder ranges of motion in consultation: coupling a red/green/blue-depth video camera to artificial intelligence. Int Orthop. 2023 Feb;47(2):299-307. + + + Zhu Y, Wu T, Wang W, Cai C, Zhu B, Lin W, Xu H, Liang Q, Wang Y. Artificial intelligence algorithm–based lumbar and spinal MRI for evaluation of efficacy of Chinkuei Shin Chewan decoction on lumbar spinal stenosis. Contrast Media Mol Imaging. 2021 Dec 29;2021:2700452. + + + Zhang H, He Q, Liu Q, He Y, Wang T, Feng Z, Zhang Q, He S. Comparative analysis of artificial intelligence and two-dimensional digital template preoperative planning–assisted total hip arthroplasty to predict prosthesis size. Chin J Tissue Eng Res. 2023;27(29):4620-7. + + + Zheng H, Feng E, Xiao Y, Liu X, Lai T, Xu Z, Chen J, Xie S, Lin F, Zhang Y. Is AI 3D-printed PSI an accurate option for patients with developmental dysplasia of the hip undergoing THA? BMC Musculoskelet Disord. 2024 Apr 22;25(1):308. + + + Gao L, Xing B. Bone cement reinforcement improves the therapeutic effects of screws in elderly patients with pelvic fragility factures. J Orthop Surg Res. 2024 Mar 18;19(1):191. + + + Baker HP, Dwyer E, Kalidoss S, Hynes K, Wolf J, Strelzow JA. ChatGPT’s Ability to Assist with Clinical Documentation: A Randomized Controlled Trial. J Am Acad Orthop Surg. 2024 Feb 1;32(3):123-9. + + + Bates NA, Huffman A, Goodyear E, Nagai T, Rigamonti L, Breuer L, Holmes BD, Schilaty ND. Physical clinical care and artificial-intelligence-guided core resistance training improve endurance and patient-reported outcomes in subjects with lower back pain. Clin Biomech (Bristol). 2023 Mar;103:105902. + + + Zhang Y, Hui Z, Qi W, Zhang J, Wang M, Zhu D. Clinical study on the safety and feasibility of AiWalker-K for lower limbs exercise rehabilitation in children with cerebral palsy. PLoS One. 2024 May 22;19(5):e0303517. + + + Thiengwittayaporn S, Wattanapreechanon P, Sakon P, Peethong A, Ratisoontorn N, Charoenphandhu N, Charoensiriwath S. Development of a mobile application to improve exercise accuracy and quality of life in knee osteoarthritis patients: a randomized controlled trial. Arch Orthop Trauma Surg. 2023 Feb;143(2):729-38. + + + Jayakumar P, Moore MG, Furlough KA, Uhler LM, Andrawis JP, Koenig KM, Aksan N, Rathouz PJ, Bozic KJ. Comparison of an artificial intelligence–enabled patient decision aid vs educational material on decision quality, shared decision-making, patient experience, and functional outcomes in adults with knee osteoarthritis: a randomized clinical trial. JAMA Netw Open. 2021 Feb 1;4(2):e2037107. + + + Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 Mar 29;372:n71. + + + Sataloff RT, Bush ML, Chandra R, Chepeha D, Rotenberg B, Fisher EW, Goldenberg D, Hanna EY, Kerschner JE, Kraus DH, Krouse JH, Li D, Link M, Lustig LR, Selesnick SH, Sindwani R, Smith RJ, Tysome J, Weber PC, Welling DB. Systematic and other reviews: criteria and complexities. Ear Nose Throat J. 2021 Jul;100(6):403-6. + + + Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ. 2020 Sep 9;370:m3164. + + + Anderson JM, Howard C, Staggs J, Steele R, Strasser T, Small T, Vassar M, Checketts JX. Inadequate reporting of complications in randomized controlled trials cited as supporting evidence underpinning AAOS CPG recommendations for hip and knee osteoarthritis: application of the CONSORT Harms checklist. J Arthroplasty. 2022 Dec;37(12):2466-2472.e2. + + + Marcus E, Teuwen J. Artificial intelligence and explanation: How, why, and when to explain black boxes. Eur J Radiol. 2024 Apr;173:111393. + + + Wyles CC, Saniei S, Mulford KL, Girod MM, Taunton MJ. Reporting Guidelines for Artificial Intelligence Use in Orthopaedic Surgery Research. J Arthroplasty. 2025 Oct;40(10):2737-2743.e1. + + + Martindale APL, Llewellyn CD, de Visser RO, Ng B, Ngai V, Kale AU, Ferrante di Ruffano L, Golub RM, Collins GS, Moher D, McCradden MD, Oakden-Rayner L, Cruz Rivera S, Calvert M, Kelly CJ, Lee CS, Yau C, Chan AW, Keane PA, Beam AL, Denniston AK, Liu X. Concordance of randomized controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines. Nature Communications. 2024;15(1):1619. + + + Shahzad R, Ayub B, Siddiqui MAR. Quality of reporting of randomised controlled trials of artificial intelligence in healthcare: a systematic review. BMJ Open. 2022 Sep 5;12(9):e061519. + + + Misir A, Yuce A. AI in Orthopedic Research: A Comprehensive Review. J Orthop Res. 2025 Aug;43(8):1508-27. + + + + + + + 42213782 + + 2026 + 05 + 29 + +
+ + 2211-1247 + + 45 + 6 + + 2026 + May + 28 + + + Cell reports + Cell Rep + + Non-centromeric CENP-A regulates epithelial-mesenchymal plasticity and heterogeneity in human cells. + + 117405 + 117405 + + 10.1016/j.celrep.2026.117405 + S2211-1247(26)00483-3 + + Centromere protein A (CENP-A), a centromeric histone H3 variant highly expressed in aggressive cancers, promotes epithelial-mesenchymal transition (EMT), yet its underlying mechanisms remain unresolved. Here, we used a reversible high-CENP-A expression system in human cells to follow EMT-state trajectories and CENP-A localization over time. Sustained CENP-A elevation shifted hybrid populations toward mesenchymal states and increased both centromeric loading and ectopic chromatin incorporation. Chromatin immunoprecipitation revealed ectopic CENP-A enrichment at EMT-associated loci. Single-nucleus multi-omics further resolved two EMT programs engaged at distinct cell cycle stages: CENP-A strengthened a pre-existing inflammatory program and triggered a developmental program. Importantly, restoring basal CENP-A levels erased these transcriptional programs and eliminated ectopic incorporation, consistent with a reversible, non-genetic mechanism. Together, our findings uncover a non-centromeric function for CENP-A in shaping epithelial-mesenchymal plasticity and cellular heterogeneity. + Copyright © 2026 The Author(s). Published by Elsevier Inc. All rights reserved. + + + + Renaud-Pageot + Charlène + C + + Institut Curie, Université PSL, Sorbonne Université, CNRS, UMR 3664 Nuclear Dynamics, Chromatin Dynamics Lab, Equipe Labellisée Ligue Contre le Cancer, 75005 Paris, France. + + + + Capocefalo + Daniele + D + + Institut Pasteur, Université Paris Cité, CNRS, UMR 3738, Machine Learning for Integrative Genomics Lab, 75015 Paris, France. + + + + Lemaire + Sébastien + S + + Institut Curie, Université PSL, Sorbonne Université, CNRS, UMR 3664 Nuclear Dynamics, Chromatin Dynamics Lab, Equipe Labellisée Ligue Contre le Cancer, 75005 Paris, France. + + + + Forest + Audrey + A + + Institut Curie, Université PSL, Sorbonne Université, CNRS, UMR 3664 Nuclear Dynamics, Chromatin Dynamics Lab, Equipe Labellisée Ligue Contre le Cancer, 75005 Paris, France. + + + + Cantini + Laura + L + + Institut Pasteur, Université Paris Cité, CNRS, UMR 3738, Machine Learning for Integrative Genomics Lab, 75015 Paris, France. + + + + Almouzni + Geneviève + G + + Institut Curie, Université PSL, Sorbonne Université, CNRS, UMR 3664 Nuclear Dynamics, Chromatin Dynamics Lab, Equipe Labellisée Ligue Contre le Cancer, 75005 Paris, France. Electronic address: genevieve.almouzni@curie.fr. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Cell Rep + 101573691 + + IM + + CENP-A + CP: developmental biology + CP: molecular biology + cell cycle + centromere + chromatin + epigenetics + epithelial-mesenchymal transition + single-nucleus multi-omics + + Declaration of interests The authors declare no competing interests. + + + + + 2025 + 10 + 1 + + + 2026 + 2 + 10 + + + 2026 + 4 + 27 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 13 + 52 + + + aheadofprint + + 42213782 + 10.1016/j.celrep.2026.117405 + S2211-1247(26)00483-3 + + + + + + 42213739 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1932-6203 + + 21 + 5 + + 2026 + + + PloS one + PLoS One + + TransGrid-CostOpt: A hybrid transformer framework for cost prediction and optimization of distribution network assets. + + e0350026 + e0350026 + + 10.1371/journal.pone.0350026 + + The prediction and optimization of distribution network asset costs is a complex problem in the power industry, involving the optimization of multiple objectives and the response to dynamic demands. Traditional methods often struggle to effectively address fluctuations in power load and the uncertainties in the supply chain, limiting their effectiveness in complex environments. To solve this issue, we propose the TransGrid-CostOpt model, an intelligent cost optimization model that integrates deep learning, multi-objective optimization, time-series forecasting, and optimization decision-making modules. TransGrid-CostOpt optimizes load forecasting and cost allocation for the distribution network by combining multi-source data, time-series load forecasting, and reinforcement learning decision strategies, reducing operational costs, improving load forecasting accuracy, and enhancing decision adaptability. Experimental results show that TransGrid-CostOpt outperforms traditional models and other advanced methods on the BuildingsBench and PJM Hourly Load Data datasets, exhibiting higher accuracy and efficiency in forecasting, cost optimization, and multi-objective balancing. Compared to classical baseline models and cutting-edge approaches, TransGrid-CostOpt demonstrates a 15% to 30% overall performance improvement. Ablation experiments confirm the critical role of each module, especially the time-series forecasting module and optimization decision-making module, in significantly enhancing the model's performance. TransGrid-CostOpt strengthens the cost management capability of the distribution network and shows strong adaptability in dynamic electricity market environments, with broad application potential. + Copyright: © 2026 Xiong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. + + + + Xiong + Wei + W + + State Grid Hubei Electric Power Co., Ltd, Wuhan, Hubei, China. + + + + Xia + Jie + J + + State Grid Hubei Electric Power Co., Ltd. Jingzhou Power Supply Company, Jingzhou, Hubei, China. + + + + Yu + YiBo + Y + + Wuhan Anju Supply Chain Co., Ltd, Wuhan, Hubei, China. + + + + Xiong + SanMing + S + + International Education College of North China Electric Power University (Baoding), Baoding, Hebei, China. + + + + Hu + HaiYang + H + + State Grid Hubei Electric Power Co., Ltd. Jingzhou Power Supply Company, Jingzhou, Hubei, China. + + + + Wan + Peng + P + 0009-0006-8464-084X + + Hubei Power Grid Design Co., Ltd., Wuhan, Hubei, China. + + + + Li + Dan + D + + Yunxia Electric Energy (Yunnan) Co., Ltd, Kunming, Yunnan, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + PLoS One + 101285081 + 1932-6203 + + IM + + + Reinforcement Machine Learning + + + Prediction Algorithms + + + Forecasting + methods + + + Predictive Learning Models + + + The authors declare that they have no competing interests. There is no financial, personal, or professional conflict that could be perceived as influencing the research or the writing of this manuscript. + + + + + 2025 + 12 + 18 + + + 2026 + 5 + 7 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 13 + 44 + + + epublish + + 42213739 + 10.1371/journal.pone.0350026 + PONE-D-25-67221 + + + + + + 42213730 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1553-7358 + + 22 + 5 + + 2026 + May + + + PLoS computational biology + PLoS Comput Biol + + A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and facilitating drug repurposing. + + e1014323 + e1014323 + + 10.1371/journal.pcbi.1014323 + + Many genetic loci were identified as associated with neuropsychiatric disorders and neurodegenerative disorders by Genome-wide association studies (GWAS). How these loci impact these diseases is unclear. Advances in deep-learning approaches and multi-omics data have the potential to link GWAS findings with disease mechanisms. Here, we proposed the Multi-omics Graph Transformer Network (MOGT), a semi-supervised graph neural network that leverages graph representation learning to model biological networks derived from multi-omics data to predict disease-associated genes. MOGT outperforms the current approaches in disease gene prediction for two psychiatric disorders and three neurodegenerative/neurological diseases. High-risk genes (HRGs) for Parkinson's disease (PD) predicted by MOGT were used to drug discovery by integrating with the CMAP database. Finally, 10 drugs were identified as potential candidates. Among them, the effect of drug UK-356618 was experimentally verified in a primary neuron model, showing that UK-356618 reversed the abnormal expression of PD-associated genes and improved the cell-level phenotypes of PD. Together, these results indicate that MOGT can be used to identify HRGs for brain disorders, and these predicted HRGs provide high-level insights into the mechanisms and treatments of brain disorders. + Copyright: © 2026 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. + + + + Li + Jiafang + J + 0009-0006-5493-5305 + + Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China. + + + Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, Guangdong, China. + + + + Li + Yifei + Y + + Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China. + + + Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, Guangdong, China. + + + + Lin + Siying + S + + Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China. + + + School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China. + + + + Rao + Jiahua + J + + School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China. + + + + Zhao + Huiying + H + 0000-0001-9134-536X + + Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China. + + + Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, Guangdong, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + PLoS Comput Biol + 101238922 + 1553-734X + + IM + + + Graph Neural Networks + + + Drug Repositioning + methods + + + Genome-Wide Association Study + + + Humans + + + Computational Biology + methods + + + Brain Diseases + genetics + drug therapy + + + Representation Machine Learning + + + Genetic Predisposition to Disease + genetics + + + Parkinson Disease + genetics + + + Animals + + + The authors have declared that no competing interests exist. + + + + + 2025 + 12 + 1 + + + 2026 + 5 + 11 + + + 2026 + 5 + 29 + 18 + 35 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 13 + 43 + + + epublish + + 42213730 + 10.1371/journal.pcbi.1014323 + PCOMPBIOL-D-25-02551 + + + + + + 42213726 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1932-6203 + + 21 + 5 + + 2026 + + + PloS one + PLoS One + + Machine learning models based on magnetic resonance imaging for predicting Lymphovascular Invasion in Invasive Breast Cancer. + + e0350085 + e0350085 + + 10.1371/journal.pone.0350085 + + Treatment strategies for invasive breast cancer require accurate lymphovascular invasion (LVI) predictions. This study aimed to investigate the feasibility and effectiveness of delta radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and radiomics signature based on T2-weighted fat suppressed imaging(T2FS) for assessing LVI in invasive breast cancer. + A total of 166 patients with resectable invasive breast cancer who underwent preoperative DCE-MRI and T2FS from July 10, 2020 to December 31, 2023 were enrolled. Radiomics features were extracted from pre-contrast phase (RFpre), second post-contrast phase (RFpost), and Delta radiomics features (RFDelta) calculated as RFpost minus RFpre. Then four radiomics signatures (RST2, RSpre, RSpost, RSDelta) were further developed based on the Random Forest model for RFT2, RFpre, RFpost and RFDelta, respectively. The predictive performance of all signatures was evaluated by receiver operating characteristic (ROC) analysis, with accuracy and area under the curve (AUC) as the main quantitative metrics. + In the test set, RSDelta (10 features) achieved the highest accuracy of 0.717 and an AUC of 0.764; RSpost (8 features) had an accuracy of 0.565 and an AUC of 0.610; RSpre (7 features) and RST2 (6 features) both showed an accuracy of 0.565 with AUCs of 0.535 and 0.662, respectively. Statistical differences were observed in predictive performance between RSDelta and RSpre, RSpost (both p < 0.05), while no significant difference was found between RSDelta and RST2 (p = 0.239). + RST2, RSpre, RSpost and RSDelta are all feasible for predicting LVI in invasive breast cancer, and RSDelta outperforms the other three radiomics signatures, which can serve as a potential non-invasive imaging biomarker for LVI prediction in clinical practice. + Copyright: © 2026 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. + + + + Li + Hong + H + 0000-0002-4531-7541 + + Department of Radiology, the Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China. + + + + Huang + Jieling + J + + Department of Radiology, Guangzhou Geriatric Hospital, Guangzhou, Guangdong, China. + + + + Hou + Jianning + J + + Department of Radiology, Guangzhou Women And Children's Medical Center, Guangzhou, China. + + + + Chen + Xinxin + X + + Department of Breast surgery, the Second Affiliated Hospital, Guangzhou Medical University,‌‌ Guangzhou, Guangdong, China. + + + + Zhi + Cheng + C + + Department of Pathology, the Second Affiliated Hospital, Guangzhou Medical University, ‌‌Guangzhou, Guangdong, China. + + + + Li + Zhiming + Z + + Department of Radiology, the Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + PLoS One + 101285081 + 1932-6203 + + IM + + + Humans + + + Breast Neoplasms + diagnostic imaging + pathology + + + Female + + + Radiomics + + + Dynamic Contrast Enhanced Magnetic Resonance Imaging + + + Machine Learning + + + Neoplasm Invasiveness + + + Magnetic Resonance Imaging + methods + + + Middle Aged + + + Predictive Learning Models + + + Lymphatic Metastasis + diagnostic imaging + + + Random Forest + + + ROC Curve + + + The authors have declared that no competing interests exist. + + + + + 2025 + 7 + 28 + + + 2026 + 5 + 8 + + + 2026 + 5 + 29 + 18 + 35 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 13 + 43 + + + epublish + + 42213726 + 10.1371/journal.pone.0350085 + PONE-D-25-38600 + + + + + + 42213702 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2767-3170 + + 5 + 5 + + 2026 + May + + + PLOS digital health + PLOS Digit Health + + Video-based detection of Delirium in hospitalized adults. + + e0001462 + e0001462 + + 10.1371/journal.pdig.0001462 + + Delirium, a dynamic neuropsychiatric condition associated with morbidity and mortality, remains underdiagnosed due to reliance on subjective, intermittent screening tools. Objective and potentially continuous identification is needed to improve clinical care. We developed and validated an analytic framework for delirium classification based on automatically extracted video features. In this prospective cohort study, patients (≥ 18 years) admitted to the inpatient medical or neurological ward of a tertiary academic center between August 2020 and March 2022 with an expected stay longer than one night were enrolled. Daily structured delirium assessments and brief video recordings were performed in consenting patients. Videos were analyzed using deep learning pose estimation to extract keypoints and calculate behavioral features based on eye, face, and limb postures and movements. Four machine learning models (logistic regression, gradient boosting, support vector machines, and random forests) were trained to predict delirium status from extracted features. Model performance was evaluated on 20 repetitions of three-fold cross-validation using the area under the curve of the receiver operating characteristics curve (AUC ROC). The cohort included 109 videos from 25 male and 25 female participants (median age: 72, IQR: 63.25-78). Twenty videos (18%) were from patients with delirium. Keypoints for this dataset were more accurately extracted using a customized ResNet-101 model developed with DeepLabCut (sensitivity 0.94, specificity 0.89, compared to human-labeled gold standards) than using off-the-shelf models. Keypoints were then used to generate behavioral features summarizing movement and postures throughout the video. A support vector machine model achieved an average delirium classification AUC ROC of 0.79 (SD ± 0.09), sensitivity of 0.71 (SD ± 0.16), and specificity of 0.78 (SD ± 0.07). This study demonstrates the feasibility of identifying delirium using brief videos in clinically heterogeneous cohorts and reveals novel features for objective identification. + Copyright: © 2026 Mendu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. + + + + Mendu + Maanasa + M + 0000-0002-0409-1769 + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Tesh + Ryan A + RA + 0000-0002-6154-6248 + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America. + + + + Pellerin + Kyle + K + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Steward + Grace E + GE + 0000-0002-0226-0880 + + Department of Neurology, Northwestern University, Chicago, Illinois, United States of America. + + + + Cerda + Ivo H + IH + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Williams + Marta + M + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Colman + Mia + M + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Shah + Simran + S + 0000-0001-6298-7558 + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Lam + Alice D + AD + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Cash + Sydney S + SS + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Westover + M Brandon + MB + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America. + + + + Kimchi + Eyal Y + EY + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + Department of Neurology, Northwestern University, Chicago, Illinois, United States of America. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + PLOS Digit Health + 9918335064206676 + 2767-3170 + + I have read the journal’s policy and the authors of this manuscript have the following competing interests: Dr. Cash and Dr. Westover report being a cofounder of, serving as a scientific advisor and consultant to, and having a personal equity interest in Beacon Biosignals outside the submitted work. Beacon Biosignals had no role in the design, data analysis, interpretation, or writing of the manuscript. + + + + + 2025 + 10 + 23 + + + 2026 + 5 + 11 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 13 + 34 + + + epublish + + 42213702 + 10.1371/journal.pdig.0001462 + PDIG-D-25-00896 + + + + + + 42213697 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1932-6203 + + 21 + 5 + + 2026 + + + PloS one + PLoS One + + A hybrid BiLSTM-transformer-GCN architecture with API fusion for adaptive transportation resource analytics. + + e0349787 + e0349787 + + 10.1371/journal.pone.0349787 + + Intelligent Transportation Systems (ITS) generate large volumes of heterogeneous spatiotemporal data from connected vehicles, road infrastructure, and external contextual services. Effectively discovering, classifying, and managing these transportation resources in real time remains challenging due to dynamic traffic conditions and nonlinear dependencies. This paper proposes a novel hybrid deep learning framework that integrates Bidirectional Long Short-Term Memory (BiLSTM), Transformer encoders, and Graph Convolutional Networks (GCN) with API-based contextual feature fusion for adaptive resource discovery and classification. A multi-objective learning strategy jointly optimizes supervised classification and unsupervised anomaly discovery through reconstruction-based latent feature refinement. Experiments conducted on the Barcelona Accident dataset and the Metro Interstate Traffic Volume dataset demonstrate the superiority of the proposed method. On the combined dataset, the framework achieves an accuracy of 0.882, F1-score of 0.864, and AUC of 0.911, outperforming traditional baselines such as Random Forest (accuracy 0.839) and deep CNN-LSTM (accuracy 0.857). For anomaly discovery, it obtains an AUROC of 0.836 and AUPRC of 0.784 on the Barcelona dataset, and AUROC of 0.819 on the Metro dataset, while achieving the lowest reconstruction error of 0.198. The ablation study shows performance degradation when API fusion (-4.1% accuracy) or GCN modeling (-2.8% accuracy) is removed, confirming their contribution. Inference latency analysis further demonstrates practical deployability with a competitive accuracy-latency trade-off. + Copyright: © 2026 Javeed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. + + + + Javeed + Mohammed Saad + MS + + Information Science, Trine University, Michigan,‌‌ Allen Park, United States of America‌. + + + + Biswas + Arindam Kishor + AK + + University of the Cumberlands, Williamsburg, Kentucky, United States of America. + + + + Hasan + Md Nahid + MN + + University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, United States of America. + + + + Maua + Jannatul + J + + Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh. + + + + Islam + Rahomotul + R + 0009-0007-1948-0322 + + Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + PLoS One + 101285081 + 1932-6203 + + IM + + + Long Short Term Memory + + + Graph Neural Networks + + + Transportation + + + Data Analytics + + + Deep Learning + + + Intelligent Systems + + + Algorithms + + + The authors have declared that no competing interests exist. + + + + + 2025 + 10 + 26 + + + 2026 + 5 + 5 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 13 + 34 + + + epublish + + 42213697 + 10.1371/journal.pone.0349787 + PONE-D-25-57885 + + + + + + 42213657 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1932-6203 + + 21 + 5 + + 2026 + + + PloS one + PLoS One + + Towards intelligent railway monitoring: A novel hybrid deep learning architecture for railway obstacle detection. + + e0349562 + e0349562 + + 10.1371/journal.pone.0349562 + + Railways are among the most efficient modes of transportation, capable of moving large quantities of goods and passengers over long distances at relatively low cost. However, accidents frequently occur due to objects or individuals present on the tracks, as trains are unable to swerve and require long braking distances. While the localization of objects within the track bed is a well-explored topic, the reliable and high-performance classification of such obstacles across all relevant categories remains an unresolved challenge. This study proposes an innovative hybrid architecture that leverages the specific visual characteristics of track bed imagery, setting a new benchmark in this domain. The hybrid design effectively leverages the strengths of ResNet50 and Swin Transformer V2, allowing the model to capture both local and global features. In addition, an Efficient Attention Module is integrated to further emphasize the most relevant features for robust obstacle classification. Using stratified five-fold cross-validation on a dataset of 2,003 images across six classes (iron bar, boulder, person, branch, canister, and barrel), the model achieved an average balanced accuracy of 99.46%. The results have implications for accident prevention, improving operational efficiency, and modernizing railway safety systems, thereby enabling the future application of automatic railway surveillance systems to ultimately enhance operational security. + Copyright: © 2026 Mai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. + + + + Mai + Christopher + C + + Chair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Hamburg, Germany. + + + + Eisentraut + Luca + L + + Chair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Hamburg, Germany. + + + + Schadt + Merlin + M + + Chair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Hamburg, Germany. + + + + Buettner + Ricardo + R + 0000-0003-2263-6408 + + Chair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Hamburg, Germany. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + PLoS One + 101285081 + 1932-6203 + + IM + + + Railroads + + + Deep Learning + + + Humans + + + Detection Algorithms + + + Convolutional Neural Networks + + + Image Processing, Computer-Assisted + methods + + + The authors have declared that no competing interests exist. + + + + + 2025 + 11 + 7 + + + 2026 + 5 + 1 + + + 2026 + 5 + 29 + 18 + 35 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 13 + 33 + + + epublish + + 42213657 + 10.1371/journal.pone.0349562 + PONE-D-25-60054 + + + + + + 42213596 + + 2026 + 05 + 29 + +
+ + 1944-8252 + + + 2026 + May + 29 + + + ACS applied materials & interfaces + ACS Appl Mater Interfaces + + 3D-Printed Conductive Aerogel Humidity Sensor for Advanced Wearable Sleep and Health Monitoring. + 10.1021/acsami.6c01464 + + Recent advances in personalized sleep medicine and home-based health monitoring have grown rapidly, yet progress remains limited by the lack of comfortable, reliable, and durable sensors for long-term respiration tracking. Here, we present a flexible humidity sensor fabricated through a freeze-drying-assisted direct-ink-writing (DIW) 3D printing strategy. This sensor is constructed from a poly(vinyl alcohol)/nanocellulose/graphene/multiwalled carbon nanotubes (PVA/CNF/Gr/MWCNTs, PCGM) composite aerogel, with a hierarchically porous conductive architecture. This aerogel was stabilized by a biocompatible PVA matrix reinforced by nanocellulose, while graphene ensures high conductivity, and it was further enhanced by carbon nanotubes in the 3D structure. Moreover, the considerable hydrogen bonding and π-π conjugation within this hybrid material contribute to exceptional interfacial stability, water-adsorption capacity, and electrical conductivity, resulting in a remarkable humidity-sensing performance with high sensitivity, fast response/recovery times, and excellent stability across a broad humidity range. Remarkably, the sensor can accurately differentiate diverse sleep postures and respiratory patterns, including normal, snoring, and coughing. Furthermore, we further enabled a 600-sample-trained deep convolutional neural network and achieved a high-precision pattern recognition, with 100% accuracy in respiratory state classification and 97% accuracy for spoken-word recognition classification. Our work provides an integrated strategy for next-generation high-performance wearable health monitoring and human-machine interaction systems. + + + + Chen + Xiaojun + X + + School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China. + + + Department of Food Science and Technology, National University of Singapore, 2 Science Drive 2, Singapore 117542, Singapore. + + + + Lin + Xitong + X + + School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China. + + + + Huang + Yuanyu + Y + + School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China. + + + + Li + Yihuan + Y + + School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China. + + + + Lian + Haishan + H + + School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China. + + + + Zhao + Zhihao + Z + + Department of Food Science and Technology, National University of Singapore, 2 Science Drive 2, Singapore 117542, Singapore. + + + + Zhou + Jiangtao + J + 0000-0003-4248-2207 + + Department of Food Science and Technology, National University of Singapore, 2 Science Drive 2, Singapore 117542, Singapore. + + + Bezos Centre for Sustainable Protein at the National University of Singapore, 2 Science Drive 2, Singapore 117542, Singapore. + + + National University of Singapore (Suzhou) Research Institute, 377 Linquan Street, Suzhou Industrial Park, Jiangsu 215123, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + ACS Appl Mater Interfaces + 101504991 + 1944-8244 + + IM + + 3D printing + Composite aerogel + Deep learning + Flexible humidity sensor + Respiration monitoring + + + + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 13 + 12 + + + aheadofprint + + 42213596 + 10.1021/acsami.6c01464 + + + + + + 42213487 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2564-1891 + + 6 + + 2026 + May + 29 + + + JMIR infodemiology + JMIR Infodemiology + + Early Depression Detection in Social Media: Monitoring of Individual Nighttime Dynamics and Large Language Model Analysis. + + e87138 + e87138 + + 10.2196/87138 + + Depression has become a major global public health challenge, and early intervention is critical for improving patient outcomes. Current depression detection techniques based on social media data (traditional risk detection) rely heavily on users' complete historical information, which cannot meet the timeliness requirements of early intervention. This underscores the need for early risk detection (ERD) methods emphasizing early-stage, real-time warning. However, existing ERD studies have notable limitations such as (1) they overlook temporal activity patterns hidden in posting time stamps, missing vital warning signals; and (2) they depend on static templates or resource-intensive sequence models, resulting in limited interpretability and inefficient use of early data, ultimately constraining their clinical applicability for early intervention. + This study aims to develop an efficient, reliable, and interpretable ERD model. The core objectives are to extract temporal activity patterns features from posting time stamps, thereby enriching feature dimensions for risk detection; to leverage large language models (LLMs) for improved text filtering precision and depression-related factor analysis; and to achieve accurate early detection of depression to support clinical intervention. + We propose the Monitoring of Individual Nighttime Dynamics (MIND) and LLM analysis model, which integrates two key innovations: (1) circadian activity dynamics: posting time stamps are transformed into temporal activity patterns, analyzing fluctuations in posting frequency and timing to derive sleep-related features, thereby compensating for the limitations of text-only approaches, and (2) LLM depression profiler: LLMs are used for dynamic text filtering, automatically removing irrelevant noise and focusing on potential depression-related cues. Based on LLM semantic understanding, latent depression risk factors are identified, enhancing interpretability for clinical treatment and robustness to noise. + Experiments on the eRisk2017 (data source: Reddit [Reddit Inc]) benchmark dataset demonstrated that MIND significantly outperformed existing baseline models in early detection sensitivity, specificity, and accuracy. By combining sleep-related features with text analysis, the model achieved interpretable, traceable predictions that can support clinical treatment. ALL relevant experimental code is publicly available. + The MIND model combines temporal activity pattern features with LLM-based text analysis, addressing the challenges of poor interpretability and inefficient use of early-stage data in existing ERD methods. It significantly enhances early detection performance, offering a new paradigm for applying social media data in ERD, thereby enabling earlier intervention and reducing the public health burden of depression. + ©Bicheng Yu, Zhichang Zhang, Lulu Ma, Jiongfu Cai, Yuanyuan Zhang. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 29.05.2026. + + + + Yu + Bicheng + B + 0009-0009-8280-6414 + + College of Artificial Intelligence and Computer Science, Northwest Normal University, Lanzhou, Gansu, China. + + + + Zhang + Zhichang + Z + 0000-0003-3306-8493 + + College of Artificial Intelligence and Computer Science, Northwest Normal University, Lanzhou, Gansu, China. + + + + Ma + Lulu + L + 0009-0009-1573-3534 + + College of Artificial Intelligence and Computer Science, Northwest Normal University, Lanzhou, Gansu, China. + + + + Cai + Jiongfu + J + 0009-0006-0608-6097 + + College of Artificial Intelligence and Computer Science, Northwest Normal University, Lanzhou, Gansu, China. + + + + Zhang + Yuanyuan + Y + 0009-0002-0645-3409 + + College of Artificial Intelligence and Computer Science, Northwest Normal University, Lanzhou, Gansu, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Canada + JMIR Infodemiology + 9918249014806676 + 2564-1891 + + IM + + + Humans + + + Large Language Models + + + Social Media + statistics & numerical data + + + Depression + diagnosis + + + Early Diagnosis + + + + circadian rhythm + depression + early risk detection + large language model + sleep analysis + + + + + + 2025 + 11 + 5 + + + 2026 + 4 + 29 + + + 2026 + 3 + 30 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 11 + 53 + + + epublish + + 42213487 + 10.2196/87138 + v6i1e87138 + + + + + + 42213480 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2369-3762 + + 12 + + 2026 + May + 29 + + + JMIR medical education + JMIR Med Educ + + Performance of Large Language Models on the Brazilian National Medical Education Examination: Comparative Benchmark Study. + + e89839 + e89839 + + 10.2196/89839 + + Large language models (LLMs) are rapidly incorporated into medical education and examination preparation; yet, most benchmarking evidence is derived from English-language material. Whether frontier commercial models and Brazilian Portuguese domain-specialized systems perform equivalently on high-stakes Brazilian medical examinations remains unclear. + This study aims to quantify and compare the performance of 9 frontier commercial LLMs and 1 Brazilian Portuguese domain-specialized system (Charcot, Voa Health) on 2026 Brazilian National Medical Education Examination (Exame Nacional de Avaliação da Formação Médica [ENAMED] 2026) and to describe the patterns of systematic between-model error as complementary quality signal. + All 100 items of ENAMED 2026 (99 valid after annulment) were administered to 10 frontier-panel models across 5 independent runs under identical Portuguese prompts (temperature=0; top-p=.95). Commercial models were accessed through a unified OpenRouter client layer (DeepSeek provider-pinned). The primary outcome was mean accuracy against the preliminary key; the secondary outcomes were convergence error (CE), normalized mean response time (NMRT), and intermodel agreement. Accuracy was analyzed with Shapiro-Wilk, Levene, Kruskal-Wallis (ε2), Dunn-Holm post hoc, and a binomial generalized linear mixed model with question and run random intercepts. NMRT excluded Charcot (different stack) and Grok 4 (latency outlier). A total of 7 open-weight and small language models were assessed as a small language model (SLM) substudy. + Frontier-panel accuracy ranged from 73.74% (365/495) for GPT-4o-mini to 96.97% (480/495) for Charcot. Accuracy was nonnormal (Shapiro-Wilk, W=0.82; P<.001) with homogeneous variance (Levene P=.26). Kruskal-Wallis showed large between-model differences (H9=47.65; P<.001; ε2=0.97). Dunn-Holm flagged 8 of 45 pairs: Charcot was separable from GPT-4o-mini, DeepSeek v3.2-exp, and Grok 4, but not from the top frontier cluster. The generalized linear mixed model preserved the ranking (all comparators odds ratio<1 vs Charcot; upper CI<1 except GPT-5). A total of 9 items met the default CE criterion; item 77 showed 10-of-10 convergence, later confirmed by Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira rectification, and sensitivity analysis preserved the CE set (5-20 items). Intermodel agreement was high (Fleiss κ=0.852; Krippendorff α=.852). Among 8 retained commercial models, NMRT correlated positively with accuracy (Spearman ρ=0.74; P=.04); these results are interpreted as descriptive of an orchestration-level latency-accuracy law. SLM accuracy ranged from 47.47% (Gemma 3 4B) to 82.22% (GPT-OSS 120B), with lower agreement (Fleiss κ=0.508) and 17 CE items. The best SLM-panel model lagged every frontier-panel model except GPT-4o-mini, with a 12-15 percentage-point gap against the top cluster. + On ENAMED 2026, a Brazilian Portuguese domain-specialized system ranked first, indistinguishable from frontier commercial cluster and above subfrontier and open-weight systems. Charcot's architecture is not publicly disclosed; these findings should be interpreted as comparative black-box evidence of performance and not as mechanistic evidence of specialization. CE was stable, and it prospectively flagged 1 rectified item, supporting its use as a quality assurance screen. + ©Francys de Luca Fernandes da Silva, Eduardo Augusto Roeder, João Victor Bruneti Severino, Matheus Nespolo Berger, Pedro Angelo Basei de Paula, Davi Ferreira, Maria Han Veiga, Thyago Proença de Moraes, Gustavo Lenci Marques. Originally published in JMIR Medical Education (https://mededu.jmir.org), 29.05.2026. + + + + Fernandes da Silva + Francys de Luca + FL + 0000-0001-9214-691X + + R. Imac. Conceição, 1155 - Prado Velho, Pontifícia Universidade Católica do Paraná, Curitiba, Paraná, Brazil. + + + + Roeder + Eduardo Augusto + EA + 0000-0002-1016-3319 + + Universidade Federal do Paraná, Curitiba, Paraná, Brazil. + + + + Bruneti Severino + João Victor + JV + 0000-0002-8649-6494 + + R. Imac. Conceição, 1155 - Prado Velho, Pontifícia Universidade Católica do Paraná, Curitiba, Paraná, Brazil. + + + Universidade Federal do Paraná, Curitiba, Paraná, Brazil. + + + + Nespolo Berger + Matheus + M + 0009-0000-0815-3288 + + Universidade Federal do Paraná, Curitiba, Paraná, Brazil. + + + + Basei de Paula + Pedro Angelo + PA + 0009-0000-6271-6862 + + Universidade Federal do Paraná, Curitiba, Paraná, Brazil. + + + + Ferreira + Davi + D + 0000-0003-1151-9652 + + Instituto Tecnológico de Aeronáutica, São José dos Campos, São Paulo, Brazil. + + + + Han Veiga + Maria + M + 0009-0008-8261-5258 + + The Ohio State University, Columbus, OH, United States. + + + + de Moraes + Thyago Proença + TP + 0000-0002-2983-3968 + + R. Imac. Conceição, 1155 - Prado Velho, Pontifícia Universidade Católica do Paraná, Curitiba, Paraná, Brazil. + + + + Lenci Marques + Gustavo + G + 0000-0002-6057-0350 + + R. Imac. Conceição, 1155 - Prado Velho, Pontifícia Universidade Católica do Paraná, Curitiba, Paraná, Brazil. + + + Universidade Federal do Paraná, Curitiba, Paraná, Brazil. + + + + eng + + Journal Article + Comparative Study + + + 2026 + 05 + 29 + +
+ + Canada + JMIR Med Educ + 101684518 + 2369-3762 + + IM + + + Large Language Models + + + Brazil + + + Humans + + + Benchmarking + methods + + + Educational Measurement + methods + standards + statistics & numerical data + + + Education, Medical + standards + methods + + + + Brazilian Portuguese + ENAMED + Exame Nacional de Avaliação da Formação Médica + artificial intelligence + benchmarking + clinical reasoning + large language models + medical education + + + + + + 2025 + 12 + 20 + + + 2026 + 5 + 8 + + + 2026 + 4 + 28 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 11 + 52 + + + epublish + + 42213480 + 10.2196/89839 + v12i1e89839 + + + + + + 42213376 + + 2026 + 05 + 29 + +
+ + 1573-501X + + + 2026 + May + 29 + + + Molecular diversity + Mol Divers + + M-Tune: imbalanced data handling in machine learning by tuning the decision threshold. + 10.1007/s11030-026-11600-8 + + Overprediction of the majority class and poor predictive performance are the major issues when training machine learning classifiers with imbalanced data. This eventually leads to a larger misclassification rate and bias towards the majority class. This is a real-world problem, especially when dealing with biological datasets. There are several popular methods, including Random Under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE), and Generalized tHreshOld ShifTing (GHOST). However, these methods either articulate the no. of samples or are biased towards the majority class. In this study, we developed a novel strategy, "M-Tune", a simple yet powerful technique for addressing the class imbalance problem. This procedure employs an ensemble approach that incorporates threshold shifting and majority voting. This new method can effectively predict the minority class. The developed method was rigorously tested with 138 drug discovery datasets. We further rigorously evaluate the performance of the M-Tune method for two classifiers with eleven different fingerprints and find that classifiers benefit from M-Tune in effectively classifying the minority class. The M-Tune method outperformed the other strategies, including the GHOST method and SMOTE and achieved comparable performance with RUS in identifying minority classes. In conclusion, we demonstrated a new threshold procedure that can be effectively applied to real-world imbalanced datasets, i.e., drug discovery data sets where the class datasets are highly imbalanced. This method aids in identifying the minority class in highly imbalanced datasets, in general, and in drug discovery datasets, in particular. Although M-Tune increases false positive predictions relative to conservative thresholding strategies, it provides a practical advantage in applications where maximizing active compound recovery is prioritized. + © 2026. The Author(s), under exclusive licence to Springer Nature Switzerland AG. + + + + Dutta + Tapos + T + + School of Computing Science, The Assam Kaziranga University, Jorhat, 785006, India. + + + + Chutia + Hillul + H + + Advanced Computation and Data Sciences Division, CSIR-North East Institute of Science and Technology, Jorhat, 785006, India. + + + + Nagamani + Selvaraman + S + + Advanced Computation and Data Sciences Division, CSIR-North East Institute of Science and Technology, Jorhat, 785006, India. nagamaniselvaraman@gmail.com. + + + Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India. nagamaniselvaraman@gmail.com. + + + + eng + + + BT/PR40188/BTIS/137/27/2021 + Department of Biotechnology, Ministry of Science and Technology, India + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Netherlands + Mol Divers + 9516534 + 1381-1991 + + IM + + Bioactive datasets + Class imbalance problem + Data handling + Machine learning + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2025 + 7 + 18 + + + 2026 + 5 + 21 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 11 + 20 + + + aheadofprint + + 42213376 + 10.1007/s11030-026-11600-8 + 10.1007/s11030-026-11600-8 + + + + Abd Elrahman SM, Abraham A (2013) A review of class imbalance problem. J Netw Innovative Comput 1:332–340 + + + Haixiang G, Yijing L, Shassng J, Mingyun G, Yuanyue H, Bing G (2017) Learning from class-imbalanced data: review of methods and applications. Expert Syst Appl 73:220–239 + + 10.1016/j.eswa.2016.12.035 + + + + Tong X, Feng Y, Li JJ (2018) Neyman-Pearson classification algorithms and NP receiver operating characteristics. Sci Adv 4:1–10 + + 10.1126/sciadv.aao1659 + + + + Kaur H, Pannu HS, Malhi AK (2019) A systematic review on imbalanced data challenges in machine learning: applications and solutions. ACM Comput Surv 52:1–36 + + + Weiss GM (2004) Mining with rarity: a unifying framework. SIGKDD Explor Newsl 6:7–19 + + 10.1145/1007730.1007734 + + + + Provost F, Fawcett T (2001) Robust classification for imprecise environments. Mach Learn 42:203–231 + + 10.1023/A:1007601015854 + + + + Japkowicz N, Stephen S (2002) The class imbalance problem: a systematic study. Intell Data Anal 6:429–449 + + 10.3233/IDA-2002-6504 + + + + López V, Fernández A, García S, Palade V, Herrera F (2013) An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci 250:113–141 + + 10.1016/j.ins.2013.07.007 + + + + Wellintz J, Jain S, Hochuli JE, Maxfield T, Muratov EN, Tropsha A, Zakharov AV (2025) One size does not fit all: revising traditional paradigms for assessing accuracy of QSAR models used for virtual screening. J Cheminform 17:7 + + 10.1186/s13321-025-00948-y + + + + Seal S, Mahale M, Ortegon MG (2025) Machine learning for toxicity prediction using chemical structures: pillars for success in the real world. Chem Res Toxicol 38:759–807 + + + Sun Y, Wong AK, Kamel MS (2009) Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell 23:687–719 + + 10.1142/S0218001409007326 + + + + V Ganganwar (2012) An overview of classification algorithms for imbalanced datasets. Int J Emerg Technol Adv Eng 2:42–47 + + + Estabrooks A, Jo T, Japkowicz N (2004) A multiple resampling method for learning from imbalanced data sets. 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+ + 1880-344X + + + 2026 + May + 29 + + + Journal of echocardiography + J Echocardiogr + + Explainable machine learning for estimation of elevated left ventricular filling pressure: a multicenter validation. + 10.1007/s12574-026-00738-x + + Guideline-recommended algorithms (GL-algorithm) often results in indeterminate left ventricular filling pressure (LVFP). Despite high accuracy, machine learning (ML) methods lack interpretability, which necessitates the development of explainable ML models for clinical use. + To develop an explainable ML model for estimating LVFP, providing patient-level interpretation using gold-standard right heart catheterization (RHC) data. + We retrospectively enrolled 956 patients who underwent echocardiography and RHC at three hospitals within a median of 3 days. Two extreme gradient boosting models were trained using data from two hospitals (n = 621) to estimate elevated pulmonary artery wedge pressure (PAWP ≥ 18 mmHg) as a surrogate for elevated LVFP. Model 1 used variables from GL-algorithm, while Model 2 used variables selected based on Shapley additive explanations (SHAP) values. Models' area under the receiver-operating characteristic curve (AUROC) for elevated LVFP were compared using external test data from the other hospital (n = 335). + Overall, 31.0% had elevated PAWP, and 42.7% were classified as indeterminate LVFP by GL-algorithm, whereas the ML models classified all patients. AUROCs of Model 1 (0.82, 95% CI 0.73-0.92) and Model 2 (0.83, 95% CI 0.75-0.91) in classifiable cases by GL-algorithm significantly outperformed that of GL-algorithm (0.72, 95% CI 0.60-0.83, p = 0.020 and 0.016, respectively), Model 2 performed equally well for indeterminate cases. SHAP force plots visualized each variable's contribution to the ML model's decision for each patient. + Explainable ML outperformed GL-algorithm in estimating LVFP, providing a user-friendly tool for clinicians with patient-level interpretability. + © 2026. Japanese Society of Echocardiography. + + + + Nakamura + Yutaka + Y + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Kagiyama + Nobuyuki + N + 0000-0002-2305-0049 + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. kgnb_27_hot@yahoo.co.jp. + + + Data Science Course, Juntendo University, Tokyo, Japan. kgnb_27_hot@yahoo.co.jp. + + + Department of Cardiovascular Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0021, Japan. kgnb_27_hot@yahoo.co.jp. + + + + Shrestha + Sirish + S + + Center for Creative Leadership, Greensboro, USA. + + + + Akama + Yuka + Y + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Kito + Shusuke + S + + Data Science Course, Juntendo University, Tokyo, Japan. + + + + Takamatsu + Sachiko + S + + Department of Nursing, The Sakakibara Heart Institute of Okayama, Okayama, Japan. + + + + Toki + Misako + M + + Department of Clinical Laboratory, The Sakakibara Heart Institute of Okayama, Okayama, Japan. + + + + Kitai + Takeshi + T + + Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan. + + + Department of Heart Failure, National Cerebral and Cardiovascular Center, Suita, Japan. + + + + Okada + Taiji + T + + Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan. + + + Division of Cardiology, Shimane University Faculty of Medicine, Izumo, Japan. + + + + Murata + Azusa + A + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Kaneko + Tomohiro + T + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Matsue + Yuya + Y + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Miyazaki + Sakiko + S + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Okazaki + Shinya + S + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Minamino + Tohru + T + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + eng + + + 25K19371 + Japan Society for the Promotion of Scienc + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Japan + J Echocardiogr + 101263153 + 1349-0222 + + IM + + Explainable AI + Heart failure + Left ventricular filling pressure + Machine learning + SHAP + + + + + + 2025 + 12 + 21 + + + 2026 + 5 + 12 + + + 2026 + 5 + 2 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 11 + 18 + + + aheadofprint + + 42213288 + 10.1007/s12574-026-00738-x + 10.1007/s12574-026-00738-x + + + + GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories. Lancet. 2018;392:1789–858. + + + Bozkurt B, Ahmad T, Alexander KM, Baker WL, Bosak K, Breathett K, et al. Heart failure epidemiology and outcomes statistics: a report of the Heart Failure Society of America. J Card Fail. 2023;29:1412–51. + + 10.1016/j.cardfail.2023.07.006 + 37797885 + 10864030 + + + + Shimokawa H, Miura M, Nochioka K, Sakata Y. Heart failure as a general pandemic in Asia. Eur J Heart Fail. 2015;17:884–92. + + 10.1002/ejhf.319 + 26222508 + + + + Savarese G, Becher PM, Lund LH, Seferovic P, Rosano GMC, Coats AJS. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res. 2023;118:3272–87. + + 10.1093/cvr/cvac013 + 35150240 + + + + Nagueh SF, Smiseth OA, Appleton CP, Byrd BF 3rd, Dokainish H, Edvardsen T, et al. Recommendations for the evaluation of Left ventricular diastolic function by echocardiography: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2016;29:277–314. + + 10.1016/j.echo.2016.01.011 + 27037982 + + + + Andersen OS, Smiseth OA, Dokainish H, Abudiab MM, Schutt RC, Kumar A, et al. Estimating left ventricular filling pressure by echocardiography. J Am Coll Cardiol. 2017;69:1937–48. + + 10.1016/j.jacc.2017.01.058 + 28408024 + + + + Lancellotti P, Galderisi M, Edvardsen T, Donal E, Goliasch G, Cardim N, et al. Echo-Doppler estimation of left ventricular filling pressure: results of the multicentre EACVI Euro-Filling study. Eur Heart J Cardiovasc Imaging. 2017;18:961–8. + + 10.1093/ehjci/jex067 + 28444160 + + + + Sato K, Grant ADM, Negishi K, Cremer PC, Negishi T, Kumar A, et al. Reliability of updated left ventricular diastolic function recommendations in predicting elevated left ventricular filling pressure and prognosis. Am Heart J. 2017;189:28–39. + + 10.1016/j.ahj.2017.03.022 + 28625379 + + + + Playford D, Strange G, Celermajer DS, Evans G, Scalia GM, Stewart S, et al. Diastolic dysfunction and mortality in 436 360 men and women: the National Echo Database Australia (NEDA). Eur Heart J Cardiovasc Imaging. 2021;22:505–15. + + 10.1093/ehjci/jeaa253 + 33245344 + 8081414 + + + + Kagiyama N, Shrestha S, Farjo PD, Sengupta PP. Artificial intelligence: practical primer for clinical research in cardiovascular disease. J Am Heart Assoc. 2019;8:e012788. + + 10.1161/JAHA.119.012788 + 31450991 + 6755846 + + + + Tamura Y, Nomura A, Kagiyama N, Mizuno A, Node K. Digitalomics, digital intervention, and designing future: the next frontier in cardiology. J Cardiol. 2024;83:318–22. + + 10.1016/j.jjcc.2023.12.002 + 38135148 + + + + Farjo PD, Yanamala N, Kagiyama N, Patel HB, Casaclang-Verzosa G, Nezarat N, et al. Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study. Eur Heart J Digit Health. 2020;1:51–61. + + 10.1093/ehjdh/ztaa008 + 37056293 + 10087019 + + + + Misumi K, Matsue Y, Nogi K, Fujimoto Y, Kagiyama N, Kasai T, et al. Derivation and validation of a machine learning-based risk prediction model in patients with acute heart failure. J Cardiol. 2023;81:531–6. + + 10.1016/j.jjcc.2023.02.006 + 36858175 + + + + Pandey A, Kagiyama N, Yanamala N, Segar MW, Cho JS, Tokodi M, et al. Deep-learning models for the echocardiographic assessment of diastolic dysfunction. JACC Cardiovasc Imaging. 2021;14:1887–900. + + 10.1016/j.jcmg.2021.04.010 + 34023263 + + + + Lancaster MC, Salem Omar AM, Narula S, Kulkarni H, Narula J, Sengupta PP. Phenotypic clustering of left ventricular diastolic function parameters: patterns and prognostic relevance. JACC Cardiovasc Imaging. 2019;12:1149–61. + + 10.1016/j.jcmg.2018.02.005 + 29680357 + + + + Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2015;28:1-39.e14. + + 10.1016/j.echo.2014.10.003 + 25559473 + + + + Nagueh SF, Sanborn DY, Oh JK, Anderson B, Billick K, Derumeaux G, et al. Recommendations for the evaluation of left ventricular diastolic function by echocardiography and for heart failure with preserved ejection fraction diagnosis: an update from the American Society of Echocardiography. J Am Soc Echocardiogr. 2025;38:537–69. + + 10.1016/j.echo.2025.03.011 + 40617625 + + + + Heidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, Colvin MM, et al. 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Joint Committee on clinical practice guidelines. J Am Coll Cardiol. 2022;79:e263–421. + + 10.1016/j.jacc.2021.12.012 + 35379503 + + + + McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: developed by the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) With the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J. 2021;42:3599–726. + + 10.1093/eurheartj/ehab368 + 34447992 + + + + Forrester JS, Diamond GA, Swan HJ. Correlative classification of clinical and hemodynamic function after acute myocardial infarction. Am J Cardiol. 1977;39:137–45. + + 10.1016/S0002-9149(77)80182-3 + 835473 + + + + Chen T, Guestrin C. XGBoost a scalable tree boosting system. Knowl discovery data Min. 2016. https://doi.org/10.48550/arXiv.1603.02754 + + 10.48550/arXiv.1603.02754 + + + + Lundberg S, Lee S-I. A unified approach to interpreting model predictions. Neural information processing systems. 2017. https://doi.org/10.48550/arXiv.1705.07874 + + + van de Bovenkamp AA, Enait V, de Man FS, Oosterveer FTP, Bogaard HJ, Vonk Noordegraaf A, et al. Validation of the 2016 ASE/EACVI guideline for diastolic dysfunction in patients with unexplained dyspnea and a preserved left ventricular ejection fraction. J Am Heart Assoc. 2021;10:e021165. + + 10.1161/JAHA.121.021165 + 34476984 + 8649534 + + + + Almeida JG, Fontes-Carvalho R, Sampaio F, Ribeiro J, Bettencourt P, Flachskampf FA, et al. Impact of the 2016 ASE/EACVI recommendations on the prevalence of diastolic dysfunction in the general population. Eur Heart J Cardiovasc Imaging. 2018;19:380–6. + + 10.1093/ehjci/jex252 + 29236978 + + + + Luke P, Eggett C, Spyridopoulos I, Irvine T. A comparative analysis of British and American Society of Echocardiography recommendations for the assessment of left ventricular diastolic function. 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BMC Med Inform Decis Mak. 2021;21:237. + + 10.1186/s12911-021-01591-x + 34362354 + 8344327 + + + + Hou N, Li M, He L, Xie B, Wang L, Zhang R, et al. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med. 2020;18:462. + + 10.1186/s12967-020-02620-5 + 33287854 + 7720497 + + + + Wu TT, Lin XQ, Mu Y, Li H, Guo YS. Machine learning for early prediction of in-hospital cardiac arrest in patients with acute coronary syndromes. Clin Cardiol. 2021;44:349–56. + + 10.1002/clc.23541 + 33586214 + 7943901 + + + + Hirata Y, Tsuji T, Kotoku J, Sata M, Kusunose K. Echocardiographic artificial intelligence for pulmonary hypertension classification. Heart. 2024;110:586–93. + + 10.1136/heartjnl-2023-323320 + 38296266 + + + + Saito Y, Omae Y, Mizobuchi S, Fujito H, Miyagawa M, Kitano D, et al. Prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure. ESC Heart Fail. 2023;10:1103–13. + + 10.1002/ehf2.14282 + 36583242 + + + + Hummel YM, Liu LCY, Lam CSP, Fonseca-Munoz DF, Damman K, Rienstra M, et al. Echocardiographic estimation of left ventricular and pulmonary pressures in patients with heart failure and preserved ejection fraction: a study utilizing simultaneous echocardiography and invasive measurements. Eur J Heart Fail. 2017;19:1651–60. + + 10.1002/ejhf.957 + 28984057 + + + + + + + + 42213198 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1573-4951 + + 40 + 1 + + 2026 + May + 29 + + + Journal of computer-aided molecular design + J Comput Aided Mol Des + + Target-aware molecule SMILES generation using a large language model with retrieval-augmented generation, multi-turn memory, and a predictive model. + 134 + 10.1007/s10822-026-00834-1 + + Given the vastness of chemical space and the cost and time requirements of high-throughput screening, resource-efficient computational strategies are needed to prioritize candidate drug molecules. Here, we evaluated whether an open-weight large language model (LLM) augmented with retrieval-augmented generation (RAG), multi-turn memory, and a pretrained drug-target interaction predictor can generate target-aware molecules without task-specific retraining. Protein-ligand-pKi examples retrieved from BindingDB, Davis, and KIBA were used as contextual guidance, while DeepPurpose provided the optimization signal during iterative SMILES refinement. Our approach produced a statistically significant increase in predicted pKi across successive multi-turn memory iterations, indicating that retrieval- and memory-guided refinement may improve target-conditioned molecular generation. The proposed framework also showed favorable molecular generation characteristics, with novelty reaching 100%, diversity up to 0.882, and uniqueness up to 1.0, suggesting that retrieval did not reduce the process to simple reproduction of known ligands but instead supported exploration of new regions of chemical space. In addition, the approach yielded supportive docking results consistent with the generation of chemically relevant candidate ligands. However, the increase in predicted affinity was accompanied by trade-offs in molecular quality, including reduced validity and drug-likeness in later iterations. Taken together, these findings suggest that this framework provides a flexible and comparatively resource-efficient strategy for target-aware de novo molecular design, while further multi-objective optimization and independent validation remain necessary. + © 2026. The Author(s). + + + + Karabowicz + Piotr + P + 0000-0002-8072-2104 + + Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269, Bialystok, Poland. piotr.karabowicz@umb.edu.pl. + + + + Charkiewicz + Radosław + R + + Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269, Bialystok, Poland. + + + Center of Experimental Medicine, Medical University of Bialystok, 15-369, Bialystok, Poland. + + + + Charkiewicz + Alicja + A + + Department of Analysis and Bioanalysis of Medicines, Medical University of Bialystok, 15-089, Bialystok, Poland. + + + + Nikliński + Jacek + J + + Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269, Bialystok, Poland. + + + + eng + + + B.SUB.26.512 + Uniwersytet Medyczny w Bialymstoku + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Netherlands + J Comput Aided Mol Des + 8710425 + 0920-654X + + + + 0 + Ligands + + + 0 + Proteins + + + 0 + Small Molecule Libraries + + + IM + + + Large Language Models + + + Ligands + + + Proteins + chemistry + + + Drug Discovery + methods + + + Small Molecule Libraries + chemistry + + + Drug Design + + + Humans + + + + Drug discovery + Large language models + Multi-turn memory + Retrieval-augmented generation + SMILES generation + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 1 + 8 + + + 2026 + 5 + 9 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 11 + 15 + + + epublish + + 42213198 + 10.1007/s10822-026-00834-1 + 10.1007/s10822-026-00834-1 + + + + Skinnider MA (2024) Invalid SMILES are beneficial rather than detrimental to chemical language models. Nat Mach Intell 6:437–448. https://doi.org/10.1038/s42256-024-00821-x + + 10.1038/s42256-024-00821-x + + + + Zheng Y, Koh HY, Ju J et al (2025) Large language models for drug discovery and development. Patterns 6:101346. https://doi.org/10.1016/j.patter.2025.101346 + + 10.1016/j.patter.2025.101346 + 41142906 + 12546459 + + + + Mienye ID, Jere N, Obaido G et al (2025) Large language models: an overview of foundational architectures, recent trends, and a new taxonomy. Discov Appl Sci 7:1027. https://doi.org/10.1007/s42452-025-07668-w + + 10.1007/s42452-025-07668-w + + + + Umer MS, Nabeel M, Athar U et al (2025) Large language models meet molecules: a systematic review of advances and challenges in AI-driven cheminformatics. Arch Computat Methods Eng. https://doi.org/10.1007/s11831-025-10437-y + + 10.1007/s11831-025-10437-y + + + + Bagal V, Aggarwal R, Vinod PK, Priyakumar UD (2022) MolGPT: molecular generation using a transformer-decoder model. J Chem Inf Model 62:2064–2076. https://doi.org/10.1021/acs.jcim.1c00600 + + 10.1021/acs.jcim.1c00600 + 34694798 + + + + Sheikholeslami M, Mazrouei N, Gheisari Y et al (2025) DrugGen enhances drug discovery with large language models and reinforcement learning. Sci Rep 15:13445. https://doi.org/10.1038/s41598-025-98629-1 + + 10.1038/s41598-025-98629-1 + 40251288 + 12008224 + + + + Song B, Li X, Chao X et al (2025) Advancements in large language models ( LLMS ): empowering drug discovery. WIREs Comput Mol Sci 15:e70054. https://doi.org/10.1002/wcms.70054 + + 10.1002/wcms.70054 + + + + Ma J, Liu J, Xu D et al (2025) Application and prospects of large language models in small-molecule drug discovery. Anal Chem 97:27453–27477. https://doi.org/10.1021/acs.analchem.5c04083 + + 10.1021/acs.analchem.5c04083 + 41366732 + 12750412 + + + + Öztürk H, Özgür A, Ozkirimli E (2018) DeepDTA: deep drug–target binding affinity prediction. Bioinformatics 34:i821–i829. https://doi.org/10.1093/bioinformatics/bty593 + + 10.1093/bioinformatics/bty593 + 30423097 + 6129291 + + + + Mswahili ME, Jeong Y-S (2024) Transformer-based models for chemical SMILES representation: a comprehensive literature review. Heliyon 10:e39038. https://doi.org/10.1016/j.heliyon.2024.e39038 + + 10.1016/j.heliyon.2024.e39038 + 39640612 + 11620068 + + + + Gao Y, Xiong Y, Gao X, et al (2023) Retrieval-augmented generation for large language models: a survey + + + Zhang P, Peng X, Han R et al (2025) Rag2Mol: structure-based drug design based on retrieval augmented generation. Briefings Bioinform 26:bbaf265. https://doi.org/10.1093/bib/bbaf265 + + 10.1093/bib/bbaf265 + + + + Hu Y, Wang Y, McAuley J (2025) Evaluating memory in LLM agents via incremental multi-turn interactions + + + OpenAI, Agarwal S, Ahmad L, et al (2025) gpt-oss-120b &amp; gpt-oss-20b Model Card + + + Grattafiori A, Dubey A, Jauhri A, et al (2024) The Llama 3 Herd of Models + + + Liu T, Hwang L, Burley SK et al (2025) BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data. Nucleic Acids Res 53:D1633–D1644. https://doi.org/10.1093/nar/gkae1075 + + 10.1093/nar/gkae1075 + 39574417 + 11701568 + + + + Huang K, Fu T, Glass LM et al (2021) DeepPurpose: a deep learning library for drug–target interaction prediction. Bioinformatics 36:5545–5547. https://doi.org/10.1093/bioinformatics/btaa1005 + + 10.1093/bioinformatics/btaa1005 + 33275143 + 8016467 + + + + Eberhardt J, Santos-Martins D, Tillack AF, Forli S (2021) AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings. J Chem Inf Model 61:3891–3898. https://doi.org/10.1021/acs.jcim.1c00203 + + 10.1021/acs.jcim.1c00203 + 34278794 + 10683950 + + + + Fleming J, Magana P, Nair S et al (2025) AlphaFold Protein Structure Database and 3D-Beacons: new data and capabilities. J Mol Biol 437:168967. https://doi.org/10.1016/j.jmb.2025.168967 + + 10.1016/j.jmb.2025.168967 + 40133787 + + + + Laban P, Hayashi H, Zhou Y, Neville J (2025) LLMs Get Lost In Multi-Turn Conversation + + + + + + + 42213197 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1438-7948 + + 26 + 1 + + 2026 + May + 29 + + + Functional & integrative genomics + Funct Integr Genomics + + An integrated computational-experimental approach identifies Malate Synthase G as therapeutic target to disrupt carbon metabolism in chronic Pseudomonas aeruginosa infection. + 116 + 10.1007/s10142-026-01889-x + + Pseudomonas aeruginosa is a leading cause of nosocomial infections, particularly in individuals with a compromised immune system. Due to its strong adaptive ability, P. aeruginosa tends to develop antibiotic resistance and establish chronic infection, making its eradication through traditional antibiotics challenging. Thus, the development of novel therapeutic targets and corresponding inhibitors is urgently required. We conducted bioinformatics analyses of gene-chip datasets (GSE10362, GSE21966) of P. aeruginosa from patients with cystic fibrosis (CF) in the Gene Expression Omnibus (GEO) database, ranging from early to late stages of infection. Differentially expressed gene (DEG) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and Weighted Gene Co-expression Network Analysis (WGCNA) were performed to identify key functional DEGs. Among these genes, candidate genes were refined using three machine learning algorithms: LASSO regression, random forest (RF), and support vector machine (SVM). Using this integrated approach and published reports, target gene was identified consequently. With reported inhibitors, a structure-based virtual screening model against the target protein was established to screen inhibitors from an FDA-approved drug library. Screened hits were experimentally validated through in vitro MIC and checkerboard broth microdilution assays to determine their antibacterial activity and the synergistic effect of tobramycin (TOB) and amikacin (AMK). Enzyme activity was measured to evaluate target protein inhibition. Transcriptome sequencing analysis was performed to explain the effect of target inhibition and reveal the possible mechanism of inhibition. We identified 210 upregulated genes in the late stage of infection. KEGG functional enrichment demonstrated that the up-regulated genes were mainly enriched in metabolic pathways. Eight metabolism-related key DEGs were obtained by the intersection of genes from metabolic pathways and genes from the key module of WGCNA. Machine learning algorithms (LASSO, RF, and SVM) and literature investigation results identified glcB, which encodes malate synthase G (MS), as a therapeutic target for developing inhibitors. In virtual screening, we formulated four rules for a screening model based on the docking results, and finally identified three FDA-approved drugs prednisolone, dienogest and carbinoxamine maleate salt (CAR) as candidates. In vitro MIC assay and checkerboard assay suggested that CAR had an antibacterial effect with a concentration-dependent trend and could enhance the effect of tobramycin and amikacin. Consistent with MIC assay, the inhibition rate in MS activity assay showed a concentration-dependent trend. Transcriptome sequencing analysis showed that carbon metabolism was remodeled when MS was inhibited, thus impairing the growth of P. aeruginosa, and the synergistic effect with TOB and AMK may be explained by the impaired ability to combat oxidative stress. This study identified and validated drug target glcB (encodes MS) and potential inhibitor CAR in treating chronic P. aeruginosa infection based on an integrated method of bioinformatics, virtual screening, and in vitro experiments. + © 2026. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. + + + + Chen + Geping + G + + Department of Pharmacy, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China. + + + + Tan + Qiutong + Q + + College of Pharmacy, Jinan University, Guangzhou, 510632, China. + + + + Zhou + Haochuan + H + + College of Pharmacy, Jinan University, Guangzhou, 510632, China. + + + + Wang + Rui + R + + Department of Pharmacy, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China. + + + + Mo + Jingyi + J + + Department of Pharmacy, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China. + + + + Xu + Jun + J + + College of Pharmacy, Jinan University, Guangzhou, 510632, China. xujun@jnu.edu.cn. + + + + Chen + Wenying + W + + Department of Pharmacy, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China. chenwenying2016@163.com. + + + + eng + + + CPA: 2023ZYS09 + Guangdong Pharmaceutical Association + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Germany + Funct Integr Genomics + 100939343 + 1438-793X + + + + EC 2.3.3.9 + Malate Synthase + + + 0 + Anti-Bacterial Agents + + + 7440-44-0 + Carbon + + + 0 + Bacterial Proteins + + + IM + + + Pseudomonas aeruginosa + drug effects + genetics + pathogenicity + metabolism + + + Humans + + + Malate Synthase + antagonists & inhibitors + genetics + metabolism + chemistry + + + Pseudomonas Infections + drug therapy + genetics + microbiology + metabolism + + + Anti-Bacterial Agents + pharmacology + + + Carbon + metabolism + + + Bacterial Proteins + genetics + metabolism + antagonists & inhibitors + + + Cystic Fibrosis + microbiology + + + Computational Biology + + + Molecular Docking Simulation + + + + Pseudomonas aeruginosa + Bioinformatics + Carbon metabolism + Malate synthase + Virtual screening + + Declarations. 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Nucleic Acids Res 52:967–976. https://doi.org/10.1093/nar/gkad1201 + + 10.1093/nar/gkad1201 + 38096062 + 10810197 + + + + Xia Y, Wang D, Pan X et al (2020) TpiA is a Key Metabolic Enzyme That Affects Virulence and Resistance to Aminoglycoside Antibiotics through CrcZ in Pseudomonas aeruginosa. mBio 11:e02079–e02019. https://doi.org/10.1128/mBio.02079-19 + + 10.1128/mBio.02079-19 + 31911486 + 6946797 + + + + Yang H, Zhou Y, Luo Q et al (2023) L-leucine increases the sensitivity of drug-resistant Salmonella to sarafloxacin by stimulating central carbon metabolism and increasing intracellular reactive oxygen species level. Front Microbiol 14:1186841. https://doi.org/10.3389/fmicb.2023.1186841 + + 10.3389/fmicb.2023.1186841 + 37250042 + 10213264 + + + + Yang J, Xu J-F, Liang S (2025) Antibiotic resistance in Pseudomonas aeruginosa: mechanisms and emerging treatment. Crit Rev Microbiol 51:841–859. https://doi.org/10.1080/1040841X.2024.2429599 + + 10.1080/1040841X.2024.2429599 + 39556143 + + + + + + + + 42213195 + + 2026 + 05 + 29 + +
+ + 1432-0584 + + + 2026 + May + 29 + + + Annals of hematology + Ann Hematol + + A macrophage-related efferocytosis-based two-gene prognostic model for acute myeloid leukemia identified by multi-omics and machine learning. + 10.1007/s00277-026-07044-7 + + Background Acute myeloid leukemia (AML) remains a lethal hematologic malignancy with high heterogeneity. Macrophage-mediated efferocytosis in the tumor microenvironment is implicated in immune suppression and disease progression. Methods We integrated single-cell and bulk transcriptomic data from public cohorts to identify genes associated with macrophages and efferocytosis in AML. Candidate genes were screened for prognosis using univariate Cox regression. A comprehensive machine learning framework, evaluating 117 algorithm combinations, was employed to construct a robust prognostic model. The optimal LASSO and random survival forest approach identified CD52 and S100A4 as core prognostic genes. The resulting two-gene model was rigorously validated using Kaplan-Meier analysis, time-dependent ROC curves, and calibration plots across multiple independent cohorts. The associations of the risk score with the immune microenvironment and drug sensitivity were further analyzed. SHapley Additive exPlanations (SHAP) analysis was applied to interpret the model's decision-making. Results The two-gene signature demonstrated stable and powerful predictive performance for overall survival in both training and external validation sets. The risk score was an independent prognostic factor and showed significant correlations with immune cell infiltration patterns and response to chemotherapeutic agents. SHAP analysis confirmed the consistent and biologically plausible contributions of CD52 and S100A4. Single-cell resolution analysis revealed their specific enrichment in distinct AML-associated macrophage subpopulations. Conclusions We developed a novel macrophage efferocytosis-based prognostic model using a multi-omics and machine learning approach. This model provides valuable insights into the immune microenvironment of AML and offers a potential tool for risk stratification and therapeutic guidance. + © 2026. The Author(s). + + + + Li + Xiaoning + X + + Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China. + + + Guangdong Engineering Research Center of Precision Immune Cell Therapy Technology, Guangzhou, Guangdong, China. + + + + Zhang + Yujie + Y + + Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China. + + + Guangdong Engineering Research Center of Precision Immune Cell Therapy Technology, Guangzhou, Guangdong, China. + + + + Wei + Xiaoying + X + + Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China. + + + Guangdong Engineering Research Center of Precision Immune Cell Therapy Technology, Guangzhou, Guangdong, China. + + + + Huang + Rui + R + + Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China. rachelchn@163.com. + + + Guangdong Engineering Research Center of Precision Immune Cell Therapy Technology, Guangzhou, Guangdong, China. rachelchn@163.com. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Germany + Ann Hematol + 9107334 + 0939-5555 + + IM + + Acute myeloid leukemia + Biomarkers + Efferocytosis + Machine learning + Prognostic model + + Declarations. Consent for publication: All authors have read and approved the final version of the manuscript and agree to the publication of this research in the journal. Ethics and consent to participate declarations: Not applicable.  Competing interests: The authors declare no competing interests. + + + + + 2025 + 12 + 2 + + + 2026 + 4 + 26 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 11 + 15 + + + aheadofprint + + 42213195 + 10.1007/s00277-026-07044-7 + 10.1007/s00277-026-07044-7 + + + + + + 42213145 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2561-326X + + 10 + + 2026 + May + 29 + + + JMIR formative research + JMIR Form Res + + Machine Learning-Based Prediction Model for 30-Day Emergency Department Revisits in a Medically Underserved Tertiary Hospital: Formative Retrospective Cohort Study. + + e87289 + e87289 + + 10.2196/87289 + + Emergency department (ED) revisits are critical quality indicators, particularly in medically underserved areas, where traditional prediction tools show limited performance. Machine learning (ML) approaches may offer improved predictive performance for identifying high-risk patients. + This formative study aimed to develop and validate an ML-based model for predicting 30-day ED revisits using electronic health records from a tertiary hospital serving a medically underserved area in South Korea and to evaluate its clinical utility through interpretability analysis and risk stratification. + This retrospective cohort study analyzed 36,230 adult patients visiting the Gangneung Asan Hospital ED in 2023. We developed and compared 3 ML models (extreme gradient boosting [XGBoost], random forest, and ElasticNet) using electronic health records. Model interpretability was ensured through Shapley additive explanations (SHAP) analysis, and clinical utility was evaluated through 5-tier risk stratification. + Among 36,230 patients, 798 (2.2%) revisited within 30 days. XGBoost achieved superior performance with an area under the receiver operating characteristic curve of 0.90 (95% CI 0.88-0.92), a sensitivity of 0.94, and a specificity of 0.69. The SHAP analysis identified ED length of stay, oxygen saturation, systolic blood pressure, computed tomography performance, antibiotic use, and liver disease as key predictors. Risk stratification demonstrated a 25-fold difference in the actual revisit rates between the lowest (152/8450, 1.8%) and the highest (686/1500, 45.7%) risk groups. + The XGBoost model demonstrated excellent predictive performance with high interpretability for 30-day ED revisit predictions. The implementation of this model could enable risk-stratified interventions and more efficient resource allocation in medically underserved settings, potentially reducing unnecessary revisits and improving patient outcomes. This formative study establishes feasibility and provides a foundation for future multicenter validation studies in similar medically underserved settings. + © Kyongmin Sun. Originally published in JMIR Formative Research (https://formative.jmir.org). + + + + Sun + Kyongmin + K + 0000-0002-2977-0880 + + Department of Emergency Medicine, College of Medicine, Gangneung Asan Hospital, Gangneung, Gangwon-do, Republic of Korea. + + + Department of Health Administration, Graduate School, Yonsei University Mirae Campus, 1 Yonsedae-gil, Heungeop-myeon, Wonju, Gangwon-do, 26493, Republic of Korea, 82 337602418. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Canada + JMIR Form Res + 101726394 + 2561-326X + + IM + + + Retrospective Studies + + + Humans + + + Tertiary Care Centers + statistics & numerical data + organization & administration + + + Emergency Room Visits + statistics & numerical data + + + Emergency Service, Hospital + statistics & numerical data + organization & administration + + + Machine Learning + + + Republic of Korea + + + Medically Underserved Area + + + Female + + + Predictive Learning Models + + + Male + + + Middle Aged + + + Adult + + + Aged + + + Boosting Machine Learning Algorithms + + + Cohort Studies + + + + SHAP + Shapley additive explanations + XGBoost + emergency department + extreme gradient boosting + machine learning + medically underserved area + prediction model + revisit + + + + + + 2025 + 11 + 6 + + + 2026 + 4 + 9 + + + 2026 + 4 + 28 + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 11 + 13 + + + epublish + + 42213145 + 10.2196/87289 + v10i1e87289 + + + + + + 42213103 + + 2026 + 05 + 29 + +
+ + 1432-5241 + + + 2026 + May + 29 + + + Aesthetic plastic surgery + Aesthetic Plast Surg + + Does Patient History Influence Capsular Contracture? An Exploratory Analysis with Machine Learning. + 10.1007/s00266-026-05919-8 + + Capsular contracture (CC) is a frequent and distressing complication of breast augmentation and reconstruction. Although numerous patient-, surgical-, and implant-related risk factors have been proposed, reliable population-level predictors remain inconsistent across studies. This study evaluates whether administrative medical history, as encoded by ICD and CPT codes, contains sufficient predictive signal to identify patients at risk for CC using machine learning. + Patients were queried from the MerativeTM MarketScan® Research Databases from 2003 to 2017 with CPT codes for implant-based breast reconstruction and augmentation. ICD codes were then used to identify all events and conditions of a patient's history. Hyperparameter-tuned random forest models were combined with multivariable logistic regression models to validate risk factors for the development of CC. + A total of 112,489 patients were included, and the rate of capsular contracture was 9.55%. In total, 4,825 common conditions and procedures were included as features in the model. The random forest's error rate was 9.54%. Prior CC was the most important variable in the model. When removed, model accuracy decreased by 0.0011% (p <0.001). Removing prior irradiation as a feature decreased model accuracy by 0.00014% (p < 0.001). In line with the previous literature, prior CC (OR = 2.47, p <0.001) and irradiation (OR = 1.16, p <0.001) were significantly associated with CC development in a multivariable logistic regression model. + Administrative medical history alone demonstrated limited predictive utility for CC development. Although established associations such as prior CC and irradiation were confirmed, their incremental contribution to predictive performance was negligible. These associations do not meaningfully enhance a clinician's ability to reliably predict which patients are at increased risk for CC. + This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 . + © 2026. The Author(s). + + + + Johnstone + Thomas M + TM + + Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA, USA. + + + + Najafali + Daniel + D + 0000-0003-1509-9229 + + Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA. danieln6@illinois.edu. + + + + Shah + Jennifer K + JK + + Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA, USA. + + + + Camacho + Justin M + JM + + Drexel University College of Medicine, Philadelphia, PA, USA. + + + + Johnstone + Chancellor + C + + Air Force Institute of Technology, Wright-Patterson AFB, Dayton, OH, USA. + + + + Nazerali + Rahim S + RS + + Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA, USA. + + + + Lee + Gordon K + GK + + Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA, USA. gordokl1@hs.uci.edu. + + + Department of Plastic Surgery, University of California, Irvine, Orange, CA, USA. gordokl1@hs.uci.edu. + + + + eng + + + UL1TR003142 + TR + NCATS NIH HHS + United States + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + Aesthetic Plast Surg + 7701756 + 0364-216X + + IM + + Alloplastic reconstruction + Artificial intelligence + Breast + Breast implants + Breast reconstruction + Capsular contracture + Complications + Contracture formation + Implant + Implant capsular contracture + Implant-based reconstruction + Machine learning + Mammaplasty + Outcomes prediction + Postoperative complications + Supervised machine learning + + Declarations. Conflict of interest: The authors declare that they have no conflicts of interest to disclose. Human and Animal Rights: Statement of human and animal rights, or ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. Data for this project were accessed using the Stanford Center for Population Health Sciences Data Core. The PHS Data Core is supported by a National Institutes of Health National Center for Advancing Translational Science Clinical and Translational Science Award (UL1TR003142) and from Internal Stanford funding. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Informed Consent: For this type of study, informed consent is not required. + + + + + 2025 + 1 + 24 + + + 2026 + 4 + 17 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 11 + 12 + + + aheadofprint + + 42213103 + 10.1007/s00266-026-05919-8 + 10.1007/s00266-026-05919-8 + + + + ISAPS international survey on aesthetic/cosmetic procedures performed in 2019. 2020. https://www.isaps.org/discover/about-isaps/global-statistics/global-survey-2019-full-report-and-press-releases-english/ . Accessed 2024. + + + Pasca A, Bonci EA, Chiuzan C, et al. 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+ + 1613-6829 + + + 2026 + May + 29 + + + Small (Weinheim an der Bergstrasse, Germany) + Small + + Harnessing and Suppressing Electron Spin-State Transitions: From Decoding to Rational Design of High-Performance Cathodes for Alkali-Ion Batteries. + + e73936 + e73936 + + 10.1002/smll.73936 + + The electron spin state of transition metal ions represents a fundamental quantum property that is increasingly recognized as a pivotal design dimension for tuning the performance of cathode materials in Li/Na/K‑ion batteries. This review begins by consolidating the foundational principles through which spin states govern electrochemical properties, establishing a robust theoretical framework that bridges atomic-scale coordination environments with macroscopic electrode behavior. It further discusses advanced experimental and computational techniques for probing complex spin states and, critically, for establishing clear structure-spin-performance relationships. A central focus is placed on the rational design of spin configurations, whether via proactive engineering or suppression of unfavorable transitions, to optimize key electrochemical processes: modulating cationic vs. anionic redox competition, enhancing structural stability by mitigating Jahn-Teller distortions and magnetic frustration, and improving charge and ion transport. The review also highlights the emerging role of spin‑sensitive machine learning as an accelerated pathway for discovering superior cathode materials. By integrating theoretical insights, methodological advances, and application‑oriented studies, this work provides a comprehensive mechanistic framework and practical guidelines for the design of next‑generation high‑performance cathodes through deliberate spin‑state control. + © 2026 Wiley‐VCH GmbH. + + + + Peng + Tao + T + 0000-0002-0234-0477 + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + Ma + Mengzhen + M + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + Liu + Shaonan + S + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + He + Junbao + J + + Henan International Joint Laboratory of MXene Materials Microstructure, Collaborative Innovation Center of Intelligent Explosion-Proof Equipment of Henan Province, College of Physics and Electronic Engineering, Nanyang Normal University, Nanyang, China. + + + + Wang + Yuanyuan + Y + + College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China. + + + + Yang + Ya + Y + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + Lu + Yang + Y + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + Luo + Yongsong + Y + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + Henan International Joint Laboratory of MXene Materials Microstructure, Collaborative Innovation Center of Intelligent Explosion-Proof Equipment of Henan Province, College of Physics and Electronic Engineering, Nanyang Normal University, Nanyang, China. + + + + Sun + Weiwei + W + 0000-0003-4079-4063 + + College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China. + + + + eng + + + 52272219 + National Natural Science Foundation of China + + + + 52472254 + National Natural Science Foundation of China + + + + 262300421381 + Natural Science Foundation of Henan Province + + + + 242300421191 + Natural Science Foundation of Henan Province + + + + 24ZDYF016 + Key Research and Development Program of Nanyang City + + + + Nanhu Scholars Program for Young Scholars of Xinyang Normal University + + + + 2026JJ50116 + the Natural Science Foundation of Hunan Province + + + + 2026JJ50116 + the Natural Science Foundation of Hunan Province + + + + + Journal Article + Review + + + 2026 + 05 + 29 + +
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+ + 1876-7591 + + + 2026 + May + 28 + + + JACC. Cardiovascular imaging + JACC Cardiovasc Imaging + + Machine Learning Model Using Pre-Cancer Therapy Cardiac Magnetic Resonance Images to Predict Cancer Therapy-Related Cardiac Dysfunction. + S1936-878X(26)00229-9 + 10.1016/j.jcmg.2026.04.007 + + Predicting risk of cancer therapy-related cardiac dysfunction (CTRCD) remains challenging. + The purpose of this study was to assess if deep learning (DL) approaches using cardiac magnetic resonance (CMR) images before cancer therapy can predict subsequent CTRCD and compare them with clinical and conventional imaging models. + Women with HER2+ breast cancer receiving anthracyclines and trastuzumab from 3 prospective studies (Toronto, Canada: EMBRACE-MRI, SPARE-HF; and Calgary, Canada: CIROC) were included. Patients were assessed before cancer therapy, after anthracycline, and trimonthly with repeated echocardiography and CMR. CTRCD was defined according to CMR. We calculated the HFA-ICOS risk score and measured CMR and echocardiography volumetric and functional parameters. Deep convolutional neural network architectures were used with pre-cancer therapy CMR short-axis cine images to develop image-based DL models to predict CTRCD. The Toronto patients were used for model derivation and internal validation; the Calgary patients were used for external validation. + A total of 229 patients (mean age 50.4 ± 9.7 years) were included: 176 in the internal (52 CTRCD events) development cohort and 53 in the external data set (14 CTRCD events). Our pre-cancer therapy CMR DL model demonstrated an AUC of 0.85 (95% CI: 0.69-0.97) and F1 score 0.69 (95% CI: 0.47-0.86) to predict CTRCD. On external validation, the DL model had an AUC of 0.80 (95% CI: 0.58-0.86) and F1 score of 0.55 (95% CI: 0.32-0.69). In comparison, the best-performing models using HFA-ICOS risk score, CMR, and echocardiographic parameters demonstrated, respectively, AUCs of 0.66 (95% CI: 0.53-0.75), 0.59 (95% CI: 0.41-0.73), 0.62 (95% CI: 0.44-0.78) and F1 scores of 0.56 (95% CI: 0.47-0.61), 0.20 (95% CI: 0.00-0.42) and 0.36 (95% CI: 0.12-0.62) to predict CTRCD. + A DL model using pre-cancer therapy CMR short-axis cines can predict future CTRCD risk better than clinical or manually quantified imaging models. + Copyright © 2026 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved. + + + + Yu + Christopher + C + + Department of Medicine, Division of Cardiology, Ted Rogers Program in Cardiotoxicity Prevention, Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada. + + + + Peikari + Mohammad + M + + Ted Rogers Computational Program, Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada. + + + + Labib + Dina + D + + Stephenson Cardiac Imaging Centre, Libin Cardiovascular Institute of Alberta, University of Calgary, Alberta, Canada. + + + + Houbois + Christian P + CP + + Sunnybrook Health Science Centre, University of Toronto, Toronto, Ontario, Canada. + + + + Steve Fan + Chun-Po + CP + + Ted Rogers Computational Program, Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada. + + + + White + James A + JA + + Stephenson Cardiac Imaging Centre, Libin Cardiovascular Institute of Alberta, University of Calgary, Alberta, Canada. + + + + Amir + Eitan + E + + Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada. + + + + Hanneman + Kate + K + + Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada. + + + + J Wintersperger + Bernd + B + + Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany. + + + + Abdel-Qadir + Husam + H + + Department of Medicine, Division of Cardiology, Ted Rogers Program in Cardiotoxicity Prevention, Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; Women's College Hospital, Toronto, Ontario, Canada. + + + + McIntosh + Chris + C + + Ted Rogers Computational Program, Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada; Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; Princess Margaret Cancer Centre, Toronto, Ontario, Canada. + + + + Thavendiranathan + Paaladinesh + P + + Department of Medicine, Division of Cardiology, Ted Rogers Program in Cardiotoxicity Prevention, Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada. Electronic address: dinesh.thavendiranathan@uhn.ca. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + JACC Cardiovasc Imaging + 101467978 + 1876-7591 + + IM + + cardio-oncology + cardiotoxicity cancer therapy–related cardiac dysfunction + heart failure + machine learning + + Funding Support and Author Disclosures This study was funded by operating grants 137132, 142456, and 147814 from the Canadian Institutes of Health Research, an Ontario Early Research Award, a Grant Miller Research grant from the University of Toronto, and AHSC AFP Innovation Funds. Dr Thavendiranathan (147814) is supported by a Canada Research Chair in Cardiooncology (CRC-2019-00097) and the Canadian Cancer Society/Canadian Institutes of Health Research W. David Hargraft Grant. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. + + + + + 2025 + 10 + 2 + + + 2026 + 4 + 7 + + + 2026 + 4 + 10 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 10 + 31 + + + aheadofprint + + 42212994 + 10.1016/j.jcmg.2026.04.007 + S1936-878X(26)00229-9 + + + + + + 42212964 + + 2026 + 05 + 29 + +
+ + 2198-3844 + + + 2026 + May + 29 + + + Advanced science (Weinheim, Baden-Wurttemberg, Germany) + Adv Sci (Weinh) + + Machine Learning Accelerated Non-Adiabatic Molecular Dynamics Elucidates Local Polarization Effects on Non-radiative Recombination in Halide Perovskites. + + e75903 + e75903 + + 10.1002/advs.75903 + + Non-radiative recombination is a critical factor limiting the optoelectronic performance of halide perovskites, yet how local polarization induced by charge redistribution regulates this process remains unclear. To gain deeper insight while reducing the high computational cost of conventional non-adiabatic molecular dynamics (NAMD), we developed Hefei-NAMD-S, a machine learning (ML) framework constructed using stacked models. The relative errors of the ML predicted non-adiabatic coupling and pure-dephasing time, calculated with respect to the first-principles values, remain below 1.10%, while the total computational time is reduced by approximately 78%, demonstrating the accuracy and efficiency of the proposed framework. NAMD simulations further reveal that the B-site local polarization is involved in regulating the non-radiative recombination process. The rubidium-substitution-doped system (FARb) and the cesium interstitial doped system exhibit recombination times of about 280 ns, nearly 2.8 times that of the pristine system, and the enhanced performance of FARb has been supported by previous experimental evidence. These results identify B-site local polarization as one of the important factors in suppressing non-radiative recombination and provide a theoretical foundation for designing perovskite materials through local polarization modulation. + © 2026 The Author(s). Advanced Science published by Wiley‐VCH GmbH. + + + + Yang + Bing + B + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Wei + Xiaoli + X + + School of Biological Science and Medical Engineering, Southeast University, Nanjing, China. + + + + Cai + Bo + B + 0000-0002-6605-5931 + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, UK. + + + + Yang + Yan + Y + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Zhu + Xinghai + X + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Liu + Yi + Y + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Xia + Junmin + J + 0000-0002-9805-5397 + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Liu + Lihui + L + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Cao + Kun + K + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Shen + Wei + W + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Xia + Pengfei + P + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Chen + Shufen + S + 0000-0003-0333-958X + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Chen + Siyu + S + + Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, UK. + + + TCM Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK. + + + + Zhao + Jin + J + + Department of Physics and ICQD/Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, China. + + + Hefei National Laboratory, University of Science and Technology of China, Hefei, China. + + + Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, USA. + + + + eng + + + 62404107 + National Natural Science Foundation of China + + + + 62304111 + National Natural Science Foundation of China + + + + 62474097 + National Natural Science Foundation of China + + + + BK20220396 + Natural Science Foundation of Jiangsu Province + + + + GDX2022010009 + Project of the State Key Laboratory of Flexible Electronics + + + + GZR2023010041 + Project of the State Key Laboratory of Flexible Electronics + + + + BZ2023059 + Science and Technology Cooperation Project of Hong Kong, Macao, and Taiwan + + + + 20KJA510005 + Natural Science Fund for Colleges and Universities in Jiangsu Province + + + + NY223076 + Natural Science Foundation of Nanjing University of Posts and Telecommunications + + + + NY222069 + Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications + + + + CSC202308320240 + China Scholarship Council + + + + KYCX23_1091 + Ministry of Education, as well as the Postgraduate Research & Practice Innovation Program of Jiangsu Province + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Germany + Adv Sci (Weinh) + 101664569 + 2198-3844 + + IM + + excited‐state dynamics + local polarization + machine learning + non‐adiabatic molecular dynamics + + + + + + 2026 + 5 + 19 + + + 2026 + 3 + 6 + + + 2026 + 5 + 21 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 10 + 22 + + + aheadofprint + + 42212964 + 10.1002/advs.75903 + + + + G. E. Eperon, S. D. Stranks, C. Menelaou, M. B. Johnston, L. M. Herz, and H. J. Snaith, “Formamidinium Lead Trihalide: A Broadly Tunable Perovskite for Efficient Planar Heterojunction Solar Cells,” Energy & Environmental Science 7 (2014): 982, https://doi.org/10.1039/c3ee43822h. + + + A. A. Zhumekenov, M. I. Saidaminov, M. A. Haque, et al., “Formamidinium Lead Halide Perovskite Crystals With Unprecedented Long Carrier Dynamics and Diffusion Length,” ACS Energy Letters 1 (2016): 32–37, https://doi.org/10.1021/acsenergylett.6b00002. + + + A. Amat, E. Mosconi, E. 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+ + 1746-0921 + + + 2026 + May + 29 + + + Future microbiology + Future Microbiol + + AI-microbial hybrid biosensors: the next generation of intelligent detection systems. + + 1 + 21 + 1-21 + + 10.1080/17460913.2026.2678101 + + The convergence of artificial intelligence (AI) and microbial biosensor technology is transforming pathogen detection, environmental surveillance, antimicrobial resistance (AMR) profiling, and precision diagnostics. Microbial biosensors exploit the specificity of living microorganisms, but signal variability, scalability limits, and interpretive challenges have constrained clinical adoption. Integration of machine learning (ML) and deep neural networks (DNNs) now enables adaptive, high-performance sensing systems. Applied to multi-sensor datasets-such as electrochemical impedance, Raman spectroscopy, and hyperspectral microscopy-convolutional neural networks (CNNs) and ensemble models achieve bacterial classification accuracies of 95-99%, while markedly reducing diagnostic turnaround times and enabling continuous surveillance. Despite rapid progress, the field remains fragmented, lacking a unified synthesis of system architectures, computational strategies, translational barriers, and regulatory considerations. This narrative review provides an integrative analysis of AI-microbial hybrid biosensors, covering biorecognition principles, AI integration approaches, system designs, clinical and environmental applications, performance metrics, and key challenges. It also highlights emerging directions, including synthetic biology, CRISPR-enabled sensing, and edge computing. By consolidating these dimensions, this review positions AI-microbial hybrid biosensors as a next-generation platform for real-time pathogen detection and adaptive biosurveillance. Literature was identified through systematic searches of Google Scholar, PubMed, Web of Science, Scopus, and IEEE Xplore (2000-2026), supplemented by manual reference screening. + + + + Hama Soor + Taib A + TA + + Medical Laboratory Department, Technical College of Health and Medical Technology, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq. + + + + Mustafa + Ayman M + AM + 0009-0005-4254-4182 + + Medical Laboratory Department, Technical College of Health and Medical Technology, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq. + + + Scientific Affairs Department, Smart Health Tower, Sulaymaniyah, Kurdistan Region, Iraq. + + + Medical Laboratory Science Department, College of Health Sciences, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq. + + + Kscien Organization for Scientific Research (Middle East Office), Sulaymaniyah, Kurdistan Region, Iraq. + + + + Abdulkarim + Meer M + MM + + Scientific Affairs Department, Smart Health Tower, Sulaymaniyah, Kurdistan Region, Iraq. + + + + Abdullah + Hiwa O + HO + + Scientific Affairs Department, Smart Health Tower, Sulaymaniyah, Kurdistan Region, Iraq. + + + Kscien Organization for Scientific Research (Middle East Office), Sulaymaniyah, Kurdistan Region, Iraq. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 29 + +
+ + England + Future Microbiol + 101278120 + 1746-0913 + + IM + + Artificial intelligence + antimicrobial resistance + deep learning + machine learning + microbial biosensors + pathogen detection + synthetic biology + + + + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 10 + 3 + + + aheadofprint + + 42212947 + 10.1080/17460913.2026.2678101 + + + + \ No newline at end of file diff --git a/data/raw/pubmed_20260530_112127.xml b/data/raw/pubmed_20260530_112127.xml new file mode 100644 index 0000000..24dcff4 --- /dev/null +++ b/data/raw/pubmed_20260530_112127.xml @@ -0,0 +1,21919 @@ + + + + + 42216193 + + 2026 + 05 + 29 + +
+ + 1742-4755 + + + 2026 + May + 29 + + + Reproductive health + Reprod Health + + Predicting short birth intervals in Bangladesh using stacked machine learning and SHAP explainability: evidence from BDHS 2022. + 10.1186/s12978-026-02367-0 + + Short birth interval (SBI) is a major public health concern, associated with adverse outcomes such as preterm birth, low birth weight, and maternal depletion. Identifying its influential predictors is essential for improving family planning and maternal health interventions. The study aimed to predict SBI and identify its influential predictors using stacked machine learning and SHAP explainability. + This study used data, obtained from the Bangladesh Demographic and Health Survey (BDHS), 2022. The dataset comprised 11,872 respondents, of whom 2,137 experienced SBI (18.0%). Class imbalance problem was addressed by applying class weighting during model training. Boruta and least absolute shrinkage and selection operator-based methods were applied to identify the most important predictors of SBI. Subsequently, multiple machine learning models (including logistic regression, random forest, extreme gradient boosting, categorical boosting, artificial neural network, and stacking ensemble) were used to predict SBI and evaluated their model performances using accuracy, precision, sensitivity, F1-score, and the area under the curve (AUC). Finally, SHAP explainability were employed to identify the most influential predictors of SBI. + The stacking ensemble model achieved the highest predictive performance compared to the individual models, with an accuracy of 65.8%, precision of 42.6%, sensitivity of 72.3%, F1-score of 53.6%, and modest AUC of approximately 0.667. The SHAP analysis showed that no educated respondents, higher parity, and do not intend to use contraceptive method were the most influential predictors of SBI. + Interventions could therefore focus on improving female education, expanding access to contraceptives, and promoting awareness of optimal birth spacing. Policymakers may incorporate SHAP explainability to support data-driven reproductive health strategies to reduce SBI in Bangladesh. + © 2026. The Author(s). + + + + Mia + Mostakim + M + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Akter + Shanti + S + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Sraboni + Takyeatun Nesa + TN + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Islam + Md Merajul + MM + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Tawabunnahar + Mst + M + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Maniruzamman + Md + M + + Statistics Discipline, Khulna University, Khulna, 9208, Bangladesh. + + + + Hossain + Md Golam + MG + + Health Research Group, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh. hossain95@yahoo.com. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Reprod Health + 101224380 + 1742-4755 + + IM + + Birth Spacing + Family Planning + Maternal and Child health + Model Interpretability + Predictive Modeling + + Declarations. Ethics approval and consent to participate: The survey was conducted in accordance with the Declaration of Helsinki, and the protocol for the 2022 BDHS received ethical clearance from both the ICF Institutional Review Board ethics committee and the Bangladesh Medical Research Council (BMRC). Informed written consent was obtained from each participant before commencement of the interview. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Conflict of interest: The authors declare that they have no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 10 + 14 + + + 2026 + 5 + 19 + + + 2026 + 5 + 29 + 23 + 45 + + + aheadofprint + + 42216193 + 10.1186/s12978-026-02367-0 + 10.1186/s12978-026-02367-0 + + + + + + 42216185 + + 2026 + 05 + 29 + +
+ + 1758-2946 + + + 2026 + May + 29 + + + Journal of cheminformatics + J Cheminform + + Towards the design of artificial sensing materials via quantum-informed explainable AI. + 10.1186/s13321-026-01232-3 + + Computational design of sensing materials remains fundamentally challenging due to the vast configurational landscape and the absence of robust property correlations that enable efficient molecular representation. These challenges become particularly critical in healthcare applications, where reliable and interpretable molecular recognition is essential for non-invasive diagnostics. To address these challenges, we developed MORE-ML, a quantum-informed AI framework that combines electronic-structure-derived properties of e-nose molecular building blocks with machine learning (ML) methods to uncover sensing mechanisms and guide the design of new systems. Within this framework, we expanded our previous dataset, MORE-Q, to MORE-QX by sampling a larger conformational space of interactions between body odor volatilomes (BOV) molecules and mucin-derived receptors, both in the gas phase and when deposited on graphene. MORE-QX provides extensive electronic binding features (BFs) computed upon BOV adsorption. Analysis of the property space revealed weak correlations between quantum-mechanical (QM) properties of building blocks and resulting BFs. Leveraging this observation, we defined electronic descriptors of building blocks as inputs for tree-based ML models to predict BFs. Benchmarking showed CatBoost models outperform alternatives, especially in transferability to unseen compounds. Through explainable AI, we reduced the high-dimensional QM property space to a compact and physically interpretable set of descriptors, revealing the properties that most influence BF predictions. Collectively, MORE-ML combines QM insights with ML to provide mechanistic understanding and rational design principles for artificial sensing materials in BOV sensing. This approach establishes a foundation for advancing materials capable of analyzing complex odor mixtures, bridging the gap between molecular-level computations and practical e-nose applications. + © 2026. The Author(s). + + + + Chen + Li + L + + Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany. + + + + Medrano Sandonas + Leonardo + L + + Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany. leonardo.medrano@tu-dresden.de. + + + + Huang + Shirong + S + + Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany. + + + + Croy + Alexander + A + + Institute of Physical Chemistry, Friedrich Schiller University Jena, 07737, Jena, Germany. + + + + Cuniberti + Gianaurelio + G + + Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany. gianaurelio.cuniberti@tu-dresden.de. + + + Dresden Center for Computational Materials Science (DCMS), TUD Dresden University of Technology, 01062, Dresden, Germany. gianaurelio.cuniberti@tu-dresden.de. + + + Cluster of Excellence CARE, TUD Dresden University of Technology and RWTH Aachen, Dresden, Germany. gianaurelio.cuniberti@tu-dresden.de. + + + Cluster of Excellence CeTI, TUD Dresden University of Technology, Dresden, Germany. gianaurelio.cuniberti@tu-dresden.de. + + + + eng + + + 101046369 + HORIZON EUROPE European Innovation Council + + + + RTG2767 + Deutsche Forschungsgemeinschaft + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + J Cheminform + 101516718 + 1758-2946 + + + Explainable AI + Healthcare + Molecular sensing + Quantum-informed descriptors + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 17 + + + 2026 + 5 + 21 + + + 2026 + 5 + 29 + 23 + 45 + + + aheadofprint + + 42216185 + 10.1186/s13321-026-01232-3 + 10.1186/s13321-026-01232-3 + + + + + + 42216176 + + 2026 + 05 + 29 + +
+ + 1472-6947 + + 26 + 1 + + 2026 + May + 29 + + + BMC medical informatics and decision making + BMC Med Inform Decis Mak + + Correction to: Simultaneous prediction of early and delayed mortality in burn patients: a comparative machine learning analysis of feature importance in a single-center retrospective study. + 194 + 10.1186/s12911-026-03563-5 + + + Motamedi + Mehran + M + + Nanotechnology Engineering, Babol Noshirvani University of Technology, Shariati Avenue, Babol, Mazandaran Province, 47148-71167, Iran. + + + + Moallemkolaei + Najibeh Mohseni + NM + + Health Information Management, Health Information Management Research Center, Kashan University of Medical Sciences, Ravandi Boulevard, Kashan, Isfahan Province, 87159-73449, Iran. + + + + Hesamirostami + Mohammadhossein + M + + Plastic and Reconstructive Surgery, Mazandaran University of Medical Sciences, Daneshgah Boulevard, Sari, Mazandaran Province, 48157-33971, Iran. hesami_plas@yahoo.com. + + + + Ghorbani + Mojtaba + M + + Plastic and Reconstructive Surgery, Mazandaran University of Medical Sciences, Daneshgah Boulevard, Sari, Mazandaran Province, 48157-33971, Iran. + + + + Arani + Leila Shokrizadeh + LS + + Health Information Management, Health Information Management Research Center, Kashan University of Medical Sciences, Ravandi Boulevard, Kashan, Isfahan Province, 87159-73449, Iran. + + + + eng + + Published Erratum + + + 2026 + 05 + 29 + +
+ + England + BMC Med Inform Decis Mak + 101088682 + 1472-6947 + + IM + + + BMC Med Inform Decis Mak. 2025 Dec 5;26(1):7. doi: 10.1186/s12911-025-03311-1. + 41350855 + + + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 44 + + + epublish + + 42216176 + 10.1186/s12911-026-03563-5 + 10.1186/s12911-026-03563-5 + + + + + + 42216170 + + 2026 + 05 + 29 + +
+ + 1475-9276 + + + 2026 + May + 29 + + + International journal for equity in health + Int J Equity Health + + Intersection of gender and class in the assessment of self-rated health in the Spanish population: a classification tree study. + 10.1186/s12939-026-02897-w + + This study assesses the influence of various specific health conditions as activity limitations and groups of chronic diseases, in estimating poor self-rated health for the Spanish population aged 30-60, stratified by the intersections of gender and social class. Moving beyond unidimensional social gradients, the research utilises decision tree analysis to identify specific configurations of SRH responses that align with the epidemiological profiles of distinct intersectional strata. + Data were drawn from the 2023 Spanish Health Survey (ESdE), focusing on a sample of 10,210 adults aged 30-60. This age bracket was selected to capture health inequality dynamics during the working life-course. Specific machine learning decision trees (J48 algorithm) were employed to assess the pathways for classifying individuals with poor SRH across six distinct intersectional groups. + Resulting decision trees differ significantly in composition, shape, and length across strata. A clear trend emerged: less skilled groups exhibited higher tree complexity, reflecting a more rapid accumulation of chronic illnesses at earlier ages. + This research contributes to the emerging field of study regarding what truly underlies individuals' general assessment of their health. The resulting decision trees illustrate the social gradient in health in a novel way that enriches the understanding of health inequalities. Instead of simply showing a linear trend across a one-dimensional variable, these trees reveal that as disadvantages accumulate through the intersection between gender and social class, the combination of relevant diseases do not only become more complex, but also distinct, in specific and unique ways for each group, as the intersectional framework predicts. + © 2026. The Author(s). + + + + Gumà-Lao + Jordi + J + + Centre for Demographic Studies, Barcelona, Spain. jguma@ced.uab.es. + + + + Barnils + Núria Pedrós + NP + + Institute for Public Health and Nursing Research, University of Bremen, Bremen, Germany. + + + Department of Epidemiology and Global Health, Umeå University, Umeå, Sweden. + + + + Parra-Casado + Daniel La + D + + Department of Sociology II, University of Alicante, Alicante, Spain. + + + + eng + + + Ramón y Cajal program (RYC2022-037781-I) + Agencia Estatal de Investigación + + + + "Generación de Conocimiento" Project [PID2022-141543OB-I00] + Agencia Estatal de Investigación + + + + 535865195 + Deutsche Forschungsgemenischaft + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Int J Equity Health + 101147692 + 1475-9276 + + IM + + Health inequity + Intersectional framework + Machine learning + Self-rated health + + Declarations. Human ethics and consent to participate: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 3 + + + 2026 + 5 + 14 + + + 2026 + 5 + 29 + 23 + 44 + + + aheadofprint + + 42216170 + 10.1186/s12939-026-02897-w + 10.1186/s12939-026-02897-w + + + + + + 42216166 + + 2026 + 05 + 29 + +
+ + 1471-2474 + + + 2026 + May + 29 + + + BMC musculoskeletal disorders + BMC Musculoskelet Disord + + Optimizing preoperative planning for total hip arthroplasty using random forest models to predict stem size and compatibility. + 10.1186/s12891-026-10022-9 + + Preoperative planning is essential for optimal outcomes post-total hip arthroplasty (THA). Appropriate stem size and compatibility with femoral dimensions are essential in THA to prevent distal fixation. Previous studies have proposed deep learning-based models with image recognition for preoperative planning to determine the best-fit stem; however, whether size and compatibility are adequately optimized remains unclear. This study developed methods that may assist surgeons in preoperative planning to estimate the stem size and compatibility using supervised machine learning models. + Two Random Forest (RF) models were developed to estimate the best-fit stem size and stem compatibility with the femoral geometry. Femoral size information measured at 10 locations from computed tomography images of 320 hips was used to train the models. The training data included the sizes and compatibility information as the target variables for supervised learning provided by a simulation software currently used in clinical practice to determine the optimal stem. As part of feature engineering, ratios derived from combinations of the 10 measured values were used as learning data. The size estimation model was designed as a seven-class classification model, whereas the compatibility estimation model was a binary classification model, predicting whether the stem will exclusively fit distally or not. Both models were tested using data from 109 hips of patients who underwent THA. The model performances were assessed using accuracy, F1 score, precision, and recall. Each parameter's impact was determined using feature importance analysis. + The size and compatibility estimation models trained without ratio information showed accuracies of 89.0% (exact-match: 46.8%) and 85.3%, respectively. When it was included, the models' performance improved, with the size and compatibility estimation accuracies increasing to 92.7% (exact-match: 39.4%) and 87.2%, respectively. Feature importance analysis highlighted that distal medullary cavity diameter is key in size estimation, while overall femoral dimensions are vital for compatibility estimation. + This study developed models that estimate the two critical factors in stem selection: size and compatibility. These models may support preoperative planning for THA using the Accolade II stem, although further validation is required to establish applicability to other implant systems. + © 2026. The Author(s). + + + + Kaneoka + Takehiro + T + 0000-0002-2742-4183 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Imagama + Takashi + T + 0000-0001-8509-1850 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Okazaki + Tomoya + T + 0000-0002-6635-3541 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Matsuki + Yuta + Y + 0000-0002-0664-4136 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Kawakami + Takehiro + T + 0000-0001-6363-560X + + Department of Orthopaedic surgery, Yamaguchi Prefectural Grand Medical Centre, Hofu, Yamaguchi, JP, Japan. + + + + Yamazaki + Kazuhiro + K + 0000-0002-1627-7368 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Sasaki + Kei + K + 0009-0003-8535-1589 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Asai + Yoshiyuki + Y + 0000-0001-5519-4306 + + Department of Systems Bioinformatics, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan. + + + + Sakai + Takashi + T + 0000-0001-6367-1299 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. cozy@yamaguchi-u.ac.jp. + + + + eng + + + JP22he0122010 + Japan Agency for Medical Research and Development (AMED) + + + + JP 23K10426 + JSPS KAKENHI + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Musculoskelet Disord + 100968565 + 1471-2474 + + IM + + femoral dimensions + machine learning + preoperative planning + random forest + total hip arthroplasty + + Declarations. Ethics approval and consent to participate: This retrospective study’s protocol was approved by the by the Ethics Committee of Yamaguchi University Graduate School of Medicine (approval number: H2021-166-2), and informed consent was obtained from all participants. The ethical standards from the 1964 Helsinki declaration and its later amendments were upheld. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2024 + 12 + 4 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 44 + + + aheadofprint + + 42216166 + 10.1186/s12891-026-10022-9 + 10.1186/s12891-026-10022-9 + + + + + + 42216115 + + 2026 + 05 + 29 + +
+ + 1471-2393 + + + 2026 + May + 30 + + + BMC pregnancy and childbirth + BMC Pregnancy Childbirth + + Predicting facility delivery and its determinants among reproductive-age women in Ethiopia using machine learning algorithm: evidence from performance monitoring for action Ethiopia 2019 dataset. + 10.1186/s12884-026-09354-0 + + Facility delivery attended by skilled health professionals is a critical strategy for reducing maternal and neonatal mortality. Despite substantial efforts to expand maternal health services in Ethiopia, a large proportion of births still occur at home. This study aimed to predict facility delivery and identify its associated factors among reproductive-age women in Ethiopia using machine learning approaches. + This study used secondary data from the 2019 Performance Monitoring for Action (PMA) Ethiopia cross-sectional household and female survey. A weighted sample of 5,413 women aged 15-49 years who had a recent birth was included in the analysis. Data extraction and preprocessing were conducted using STATA version 17, while machine learning models were implemented using Python version 3.11.5. Seven supervised machine learning algorithms were developed to predict facility delivery. Model performance was evaluated using accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC). Data preparation included feature engineering, data splitting, handling missing values, addressing class imbalance, and outlier detection. SHapley Additive exPlanations (SHAP) were applied to interpret the contribution of important predictors to model predictions. + Among the evaluated models, the Random Forest algorithm demonstrated the best predictive performance, achieving 74.15% accuracy, 72.58% recall, 76.27% F1-score, and 80.36% precision on test data. The most influential predictors of facility delivery included higher household wealth status, secondary or higher maternal education, television ownership, discussion of delivery location with a partner, partner encouragement for antenatal care attendance, family planning use, maternal age between 20 and 34 years, age at first sexual intercourse of 19 years or older, awareness of emergency contacts, and completion of primary education. + Interpretable machine learning approaches can provide useful insights for identifying factors associated with facility delivery. The findings highlight key socioeconomic and behavioral characteristics that may inform targeted strategies to improve utilization of facility-based delivery services and support efforts to enhance maternal health outcomes in Ethiopia. + © 2026. The Author(s). + + + + Degefa + Siraj Muhidin + SM + + Department of Health Informatics, College of Health Science, Mattu University, Mettu, Ethiopia. sirajmuhidin6@gmail.com. + + + + Hailu + Beriso Alemu + BA + + Department of Health Informatics, College of Health Science, Mattu University, Mettu, Ethiopia. + + + + Serbessa + Naol Gonfa + NG + + Department of Health Informatics, College of Health Science, Mattu University, Mettu, Ethiopia. + + + + Tsehay + Asmamaw Ketemaw + AK + + School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia. + + + + Sedi + Tigist Tollessa + TT + + Department of Health Informatics, College of Medicine and Health Sciences, Arbaminch University, Arbaminch, Ethiopia. + + + + Woldekidan + Eden Ketema + EK + + Department of Health Informatics, College of Medicine and Health Sciences, Arbaminch University, Arbaminch, Ethiopia. + + + + Ahmed + Mohammedjud Hassen + MH + + School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia. + + + + Adem + Jibril Bashir + JB + + Department of Public Health, College of medicine and Health sciences, Arsi University, Asella, Ethiopia. + + + + Wale + Agmasie Damtew + AD + + Department of Health Informatics, School of Public Health, Asrat Woldeyes Health Science Campus, Debre Berhan University, Debre Berhan, Ethiopia. + + + + Guadie + Habtamu Alganeh + HA + + School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia. + + + + Asemahagn + Mulusew Andualem + MA + + School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia. + + + + eng + + Journal Article + + + 2026 + 05 + 30 + +
+ + England + BMC Pregnancy Childbirth + 100967799 + 1471-2393 + + IM + + Determinants + Ethiopia + Facility delivery + Women + + Declarations. Ethics approval and consent to participate: For this study, secondary anonymized data were used, and permission to access the dataset was obtained from the Performance Monitoring for Action (PMA) data repository through the official PMA data access portal: PMA Dataset Access Portal. Ethical clearance for this secondary data analysis was obtained from the Institutional Review Board (IRB) of Bahir Dar University, College of Medicine and Health Sciences. The study was conducted in accordance with the Declaration of Helsinki. Since the dataset was fully anonymized and publicly available for research purposes, informed consent from participants was not required for this secondary analysis. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2024 + 11 + 7 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 42 + + + aheadofprint + + 42216115 + 10.1186/s12884-026-09354-0 + 10.1186/s12884-026-09354-0 + + + + + + 42216109 + + 2026 + 05 + 29 + +
+ + 2045-3701 + + + 2026 + May + 29 + + + Cell & bioscience + Cell Biosci + + Decoding the role of H19 in cholestatic liver injury using snRNA-seq, spatial transcriptomics, and machine learning-based disease prediction. + 10.1186/s13578-026-01590-3 + + Despite recent advances, Primary Sclerosing Cholangitis (PSC)-a chronic obstructive biliary disease-still lacks effective therapies to prevent disease progression or the need for liver transplantation. Moreover, up to 30% of transplant recipients experience recurrence. Long non-coding RNA H19 (H19) has been implicated in promoting PSC progression, yet the cellular and molecular mechanisms underlying its pathogenic role remain incompletely understood. + Liver tissues from age- and sex-matched wild type (WT), H19 knockout (H19KO), Mdr2 knockout (Mdr2KO), and double-knockout (DKO; Mdr2KO/H19KO) mice were analyzed using single-nucleus RNA sequencing (snRNAseq) and GeoMx spatial transcriptomics to define the cell type and spatially specific effects of H19 deletion in cholestatic liver injury. Machine learning models were built to develop cell-type-specific gene prediction signatures and cross-validated using the human dataset GSE243981. A disease-associated cholangiocyte subcluster that increased in Mdr2KO but was markedly reduced in DKO mice was identified. SPP1 signaling was significantly dysregulated in cholestatic liver injury and mitigated following H19 deletion. Translationally conserved healthy (Clu, Spp1) and diseased (Csmd1, Slco3a1, Cftr) cholangiocyte markers were identified. When validated in a human patient dataset (GSE243981), our machine-learning prediction models achieved AUC values > 0.869. Finally, spatial analyses demonstrated that the mitigation of disease-associated gene expression following H19 deletion was specifically restricted to hepatocytes within the bile duct region. + H19 deletion mitigates cholestatic injury by suppressing pathogenic cholangiocyte states, normalizing Spp1-mediated signaling, and shifting transcriptional programs specifically within the periductal niche. Furthermore, our machine-learning signatures demonstrate robust cross-species translation and may benefit future post-transplant analytics and disease recurrence predictions. + © 2026. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. + + + + Way + Grayson W + GW + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Jiang + Xixian + X + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Lu + Hongkun + H + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Wu + Nan + N + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Zhao + Derrick + D + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Tai + Yun-Ling + YL + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Bayatpour + Sareh + S + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Wang + Xuan + X + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Zhou + Huiping + H + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. huiping.zhou@vcuhealth.org. + + + Richmond Veterans Affairs Medical Center, Richmond, VA, 23249, USA. huiping.zhou@vcuhealth.org. + + + + eng + + + 5T32AA029975 + GF + NIH HHS + United States + + + DK115377, 2R56DK115377-05A1, 5R01AA030180, R01DK139587, NIH-NCI P01CA275740 + GF + NIH HHS + United States + + + 5I01BX005730,1 IS1 BX004777-01 and 1IS1BX005517-01, IK6BX004477 + U.S. Department of Veterans Affairs + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Cell Biosci + 101561195 + 2045-3701 + + + Cholestasis + H19 + Long non-coding RNA + Machine learning + Primary sclerosing cholangitis + SPP1 + + Declarations. Conflict of interest: Huiping Zhou is the corresponding author and is also an Editor for the journal. Ethics approval and consent to participate: All animal experiments were performed following institutional guidelines for ethical animal studies and approved by the Virginia Commonwealth University and Richmond VA Medical Center Institutional Animal Care and Use Committee, Virginia, USA. All patient samples used followed Richmond VA Medical Center Institutional Review Board guidelines for de-identified consenting patients. Consent for publication: All authors have given their consent for publication. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 12 + 11 + + + 2026 + 5 + 7 + + + 2026 + 5 + 29 + 23 + 40 + + + aheadofprint + + 42216109 + 10.1186/s13578-026-01590-3 + 10.1186/s13578-026-01590-3 + + + + + + 42216097 + + 2026 + 05 + 29 + +
+ + 1758-9193 + + + 2026 + May + 29 + + + Alzheimer's research & therapy + Alzheimers Res Ther + + EEG biomarkers can predict early-stage Alzheimer's disease and correlate with intracerebral pathology: a multimodal machine learning study. + 10.1186/s13195-026-02096-3 + + Early recognition of Alzheimer's disease (AD) is crucial for timely intervention and delaying disease progression. Electroencephalogram (EEG) technology provides a direct reflection of the brain's dynamic activity. However, the relationship between potential EEG features and cognitive function in early-stage AD patients, as well as cerebrospinal fluid (CSF) pathological biomarkers, remains unclear. + This study included 101 patients with mild cognitive impairment (MCI) and mild AD, alongside 69 healthy controls (HC) matched for gender, age, and educational attainment. Extracting EEG power spectral density (PSD) and microstates analysis features as training features for machine learning (ML), we employed five ML algorithms-Support Vector Machines (SVM), Logistic Regression (LR), Random Forests (RF), XGBoost, and LightGBM-for training and testing. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). SHapley Additive exPlanations (SHAP) plots were employed to elucidate variable importance within the model, and sequential forward selection (SFS) was utilised to identify potential features. Correlation analysis and mediation analysis were conducted to investigate the relationships between EEG features, CSF pathological biomarkers, and cognitive function. + The LR model demonstrated the highest average predictive performance in the training set (mean AUC = 0.859 ± 0.059). The model incorporating PSD and microstates features demonstrated optimal predictive performance in the test set (AUC = 0.949, 95% CI: 0.877-1.000), outperforming any single-feature model. Based on SHAP and SFS analyses, six potential EEG indicators were identified: central region delta frequency band, central region theta frequency band, temporal region beta frequency band, microstate mean duration, microstate C duration, and the transition probability from microstate C to A. Mediation analysis revealed a significant negative correlation between central region theta frequency band and CSF Aβ₁₋₄₂ levels (r = - 0.31, p = 0.015), and the central region theta frequency band mediated the relationship between Aβ₁₋₄₂ levels and Mini-Mental State Examination (MMSE) scores (indirect effect = 0.0007, 95% CI: 0.0001-0.0013). + The combined application of EEG and ML enables efficient classification diagnosis of early-stage AD, and EEG is correlated with intracerebral pathological biomarkers and cognitive impairment. + © 2026. The Author(s). + + + + Geng + Zhi + Z + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Song + Wenqian + W + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Gao + Chen + C + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Zhu + Yun + Y + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Wu + Yue + Y + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + Department of Sleep Psychology, The Second Hospital of Anhui Medical University, Anhui Medical University, Hefei, China. + + + + Song + Bo + B + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Guo + Haihua + H + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Wu + Jiayun + J + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Fang + Miao + M + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Yan + Yibing + Y + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Zhou + Shanshan + S + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Hu + Panpan + P + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Tian + Yanghua + Y + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Wu + Xingqi + X + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. wuxq@fy.ahmu.edu.cn. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. wuxq@fy.ahmu.edu.cn. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. wuxq@fy.ahmu.edu.cn. + + + + Wang + Kai + K + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. kwang@ahmu.edu.cn. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. kwang@ahmu.edu.cn. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. kwang@ahmu.edu.cn. + + + Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, China. kwang@ahmu.edu.cn. + + + + Wei + Ling + L + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. weil@fy.ahmu.edu.cn. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. weil@fy.ahmu.edu.cn. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. weil@fy.ahmu.edu.cn. + + + + eng + + + 82101498 + National Natural Science Foundation of China + + + + 82371201 + National Natural Science Foundation of China + + + + 82371201 + National Natural Science Foundation of China + + + + 2021kj19 + The 2021 Youth Foundation training program of the First Affliated Hospital of Anhui Medical University + + + + 202204295107020006 + The Anhui Province Clinical Medical ResearchTransformation Special Project + + + + XZZR202402048 + The Natural Science Foundation of theXizang Autonomous Region Group Medical Aid Project to Xizang + + + + 2022zhyx-B11 + Research Fund of Anhui Institute of Translational Medicine + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Alzheimers Res Ther + 101511643 + + IM + + Alzheimer’s disease + CSF biomarkers + EEG microstates + EEG power spectral density + Machine learning + + Declarations. Ethics approval and consent to participate: The studies involving human participants were reviewed and approved by the Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University. The patients/participants provided their written informed consent to participate in this study. Consent for publication: All participants provided consent for the publication of this manuscript. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 12 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 23 + 40 + + + aheadofprint + + 42216097 + 10.1186/s13195-026-02096-3 + 10.1186/s13195-026-02096-3 + + + + + + 42216034 + + 2026 + 05 + 29 + +
+ + 1475-2867 + + + 2026 + May + 29 + + + Cancer cell international + Cancer Cell Int + + DNA damage response signature-based prognostic genes for intrahepatic cholangiocarcinoma: a combined analysis of machine learning and biological experiments. + 10.1186/s12935-026-04355-7 + + Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive subtype of primary liver cancer with insidious onset, early metastasis, and poor prognosis. DNA damage response (DDR) dysfunction is linked to ICC tumorigenesis, progression, and drug resistance. However, the detailed mechanisms remain underexplored. + DDR activity was quantified by ssGSEA across 6 independent ICC cohorts. DDR risk model was analyzed using 10 machine learning algorithms to construct 101 combined models, and the optimal Lasso + RSF model was adopted by c-index. The core gene SFN was selected for experimental validation. Single‑cell RNA sequencing and spatial transcriptomics were used to explore cellular localization and tissue distribution of signature genes. In vitro assays were performed in ICC cell lines after SFN knockdown to assess cell proliferation and chemosensitivity. + The DDR risk model showed stable prognostic performance in training and external validation cohorts. High‑risk patients exhibited poor prognosis, immunosuppressive microenvironment and reduced sensitivity to multiple chemotherapeutic agents. SFN was specifically enriched in malignant cells. Knockdown of SFN significantly suppressed cell proliferation and colony formation, enhanced sensitivity to cisplatin and gemcitabine, and increased γ‑H2AX expression indicating elevated DNA double‑strand breaks. + We constructed a robust DDR‑based prognostic model for ICC that enables accurate risk stratification and prediction of therapeutic response. SFN serves as a critical driver of chemoresistance and a potential therapeutic target. These results offer novel biomarkers and mechanistic insights to facilitate precision medicine for ICC patients. + © 2026. The Author(s). + + + + Lu + Kai + K + + Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Li + Hanqi + H + + Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Wu + Yinying + Y + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Dong + Xuyuan + X + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Dong + Danfeng + D + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Fan + Yangwei + Y + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Li + Enxiao + E + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Sun + Liankang + L + + Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. sunliankang@xjtu.edu.cn. + + + + Shi + Yu + Y + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. shiyu@xjtu.edu.cn. + + + + eng + + + 2022JQ-846 + Natural Science Basic Research Program of Shaanxi + + + + 2021QN-01,2022QN-15 + Research Foundation of the First Affiliated Hospital of Xi'an Jiaotong University + + + + xzy012022103 + Fundamental Research Funds for the Central Universities + + + + 82303663 + National Natural Science Foundation of China + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Cancer Cell Int + 101139795 + 1475-2867 + + + DNA damage response + Experimental validation + Intrahepatic cholangiocarcinoma + Machine learning + Prognostic markers + + Declarations. Ethics approval and consent to participate: All data utilized in this study were retrieved from publicly available online databases, as specified in the manuscript. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 8 + 3 + + + 2026 + 5 + 23 + + + 2026 + 5 + 29 + 23 + 38 + + + aheadofprint + + 42216034 + 10.1186/s12935-026-04355-7 + 10.1186/s12935-026-04355-7 + + + + + + 42216026 + + 2026 + 05 + 29 + +
+ + 1757-6512 + + + 2026 + May + 29 + + + Stem cell research & therapy + Stem Cell Res Ther + + KRT6A derived from mesenchymal stem cells as a potential biomarker and therapeutic target for alopecia areata: insights from multi-omics analysis and experimental evidence. + 10.1186/s13287-026-05077-3 + + Mesenchymal stem cells (MSCs) secretome have shown promise in the treatment of alopecia areata (AA). However, the key therapeutic genes remain unclear. This study aimed to identify potential critical therapeutic molecules using multi-omics approaches. + MSCs extracellular vesicles related genes was retrieved from ExoCarta database, and which integrated with transcriptomic and proteomic sequencing data from AA for cross-analysis to identify AA-related therapeutic genes. Mfuzz clustering, PPI networks, and machine learning algorithms were employed to potential key therapeutic factors. Functional mechanisms were explored through gene expression analysis, immune infiltration, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics. AA mice were used for experimental validation. + We identified 146 MSCs-derived genes with potential therapeutic relevance for AA. Enrichment analysis suggests that they are involved in skin development and differentiation. Through Mfuzz clustering, 13 machine learning algorithms, and combined with SHAP interpretability analysis, the KRT6A was we further identified as the most critical candidate gene. Validation analyses demonstrated that KRT6A serves as a robust diagnostic marker for AA (AUC = 0.989), a predictor of disease severity (AUC = 0.769), and a protective factor (HR = 0.52, 95%CI: 0.34-0.79). Immune infiltration analysis revealed severe immune dysregulation in AA, and KRT6A was significantly negative correlated with dysregulated immune cells. By exploring the expression source of KRT6A, we found that it was mainly enriched in skin hair follicle tissues, and single-cell subset analysis and spatial transcriptomics further mapped KRT6A to hair follicle stem-like cells from the cellular level, yet KRT6A was significantly downregulated in AA models. Primary human hair follicle mesenchymal stem cells (HFMSCs) further confirmed KRT6A localization. Furthermore, KRT6A-overexpressing HFMSCs exhibited enhanced migration, whereas KRT6A knockdown reduced migration and suppressed WNT pathway activation. Conditioned medium from KRT6A-overexpressing HFMSCs effectively alleviated alopecia and reduced apoptosis in AA mice, whereas KRT6A knockdown reversed these effects. + MSCs-derived KRT6A may serve as a potentia biomarker and therapeutic target for AA, which provide novel insights into AA pathogenesis and may help the development of targeted therapeutic strategies. + © 2026. The Author(s). + + + + Peng + Shixiong + S + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. + + + + Deng + Jia + J + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. + + + + Meng + Xiuming + X + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. + + + + Deng + Shuangjie + S + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. + + + + Yan + Wenjie + W + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. ywj716@glmu.edu.cn. + + + + Huang + Xi + X + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. professorhuangxi@163.com. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Stem Cell Res Ther + 101527581 + 1757-6512 + + IM + + Alopecia areata + KRT6A + Machine learning + Mesenchymal stem cells + Multi-omics + + Declarations. Ethics approval and consent to participate: The animal research, entitled “KRT6A from Mesenchymal Stem Cells Exosomes as a Potential Biomarker and Therapeutic Target for Alopecia Areata: Insights from Multi-omics Analysis and Experimental Evidence”, was approved by the Animal Ethics Committee of Guilin Medical University (GLMU-IACUC-20251084) on August 13, 2025, and was performed in accordance with the ARRIVE 2.0 guidelines. The human-related research in this study, which was part of the project entitled “Exosome Multimodal Analysis Reveals the Mechanism by Which Mesenchymal Stem Cell Exosomes Regulate Hair Follicle Regeneration”, was approved by the Ethics Committee of the Affiliated Hospital of Guilin Medical University on February 18, 2025 (No. 2025QTLL-10), and was conducted in compliance with the principles of the Declaration of Helsinki. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 10 + 11 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 37 + + + aheadofprint + + 42216026 + 10.1186/s13287-026-05077-3 + 10.1186/s13287-026-05077-3 + + + + + + 42216024 + + 2026 + 05 + 29 + +
+ + 2050-6511 + + + 2026 + May + 29 + + + BMC pharmacology & toxicology + BMC Pharmacol Toxicol + + Network toxicology and bioinformatics reveal potential molecular links between cadmium exposure and pancreatic cancer. + 10.1186/s40360-026-01156-6 + + Environmental cadmium (Cd) pollution poses a severe threat to human health due to its strong bioaccumulation and high carcinogenicity. Although Cd exposure has been linked to various cancers, its specific role in pancreatic cancer (PC) remains unclear. This study aimed to explore the molecular mechanisms underlying Cd-associated PC and identify potential diagnostic and therapeutic targets. + Transcriptomic data of PC were obtained from the GEO database to screen differentially expressed genes (DEGs), which were then intersected with Cd-related genes retrieved from the Comparative Toxicogenomics Database (CTD). Functional enrichment analyses, including GO and KEGG, were conducted to identify key biological processes and pathways. Multiple machine learning algorithms were used to construct a diagnostic model and identify hub genes. Furthermore, molecular docking and molecular dynamics simulations were performed to evaluate drug-target interactions, and in vitro experiments were used to validate the effects of Cd exposure on PC cell proliferation and invasiveness. + A total of five key genes-FN1, COL5A1, AHR, COL3A1, and TIMP1-were identified as core regulators of Cd-associated PC. Functional analyses indicated that Cd primarily affects the extracellular matrix (ECM) metabolic and collagen remodeling pathways. Experimental results confirmed that Cd exposure significantly enhanced the proliferation and invasiveness of PC cells. Molecular docking and dynamics simulations revealed that the EGFR inhibitor dacomitinib exhibited favorable predicted binding affinity to FN1, suggesting its potential as a therapeutic agent. + This study reveals that Cd may promote the malignant progression of PC by regulating ECM remodeling through key genes such as FN1. Dacomitinib shows promise as an FN1-associated therapy, offering new insights for the precise prevention and treatment of Cd-associated PC. + © 2026. The Author(s). + + + + Liu + Shuyuan + S + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. + + + + Lu + Xinyan + X + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + + Li + Desheng + D + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + + Ma + Jingyuan + J + + The First Clinical Medical College, Liaoning University of Traditional Chinese Medicine, Shenyang, China. + + + + Zhang + Yunshu + Y + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. + + + + Li + Junchen + J + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. + + + + Wan + Xing + X + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. wanx03@dmu.edu.cn. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. wanx03@dmu.edu.cn. + + + + Shang + Dong + D + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. shangdong@dmu.edu.cn. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. shangdong@dmu.edu.cn. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. shangdong@dmu.edu.cn. + + + + Zhang + Qingkai + Q + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. zhangqk@dmu.edu.cn. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. zhangqk@dmu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Pharmacol Toxicol + 101590449 + 2050-6511 + + IM + + Cadmium + Machine learning + Molecular dynamic simulation + Network toxicology + Pancreatic cancer + + Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 11 + 11 + + + 2026 + 5 + 20 + + + 2026 + 5 + 29 + 23 + 37 + + + aheadofprint + + 42216024 + 10.1186/s40360-026-01156-6 + 10.1186/s40360-026-01156-6 + + + + + + 42216013 + + 2026 + 05 + 29 + +
+ + 1749-7922 + + + 2026 + May + 29 + + + World journal of emergency surgery : WJES + World J Emerg Surg + + Machine learning models for early mortality prediction in trauma patients using public data: a nationwide retrospective study. + 10.1186/s13017-026-00706-x + + Trauma is a leading cause of morbidity and mortality worldwide, particularly in younger populations. Early identification of high-risk trauma patients is critical for timely interventions and improved outcomes. Although artificial intelligence and machine learning have demonstrated promise in healthcare, their application in trauma mortality prediction has been limited. + This study developed and validated machine learning models to predict mortality in trauma patients using a large public dataset from the National Community-Based Critical Injury Survey (South Korea, 2016-2020). Overall, 207,012 cases were analyzed. Six machine learning algorithms, including logistic regression, k-nearest neighbor, decision tree, random forest (RF), extreme gradient boosting (XGB), and multi-layer perceptron, were trained and evaluated. Their performance was assessed using the areas under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC), and other metrics. Shapley additive explanations (SHAP) scores were used to interpret feature importance. + The XGB model demonstrated the highest performance (AUROC 0.985; AUPRC 0.957), followed closely by the RF model (AUROC 0.984; AUPRC 0.956). Performance remained stable during the COVID-19 period, supporting the model's temporal robustness under systemic disruption. SHAP analysis identified clinically actionable features such as out-of-hospital cardiac arrest, injury severity score, age, and time to transfusion. Unlike many prior studies based on small or single-center datasets, our model was developed using a nationally representative cohort and prioritized interpretability, scalability, and generalizability. + This study presents a high-performing, interpretable machine learning framework for early mortality risk stratification in trauma patients using nationwide registry data. The strong discrimination and temporal robustness of the model support its value as a system-level prediction tool; however, further calibration analyses, external validation, and prospective implementation studies are required before integration into clinical workflows. + © 2026. The Author(s). + + + + Baik + Seung Min + SM + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Lee + Jae Gil + JG + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Shim + Hongjin + H + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Kim + Sujung + S + + Division of Acute Care Surgery, Department of Surgery, Korea University Anam Hospital, Korea University College of Medicine, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Republic of Korea. + + + + Hong + Kyung Sook + KS + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Yi + Heejung + H + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Lee + Kyung Hyun + KH + + Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. + + + + Lee + Jae-Myeong + JM + + Division of Acute Care Surgery, Department of Surgery, Korea University Anam Hospital, Korea University College of Medicine, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Republic of Korea. ljm3225@hanmail.net. + + + + eng + + + RS-2023-00240766 + National Research Foundation of Korea + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + World J Emerg Surg + 101266603 + 1749-7922 + + IM + + Artificial intelligence + Clinical decision-making + Emergency medical services + Injury severity score + + Declarations. Ethics approval and consent to participate: The Ewha Womans University Mokdong Hospital Institutional Review Board deemed this study exempt from ethics review and waived the informed consent requirement. Consent for publication: Not applicable. Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 10 + 20 + + + 2026 + 5 + 23 + + + 2026 + 5 + 29 + 23 + 37 + + + aheadofprint + + 42216013 + 10.1186/s13017-026-00706-x + 10.1186/s13017-026-00706-x + + + + + + 42215973 + + 2026 + 05 + 29 + +
+ + 1472-6963 + + + 2026 + May + 29 + + + BMC health services research + BMC Health Serv Res + + Why are rural hospitals closing in the U.S.? Predictors identified using explainable machine learning. + 10.1186/s12913-026-14810-w + + Rural hospital closures in the U.S. reduce access to essential healthcare services and worsen health and economic outcomes in rural communities. This study uses national longitudinal data and explainable machine learning (XML) to identify and interpret risk factors of rural hospital closures. + We conducted a retrospective longitudinal study of U.S. rural hospitals using national datasets from 2011 to 2022, integrating hospital-level financial, operational, policy, population, and community data. Longitudinal feature engineering and statistical summarization were performed, class imbalance and multicollinearity were addressed using random undersampling and variance inflation factor (VIF) analysis, and an XGBoost classifier was trained using stratified three-fold cross-validation and Optuna for hyperparameter tuning. Model explainability was assessed using SHapley Additive exPlanations (SHAP). + Among 2,683 rural hospitals, 139 (5.2%) closed during the study period. The model achieved a recall of 86%, accuracy of 75%, and an AUC of 89%. The key variables linked to closure risk are sustained financial weakness and volatility (e.g., total margin, variability in return on equity, and current ratio), operational instability (low patient volume and variability in bed-days available), policy context (e.g., years since Medicaid expansion), and community context, including sociodemographic characteristics of the service area (e.g., proportion of Black or African American residents), which may reflect underlying structural and socioeconomic conditions rather than direct predictors or causal drivers of closure. + These findings indicate that the risk of closure of rural hospitals reflects accumulated long-term financial and operational instability, as well as policy and community context, rather than short-term distress, highlighting the value of longitudinal and explainable modeling approaches. + Longitudinal data integrated into XML provides a practical approach for early warning systems that enable proactive monitoring and timely interventions, particularly for hospitals serving historically underserved communities. + Not applicable. + © 2026. The Author(s). + + + + Balakrishnan + Kiruthika + K + + National Center for Rural Health Professions, University of Illinois College of Medicine Rockford, 1601 Parkview Avenue, Rockford, IL, 61107, USA. bkiruthi@uic.edu. + + + AI.Health4All Center, University of Illinois Chicago, 1747 West Roosevelt Road, Chicago, IL, 60608, USA. bkiruthi@uic.edu. + + + + Abuhay + Tesfamariam M + TM + + Department of Medicine, University of Illinois Chicago, 840 S. Wood St., Chicago, IL, 60612, USA. + + + AI.Health4All Center, University of Illinois Chicago, 1747 West Roosevelt Road, Chicago, IL, 60608, USA. + + + + Hinkle + Hana E + HE + + National Center for Rural Health Professions, University of Illinois College of Medicine Rockford, 1601 Parkview Avenue, Rockford, IL, 61107, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Health Serv Res + 101088677 + 1472-6963 + + IM + + Early warning systems + Explainable machine learning + Hospital closure risk factors + Longitudinally derived features + Rural hospital closures + + Declarations. Ethics consent to participate: This study is a retrospective analysis of publicly available data and published literature and did not involve human participants, identifiable private information, or the collection of new data requiring ethical approval or informed consent. Consent for publication: Not applicable Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 10 + + + 2026 + 5 + 15 + + + 2026 + 5 + 29 + 23 + 36 + + + aheadofprint + + 42215973 + 10.1186/s12913-026-14810-w + 10.1186/s12913-026-14810-w + + + + + + 42215935 + + 2026 + 05 + 29 + +
+ + 1472-6947 + + + 2026 + May + 29 + + + BMC medical informatics and decision making + BMC Med Inform Decis Mak + + Explainable machine learning for the prediction of Alzheimer's disease-related cognitive impairment: a consensus feature selection approach. + 10.1186/s12911-026-03585-z + + Early identification of Alzheimer's disease-related cognitive impairment remains challenging, and existing machine learning (ML) models often suffer from feature instability and limited interpretability. This study developed robust and explainable ML models using cerebrospinal fluid (CSF) biomarkers by systematically comparing sparsity-based (LASSO), importance-based (Boruta), and consensus feature selection strategies. + A publicly available cohort of 333 individuals (91 cognitively impaired, 242 cognitively normal) was analyzed. Data were split into training (70%) and independent test (30%) sets. Multiple classifiers, including Elastic Net-regularized logistic regression (LR), support vector machine (SVM), random forest, XGBoost, and Naive Bayes (NB), were trained using repeated 5-fold cross-validation (10 repetitions; 10 × 5-fold cross-validation) with class weighting. Model performance was evaluated using discrimination, calibration, and clinical utility metrics, and interpretability was assessed using SHAP. + All models demonstrated strong discriminative performance on the test set (AUROC 0.861-0.958). LASSO-based models showed high specificity, Boruta-based models achieved higher sensitivity, and consensus-based models provided the most balanced performance. The consensus-LR and -SVM models achieved AUROC values of 0.954 and 0.951, respectively. Beyond discrimination, the consensus-LR model demonstrated good calibration and consistent net clinical benefit in decision curve analysis, analyses that remain relatively underreported in the Alzheimer's disease machine learning literature. SHAP analyses highlighted biologically plausible contributions from key biomarkers, including tau, Aβ42, NT-proBNP, pancreatic polypeptide, and IL-7. + In summary, stable and interpretable ML models for Alzheimer's disease-related cognitive impairment can be developed using CSF-derived biomarkers obtained through lumbar puncture. The proposed consensus-based feature selection framework improves feature stability and model transparency, facilitating the discrimination between cognitively normal and impaired individuals and providing a foundation for future external validation studies. + © 2026. The Author(s). + + + + Cantaş Türkiş + Fulden + F + 0000-0002-7018-7187 + + Division of Biostatistics, Faculty of Medicine, Muğla Sıtkı Koçman University, Muğla, 48000, Turkey. fuldencantas@mu.edu.tr. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Med Inform Decis Mak + 101088682 + 1472-6947 + + IM + + Calibration + Clinical utility + Explainability + Feature selection + Machine learning + + Declarations. Ethics approval and consent to participate: Not applicable. This study is a secondary analysis of publicly available, anonymized data obtained from an open-access repository. Ethical approval and informed consent were obtained in the original study, and no additional ethical approval was required for the present analysis. The study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication: Not applicable. The study does not include any individual-level identifiable data. Authors’ information: Fulden Cantaş Türkiş is an Assistant Professor at Muğla Sıtkı Koçman University, Faculty of Medicine, Department of Biostatistics. Her research interests include biostatistics, time-to-event data analysis, machine learning, artificial intelligence in healthcare, explainable artificial intelligence, predictive modeling, and statistical learning using real-world clinical data. She collaborates with multiple interdisciplinary research groups as the lead biostatistician and provides statistical consultancy for clinical and translational research projects. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 22 + + + 2026 + 5 + 20 + + + 2026 + 5 + 29 + 23 + 35 + + + aheadofprint + + 42215935 + 10.1186/s12911-026-03585-z + 10.1186/s12911-026-03585-z + + + + + + 42215930 + + 2026 + 05 + 29 + +
+ + 1472-6947 + + + 2026 + May + 29 + + + BMC medical informatics and decision making + BMC Med Inform Decis Mak + + Beyond prediction: explainable machine learning for ferritin estimation from complete blood count. + 10.1186/s12911-026-03601-2 + + Serum ferritin is a primary marker of body iron stores; however, its diagnostic utility is limited by cost and non-specific elevation during inflammatory states. This study aimed to develop and validate explainable machine learning (ML) models to estimate ferritin status using only widely available and cost-effective complete blood count (CBC) and demographic parameters. + This prospective study analyzed 12,450 adult laboratory records. To ensure ferritin reflected true iron status, cases with inflammation (elevated white blood cell count or erythrocyte sedimentation rate) were excluded. A balanced dataset of 7,200 samples was used to train five supervised ML algorithms, including Logistic Regression, XGBoost, and Artificial Neural Network (ANN), for both binary classification and regression tasks. Model interpretability was assessed using SHapley Additive Explanations (SHAP) to evaluate global feature importance. + The ANN model achieved the best classification performance, with an area under the curve of 0.837 and an F1-score of 0.760 in discriminating low ferritin levels (< 30 ng/mL) from normal values. Although the regression model demonstrated a moderate global R² of 0.32, it showed higher predictive accuracy in lower ferritin ranges critical for diagnosing iron deficiency. SHAP analysis revealed Sex, Mean Corpuscular Hemoglobin, and Red Cell Distribution Width as the most influential predictors, consistent with iron-restricted erythropoiesis. + ML models based solely on routine hemogram data provide a reliable, zero-cost screening approach for iron deficiency. The integration of explainable AI enhances clinical credibility and supports implementation as an accessible decision support tool. + © 2026. The Author(s). + + + + Us + Ramazan Berk + RB + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. berkus62@gmail.com. + + + + Panatli + Oguzhan + O + + Department of Biomedical Engineering, Faculty of Engineering, Ankara University, Gölbaşı, Ankara, 06830, Türkiye. + + + Department of Computer Engineering, Faculty of Engineering, Ankara University, Gölbaşı, Ankara, 06830, Türkiye. + + + + Bozkürk + Mine Büşra + MB + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Öztürk + Atakan + A + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Sağlam + Suadiye + S + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Kösem + Arzu + A + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Öztürk + Alpaslan + A + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Bostanci + Gazi Erkan + GE + + Department of Computer Engineering, Faculty of Engineering, Ankara University, Gölbaşı, Ankara, 06830, Türkiye. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Med Inform Decis Mak + 101088682 + 1472-6947 + + IM + + Clinical decision support systems + Complete blood count + Explainable artificial intelligence + Ferritin + Iron deficiency + SHAP + + Declarations. Ethics approval and consent to participate: The study protocol was approved by the Ankara Etlik City Hospital Ethics Committee (Approval No: AEŞH-BADEK1-2025-032, Date: 02/07/2025) and was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all participants prior to inclusion in the study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 6 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 23 + 35 + + + aheadofprint + + 42215930 + 10.1186/s12911-026-03601-2 + 10.1186/s12911-026-03601-2 + + + + + + 42215875 + + 2026 + 05 + 29 + +
+ + 1480-9222 + + + 2026 + May + 29 + + + Biological procedures online + Biol Proced Online + + A novel prognostic model based on MVIRGs identifies ANGPT2 as a key target driving the malignant progression of HCC. + 10.1186/s12575-026-00347-z + + Hepatocellular carcinoma (HCC) is a highly lethal malignancy with high recurrence and limited therapies. Microvascular invasion (MVI) is a critical prognostic factor, but its molecular mechanisms and precise predictive tools are lacking. This study aimed to identify key MVI-associated genes (MVIRGs), develop a prognostic model, and elucidate the role of ANGPT2 in HCC progression and therapeutic responses. + Machine learning on multi-cohort data (TCGA, ICGC) identified MVIRGs and constructed a prognostic model. ANGPT2, the core oncogenic MVIRG, was validated clinically (IHC) and functionally (in vitro and in vivo). We analyzed its association with the tumor immune microenvironment (TIME), immune checkpoint inhibitors (ICIs), and drug sensitivity. + A powerful six-MVIRG prognostic model was developed, with ANGPT2 identified as the sole oncogenic gene. Clinically, ANGPT2 was significantly overexpressed, correlating strongly with aggressive features like MVI, vascular invasion, and advanced stages, serving as a robust poor prognostic biomarker for OS and PFS. Functionally, ANGPT2 knockdown significantly inhibited HCC cell growth and migration in vitro, and inhibited tumor growth in vivo. Bioinformatics revealed ANGPT2 fosters an immunosuppressive TIME, upregulating immune checkpoints (e.g., PD-L1) to mediate resistance to ICIs. High ANGPT2 expression showed a differential drug response: resistance to targeted therapies (e.g., EGFR-TKIs) but paradoxically increased sensitivity to chemotherapies (e.g., docetaxel, paclitaxel). + This study constructed a powerful MVIRG model and identified ANGPT2 as a core oncogenic molecule. ANGPT2 drives HCC progression, mediates immune resistance, and differentially regulates drug sensitivity, making it a key biomarker for prognosis and treatment guidance. + © 2026. The Author(s). + + + + Li + Xian + X + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Tan + Zhouli + Z + + The Fifth School of Clinical Medicine, Anhui Medical University, Hefei, China. + + + Navy Clinical College, Anhui Medical University, Beijing, China. + + + + Feng + Shanru + S + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Yu + Qiang + Q + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Zhou + Jian + J + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Fan + Jia + J + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Xu + Yang + Y + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. xuyangdoctor@163.com. + + + + eng + + + 82073272 + National Natural Science Foundation of China + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Biol Proced Online + 100963717 + 1480-9222 + + + ANGPT2 + Hepatocellular carcinoma + MVIRGs + Microvascular invasion + Prognostic model + + Declarations. Ethics approval and consent to participate: Ethical approval was provided by the Ethics Committee of Zhongshan Hospital, and written informed consent was obtained from each patient (Approval number: No. Y2020-374). All animal experiments involved in this study were reviewed and approved by the Ethics Committee of Zhongshan Hospital, Fudan University, Xiamen Hospital (Approval number: No. Y2024-021). We confirm that all experiments were performed in accordance with the approved protocols and all relevant guidelines and regulations for the care and use of laboratory animals. Regarding the tumor burden, the maximum tumor size/burden permitted by our Ethics Committee/IRB is a tumor volume not exceeding 2000 mm3 or a longest tumor diameter not exceeding 2.0 cm. We confirm that this maximum permitted tumor size/burden was not exceeded at any point during the study. Clinical trial number: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 1 + 31 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 23 + 33 + + + aheadofprint + + 42215875 + 10.1186/s12575-026-00347-z + 10.1186/s12575-026-00347-z + + + + + + 42215857 + + 2026 + 05 + 29 + +
+ + 1471-2164 + + + 2026 + May + 30 + + + BMC genomics + BMC Genomics + + PRIME: An evaluation framework for protein representation inference and generalization in viral mutation space. + 10.1186/s12864-026-12976-5 + + Protein language models (PLMs) have revolutionized protein fitness prediction, yet their application to rapidly evolving viral pathogens is often confounded by extreme sequence homology. This homology leads to "data leakage" in standard random validation splits, yielding inflated performance metrics that fail to translate into real-world biosurveillance utility. + We present Protein Representation Inference for Mutation Evaluation (PRIME), a framework that integrates domain-specific fine-tuning with a rigorous position-stratified validation protocol to evaluate viral threats. Using a dataset of 347,432 SARS-CoV-2 receptor binding domain (RBD) sequences, we demonstrate that while random training data split yields deceptive R2 values (> 0.90), they fail to generalize to novel mutational sites. By benchmarking models up to 650 M parameters, we show that domain-specific fine-tuning of the ESM-C 600 M model with correctly stratified data provides an initial demonstration of predictive signal for binding affinity and expression at unseen mutational sites of binding affinity and expression on unseen sites (R2 ~0.23), a significant advancement over base foundation models which exhibit no predictive power (R2 <0). PRIME's embedding-based clustering identified 3.03% of bat coronavirus sequences as candidates for further experimental prioritization based on their functional similarity to human-infective strains in embedding space, offering a perspective complementary to traditional phylogenetic methods. + PRIME establishes a new benchmark for the application of PLMs in pathogen surveillance. Our findings demonstrate that state-of-the-art models and fine-tuning, when paired with stratified validation, provide biologically meaningful insights into pathogen evolution and zoonotic risk. + © 2026. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. + + + + Gibson + Kaetlyn + K + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Li + Po-E + PE + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Li + Valerie + V + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Dix + Martha + M + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Hung + Li-Wei + LW + + Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Stelle + George Widgery + GW + + Applied Computer Science Group, Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Babinski + Michal + M + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Chain + Patrick + P + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Hu + Bin + B + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. bhu@lanl.gov. + + + + eng + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + + Journal Article + + + 2026 + 05 + 30 + +
+ + England + BMC Genomics + 100965258 + 1471-2164 + + IM + + Biosurveillance + Gomology leakage + Host tropism prediction + Machine learning efficiency + Model fine-tuning + Phenotype prediction + Position-stratified validation + Protein language models + + Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 12 + + + 2026 + 5 + 18 + + + 2026 + 5 + 29 + 23 + 32 + + + aheadofprint + + 42215857 + 10.1186/s12864-026-12976-5 + 10.1186/s12864-026-12976-5 + + + + + + 42215832 + + 2026 + 05 + 29 + +
+ + 1573-6822 + + + 2026 + May + 29 + + + Cell biology and toxicology + Cell Biol Toxicol + + Integrative multi-omics, machine learning, and experimental validation reveal that TPM1 suppresses M2 macrophage polarization and enhances chemosensitivity in acute myeloid leukemia. + 10.1007/s10565-026-10201-3 + + Acute myeloid leukemia (AML) remains a therapeutic challenge due to chemoresistance and immunosuppression. This study aimed to discover novel regulatory mechanisms in AML pathogenesis. + Integrative transcriptomic (GSE30029) and proteomic (PXD008378) analyses with machine learning (LASSO, SVM-RFE, random forest) were performed to identify core AML regulators. Functional validation was conducted in AML cell lines (HL-60, NB4), including cell proliferation and apoptosis assays, cytarabine (Ara-C) sensitivity testing, and a subcutaneous xenograft model. Macrophage polarization was evaluated in co-culture systems. Molecular mechanisms were explored via MeRIP, RIP, and mRNA stability assays. + Tropomyosin 1 (TPM1) was identified as a potential key factor in AML. TPM1 was downregulated in AML tissues and cells. TPM1 overexpression inhibited AML cell proliferation, promoted apoptosis and oxidative stress, and enhanced sensitivity to Ara-C. Furthermore, TPM1 upregulation attenuated M2 macrophage polarization. Mechanistically, the m6A eraser fat mass and obesity-associated protein (FTO) destabilized TPM1 mRNA in an m6A-dependent manner. Downregulation of TPM1 reversed the effects of FTO depletion on AML cell proliferation, Ara-C sensitivity, and M2 macrophage polarization. + This study unveils a novel FTO/m6A/TPM1 axis that coordinately governs AML cell growth, chemosensitivity, and macrophage polarization, highlighting it as a promising therapeutic target. + © 2026. The Author(s). + + + + Li + Yue + Y + + Department of Clinical Laboratory, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 222, West Huanhu 3Rd Road, Lingang New City 201306, Pudong New Area, Shanghai, China. + + + + Wei + Baosheng + B + + Department of Clinical Laboratory, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 222, West Huanhu 3Rd Road, Lingang New City 201306, Pudong New Area, Shanghai, China. + + + + Hu + Liuping + L + + Department of Clinical Laboratory, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 222, West Huanhu 3Rd Road, Lingang New City 201306, Pudong New Area, Shanghai, China. huhuliuping@163.com. + + + + Du + Yuzhen + Y + + Department of Clinical Laboratory, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 222, West Huanhu 3Rd Road, Lingang New City 201306, Pudong New Area, Shanghai, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Switzerland + Cell Biol Toxicol + 8506639 + 0742-2091 + + IM + + Acute myeloid leukemia + Chemosensitivity + M6A eraser + Macrophage polarization + Tropomyosin 1 + + Declarations. Ethics approval: The present study was approved by the ethical review committee of Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine with approval No.20250222. Written informed consent was obtained from all enrolled patients. Human study: This study was carried out in strict accordance with the relevant regulations and requirements of the Declaration of Helsinki. Written informed consents were obtained from all participants and this study was permitted by the Ethics Committee of Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. Animal study: The procedures performed on all animals complied with the standards formulated by the Experimental Animal Ethics Committee of Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and obtained the approval of the said committee. Conflicts of interest: The authors declare no competing interests. Clinical trial number: Not applicable + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 31 + + + 2026 + 4 + 23 + + + 2026 + 5 + 29 + 23 + 26 + + + aheadofprint + + 42215832 + 10.1007/s10565-026-10201-3 + 10.1007/s10565-026-10201-3 + + + + + + 42215825 + + 2026 + 05 + 29 + +
+ + 1618-1905 + + + 2026 + May + 30 + + + International microbiology : the official journal of the Spanish Society for Microbiology + Int Microbiol + + Gut microbial alterations and functional shifts in patients with hypertriglyceridemia: insights from a northwestern Chinese metagenomic study. + 10.1007/s10123-026-00845-w + + Although hypertriglyceridemia (HTG) is a significant contributor to lipid-associated pathologies such as atherosclerotic cardiovascular disease, its regulation by host‒microbiome interactions remain insufficiently characterized. While the gut microbiota (GM) is known to influence cholesterol metabolism, its specific role in systemic triglyceride (TG) homeostasis, particularly in non-Western populations, is poorly defined. This study aimed to identify preliminary robust GM signatures associated with HTG and to assess their translational potential using integrated multiomics and explainable machine learning approaches. In a cross-sectional investigation of 50 well-phenotyped adults from Northwest China, we combined 16S rRNA sequencing, shotgun metagenomics, and ensemble machine learning (LightGBM/XGBoost) to elucidate the associations between the GM and TGs. Microbial features were rigorously linked to serum lipid profiles through dual-algorithm validation and SHAP interpretability analysis, while functional potential was assessed via KEGG pathway mapping. Subjects with HTG exhibited a distinct gut microbial configuration, marked by consistent enrichment of Faecalibacterium and Bacteroides coprocola (positively correlated with serum TG levels) and depletion of Bifidobacterium pseudocatenulatum and Lactobacillus salivarius (inversely correlated). Machine learning converged on five exploratory consensus biomarker taxa, three of which were independently confirmed by LEfSe analysis (Faecalibacterium). Functional profiling further revealed the upregulation of microbial starch and sucrose metabolism pathways in the HTG cohort. Our findings establish a preliminary gut microbial signature for HTG patients and suggest context‑dependent associations of butyrate-producing taxa such as Faecalibacterium. By integrating multiomics with explainable artificial intelligence, this work addresses key challenges in reproducibility and mechanistic inference in microbiome research. These results pave the way for novel microbiota-targeted therapeutic strategies, including precision probiotics and dietary interventions, to modulate lipid metabolism, pending further validation in expanded cohorts and functional studies. + © 2026. The Author(s). + + + + Lv + Jing + J + + Department of Clinical Laboratory, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Wang + Ji-Han + JH + + Yan'an Medical College of Yan'an University, Yan'an, Shaanxi, 716000, China. + + + + Wang + Yang-Yang + YY + + School of Physics and Electronic Information, Yan'an University, Yan'an, Shaanxi, 716000, China. + + + + Huang + Jing + J + + Department of Clinical Pharmacy, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Chen + Fen-Rong + FR + + Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710000, China. + + + + Fang + Sa + S + + Department of Gastroenterology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Wang + Xiao-Juan + XJ + + Department of Gastroenterology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Li + Zi-Tong + ZT + + Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Shi + Yu-Peng + YP + + Department of Gastroenterology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. xjshiyupeng@163.com. + + + + Guo + Lei + L + + Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. guolei0711@163.com. + + + + eng + + + 2025JC-YBMS-916 + Shaanxi Natural Science Foundation of China + + + + No. 81702067 and 82560411 + National Natural Science Foundation of China + + + + + Journal Article + + + 2026 + 05 + 30 + +
+ + Switzerland + Int Microbiol + 9816585 + 1139-6709 + + IM + + Biomarker + Gut microbiota + Hypertriglyceridemia + Machine learning + Triglyceride + + Declarations. Competing interests: The authors declare no competing interests. Consent to participate: Informed consent was provided by all recruited subjects prior to the study. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 18 + + + 2026 + 5 + 22 + + + 2026 + 5 + 11 + + + 2026 + 5 + 29 + 23 + 26 + + + aheadofprint + + 42215825 + 10.1007/s10123-026-00845-w + 10.1007/s10123-026-00845-w + + + + The Writing Committee Of The Report On Cardiovascular Health And Diseases In China (2022) Key points of Report on Cardiovascular Health and Diseases in China 2021. Chin J Cardiovasc Res, 20(7): 577–596 + + + An HM et al (2011) Antiobesity and lipid-lowering effects of Bifidobacterium spp. in high fat diet-induced obese rats. 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+ + 1550-7416 + + 28 + 4 + + 2026 + May + 29 + + + The AAPS journal + AAPS J + + New Frontiers of Drug Development Through the Use of New Approach Methodologies. + 112 + 10.1208/s12248-026-01263-6 + + On November 2025, AAPS PharmSci 360 convened a symposium that included experts in the application of various types of New Approach Methodologies (NAMs). They shared their experiences and insights on topics that included microphysiological systems (MPS), the use of 3D organoids, and in silico tools. MPS systems were explored from the perspective of their testing and qualification through an academia-industry-government tissue chip testing consortium whose mission is to perform context-of-use-based testing of MPS and conduct comparative evaluations to support the use of MPS systems in safety assessments. 3D organoids were described and insights shared on how and when they may provide an appropriate tool for evaluating drug safety and effectiveness, and their use in supporting personalized medicine. Regarding in silico tools, examples were provided to describe their growing utility in predicting drug toxicity, population variability, and its potential benefits over traditional evidence-based toxicology. These tools include in silico models (e.g., physiologically based pharmacokinetic models), Artificial Intelligence (AI), and Machine Learning (ML) which, unlike other aspects of model informed drug development, reduce reliance on predefined models and allows for the integration of diverse sources of data. Computationally, these tools can generate predictions in hours where it would otherwise have taken weeks or months (and extensive experimentation). Within this meeting report, we highlight the key issues discussed during that symposium and share additional aspects to consider when developing a NAMs-based roadmap for specific contexts of use. + © 2026. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. + + + + Martinez + M N + MN + 0000-0002-4512-853X + + Food and Drug Administration, Center for Veterinary Medicine, Office of Generic Animal Drugs, CPK1 5001 Campus Drive, College Park, MD, 20740-3835, USA. marilyn.martinez@fda.hhs.gov. + + + + Mochel + J P + JP + 0000-0002-0997-3111 + + Precision One Health Initiative and Biomedical & Translational Sciences Institute (One Health), Diane L. Wynocker Endowed Professor, Athens, GA, USA. + + + + Allenspach + K + K + 0000-0002-2168-5809 + + Precision One Health and Department of Pathology, College of Veterinary Medicine, The University of Georgia, 501 D. W. Brooks Drive, Athens, GA, 30602, USA. + + + + Zdyrski + C + C + 0000-0002-7556-5298 + + Precision One Health and College of Veterinary Medicine, The University of Georgia, 501 D. W. Brooks Drive, Athens, GA, 30602, USA. + + + + Krishna + S + S + 0000-0002-4960-2753 + + Physicians Committee for Responsible Medicine, 5100 Wisconsin Ave., NW, Suite 400, Washington, DC, 20016, USA. + + + + Rusyn + I + I + 0000-0001-9340-7384 + + Department of Veterinary Physiology and Pharmacology, Texas A&M College of Veterinary Medicine & Biomedical Sciences, 4466 TAMU, College Station, TX, 77843, USA. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 29 + +
+ + United States + AAPS J + 101223209 + 1550-7416 + + IM + + + Drug Development + methods + trends + + + Humans + + + Microphysiological Systems + + + Computer Simulation + + + Organoids + + + Animals + + + Machine Learning + + + Artificial Intelligence + + + + AI + NAMs + PBPK models + organ-on-a-chip + organoid + + Declarations. Disclaimer: The findings and conclusions in this research have not been formally disseminated by the Food and Drug Administration and should not be construed to represent any Agency endorsement, determination, or policy. The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the FDA. Animal Use and Assurances: No animals were used in the writing of this Commentary. A.I. Statement: "No, but part of the graphical abstract had assist from Google AI. This was indicated in the figure.". Conflict of interest: Ivan Rusyn has no conflicts of interest to declare. Jonathan Mochel is a co-founder of 3D Health Solutions Inc., a small biotechnology company specializing in the development of animal-derived organoids for drug testing. Karin Allenspach co-founder of 3D Health Solutions. She serves as a consultant for Ceva Animal Health, Antech Diagnostics, hills, Royal Canin, Purina, and Heel GmbH. Christopher Zdyrski is the Director of Research and Product Development at 3D Health Solutions. Shagun Krishna has no conflicts of interest to declare. Marilyn N. Martinez has no conflicts of interest to declare. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 3 + 12 + + + 2026 + 5 + 18 + + + 2026 + 5 + 29 + 23 + 25 + + + epublish + + 42215821 + 10.1208/s12248-026-01263-6 + 10.1208/s12248-026-01263-6 + + + + Avila AM, Bebenek I, Mendrick DL, Peretz J, Yao J, Brown PC. 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+ + 2730-6011 + + + 2026 + May + 29 + + + Discover oncology + Discov Oncol + + Exercise-related genes predicts overall survival and tumor immune microenvironment, and identifies the biological role of SLC52A2 in breast cancer. + 10.1007/s12672-026-05321-5 + + Breast cancer (BRCA) is the most common malignant tumor in women, with prognoses differing markedly by molecular subtype. Exercise is known to be related to tumor occurrence and progression, yet its role in BRCA remains understudied. This research seeks to explore the prognostic and immunomodulatory functions of exercise-related genes (ERGs) in BRCA, and their links to molecular subtypes, so as to inform targeted and precision treatment. + We obtained BRCA gene expression profiles and clinical data from the TCGA-BRCA project. Using LASSO, random forest, gradient boosting machine (GBM), stepwise Cox regression (Stepcox), and extreme gradient boosting (XGBOOST) algorithms, we screened prognostic signature genes from the intersecting ERGs, determined by differential expression analysis and weighted gene co-expression network analysis (WGCNA), established and validated a novel risk prediction model. We further analyzed biological functions, mutation profiles, immune traits, and molecular subtype associations across different risk groups. Additionally, the prognostic, biological and immunological roles of the core risk biomarker in the exercise-related signature was evaluated. + WGCNA identified 345 differentially expressed ERGs in BRCA, and five machine learning algorithms further screened 6 prognostic genes (SNX22, SLC52A2, TNFRSF18, NFE2, EZR, and EMP1). The low-risk group had significantly higher overall survival rate than the high-risk group, a difference driven by the functional roles of the two groups' differentially expressed genes, which regulated the cell cycle and epithelial-mesenchymal transition (EMT). Additionally, the high- and low-risk groups showed distinct mutation and immunomodulatory features. Consensus clustering of prognosis between the groups linked these differences to BRCA molecular subtypes. As a core candidate factor, SLC52A2 contributed to BRCA progression and poor prognosis in patients by participating in multiple signaling pathways and regulating immune cells. + ERGs-based prognostic signatures could distinguish clinical outcomes of BRCA patients and were correlated with immune regulation and mutation patterns. The ERGs-constructed risk scoring system and molecular subtypes have the potential to serve as preclinical biological and immunological markers, paving the way for clinical management and treatment of BRCA. + © 2026. The Author(s). + + + + Chen + Caiping + C + + Department of Breast Surgery, The Affiliated Hospital of Jiaxing University (The First Hospital of Jiaxing), Jiaxing, China. + + + + Han + Chao + C + + Department of Breast Surgery, The Affiliated Hospital of Jiaxing University (The First Hospital of Jiaxing), Jiaxing, China. + + + + Xue + Li + L + + Department of Breast Surgery, The Affiliated Hospital of Jiaxing University (The First Hospital of Jiaxing), Jiaxing, China. + + + + Lu + Xiang + X + + Department of Breast Surgery, The Affiliated Hospital of Jiaxing University (The First Hospital of Jiaxing), Jiaxing, China. 00187649@zjxu.edu.cn. + + + + eng + + + 2023-FC-001 + Jiaxing Key Discipiline of Medcine - -Surgery Med (Mastopathy) + + + + 2023-PYXK-001 + Jiaxing Provinces and Cities Jointly Cultivate Discipline - -General Surgery (Minimally Invasive) + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + Discov Oncol + 101775142 + 2730-6011 + + + Breast cancer + Exercise + Genetic mutation + Immune response + Molecular cluster + Prognosis + + Declarations. Ethics approval and consent to participate: The cohort data used in this article were all from online databases and therefore did not involve ethics. Consent to participate is not applicable to this study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 11 + 28 + + + 2026 + 5 + 26 + + + 2026 + 5 + 29 + 23 + 25 + + + aheadofprint + + 42215809 + 10.1007/s12672-026-05321-5 + 10.1007/s12672-026-05321-5 + + + + + + 42215746 + + 2026 + 05 + 29 + +
+ + 1435-2451 + + + 2026 + May + 30 + + + Langenbeck's archives of surgery + Langenbecks Arch Surg + + Relationship between ileocolic arterial stump length and lymph node yield - a retrospective study using machine learning. + 10.1007/s00423-026-04048-9 + + In right-sided colon cancer surgery, ileocolic artery stump length may reflect the extent of mesenteric resection and lymph node harvest. This study evaluated the association between postoperative stump length measured on CT, lymph node yield, and recurrence or progression within 24 months. + This single-center retrospective study included 64 patients who underwent right or extended right hemicolectomy for right-sided colon adenocarcinoma between 2016 and 2024. Right-sided tumors were defined as those located in the cecum, ascending colon, hepatic flexure, or proximal transverse colon. Only primary surgical cases were included. Ileocolic artery stump length was measured on contrast-enhanced CT scans performed approximately 12 months postoperatively (± 1 month) by two independent radiologists. A Random Forest regression model using stump length as the only predictor variable was developed to estimate lymph node yield. Model performance was assessed using 10-fold cross-validation with R² and mean squared error (MSE). Exploratory recurrence/progression analysis used logistic regression with natural cubic splines. + Mean stump length was 2.03 cm (range 1.0-4.5 cm), and median lymph node yield was 17 (range 10-55). Adequate nodal evaluation (≥ 12 nodes) was achieved in 85.9% of patients. The model demonstrated a consistent inverse relationship between stump length and lymph node yield (R² = 0.78; MSE = 0.16). Exploratory analysis suggested a non-significant trend toward higher recurrence or progression with longer stumps. + Longer ileocolic artery stumps were associated with reduced lymph node yield, suggesting stump length may reflect the extent of mesenteric resection. External validation is required before clinical application. + © 2026. The Author(s). + + + + Zayas-Borquez + Rogelio + R + 0000-0003-1751-4211 + + Universidad Nacional Autónoma de México, Mexico City, Mexico. rogeliozb94@unam.edu. + + + + Arizaga-Ramírez + María Sofía + MS + 0009-0004-2981-0530 + + Department of Radiology and Imaging, National Institute of Medical Sciences and Nutrition "Salvador Zubirán", Mexico City, Mexico. + + + + Canto-Losa + Jorge + J + 0000-0003-3768-3688 + + Universidad Nacional Autónoma de México, Mexico City, Mexico. jorge.canto@incmnsz.mx. + + + + Gándara-Lois + Ana C + AC + 0009-0001-0265-1726 + + Department of Radiology and Imaging, National Institute of Medical Sciences and Nutrition "Salvador Zubirán", Mexico City, Mexico. + + + + Salgado-Nesme + Noel + N + 0000-0002-3559-5070 + + Department of Colorectal Surgery, National Institute of Medical Sciences and Nutrition "Salvador Zubirán", Vasco de Quiroga 15, Belisario Domínguez Seccion 16, Tlalpan, Mexico City, PC 14080, Mexico. + + + + Alvarez-Bautista + Francisco + F + 0000-0003-2645-832X + + Department of General and Endoscopic Surgery, "Dr. Manuel GEA González" General Hospital, Mexico City, Mexico. + + + + Santes + Oscar + O + 0000-0002-6393-4607 + + Department of Colorectal Surgery, National Institute of Medical Sciences and Nutrition "Salvador Zubirán", Vasco de Quiroga 15, Belisario Domínguez Seccion 16, Tlalpan, Mexico City, PC 14080, Mexico. + + + + eng + + Journal Article + + + 2026 + 05 + 30 + +
+ + Germany + Langenbecks Arch Surg + 9808285 + 1435-2443 + + IM + + Central vascular ligation + Complete mesocolic excision + Lymph node retrieval + Machine learning + Right hemicolectomy + + Declarations. Ethics approval: This study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board of the corresponding institution. Given the retrospective nature of the study and the use of anonymized data, the requirement for informed consent was waived. Informed consent: Informed consent was waived due to the retrospective design of the study and the use of anonymized patient data. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 16 + + + 2026 + 4 + 11 + + + 2026 + 5 + 29 + 23 + 23 + + + aheadofprint + + 42215746 + 10.1007/s00423-026-04048-9 + 10.1007/s00423-026-04048-9 + + + + + + 42215700 + + 2026 + 05 + 29 + +
+ + 1437-1596 + + + 2026 + May + 30 + + + International journal of legal medicine + Int J Legal Med + + Proteomic profiling of bone for the estimation of post-mortem interval and post-mortem submersion interval: a systematic review. + 10.1007/s00414-026-03844-8 + + Accurate estimation of the Post-Mortem Interval (PMI) and Post-Mortem Submersion Interval (PMSI) remains a persistent challenge in forensic science, especially when traditional morphological and entomological methods fail due to advanced decomposition or in aquatic environments. Proteomic profiling of bone tissues has recently emerged as a promising approach, leveraging the predictable degradation patterns of bone proteins to estimate time since death more reliably. This systematic review, conducted in accordance with PRISMA guidelines, analyzed 24 peer-reviewed studies focusing on the application of proteomic techniques to bone tissue for PMI and PMSI estimation. The included studies were evaluated based on sample type, analytical techniques used, identified biomarkers, environmental conditions assessed, and the overall reliability and reproducibility of the findings. The review found that specific bone proteins, particularly collagen, osteocalcin, fetuin-A, etc. exhibited consistent degradation patterns that correlated strongly with elapsed post-mortem time. Cortical bone was identified as a more stable and informative matrix compared to trabecular bone. Mass spectrometry, especially LC-MS/MS, emerged as the predominant analytical technique due to its high sensitivity and accuracy in detecting low-abundance proteins over extended PMIs and PMSIs. However, protein degradation rates were significantly influenced by environmental variables such as temperature, humidity, soil pH, and microbial activity. This review also emphasizes the transformative role of bone proteomics in advancing forensic science while identifying key gaps that must be addressed to achieve global standardization and practical implementation in diverse forensic contexts. The integration of proteomics with other emerging technologies, such as machine learning algorithms and computational modeling, may further enhance the precision of PMI and PMSI estimation in future applications. + © 2026. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. + + + + Dalal + Jyoti + J + 0000-0002-4690-6452 + + School of Forensic Sciences, Amity University Punjab, Mohali, Punjab, India. jyotidalal417@gmail.com. + + + + Kumari + Pallavi + P + + School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India. + + + Department of Forensic Science, University Institute Allied Health Sciences, Chandigarh University, Mohali, Punjab, India. + + + + Gupta + Vinayak + V + + School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India. + + + + Chhikara + Anjali + A + + School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 30 + +
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+ + 1546-170X + + + 2026 + May + 29 + + + Nature medicine + Nat Med + + From donor lungs to digital twins. + 10.1038/d41591-026-00029-z + + + O'Leary + Karen + K + + + eng + + News + + + 2026 + 05 + 29 + +
+ + United States + Nat Med + 9502015 + 1078-8956 + + IM + + Machine Learning + Technology + Translational research + + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 22 + + + aheadofprint + + 42215696 + 10.1038/d41591-026-00029-z + 10.1038/d41591-026-00029-z + + + + + + 42215691 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + O²RDL-net for joint risk classification and delay forecasting in logistics systems using interaction amplified deep. + 10.1038/s41598-026-55703-6 + + Transportation and logistics systems face frequent delays and operational risks due to congestion, human factors, and rapidly changing conditions. Predicting delays and identifying risk levels in advance is therefore essential for reliable logistics planning. Most existing methods handle delay forecasting and risk classification separately and often fail to capture complex temporal and interaction-driven patterns. To overcome these limitations, this study proposes O²RDL-Net, a load-aware and interaction-amplified deep learning framework for joint risk classification and delay forecasting. The proposed model integrates higher-order operational indicators, including the Risk Accumulation Index, Delay Sensitivity Indicator, Human-Operational Interaction Score, and Logistics System Representation, to capture cumulative risk, short-term sensitivity, and system-level interactions. A spatio-temporal attention encoder with risk regime modulation is used to improve learning stability and generalization. Experimental results show that O²RDL-Net achieves superior performance compared to baseline models, with a mean absolute error of 2.05, RMSE of 3.01, and R² of 0.91 for delay forecasting. For risk classification, the model reaches an accuracy of 92%, precision of 91%, recall of 92%, and F1-score of 91%. Interpretability analysis using SHAP and local explanations confirms that the model relies on meaningful and domain-consistent features. Overall, O²RDL-Net provides an accurate, stable, and interpretable solution for real-world logistics risk and delay management. + © 2026. The Author(s). + + + + Yang + Xiao + X + + Low-altitude Management and Digital Economy College, XIHANG UNIVERSITY, Xi'an, 710077, China. jonah-y@xaau.edu.cn. + + + + eng + + + Grant No. 2025YB0214 + Research on the Coupled and Coordinated Development of Transportation and Modern Industrial System in Shaanxi Province + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Artificial intelligence + Deep Learning + Feature Construction + Forecasting + Logistics Network + Machine Learning + Risk Prediction + Supply Chain + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 9 + + + 2026 + 5 + 26 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215691 + 10.1038/s41598-026-55703-6 + 10.1038/s41598-026-55703-6 + + + + + + 42215678 + + 2026 + 05 + 29 + +
+ + 2398-6352 + + + 2026 + May + 29 + + + NPJ digital medicine + NPJ Digit Med + + Detecting dataset bias in medical AI using a generalized and modality agnostic auditing approach. + 10.1038/s41746-026-02807-y + + Despite many success stories along the path of Artificial Intelligence's (AI) rise in healthcare, there are comparably many reports of significant shortcomings and unexpected behavior of AI in deployment. A major risk is AI's reliance on association-based learning, where non-representative machine learning datasets can amplify latent bias during training and hide it during testing. To unlock new tools capable of detecting such AI bias issues, we present Generalized Attribute Utility and Detectability-Induced bias Testing (G-AUDIT). G-AUDIT is a data modality-agnostic dataset auditing approach that automatically quantifies shortcut learning risks by examining the interplay between task-level annotations, sensor-level measurements, and patient, environmental, and acquisition characteristics. We demonstrate the broad applicability of this method by analyzing a range of medical datasets across three distinct modalities (images, text, and tabular data) and machine learning tasks, successfully identifying potential shortcuts commonly overlooked by traditional qualitative methods. + © 2026. The Author(s). + + + + Drenkow + Nathan + N + + The Johns Hopkins University, Baltimore, MD, USA. nathan.drenkow@jhuapl.edu. + + + The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA. nathan.drenkow@jhuapl.edu. + + + + Pavlak + Mitchell + M + + The Johns Hopkins University, Baltimore, MD, USA. + + + + Harrigian + Keith + K + + The Johns Hopkins University, Baltimore, MD, USA. + + + + Zirikly + Ayah + A + + The Johns Hopkins University, Baltimore, MD, USA. + + + Department of Computer Science, School of Engineering and Applied Science, George Washington University, Washington, DC, USA. + + + + Subbaswamy + Adarsh + A + + Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA. + + + + Farhangi + Mohammad Mehdi + MM + + Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA. + + + + Petrick + Nicholas + N + + Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA. + + + + Unberath + Mathias + M + + The Johns Hopkins University, Baltimore, MD, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Digit Med + 101731738 + 2398-6352 + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 6 + 3 + + + 2026 + 5 + 18 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215678 + 10.1038/s41746-026-02807-y + 10.1038/s41746-026-02807-y + + + + + + 42215677 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Stacked multi-fusion CNN: an adaptive attention model for privacy preserving deepfake forensics. + 10.1038/s41598-026-55325-y + + The emergence of Generative Artificial Intelligence (Gen-AI) and Generative Adversarial Network (GAN)-based deepfakes poses significant security risks in sociocultural and sociopolitical domains. This necessitates the development of advanced and effective detection methods to prevent vulnerability in social networks. Classical Machine Learning (ML) algorithms have their limitations, especially in classifying the deepfakes. To address these issues, this paper suggests a privacy-preserving Stacked Multi-Fusion (SMF) Convolutional Neural Network (CNN) approach to classify deepfakes. An improved CNN model is proposed, integrating an adaptive multi-scale attention framework with enhanced residual blocks and a Squeeze-and-Excitation (SE) mechanism. The selection of these components is backed with an ablation study to report the individual contribution to the overall optimal architecture. A hybrid lossless multilayer cryptosystem based on a chaos-based approach, Deoxyribonucleic Acid (DNA)-based computing, etc., is developed to secure images in cloud storage. The efficacy of the proposed SMF model is validated using the 140K Real and Fake Faces (RFF) image dataset. The proposed approach was found to achieve stable performance with minimal variation in various tests. It achieved a test accuracy and ROC-AUC of 97.80 and 99.79, respectively. This paper provides comprehensive relevant factors for building effective synthetic media detection systems. + © 2026. The Author(s). + + + + Rout + Jayanti + J + + P. G. Department of Computer Science, Fakir Mohan University, Balasore, 756019, Odisha, India. + + + + Mishra + Minati + M + + P. G. Department of Computer Science, Fakir Mohan University, Balasore, 756019, Odisha, India. + + + + Barik + Ram Chandra + RC + + Department of CSE, C. V. Raman Global University, Bhubaneswar, 752054, Odisha, India. + + + + Yadav + Devendra Kumar + DK + + School of Computer Science and Engineering, XIM University, Bhubaneswar, 752050, Odisha, India. devendra@xim.edu.in. + + + + Prasad + Dilip K + DK + + Department of Computer Science, UiT The Arctic University of Norway, Tromsø, 9037, Norway. + + + + Sekh + Arif Ahmed + AA + + Department of Computer Science, UiT The Arctic University of Norway, Tromsø, 9037, Norway. arif.a.sekh@uit.no. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Convolutional neural network + Deepfake detection + Squeeze-and-excitation attention + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 10 + 25 + + + 2026 + 5 + 23 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215677 + 10.1038/s41598-026-55325-y + 10.1038/s41598-026-55325-y + + + + + + 42215670 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + A hybrid AI vision model for single-camera tracking of rehabilitation exercises: a proof of concept in clinical settings. + 10.1038/s41598-026-55431-x + + Accurate measurement of range of motion (ROM) during physical rehabilitation is traditionally achieved using goniometers or multi-camera, marker-based motion capture systems. The latter, while highly accurate, require specialised laboratory infrastructure, are costly, and are unsuitable for home-based use. There is growing interest in markerless, camera-based alternatives that leverage artificial intelligence (AI) for joint angle estimation. This study presents a preliminary proof-of-concept evaluation of a hybrid AI vision system, combining the MediaPipe Pose framework (version 0.8.9.1, Full model variant) with a Mamdani-type fuzzy inference system, for joint angle estimation and exercise repetition counting using a single consumer-grade camera. Fourteen healthy adult rehabilitation staff members performed three exercises (elbow flexion, knee extension, and hip external rotation) simultaneously recorded by the proposed AI system and by a reference optical motion capture system (C-Motion Visual3D™, 14 calibrated infrared cameras, 53 retro-reflective markers). Reliability was assessed using two-way mixed-effects intraclass correlation coefficients (ICC[Formula: see text]) with absolute agreement. Individual-measure ICC values ranged from poor to good across joints and conditions (range: 0.005-0.68). The highest reliability was observed for the right hip external rotation ROM (individual ICC [Formula: see text]; average ICC [Formula: see text], 95% CI: 0.63-0.89). Several measures showed poor reliability, including the right elbow flexion ROM (individual ICC [Formula: see text]) and the minimum angle of the left hip external rotation (individual ICC [Formula: see text]). Confidence intervals were wide throughout. This proof of concept demonstrates that a single-camera AI system can capture general trends in joint angles during simple rehabilitation exercises in healthy adults under controlled conditions. However, reliability is inconsistent and frequently poor, and the system has not been evaluated in patient populations with pathological movement patterns. These findings do not support clinical deployment at this stage. Substantial further development and validation in patient cohorts are required before clinical or home-based use can be considered. + © 2026. The Author(s). + + + + Das + Utpal Chandra + UC + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. + + + School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Kelvin Grove, Brisbane, QLD, 4059, Australia. + + + Centre for Biomedical Technologies (CBT), Queensland University of Technology, Brisbane, QLD, 4000, Australia. + + + + Le + Ngoc Thien + NT + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. + + + + Vitoonpong + Timporn + T + + Department of Rehabilitation Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand. timporn.v@chula.ac.th. + + + Department of Rehabilitation Medicine, King Chulalongkorn Memorial Hospital & The Thai Red Cross Society, Bangkok, 10330, Thailand. timporn.v@chula.ac.th. + + + + Pongmala + Chatkaew + C + + Department of Radiology, University of Michigan, Ann Arbor, 48104, MI, USA. + + + Department of Neurology, University of Michigan, Ann Arbor, 48104, MI, USA. + + + + Prapinpairoj + Chalermdej + C + + Department of Rehabilitation Medicine, King Chulalongkorn Memorial Hospital & The Thai Red Cross Society, Bangkok, 10330, Thailand. + + + + Anannub + Kawee + K + + Health Service Center, Chulalongkorn University, Bangkok, 10330, Thailand. + + + + Kaewplung + Pasu + P + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. + + + + Chaitusaney + Surachai + S + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. surachai.c@chula.ac.th. + + + + Benjapolakul + Watit + W + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. watit.b@chula.ac.th. + + + + eng + + + B04G640068 + National Science, Research and Innovation Fund (NSRF) via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + Competing interests: The authors declare no competing interests. Ethical approval: The research conducted with healthy human participants at Chulalongkorn University in Thailand complied with the Regulations Governing Life Science and Medical Research Involving Human Subjects. Approval was secured from the Institutional Review Board of the Faculty of Medicine, Chulalongkorn University, Thailand (COA No. 0495-2022; IRB No. 846-64), in accordance with the Declaration of Helsinki, The Belmont Report, CIOMS Guidelines, and ICH-GCP. Informed consent: All study participants provided written informed consent for participation and for the publication of identifiable information and images. Participants were fully informed of the study objectives, dissemination plans, and potential risks and benefits prior to enrolment. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 9 + 20 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215670 + 10.1038/s41598-026-55431-x + 10.1038/s41598-026-55431-x + + + + + + 42215668 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Autism spectrum disorder identification using machine learning models on MRI data. + 10.1038/s41598-026-55163-y + + Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects sensory processing, speech, behavior and identifying the condition at an early stage is necessary to provide the treatment. The existing diagnostic methods are mainly based on personalized subjective and time consuming. This study introduces an objective diagnostic method by analyzing technical noise signature producing the Quality Vector which is extracted from multimodal MRI data. This work leverages Machine Learning (ML) and Deep Learning (DL) techniques on this Quality vector for improving ASD detection. The methodology leveraged technical Quality Assessment Protocol (QAP) metrics from the ABIDE II repository, integrating structural (sMRI), functional (fMRI), and diffusion (DTI) modalities. The methodology utilized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for outlier removal and Principal Component Analysis (PCA) for dimensionality reduction. A classification pipeline was implemented using a 1D-Residual Network (1D-ResNet), Convolutional Neural Network(CNN), SVM, kNN and a proposed Voting Ensemble (SVM, KNN, and XGBoost), validated through stratified 10-fold cross-validation. In this work, the proposed ensemble emerged as the superior model, achieving a diagnostic accuracy of 95.84%. These findings indicate that technical biomarkers derived from imaging quality metrics are highly useful for ASD detection. This research demonstrates that technical quality metrics, often dismissed as noise, contain significant diagnostic value. The Quality Vector framework provides a computationally efficient and objective tool for ASD identification. The high classification performance suggests that the biomarkers obtained from MRI have a potential future scope in research on early ASD diagnosis and detection. + © 2026. The Author(s). + + + + V + Milner Paul + MP + + Department of Electrical Engineering, National Institute of Technology Manipur (NITM), Manipur, India. vithayathilmilner@gmail.com. + + + Department of Electrical and Electronics Engineering, Christ College of Engineering, Thrissur, Kerala, 680125, India. vithayathilmilner@gmail.com. + + + + Babu + Caren + C + + Department of Electronics and Communication Engineering, Christ College of Engineering, Thrissur, Kerala, 680125, India. + + + + Jose + Suni + S + + Department of Computer Science and Engineering, Christ College of Engineering, Thrissur, Kerala, 680125, India. + + + + Adhikari + Shuma + S + + Department of Electrical Engineering, National Institute of Technology Manipur (NITM), Manipur, India. + + + + Singh + Loitongbam Surajkumar + LS + + Department of Electronics and Communication Engineering, National Institute of Technology Manipur (NITM), Manipur, India. + + + + Francis + Jijo + J + + Department of Zoology, Christ College (Autonomous), 680125, Irinjalakuda, India. + + + + Parekkattil + Adarsh V + AV + + Department of Electronics and Communication, Indian Institute of Technology Roorkee (IITR), Roorkee, India. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Autism spectrum disorder + Convolutional neural network (CNN) + Early detection + Machine learning + Magnetic resonance imaging + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 7 + 8 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215668 + 10.1038/s41598-026-55163-y + 10.1038/s41598-026-55163-y + + + + + + 42215666 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Blockchain-driven machine learning-enabled intrusion-resilient authenticated key agreement protocol for edge-centric IoT systems. + 10.1038/s41598-026-55266-6 + + The edge computing-based Internet of Things (IoT) system minimizes latency by processing data locally, reducing the distance it needs to travel. Processing data in proximity to its source enables rapid decision-making and real-time reactions. The edge-based IoT has several possible uses, including smart cities, smart healthcare, industrial automation & processing, smart farming, and many more. In this paper, we propose a blockchain-driven machine learning-enabled intrusion-resilient authenticated key agreement scheme for edge-centric IoT systems (in short, BMAS-EIoT), which is equipped with the features of authentication, key management, and machine learning-based intrusion detection. In BMAS-EIoT, we provide the network and threat models to enhance comprehension of the organization and deployment of devices and systems, as well as the potential threats to the system. BMAS-EIoT has been observed to possess protection against a variety of potential attacks during the security investigation. Moreover, it has been observed that BMAS-EIoT outperforms other present schemes in terms of performance comparison. A practical implementation of BMAS-EIoT is provided to evaluate the effectiveness of its key components, including intrusion detection and blockchain implementation. Furthermore, BMAS-EIoT possesses supplementary noteworthy capabilities and enhanced security attributes. + © 2026. The Author(s). + + + + Karnatak + Vijay + V + + Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India. + + + + Tripathi + Neha + N + + Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India. + + + + Wazid + Mohammad + M + + Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India. + + + + Mittal + Saksham + S + + Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, 248002, Uttarakhand, India. + + + + Das + Ashok Kumar + AK + + Center for Security, Theory and Algorithmic Research, International Institute of Information Technology, Hyderabad, 500032, India. ashok.das@iiit.ac.in. + + + Department of Computer Science and Engineering, College of Informatics, Korea University, 145 Anam-ro, Seongbuk-gu, 02841, Seoul, South Korea. ashok.das@iiit.ac.in. + + + + K + Vivekananda Bhat + VB + + Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India. kv.bhat@manipal.edu. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Authentication + Blockchain + Edge computing + Internet of things + Intrusion detection + Security + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 3 + 23 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215666 + 10.1038/s41598-026-55266-6 + 10.1038/s41598-026-55266-6 + + + + + + 42215649 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Machine learning based approaches for structure activity relationship analysis of heparanase inhibitors. + 10.1038/s41598-026-55098-4 + + Human Heparanase (HPSE), the only mammalian endo-β-D-glucuronidase, plays an important role in extracellular matrix remodeling and the release of heparin-bound growth factors. Its overexpression is strongly correlated with increased tumor growth, angiogenesis, metastasis, and inflammation, highlighting HPSE as a compelling therapeutic target for oncology and inflammatory diseases. This study aimed to develop and validate a robust computational workflow for predicting the activity class of potential HPSE inhibitors using curated data from the ChEMBL database. Bioactivity data ([Formula: see text]) for known HPSE inhibitors were extracted and put through a meticulous data curation process, which included chemical structure standardization, molecular weight filtering, and final deduplication based on standardized isomeric SMILES to ensure structural uniqueness. Continuous [Formula: see text] values (nM) were converted to [Formula: see text] and subsequently categorized into three activity classes: A ([Formula: see text] and [Formula: see text]), B ([Formula: see text] and [Formula: see text]), and C ([Formula: see text] and [Formula: see text]) for multi-class classification. Molecular representations included two-dimensional physicochemical descriptors, Morgan fingerprints, and three-dimensional descriptors derived from optimized low-energy conformers generated using ETKDGv3 and MMFF94s. Multiple machine learning classifiers were evaluated using pipelines incorporating imputation, scaling, optional Principal Component Analysis (PCA) dimensionality reduction applied to the combined feature sets, and SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance. Models were trained and optimized using randomized search cross-validation on an 80% training split, maximizing balanced accuracy. The best-performing model pipeline (RF_B, a Random Forest with PCA on 2D+Morgan Fingerprints+3D features) achieved approximately 80% accuracy and 78.5% balanced accuracy on the held-out 20% test set. The final validated model was successfully utilized to predict the activity classes of new, unseen compounds. This comprehensive pipeline provides a validated tool for classifying HPSE inhibitors derived from ChEMBL data, potentially aiding virtual screening efforts and guiding hit prioritization in drug discovery campaigns targeting HPSE. + © 2026. The Author(s). + + + + Shanbhogue + Rachana V + RV + + Manipal Institute of Technology, Manipal Academy of Higher Education, 576104, Manipal, Karnataka, India. shanbhoguerachana@gmail.com. + + + + Gandhi + Neha S + NS + + Department of Biotechnology and Chemical Engineering, Manipal University Jaipur, 303007, Rajasthan, India. neha.gandhi@jaipur.manipal.edu. + + + School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, Queensland, Brisbane, Australia. neha.gandhi@jaipur.manipal.edu. + + + + B + Shanthi P + SP + + Manipal Institute of Technology, Manipal Academy of Higher Education, 576104, Manipal, Karnataka, India. shanthi.moorkoth@manipal.edu. + + + + Navada + Sandhyalaxmi G + SG + + Manipal Institute of Technology, Manipal Academy of Higher Education, 576104, Manipal, Karnataka, India. sandhya.girish@manipal.edu. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 11 + 10 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 20 + + + aheadofprint + + 42215649 + 10.1038/s41598-026-55098-4 + 10.1038/s41598-026-55098-4 + + + + + + 42215613 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + A temporal adaptive dictionary-constrained LDA and Bi-calibrated dual granularity DTM framework for dynamic topic evolution analysis in academic papers. + 10.1038/s41598-026-54348-9 + + With the explosive growth of academic achievements in artificial intelligence (AI), accurately capturing the dynamic evolution of research topics is crucial for research topic selection, policy-making, and industrial innovation. To address the limitation that traditional topic models struggle to balance static topic recognition accuracy and dynamic evolution tracking capability, this study proposes a unified framework: Temporal Adaptive Dictionary-Constrained Latent Dirichlet Allocation (LDA) combined with Bi-calibrated Dual Granularity Dynamic Topic Model (DTM). We conduct a systematic empirical analysis of dynamic topic evolution in the AI field using English AI papers retrieved from the Web of Science (WOS) Core Collection (2014-2023). Specifically, we first optimize the topic initialization process of LDA using a domain-specific dictionary to enhance the accuracy of static topic clustering. Second, we take the core topics identified by the optimized LDA as prior knowledge and input them into DTM, while optimizing the traditional single temporal granularity to an "annual + quarterly" dual granularity to accurately track the changes in topic strength, keyword composition, and cross-topic association relationships over time. Finally, we verify the performance of the proposed model using perplexity and topic coherence indicators, and reveal the coupling characteristics between different topics through correlation analysis. Results indicate that six core topic clusters have emerged in the AI field, with "generative AI", "large language models", and "multimodal learning" evolving into explosive topics after 2021, traditional machine learning shows a declining trend, with its core focus shifting toward "few-shot learning" and "edge computing adaptation". The top three topic couplings are "generative AI-large language models", "computer vision-multimodal learning" and "reinforcement learning-robotics". The proposed combined model outperforms single models, achieving a 17.3% reduction in perplexity and a 23.5% decrease in topic drift rate, thereby providing valuable decision support for researchers seeking to grasp field frontiers and management departments for optimizing resource allocation. + © 2026. The Author(s). + + + + Yin + Xueqi + X + + School of International Studies, Tianjin University of Commerce, Tianjin, 300134, China. yinxq@tjcu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Academic paper analysis + Artificial intelligence + Dynamic topic model + Latent dirichlet allocation + Text mining + Topic evolution + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 30 + + + 2026 + 5 + 18 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215613 + 10.1038/s41598-026-54348-9 + 10.1038/s41598-026-54348-9 + + + + + + 42215604 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Development and validation of a noninvasive machine learning model using urinary extracellular vesicle physical parameters for prostate cancer diagnosis. + 10.1038/s41598-026-54920-3 + + Urinary extracellular vesicles (uEVs) are promising biomarkers for prostate cancer (PCa). Although novel uEV-based biomarkers have advanced early diagnosis, their detection processes remain cumbersome and cost-prohibitive. The physical parameters of uEVs offer untapped predictive potential for PCa, providing a more convenient, rapid, and cost-effective diagnostic approach. This study aims to construct a predictive model integrating uEV physical parameters with machine learning (ML) algorithms to enhance the early diagnosis of PCa. Urine samples were collected from 222 eligible participants. uEVs were isolated using a commercial kit, and their concentration and size parameters were quantified via nanoparticle tracking analysis (NTA). Utilizing these physical parameters, five ML algorithms were trained and evaluated to identify the optimal diagnostic model for PCa. Model performance was systematically assessed using the area under the receiver operating characteristic curve (AUC), learning curves, calibration curves, and decision curve analysis (DCA). Additionally, SHapley Additive exPlanations (SHAP) were employed to visualize the contributions of key predictors. Analysis of uEV physical parameters revealed that, compared to benign controls, PCa patients exhibited a significantly higher proportion of 30-150 nm uEVs and a smaller overall particle size (p < 0.001). Among the evaluated algorithms, the eXtreme Gradient Boosting (XGBoost) model demonstrated superior performance. For discriminating PCa from benign prostatic hyperplasia (BPH), the XGBoost model achieved AUC values of 0.934 and 0.864 in the training and testing cohorts, respectively. DCA demonstrated that the XGBoost model yielded a higher clinical net benefit across a threshold probability range of 0-60%. Overall, the diagnostic efficacy and clinical utility of the XGBoost model significantly outperformed routine clinical parameters, including prostate-specific antigen (PSA) and prostate-specific antigen density (PSAD). This study successfully developed and validated a noninvasive ML-based diagnostic model utilizing the physical parameters of uEVs. This approach serves as a preliminary adjunctive tool to assist clinicians in accurately identifying prostate cancer, thereby potentially reducing the incidence of unnecessary prostate biopsies. + © 2026. The Author(s). + + + + Jiang + Kangxian + K + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + Department of Surgical Critical Care Medicine, Beijing Shijitan Hospital, Capital Medical University, No.10, Tieyi Road, Beijing, 100038, China. + + + + Pan + Dongwei + D + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Yu + Jiayin + J + + Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + + Cai + Wenbing + W + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Zhang + Bingfeng + B + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Wang + Zuheng + Z + + Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China. + + + Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + + Lu + Yuqing + Y + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + + Wang + Dianyu + D + + Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China. + + + + Li + Xiao + X + + Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China. + + + Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + + Zheng + Deyong + D + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Wang + Mingda + M + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Wang + Fubo + F + + Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China. wangfubo@gxmu.edu.cn. + + + Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, Guangxi, China. wangfubo@gxmu.edu.cn. + + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. wangfubo@gxmu.edu.cn. + + + + Chen + Junyi + J + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. chenjunyidoctor@163.com. + + + + eng + + + 82372828, + National Natural Science Foundation of China + + + + 2023GXNSFFA026003 + Science Foundation for Distinguished Young Scholars of Guangxi + + + + AA22096030 + Science and Technology Major Project of Guangxi + + + + 2021015 + Yongjiang Program of Nanning + + + + 2021GGA038 + the Science and Technology Plan Project of Fujian Provincial Health Commission + + + + 2022C035R + High-level Talents Innovation and Entrepreneurship Project of Quanzhou Science and Technology Plan + + + + 2022J01277 + Natural Science Foundation of Fujian Province + + + + 2025CXB023 + Fujian Provincial Health Commission + + + + 2026J001703 + Fujian Provincial Natural Science Foundation of China + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Diagnosis + Machine learning + NTA + Prostate cancer + Urinary extracellular vesicles + + Declarations. Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: The studies involving human participants were reviewed and approved by the Ethics Committee of Shanghai Changhai Hospital, the Ethics Committee of Taizhou People’s Hospital, and the Ethics Committee of Zhongda Hospital. (Approval No. CHEC2013-115).Clinical trial number: not applicable. This study strictly adhered to the ethical principles outlined in the Declaration of Helsinki, ensuring that all data were derived from actual clinical cases. Informed consent: Informed consent was obtained from each patient or their legal representative before sample collection. Consent for publication: Not applicable. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 10 + 14 + + + 2026 + 5 + 21 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215604 + 10.1038/s41598-026-54920-3 + 10.1038/s41598-026-54920-3 + + + + + + 42215600 + + 2026 + 05 + 29 + +
+ + 2399-3669 + + + 2026 + May + 29 + + + Communications chemistry + Commun Chem + + CholBindNet as an interpretable neural network for cholesterol-binding site classification. + 10.1038/s42004-026-02064-w + + Cholesterol is a key modulator of membrane protein structure and function, yet predicting cholesterol-binding sites remains challenging due to its non-druglike physicochemical properties. Here, we curated more than 800 high-resolution transmembrane protein structures containing cholesterol, and developed an interpretable, atom-based graph neural network, called CholBindNet. A positive-unlabeled (PU) training strategy was employed to address the scarcity of negative samples due to the promiscuous nature of cholesterol binding. We show that CholBindNet substantially outperforms existing machine learning models trained on general ligand-binding datasets, including AlphaFold3, P2Rank, and DiffDock. The performance and generalizability of the model on unseen membrane proteins were further demonstrated by rapidly assessing cholesterol-binding sites in the PIEZO2 ion channel against all-atom molecular dynamics (MD) simulations conducted on Anton3 supercomputer. Additionally, strong model interpretability was achieved for CholBindNet through atom-level feature encoding, Grad-CAM visualization, and attention-based scoring analysis. Overall, CholBindNet provides an efficient and scalable approach for classifying and ranking cholesterol-binding sites on membrane proteins, achieving performance comparable to computationally expensive MD simulations while offering rich biophysical insights into the atomic-level spatial patterns beyond amino-acid sequence. This work lays the foundation for future deep-learning models targeting membrane protein drug-binding sites and cholesterol-modulated therapeutics. + © 2026. The Author(s). + + + + Hernandez + Alexis + A + + Computer Science Department, California State Polytechnic University, Pomona, CA, USA. + + + Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA, USA. + + + + Bhatt + Aashish + A + + Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA, USA. + + + + Revilla + Ivan + I + + Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA, USA. + + + + Levine + Jacob Ede + JE + + Computer Science Department, California State Polytechnic University, Pomona, CA, USA. + + + + Kosaraju + Sai Chandra + SC + 0000-0002-4332-6217 + + Computer Science Department, California State Polytechnic University, Pomona, CA, USA. skosaraju@cpp.edu. + + + + Luo + Yun Lyna + YL + 0000-0003-3581-754X + + Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA, USA. luoy@westernu.edu. + + + + eng + + + GM130834 + U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Commun Chem + 101725670 + 2399-3669 + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 1 + 1 + + + 2026 + 5 + 4 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215600 + 10.1038/s42004-026-02064-w + 10.1038/s42004-026-02064-w + + + + + + 42215593 + + 2026 + 05 + 29 + +
+ + 2397-768X + + + 2026 + May + 29 + + + NPJ precision oncology + NPJ Precis Oncol + + Early detection of gastric cancer: a novel circulating microbiome DNA based liquid biopsy assay. + 10.1038/s41698-026-01532-9 + + Microorganisms play significant roles in gastric cancer (GC) progression. However, it is unknown whether circulating microbiome DNA (cmDNA) possesses GC specific features and could serve as diagnostic biomarker for GC detection. In this study, one cohort of 586 participants from Zhejiang Cancer Hospital were divided randomly into the training and testing datasets. Another cohort of 299 participants enrolled from three hospitals was used as an independent validation cohort. The cmDNA from plasma samples were analyzed by sequencing and various tools. The significant features of cmDNA were used as inputs to establish a machine learning diagnostic model (cmDNA-MLM). The cmDNA-MLM achieved the area under receiver operating characteristic curve (AUC) of 0.831 for GC across all stages in the testing cohort and 0.914 in the independent validation cohort. Notably, this model demonstrated strong performance in detecting early-stage GC, achieving an AUC of 0.792 for stage I GC in the validation cohort and exhibited favorable sensitivities across various molecular subtypes. The stage shift analysis showed a notable increase in the number of patients diagnosed at stage I. This cmDNA-MLM exhibited promising performance in early GC detection, which could be used as a clinical liquid biopsy methodology after more clinical validation studies. + © 2026. The Author(s). + + + + Chen + Yongyi + Y + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China. + + + + Han + Xinhong + X + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Luo + Miao + M + + Department of Clinical Laboratory, Chongqing Yubei District People's Hospital, Chongqing, China. + + + + Luo + Liulin + L + + Department of Clinical Laboratory, Shanghai Yangpu Hospital, Tongji University School of Medicine, Shanghai, China. + + + + Wang + Tingzhang + T + + Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou, China. + + + + Kong + Congcong + C + + Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou, China. + + + + Ye + Liuqing + L + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Jin + Jiangang + J + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Hou + Dingding + D + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Liao + Haiqi + H + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Wang + Zhonglin + Z + + OmixScience Laboratory, OmixScience Co. Ltd., Hangzhou, China. + + + + Xue + Wei + W + + OmixScience Laboratory, OmixScience Co. Ltd., Hangzhou, China. + + + + Lin + Ziao + Z + + OmixScience Research Institute, OmixScience Co. Ltd., Shenzhen, China. + + + Liangzhu Laboratory, Zhejiang University, Hangzhou, China. + + + + Xu + Songxiao + S + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. xusx@zjcc.org.cn. + + + School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China. xusx@zjcc.org.cn. + + + + eng + + + 82302622 + National Natural Science Foundation of China + + + + 2022KY675 + Zhejiang Province Medical and Health Technology Project + + + + 2024KY862 + Zhejiang Province Medical and Health Technology Project + + + + 2024C03050 + "Pioneer" and "Leading Goose" R&D Program of Zhejiang + + + + 2022C03002 + "Pioneer" and "Leading Goose" R&D Program of Zhejiang + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Precis Oncol + 101708166 + 2397-768X + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 9 + 16 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215593 + 10.1038/s41698-026-01532-9 + 10.1038/s41698-026-01532-9 + + + + + + 42215588 + + 2026 + 05 + 29 + +
+ + 2398-6352 + + + 2026 + May + 29 + + + NPJ digital medicine + NPJ Digit Med + + Development and validation of a machine learning-based diagnostic system for 22 pediatric respiratory pathogens: a large-scale multicenter study. + 10.1038/s41746-026-02818-9 + + Respiratory tract infections (RTIs) are a significant cause of morbidity in children, caused by a wide range of pathogens. As treatment strategies depend on the causative pathogen, early and accurate diagnosis is crucial. We developed and validated an interpretable Pathogen Diagnostic System for Pediatric Respiratory Infections (Pathog-PDx), conducting a multicenter study involving 134,500 hospitalized children across three clinical centers and two databases. The model integrated 42 clinical and laboratory features from electronic health records (EHR) to enable early pathogen identification. Prospective validation was carried out on an independent cohort of 1338 children to assess the real-world applicability of the model. Pathog-PDx accurately distinguished 22 pathogen subtypes and outperformed conventional models in identifying both single and mixed infections. The model achieved high classification performance for key pathogens, such as influenza virus (AUC = 0.95; Sn: 0.88; Sp: 0.86), with mean AUCs of 0.88 for various pathogens of RTIs. The model has been deployed as a web-based decision support system, which is freely accessible at https://pathogpdx.zzu.edu.cn. Altogether, Pathog-PDx represents a potential tool for the early identification of respiratory tract pathogens in pediatric patients, which can provide actionable predictions ahead of conventional test results to guide timely and targeted therapy. + © 2026. The Author(s). + + + + Su + Dubin + D + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China. + + + + Chen + Qun + Q + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Xiamen Cell Therapy Research Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China. + + + + Xu + Ruizhi + R + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China. + + + + Chen + Qihong + Q + + Department of Pediatrics, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Department of Pediatrics, Pediatric Key Laboratory of Xiamen, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China. + + + + Ma + Chiyuan + C + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China. + + + + Chen + Xi + X + + Department of Clinical Laboratory, Children's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, Fujian, China. + + + + Yang + Yunyun + Y + + Department of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + + Qin + Feibo + F + + Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China. + + + + Zhou + Ziwei + Z + + Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China. + + + + Li + Sixian + S + + Department of Functional Neurology, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China. + + + + Peng + Jie + J + + Department of Pediatrics, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Department of Pediatrics, Pediatric Key Laboratory of Xiamen, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China. + + + + Ge + Dandan + D + + Department of Pediatrics, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Department of Pediatrics, Pediatric Key Laboratory of Xiamen, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China. + + + + Fan + Jiajun + J + + Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, Champaign, IL, USA. + + + + Li + Xingyu + X + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + + Wang + Yanbo + Y + + Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China. + + + + Yang + Yungang + Y + + Department of Pediatrics, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. xmyyg@sina.com. + + + Department of Pediatrics, Pediatric Key Laboratory of Xiamen, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China. xmyyg@sina.com. + + + + Guo + Yaping + Y + + Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China. guoyaping@zzu.edu.cn. + + + + Yang + Jingjing + J + + Department of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. Jingjingyang_xmu@126.com. + + + + Ning + Wanshan + W + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. ningwanshan@xmu.edu.cn. + + + + eng + + + 3502Z20214001 + Project of Xiamen Cell Therapy Research Center + + + + 32570802 + National Natural Science Foundation of China + + + + 32400532 + National Natural Science Foundation of China + + + + 3502Z20254ZD1032 + the Guided Project of Medical and Health Care in Xiamen + + + + 3502Z20254ZD1004 + the Guided Project of Medical and Health Care in Xiamen + + + + 2021ZD0201300 + National Key R&D Program of China + + + + 2024GGB18 + Fujian Provincial Health Technology Project + + + + 2025D022 + Fujian Science and Technology Program Guiding Project + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Digit Med + 101731738 + 2398-6352 + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 8 + 19 + + + 2026 + 5 + 15 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215588 + 10.1038/s41746-026-02818-9 + 10.1038/s41746-026-02818-9 + + + + + + 42215586 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Ethnicity-specific molecular subtypes and a machine-learning risk model in Asian patients with non-muscle-invasive bladder cancer. + 10.1038/s41598-026-55438-4 + + Non-muscle-invasive bladder cancer (NMIBC) has high rates of recurrence and progression, yet its molecular landscape across different ethnicities remains poorly defined. We assembled a meta-cohort of 202 Asian NMIBC transcriptomes and identified three novel molecular subtypes (AC0, AC1, AC2) using non-negative matrix factorization clustering. These subtypes, each associated with distinct biological pathways and clinical outcomes, were externally validated across 22 datasets. Subtype-specific biomarkers (RABL6, MYBL2, RAD54L, and FAM64A) were confirmed by immunohistochemistry in an independent Asian cohort (n = 210), enabling the development of a machine-learning-based risk stratification model. AC1, characterized by aggressive features and enrichment of cell cycle and oncogenic signaling pathways, exhibited the poorest prognosis. Notably, after adjustment for T-stage and grade, the combined risk model was independently prognostic in Asian cohorts but not in European cohorts, underscoring ethnic differences in NMIBC biology. Our findings delineate distinct molecular subtypes and introduce a clinically applicable, ethnicity-specific prognostic model for Asian patients with NMIBC. This study emphasizes the importance of incorporating ethnic diversity into precision oncology frameworks and provides a foundation for personalized therapeutic and surveillance strategies in underrepresented populations. + © 2026. The Author(s). + + + + Jung + Minsun + M + + Department of Pathology, Yonsei University College of Medicine, Seoul, Republic of Korea. + + + + Park + Sangyong + S + + Department of Statistics, Seoul National University, Seoul, Republic of Korea. + + + + Jang + Insoon + I + + Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea. + + + Blood Transfusion Research Institute, Korean Red Cross, Wonju, Gangwon-do, Republic of Korea. + + + + Han + Dohyun + D + + Department of Transdisciplinary Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul, Republic of Korea. + + + Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea. + + + + Cho + Yong Mee + YM + + Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. + + + + Kim + Kwangsoo + K + + Department of Transdisciplinary Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul, Republic of Korea. kksoo716@gmail.com. + + + Department of Medicine, Seoul National University, Seoul, Republic of Korea. kksoo716@gmail.com. + + + + Moon + Kyung Chul + KC + + Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea. blue7270@snu.ac.kr. + + + Kidney Research Institute, Medical Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea. blue7270@snu.ac.kr. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Cluster Analysis + Ethnicity + Machine Learning + Non-Muscle Invasive Bladder Neoplasms + Tumor Biomarkers + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 5 + 13 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215586 + 10.1038/s41598-026-55438-4 + 10.1038/s41598-026-55438-4 + + + + + + 42215583 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Wireless charging ferrite core design with improved material efficiency for mobile devices by enhancing outer-region flux density. + 10.1038/s41598-026-55270-w + + Wireless power transfer (WPT) systems integrated with machine learning (ML) techniques have emerged as promising solutions for enhancing performance and efficiency. Material efficiency, which involves achieving high performance with minimal material use, is crucial for mobile devices to enhance user satisfaction. This paper proposes a core structure design methodology for WPT systems that enhances mutual inductance (MI) by segmenting and assembling the ferrite core, thereby improving material efficiency within limited space constraints. For genetic algorithm (GA)-based optimization, an ML estimator is implemented to replace iterative finite element method simulations during the GA optimization loop, reducing the average computation time from 203s to 86μs and significantly accelerating the GA optimization process. Furthermore, the designed core structure exhibits improved uniformity of magnetic flux density by enhancing the outer-region magnetic flux density between the coils, thereby contributing to higher MI. Experimental results show that the proposed core structure achieves a 17.44% higher normalized MI compared to conventional structures, using less material through an asymmetrical configuration, which also reduces weight and cost. Furthermore, the optimized asymmetrical structure demonstrates robustness under lateral misalignment conditions ([Formula: see text] mm), consistently maintaining higher material efficiency than the full-piece reference even under spatial offsets. These findings demonstrate the potential of ML-driven approaches for developing high-performance, computationally efficient, and cost-effective WPT systems. + © 2026. The Author(s). + + + + Park + Yohan + Y + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. + + + + Shah + Syed Ahson Ali + SAA + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. + + + + Cho + Woo-Young + WY + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. + + + + Park + Hyojun + H + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. + + + + Lee + Hyunwoong + H + + Oceansbio, Seoul, 04303, South Korea. + + + + Kim + Yun-Su + YS + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. yunsukim@gist.ac.kr. + + + Research Institute for Solar and Sustainable Energies, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. yunsukim@gist.ac.kr. + + + + eng + + + No. RS-2026-25533249 + Korea Institute of Energy Technology Evaluation and Planning + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Ferromagnetic cores design + Genetic algorithm + Inductive power transfer + Mutual inductance + Wireless power transfer + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 3 + + + 2026 + 5 + 23 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215583 + 10.1038/s41598-026-55270-w + 10.1038/s41598-026-55270-w + + + + + + 42215579 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Machine learning based detection of subacute ruminal acidosis in early lactation dairy cows using multi-sensor behavioral, physiological, and milk production data. + 10.1038/s41598-026-55162-z + + Subacute ruminal acidosis (SARA) is a common metabolic disorder in early lactation dairy cows that negatively affects rumen function, milk production, and animal welfare. Early identification remains challenging because clinical signs are often subtle and transient. The aim of this study was to evaluate whether multi sensor behavioral, physiological, and milk-production data could be used to identify cows experiencing SARA using machine-learning approaches. The study included early-lactation Holstein cows during the first 100 days in milk. The final dataset comprised 636 cow-day observations, including 134 SARA cases and 502 clinically healthy controls. Cow identification numbers were additionally used as grouping variables during cross-validation to ensure complete separation of individual animals between training and validation subsets. SARA was defined based on continuous ruminal pH measurements, where cows were classified as SARA when ruminal pH remained between 5.2 and 5.8 for at least 180 min per day. Sensor derived variables included rumination time, activity, water intake, reticulorumen temperature, milk yield, and milk composition obtained from intraruminal boluses and an in-line milk analyzer. Six supervised machine learning classifiers were developed to classify SARA status based on combined sensor data. Among the evaluated models, SVM demonstrated the highest discriminatory performance, which achieved an area under the curve (AUC) of 0.97, accuracy of 0.95, sensitivity of 0.86, and specificity of 0.98 under repeated cow-level grouped cross-validation. Random forest showed similar performance (AUC = 0.97; accuracy = 0.93 and 0.98, respectively). Across all models, specificity was consistently higher than sensitivity, indicating that healthy cows were classified more accurately than SARA cases. These results demonstrate that integrated behavioral, physiological, and milk production data obtained from automated sensor systems can support classification of cows experiencing SARA under commercial farm conditions. The findings support the potential of multi sensor monitoring systems combined with machine learning classifiers as a tool for automated detection of rumen health disturbances in precision dairy farming. + © 2026. The Author(s). + + + + Grigė + Samanta + S + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. Samanta.grige1@lsmu.lt. + + + + Girdauskaitė + Akvilė + A + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Grigas + Ovidijus + O + + Faculty of Informatics, Kaunas University of Technology, 50254, Kaunas, Lithuania. + + + + Japertas + Sigitas + S + + Practical Training and Research Center, Lithuanian University of Health Sciences, Topolių g. 6, 54310, Kaunas, Lithuania. + + + + Malašauskienė + Dovilė + D + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Televičius + Mindaugas + M + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Urbutis + Mingaudas + M + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Džermeikaitė + Karina + K + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Krištolaitytė + Justina + J + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Antanaitis + Ramūnas + R + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Biosensors + Dairy cattle + Innovations + Machine learning + + Declarations. Competing interests: The authors declare no competing interests. Ethics approval: All animal procedures were reviewed and approved by the Institutional Animal Care and Use Committee of the Lithuanian University of Health Sciences (Protocol No. G2-227, approved on 7 March 2025). + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 1 + 2 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 23 + 18 + + + aheadofprint + + 42215579 + 10.1038/s41598-026-55162-z + 10.1038/s41598-026-55162-z + + + + + + 42215538 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Transition of blue space in the urban area of khulna city corporation using geospatial and machine learning techniques. + 10.1038/s41598-026-52656-8 + + Urban blue-space loss and grey-space expansion have significantly altered the surface water systems of the Khulna City Corporation, increasing the environmental degradation and hydrological vulnerability of this climate-sensitive coastal city. This study used multi-temporal Landsat imagery, including Landsat 5 TM data from 1994 to 2004 and Landsat 8 OLI data from 2014 to 2024. The analysis integrated the Normalized Difference Vegetation Index, Normalized Difference Built-up Index, and Normalized Difference Water Index with Random Forest classification and post-classification change detection to quantify long-term land-use/land-cover transitions and urban blue-space transformation. Post-classification comparison generated directional transition matrices documenting conversion pathways among land-cover categories. The results show that surface water bodies have decreased from 4.20 km2 in 1994 to 2.27 km2 in 2024, while the built-up area has increased from 25.9 to 53.3% of the area studied. The accuracy assessment resulted in an overall accuracy of 84.5 to 89.3%, with Kappa coefficients ranging from 0.78 to 0.86. Water body classifications have remained reliable throughout all periods, which has supported the use of classifiable maps for long-term migration analysis. Temporal analysis revealed two main phases: an early phase characterized by mixed land reclamation and later phases dominated by conversion of water bodies into built-up areas. Spatial analysis identified critical hotspots of canal infill, wetland loss, and riverbank modification, mainly in central and southern municipal zones. These findings highlight the need to treat urban blue spaces as critical infrastructure and to promote integrated management, natural drainage solutions and regular remote sensing monitoring. The study frames the loss of blue space as a process of urban transformation and advocates better monitoring of urban water systems in coastal secondary coastal cities. + © 2026. The Author(s). + + + + Hossan + Md Refath + MR + + Institute of Disaster Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh. + + + + Hossain + Mohammad Ismail + MI + + Institute of Disaster Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh. ihossain@idm.kuet.ac.bd. + + + + Islam + Md Wahidul + MW + + Department of Chemistry, Gopalganj Science and Technology University, Gopalganj, 8100, Bangladesh. + + + + Shaon + Zahid Hassan + ZH + + Institute of Disaster Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + GIS analysis + Land cover change + Remote sensing + Surface water decline + Urban expansion + + Declarations. Competing interests: The authors declare no competing interests. Consent to participate: This study did not involve any human participants, animal subjects, or personally identifiable. data, and therefore did not require ethical approval. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 4 + 6 + + + 2026 + 5 + 6 + + + 2026 + 5 + 29 + 23 + 17 + + + aheadofprint + + 42215538 + 10.1038/s41598-026-52656-8 + 10.1038/s41598-026-52656-8 + + + + + + 42215534 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + An explainable spatio-temporal deep learning framework for crop yield prediction and recommendation. + 10.1038/s41598-026-54439-7 + + Agriculture plays a critical role in ensuring global food security, yet crop yield variability driven by climate change, soil heterogeneity, and environmental fluctuations poses persistent challenges. Accurate yield forecasting and intelligent crop recommendation are essential for sustainable and efficient farm management. This study proposes a novel AI-driven framework for precise crop yield prediction and data-driven crop selection. The framework integrates systematic data preprocessing, hybrid feature selection, deep learning, and attention-based modeling to capture complex nonlinear relationships within agricultural datasets. Raw data, including soil properties, topography, climatic variables, and historical yield records, are processed using median-based imputation, normalization, and Z-score outlier detection to enhance reliability. A multi-stage hybrid feature selection approach combining Minimum Redundancy Maximum Weight, Sequential Forward Subset Selection, and Recursive Fisher Score identifies the most informative features while reducing redundancy. Yield prediction is performed using an attention-enhanced hybrid kernel Extreme Learning Machine (ELM). Crop recommendation is achieved through a Spatio-Temporal Explainable Group-Enhanced Transformer Network (STX-GTNET) optimized with the PantheraCobra metaheuristic. Model interpretability is ensured using Grad-CAM and Integrated Gradients. Experimental results demonstrate strong performance, achieving an RMSE of 281.6 and R² of 0.94 for yield prediction, and 98.4% accuracy with a 0.991 ROC-AUC for crop recommendation. + © 2026. The Author(s). + + + + Piyar S + Nifasath + N + + Department of Computer Science and Engineering, Anna University, Chennai, 600 025, India. nifasathpiyar@gmail.com. + + + + S + Dr Baulkani + DB + + Department of Electronics and Communication Engineering, Government College of Engineering, Bodinayakkanur, India. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Crop recommendation system + Crop yield prediction + Explainable artificial intelligence and precision agriculture + Spatio-temporal deep learning + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 24 + + + 2026 + 5 + 19 + + + 2026 + 5 + 29 + 23 + 17 + + + aheadofprint + + 42215534 + 10.1038/s41598-026-54439-7 + 10.1038/s41598-026-54439-7 + + + + + + 42215508 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + The role of biopsychosocial factors in classifying pain intensity across various chronic pain conditions. + 10.1038/s41598-026-46612-9 + + Chronic pain is a multifaceted condition shaped by biological, psychological, and social factors, which challenges its diagnosis and treatment. While prior research largely focused on sensory profiles to distinguish pain mechanisms, this study adopted a biopsychosocial perspective with the key addition of experimental pain paradigms to identify factors explaining variance in chronic pain severity across diverse etiologies. The dataset comprised 101 individuals with chronic pain and 63 pain-free controls. Participants were classified into three groups (no pain, mild-to-moderate pain, and severe pain) using three models: a sensory-profile-based model including quantitative sensory testing (QST model), a biopsychosocial model incorporating QST alongside psychological and social variables (biopsychosocial model), and a model using the same biopsychosocial data but excluding QST measures (noQST model). The QST model achieved an accuracy of 0.51 and an F1-score of 0.48. In contrast, the biopsychosocial model performed best, with both accuracy and F1-score of 0.71, while the noQST model reached 0.60 for both metrics. Key predictors in the best-performing model included quality of life, loss of sensation, depressive mood, pain catastrophizing, anxiety, fatigue, and general health. Overall, biopsychosocial factors enhanced chronic pain severity classification beyond sensory profiles alone across heterogeneous pain etiologies. + © 2026. The Author(s). + + + + De Schoenmacker + Iara + I + + Biomedical Data Science Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich, 8092, Zurich, Switzerland. deschoeia@gmail.com. + + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. deschoeia@gmail.com. + + + Biomedical Data Science Lab, Swiss Federal Institute of Technology (ETH), Lengghalde 2, 8008, Zurich, Switzerland. deschoeia@gmail.com. + + + + Monzon + Maria + M + + Biomedical Data Science Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich, 8092, Zurich, Switzerland. + + + + Sirucek + Laura + L + + Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland. + + + Department of Chiropractic Medicine, Integrative Spinal Research Group, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark. + + + + Scheuren + Paulina S + PS + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada. + + + Department of Anesthesiology, Pharmacology and Therapeutics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada. + + + + Lütolf + Robin + R + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Gorrell + Lindsay M + LM + + Department of Chiropractic Medicine, Integrative Spinal Research Group, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Brunner + Florian + F + + Physical Medicine and Rheumatology, Balgrist University Hospital, Zurich, Switzerland. + + + + Curt + Armin + A + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Rosner + Jan + J + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + Danish Pain Research Center, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark. + + + + Schweinhardt + Petra + P + + Department of Chiropractic Medicine, Integrative Spinal Research Group, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Hubli + Michèle + M + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Jutzeler + Catherine R + CR + + Biomedical Data Science Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich, 8092, Zurich, Switzerland. + + + Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland. + + + + eng + + + PF198F + International Foundation for Research in Paraplegia + + + + P500PB_214416 + Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung + + + + PZ00P3_186101 + Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung + + + + RT-2023-3173 + Michael Smith Health Research BC + + + + R359-2020-2620 + Lundbeck Foundation + + + + 380 + Strategic Focus Area "Personalized Health and Related Technologies (PHRT)" of the Swiss Federal Institutes of Technology + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Biopsychosocial model + Chronic pain + Machine learning + Pain classification + Quantitative sensory testing + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 5 + 21 + + + 2026 + 3 + 26 + + + 2026 + 5 + 29 + 23 + 16 + + + aheadofprint + + 42215508 + 10.1038/s41598-026-46612-9 + 10.1038/s41598-026-46612-9 + + + + + + 42215501 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Predictive analysis on mechanical properties, material flow and thermal analysis of friction stir welded dissimilar AA2014/AA7075 Al-alloy joints using machine learning. + 10.1038/s41598-026-48688-9 + + Aluminum alloys are the most ideal materials for aerospace applications, and joining of a light weighted materials with an eco-friendly method is encouraging ongoing research into the green welding technique. In the current work solid-state joints of 10 mm thick AA2014/AA7075 Al-alloys dissimilar material joints with hybrid pin profiles were fabricated by using Friction Stir Welding (FSW) technique. The microstructure and mechanical properties of weld joints, temperature analysis of the welds processed under various conditions is investigated. An Artificial Neural Network (ANN) model is used to predict the mechanical properties of the welded joints fabricated at different conditions. The weld joints processed with hybrid pentagonal pin profile exhibits the highest UTS and YS measuring as 294 and 267 MPa respectively. The ANN showed strong prediction using a dataset of 27 experimentally obtained results providing a reliable data-driven method for forecasting UTS and optimizing FSW parameters. The weld joints processed at 900 rpm 31. 5 mm/min 2° tilt angle 0 mm tool offset and pentagonal pin profile revealed 62% of joint efficiency, which is suitable for the suggested AWS requirements for aerospace applications must be met by 60%. ANN model provided a good agreement with experimental results (R² = 0.93). However, due to the relatively small dataset (L27 design). + © 2026. The Author(s). + + + + Rao + R Venkateswara + RV + + Department of Mechanical Engineering, Vignana Bharathi Institute of Technology, Telangana, 501301, India. + + + + Kumar + M Senthil + MS + + School of Mechanical Engineering, Vellore Institute of Technology, Chennai, 600127, India. msv305@yahoo.co.in. + + + + Vanmathi + M + M + + Department of Electronics and Communication Engineering, B S Abdurrahman Crescent Institute of Science and Technology, Chennai, 60048, India. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Dissimilar Materials + FSW Process + Mechanical Properties + Process Parameters + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 10 + 10 + + + 2026 + 4 + 9 + + + 2026 + 5 + 29 + 23 + 16 + + + aheadofprint + + 42215501 + 10.1038/s41598-026-48688-9 + 10.1038/s41598-026-48688-9 + + + + + + 42215494 + + 2026 + 05 + 29 + +
+ + 2396-8370 + + + 2026 + May + 29 + + + NPJ science of food + NPJ Sci Food + + Novel insights into the binding mechanisms of selected aldehydes during heat-induced protein unfolding. + 10.1038/s41538-026-00874-9 + + This investigation aimed to clarify the binding mechanisms between six aldehydes and myofibrillar proteins (MPs), with a structural explanation in response to stage-heating treatments. The conformational intermediates of MPs, which form during heat processing, were systematically characterized to elucidate their role in aldehyde binding and flavor retention. Machine learning results suggested that high-temperature boiling promoted extensive protein denaturation and aggregation, while subsequent low-temperature stewing induced partial rearrangement. Thermodynamic parameters indicated that hexanal-MPs formation was primarily driven by hydrogen bonding, whereas other longer-chain and unsaturated aldehydes penetrated hydrophobic pockets. Proteomics revealed that saturated aldehydes predominantly formed Schiff bases with the lysine ε-amino group. Unsaturated aldehydes, especially (E, E)-2,4-decadienal, undergo both Schiff base reactions and Michael addition with cysteine, histidine, and tryptophan residues. The retention/release behavior of aldehydes during processing is determined by covalent and non-covalent interactions. These results provide a scientific basis for precisely controlling flavor quality in meat products. + © 2026. The Author(s). + + + + Wang + Jingfan + J + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. + + + Laboratory of Chemistry of Natural Molecules, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium. + + + Laboratory of Analytical Chemistry, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium. + + + + Wang + Tianze + T + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. + + + + Yang + Ping + P + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. + + + + Han + Dong + D + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. orange_1101@126.com. + + + + Zhang + Chunhui + C + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. dr_zch@163.com. + + + + Jia + Wei + W + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. + + + Jiangsu Chaoyue Agricultural Development Co. Ltd, Taizhou, China. + + + + Purcaro + Giorgia + G + + Laboratory of Analytical Chemistry, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium. + + + + Fauconnier + Marie-Laure + ML + + Laboratory of Chemistry of Natural Molecules, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium. + + + + eng + + + No. 32472392 + the National Natural Science Foundation of China + + + + ASTIP-Y2025QC29 + the Agricultural Science and Technology Innovation Program + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Sci Food + 101739627 + 2396-8370 + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 12 + 25 + + + 2026 + 4 + 28 + + + 2026 + 5 + 29 + 23 + 15 + + + aheadofprint + + 42215494 + 10.1038/s41538-026-00874-9 + 10.1038/s41538-026-00874-9 + + + + + + 42215477 + + 2026 + 05 + 29 + +
+ + 2731-6068 + + + 2026 + May + 29 + + + npj aging + NPJ Aging + + Latent biochemical phenotypes delineate divergent health trajectories in older adults. + 10.1038/s41514-026-00415-4 + + Ageing heterogeneity hampers prevention and care. We used routine biochemical panels and unsupervised learning to identify latent phenotypes in community-dwelling older adults. In 1491 participants from the Toledo Study for Healthy Ageing (TSHA) with ~10-11 years of follow-up, 39 blood biomarkers were dimension-reduced and clustered, yielding three phenotypes: Healthy, Metabolic (subclinical dysmetabolism), and Haematological (low erythroid/renal profile). Phenotypes differed in functional capacity, frailty, and independence at baseline (all p < 0.05 after age/sex adjustment) and predicted long-term mortality (Metabolic women HR = 1.49, p = 0.016). Sex-specific analyses revealed distinct disease-trajectory patterns (e.g., hypertension in Metabolic women HR = 1.30, p = 0.005; thrombosis in Haematological men HR = 7.20, p = 0.018; syncope in Haematological women HR = 1.88, p = 0.009). Findings are partially replicated in a cohort of physically active older adults (EXERNET), supporting the generalizability of the Metabolic phenotype. Standard laboratory data, integrated through machine learning, capture ageing-relevant biology and stratify future risk without specialised assays, enabling low-cost, scalable precision prevention. + © 2026. The Author(s). + + + + González-Martos + Raquel + R + + Centro de Tecnología Biomédica (CTB), Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Madrid, Spain. + + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. + + + Nageru S.L, Madrid, Spain. + + + + Rodríguez-Gómez + Irene + I + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. + + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. + + + Geriatrics Department, Hospital Universitario de Toledo, Toledo, Spain. + + + + Galeano + Javier + J + + Grupo de Sistemas Complejos, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Madrid, Spain. + + + Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain. + + + + Ara + Ignacio + I + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. + + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. + + + + Alegre + Luis M + LM + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. + + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. + + + + Rodríguez-Mañas + Leocadio + L + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Geriatrics Department, Getafe University Hospital, Getafe, Spain. + + + + Garcia-Garcia + Francisco J + FJ + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. + + + Geriatrics Department, Hospital Universitario de Toledo, Toledo, Spain. + + + + Ramírez-Castillejo + Carmen + C + + Centro de Tecnología Biomédica (CTB), Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Madrid, Spain. + + + Departamento de Oncología, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain. + + + + Guadalupe-Grau + Amelia + A + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. amelia.guadalupe@uclm.es. + + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. amelia.guadalupe@uclm.es. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. amelia.guadalupe@uclm.es. + + + + eng + + + IND2022/BMD-23595 + Comunidad de Madrid + + + + CD23/00236 + Instituto de Salud Carlos III + + + + PI10/01532, PI031558 + Instituto de Salud Carlos III + + + + PI2010/020 + Junta de Comunidades de Castilla-La Mancha + + + + 03031-00 + Consejería de Sanidad de Castilla-La Mancha + + + + CB16/10/00456 + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable + + + + CB16/10/00477 + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Aging + 9918402285106676 + 2731-6068 + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 11 + 25 + + + 2026 + 5 + 18 + + + 2026 + 5 + 29 + 23 + 15 + + + aheadofprint + + 42215477 + 10.1038/s41514-026-00415-4 + 10.1038/s41514-026-00415-4 + + + + + + 42215447 + + 2026 + 05 + 29 + +
+ + 2041-1723 + + + 2026 + May + 29 + + + Nature communications + Nat Commun + + Multifactorial sheltering in peristromal niches shapes in vivo responses of lung cancers to targeted therapies. + 10.1038/s41467-026-73454-w + + Targeted therapies induce strong clinical responses but fail to eliminate advanced cancers, as a subset of tumor cells survives within residual disease and eventually develops resistance. While numerous cell-intrinsic and microenvironmental mechanisms have been implicated in this survival, their relative contributions remain poorly defined. Using spatial histological inferences from ALK + NSCLC models, we show that peristromal niches protect tumor cells from elimination, enabling in vivo persistence. This spatially restricted sheltering provides an ecological rescue mechanism that sustains residual populations, enabling their eventual evolutionary escape. Mechanistically, this protective effect reflects an integrated action of multiple juxtacrine and paracrine signals. This complexity limits the utility of targeting individual mechanisms of protection, favoring a shift towards exploiting orthogonal collateral sensitivities of residual disease. We find that adaptive HER2 upregulation, associated with both cell-intrinsic and stroma-mediated persistence, can be exploited by the antibody-drug conjugate T-DXd to dramatically enhance therapeutic responses and suppress tumor relapse. + © 2026. The Author(s). + + + + Desai + Bina + B + 0000-0003-0454-7583 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + Cancer Biology Ph.D. Program, University of South Florida, Tampa, FL, USA. + + + + Miti + Tatiana + T + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Prabhakaran + Sandhya + S + 0000-0002-9537-507X + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Miroshnychenko + Daria + D + 0000-0001-6425-5000 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Kumar + Pragya + P + 0000-0002-5691-7016 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Henry + Menkara + M + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Marusyk + Viktoriya + V + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Siqueira + Natalia Souza Nunes + NSN + 0000-0002-7877-2655 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Gatenbee + Chandler + C + 0000-0002-9730-5964 + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Ozakinci + Hilal + H + + Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Bui + Marilyn + M + 0000-0003-4963-2255 + + Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + Department of Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Boyle + Theresa A + TA + 0000-0001-5950-3970 + + Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Scott + Jacob + J + 0000-0003-2971-7673 + + Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA. + + + + Altrock + Philipp M + PM + 0000-0001-7731-3345 + + Department of Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany. + + + + Pellini + Bruna + B + + Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + Department of Thoracic Oncology, Miami Cancer Institute, Baptist Health South Florida, Miami, FL, USA. + + + + Haura + Eric + E + + Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Anderson + Alexander R A + ARA + 0000-0002-2536-4383 + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Basanta + David + D + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Marusyk + Andriy + A + 0000-0002-0087-9575 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. andriy.marusyk@moffitt.org. + + + Department of Molecular Medicine, University of South Florida, Tampa, FL, USA. andriy.marusyk@moffitt.org. + + + + eng + + + NIH U54 CA274507 + U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Nat Commun + 101528555 + 2041-1723 + + IM + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 4 + 2 + + + 2026 + 5 + 8 + + + 2026 + 5 + 29 + 23 + 14 + + + aheadofprint + + 42215447 + 10.1038/s41467-026-73454-w + 10.1038/s41467-026-73454-w + + + + + + 42215401 + + 2026 + 05 + 29 + +
+ + 1879-2022 + + + 2026 + May + 13 + + + ISA transactions + ISA Trans + + Interpretable physical-informed machine learning for dynamic characteristics prediction of air spring with limited measurement information. + S0019-0578(26)00247-8 + 10.1016/j.isatra.2026.05.010 + + Pipe-type air spring with auxiliary airbag (ASAA) are important vibration isolation component due to its tunable volume and adjustable dynamic characteristics. However, its dynamic modeling is challenging because airbag expansion work, rubber bladder viscoelasticity, and turbulence-dominated throttle dynamics are strongly coupled under large-amplitude excitations. Traditional data-driven models often require large datasets and lack interpretability. To address this issue, this study proposes an interpretable physics-informed machine learning (PIML) framework for ASAA dynamic prediction. A reduced-order physical model is first developed to provide baseline predictions of stiffness and lag angle, and the discrepancies between physical predictions and experimental measurements are then used to construct residual datasets. An interpretable Kolmogorov-Arnold Network (KAN) is employed to learn these residuals through adaptive B-spline activation functions and derive explicit symbolic correction terms. Experimental validation shows that the proposed hybrid model improves prediction accuracy while retaining physical interpretability, outperforming pure data-driven, advanced physics-data and physical models. + Copyright © 2026 ISA. Published by Elsevier Ltd. All rights reserved. + + + + Qin + Wu + W + + School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China. Electronic address: qw@ecjtu.edu.cn. + + + + Liao + Xundong + X + + School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China. Electronic address: 2023038080200027@ecjtu.edu.cn. + + + + Qi + Junyu + J + + State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China; Reutlingen University, Alteburgstraße 150, Reutlingen 72762, Germany. Electronic address: junyu.qi@reutlingen-university.de. + + + + He + Yi + Y + + School of Mechanical Engineering, South China University of Technology, China. Electronic address: 202111080705@mail.scut.edu.cn. + + + + Liu + Feifei + F + + School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China. Electronic address: 2971@ecjtu.edu.cn. + + + + Chen + Zhuyun + Z + + State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: mezychen@gdut.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + United States + ISA Trans + 0374750 + 0019-0578 + + IM + + Air spring + Data-driven + Dynamic characteristic predictions + Physics-informed Machine Learning + + Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 9 + 12 + + + 2026 + 5 + 10 + + + 2026 + 5 + 10 + + + 2026 + 5 + 29 + 21 + 58 + + + aheadofprint + + 42215401 + 10.1016/j.isatra.2026.05.010 + S0019-0578(26)00247-8 + + + + + + 42215359 + + 2026 + 05 + 29 + +
+ + 1879-0828 + + + 2026 + May + 29 + + + European journal of internal medicine + Eur J Intern Med + + Artificial intelligence in cardiovascular care for internal medicine: from promising algorithms to useful clinical services. + + 106944 + 106944 + + 10.1016/j.ejim.2026.106944 + S0953-6205(26)00249-9 + + Artificial intelligence is expanding rapidly in cardiovascular medicine, but its value in internal medicine depends less on raw model performance than on whether it improves triage, risk targeting, and coordination of care within real-world workflows. Rather than cataloguing applications, this review focuses on three use cases with near-term relevance in internal medicine: AI-augmented electrocardiography, risk prediction for targeted prevention, and AI-enabled clinical decision support, including selected applications of large language models. Across these domains, evaluation should extend beyond discrimination to calibration, positive predictive value at action thresholds, net benefit, alert burden, override rates, and downstream testing. Cardiovascular AI should not be judged as an autonomous decision-maker, but as a supervised component of care delivery. Its usefulness depends on predefined confirmation pathways, integration into existing information systems, actionable outputs, user training, and post-deployment monitoring for drift, safety, and equity. We propose a practical implementation lens - Train, Explain, Integrate, Accompany (TEIA) - to structure deployment in routine care, and summarize governance issues relevant to European practice, including intended use, interoperability, traceability, cybersecurity, and lifecycle oversight. The central question is not whether an AI tool can classify or predict, but whether its use in routine care supports better decisions with acceptable workload, safer pathways, and measurable clinical value. + Copyright © 2026 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved. + + + + Desroche + Louis-Marie + LM + + Department of Cardiology, North Site, CHU de La Réunion, Allée des Topazes, Saint-Denis, La Réunion 97400, France; CIC-EC INSERM 1410, CHU de La Réunion, Saint-Denis, France. Electronic address: louis-marie.desroche@chu-reunion.fr. + + + + Trimaille + Antonin + A + + Department of Cardiology, Strasbourg University Hospital, Strasbourg, France; UR3074, Translational Cardiovascular Medicine, University of Strasbourg, Strasbourg, France; GERCA (Groupe pour l'Enseignement et la Recherche Cardiologique en Alsace), Strasbourg, France. + + + + Lequeux + Benoît + B + + Department of Cardiology, Poitiers University Hospital, Poitiers, France. + + + + Homehr + Nicolas + N + + Primary Care Practice, Lherm, France; CPTS du Sud Toulousain, Haute-Garonne, France. + + + + Girerd + Nicolas + N + + CHRU de Nancy, Centre d'Investigations Cliniques Plurithématique 1433, INSERM, Université de Lorraine, INSERM U1116 - DCAC, and F-CRIN INI-CRCT(Cardiovascular and Renal Clinical Trialists), Nancy, France. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 29 + +
+ + Netherlands + Eur J Intern Med + 9003220 + 0953-6205 + + IM + + Artificial intelligence + Cardiovascular disease + Clinical decision support + Electrocardiography + Governance + Implementation + Internal medicine + Machine learning + Risk prediction + + Declaration of competing interest AT has received a fellowship grant from Edwards Lifescience. NG declares honoraria from Alnylam, AstraZeneca, Bayer, Boehringer, Cardiostory, Lilly, Echosens, GSK, NP medical, Novartis, Novo Nordisk, Roche diagnostics. The other authors declare no conflict of interest. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 4 + 29 + + + 2026 + 5 + 12 + + + 2026 + 5 + 29 + 21 + 57 + + + aheadofprint + + 42215359 + 10.1016/j.ejim.2026.106944 + S0953-6205(26)00249-9 + + + + + + 42215334 + + 2026 + 05 + 29 + +
+ + 2588-9311 + + + 2026 + May + 29 + + + European urology oncology + Eur Urol Oncol + + AI-MIRACLE: Artificial Intelligence and MultIpaRAmetric MRI Predict CLinical OutcomEs to Neoadjuvant Immunotherapy in Patients with Muscle-invasive Bladder Cancer Undergoing Radical Cystectomy. + S2588-9311(26)00133-1 + 10.1016/j.euo.2026.05.006 + + Neoadjuvant immune-checkpoint inhibitors (ICIs) in muscle-invasive bladder cancer (MIBC) were tested in patient's ineligible for cisplatin-based chemotherapy. The PURE-01 trial (NCT02736266) evaluated three courses of pembrolizumab before radical cystectomy (RC). We developed AI-MIRACLE, an international study assessing artificial intelligence (AI) and multiparametric magnetic resonance imaging (mpMRI) for predicting treatment response. + This multi-institutional study included data acquisition in Italy, and centralized analysis in the United States. Among 112 PURE-01 patients, pre- and post-ICI MRIs were analyzed. T2-weighted signal intensities were standardized for radiomics (Image Biomarker Standardization Initiative-compatible Python-based Computational Environment for Radiological Research (pyCERR)) and deep feature extraction (AI-BLADE toolbox using VGG19). Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI data underwent model-based analysis. Supervised machine learning algorithms (elastic net, random forest) were trained and cross-validated to predict pathological major response (pMR:ypT<2N0 residual disease) and pathological complete response (pCR: ypT0) pathological response. + The predictive models using post-ICI mpMRI with either a combination of radiomics and DCE-derived features or radiomics alone achieved the same high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.96 for pMR. A shape-based radiomic model achieved an AUC of 0.86 for predicting pCR. These models outperformed benchmark models based on clinical predictors. + Shape-based radiomics, DCE-derived features, and deep features may serve as noninvasive imaging biomarkers for predicting response to neoadjuvant pembrolizumab in MIBC. This imaging-based approach provides a non-invasive assessment of treatment response following neoadjuvant immunotherapy, which may help inform bladder-preserving management decisions prior to definitive surgery. + Copyright © 2026 The Author(s). Published by Elsevier B.V. All rights reserved. + + + + Necchi + Andrea + A + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy; Department of Medical Oncology, Vita-Salute San Raffaele University, Milan, Italy. Electronic address: necchi.andrea@hsr.it. + + + + Brembilla + Giorgio + G + + Department of Medical Oncology, Vita-Salute San Raffaele University, Milan, Italy; Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Whiting + Karissa + K + + Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Arita + Yuki + Y + + Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Akin + Oguz + O + + Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Apte + Aditya + A + + Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Awais + Muhammad + M + + Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Lema-Dopico + Alfonso + A + + Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Paudyal + Ramesh + R + + Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Cosenza + Michele + M + + Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Maiorano + Brigida Anna + BA + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Tateo + Valentina + V + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Cigliola + Antonio + A + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Mercinelli + Chiara + C + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + De Cobelli + Francesco + F + + Department of Medical Oncology, Vita-Salute San Raffaele University, Milan, Italy; Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Capanu + Marinela + M + + Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Shukla-Dave + Amita + A + + Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. Electronic address: davea@mskcc.org. + + + + Schwartz + Lawrence H + LH + + Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Netherlands + Eur Urol Oncol + 101724904 + 2588-9311 + + IM + + Bladder cancer + Deep feature + MRI + Radiomics + + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 12 + 18 + + + 2026 + 4 + 28 + + + 2026 + 5 + 6 + + + 2026 + 5 + 29 + 21 + 56 + + + aheadofprint + + 42215334 + 10.1016/j.euo.2026.05.006 + S2588-9311(26)00133-1 + + + + + + 42215211 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1095-9998 + + 139 + + 2026 + Oct + + + Food microbiology + Food Microbiol + + Single-cell Raman spectroscopy-machine learning combination: Rapid and accurate strain-level identification of Bifidobacterium animalis subsp. lactis J12. + + 105131 + 105131 + + 10.1016/j.fm.2026.105131 + S0740-0020(26)00095-X + + Rapid and accurate strain-level identification of Bifidobacterium animalis is critical for probiotic quality control and intellectual property protection in the food industry. In this study, a novel identification method for Bifidobacterium animalis subsp. lactis J12 was established by integrating single-cell Raman spectroscopy (SCRS) with machine learning (ML) algorithms. A total of 500-1000 valid single-cell Raman spectra were collected for each of five Bifidobacterium animalis subsp. lactis strains and seven heterologous lactic acid bacterial strains. The SCRS data were processed and optimized using t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and support vector machine (SVM) algorithms. Results showed that stationary-phase cells exhibited the most distinctive chemical fingerprints, enabling the SVM model to identify J12 among the five target strains with an accuracy of over 99%. The established method displayed high robustness: classification accuracy and recall for lyophilized bacterial powders both exceeded 99.8%; the predicted proportions of strains in artificial mixed samples were highly consistent with theoretical values, with a mean absolute error (MAE) of <0.5% and a correlation coefficient (R2) of >0.998; and the identification accuracy for J12 isolates from commercial yogurt remained above 99%. Comparative genomics analysis confirmed that the unique Raman phenotypic characteristics of J12 were correlated with its specific genomic features, verifying the method's reliability at the genetic level. This label-free and pure culture-free SCRS-ML approach provides a rapid and high-precision tool for strain-specific identification in the probiotic industry, and fills the technical gap in rapid detection of highly homologous B. animalis strains. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Jiang + Yixuan + Y + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: 15245133725@163.com. + + + + Deng + Yu + Y + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: 18184400300@163.com. + + + + Feng + Bing + B + + Qingdao Singlecell Biotechnology Co., Ltd., Qingdao, 266000, China. Electronic address: fengbing@singlecellbiotech.com. + + + + Zhang + Hongxing + H + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: hxzhang511@163.com. + + + + Xie + Yuanhong + Y + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: xieyuanh@163.com. + + + + Gao + Jingyu + J + + Science and Technology Research Center of China Customs, Beijing, 100026, China. Electronic address: gjyhgxy@sina.com. + + + + Song + Xiaodong + X + + Inner Mongolia Mengniu Dairy (Group) Co., Ltd., Key Laboratory of Dairy Quality Intelligence Monitoring Technology, State Administration for Market Regulation, Hohhot, 011517, China. Electronic address: songxiaodong@mengniu.cn. + + + + Jin + Junhua + J + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: jinjunhua002008@163.com. + + + + eng + + Journal Article + Evaluation Study + + + 2026 + 04 + 22 + +
+ + England + Food Microbiol + 8601127 + 0740-0020 + + IM + + + Bifidobacterium animalis + classification + isolation & purification + chemistry + genetics + + + Spectrum Analysis, Raman + methods + + + Machine Learning + + + Support Vector Machine + + + Single-Cell Analysis + methods + + + Probiotics + + + Yogurt + microbiology + + + Discriminant Analysis + + + + Bifidobacterium animalis + Machine learning + Probiotics + Quality control + SCRS + Strain identification + + Declaration of competing interest The authors declare no conflicts of interest. + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 18 + + + 2026 + 4 + 20 + + + 2026 + 4 + 21 + + + 2026 + 5 + 29 + 20 + 55 + + + ppublish + + 42215211 + 10.1016/j.fm.2026.105131 + S0740-0020(26)00095-X + + + + + + 42215204 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1095-9998 + + 139 + + 2026 + Oct + + + Food microbiology + Food Microbiol + + Climatic factors and initial microbiota-driven prediction of the accumulated microbiota at the end of stacking fermentation for soy sauce aroma type baijiu. + + 105124 + 105124 + + 10.1016/j.fm.2026.105124 + S0740-0020(26)00088-2 + + Most traditional fermented foods are produced through spontaneous fermentation, which contain complex microbiota. However, it is still unclear what the key driving factors of the microbiota are and how to predict the microbiota at the end of fermentation. This study employed a typical spontaneous fermentation process, stacking fermentation for soy sauce aroma type baijiu, as a case to predict the microbiota at the end of fermentation based on key driving factors. The microbiota at the start of fermentation was mainly provided from the starter (daqu), air, and the ground of the fermentation room, contributing 45.22%, 10.05%, and 7.48% of the bacteria, and 41.83%, 10.77% and 1.21% of the fungi, respectively. Average air temperature and average photosynthetic photon flux density were identified as key climatic factors driving microbial succession. Finally, a Transformer-based machine learning model was constructed to predict the microbiota at the end of fermentation based on key climatic factors and the initial microbiota. 29 out of 34 dominant genera (average relative abundance ≥1%) could be accurately predicted (NRMSE <30%). Aitchison distance between true and predicted value using Transformer model was significantly lower than that using Random Forest model (P < 0.001). This work revealed the roles of climatic factors and the microbiota at the start of fermentation in shaping the microbiota at the end of fermentation. The constructed predictive model could facilitate the regulation of the microbiota in food fermentation. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + He + Binghao + B + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. + + + + Zheng + Yifu + Y + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. + + + + Ban + Shibo + S + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. + + + + Madadi + Meysam + M + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. + + + + Khaneghah + Amin Mousavi + AM + + Faculty of Biotechnologies (BioTech), ITMO University, 9 Lomonosova Street, Saint Petersburg, 191002, Russia. Electronic address: mousavi.amin@gmail.com. + + + + Aadil + Rana Muhammad + RM + + National Institute of Food Science and Technology, University of Agriculture, Faisalabad, 38000, Pakistan. Electronic address: muhammad.aadil@uaf.edu.pk. + + + + Wu + Qun + Q + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. Electronic address: wuq@jiangnan.edu.cn. + + + + eng + + Journal Article + + + 2026 + 04 + 16 + +
+ + England + Food Microbiol + 8601127 + 0740-0020 + + IM + + + Fermentation + + + Soy Foods + microbiology + analysis + + + Microbiota + + + Bacteria + classification + genetics + isolation & purification + metabolism + + + Climate + + + Odorants + analysis + + + Fungi + classification + genetics + metabolism + isolation & purification + + + Fermented Foods + microbiology + analysis + + + Food Microbiology + + + + Climate + Fermentation + Machine learning + Microbiota + Soy sauce aroma type baijiu + Succession distance + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 2 + + + 2026 + 4 + 12 + + + 2026 + 4 + 16 + + + 2026 + 5 + 29 + 20 + 55 + + + ppublish + + 42215204 + 10.1016/j.fm.2026.105124 + S0740-0020(26)00088-2 + + + + + + 42215160 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1876-4452 + + 66 + 3 + + 2026 + May + + + Science & justice : journal of the Forensic Science Society + Sci Justice + + On-site strip-type drug test kit enhanced by AI-based image analysis. + + 101408 + 101408 + + 10.1016/j.scijus.2026.101408 + S1355-0306(26)00014-6 + + The demand for technology that can rapidly and precisely detect drugs in the field is increasing because of the growing number of criminal incidents involving the tampering of beverages with drugs. This paper presents the development and validation of a strip-type drug test kit that is user-friendly and portable, reduces the consumption of reagents that are hazardous to humans, and produces less waste. A drawback of strip-type drug test kits is that they depend on visual interpretation of the results, which can vary depending on user skill, lighting, and slight color changes. To address this limitation, this study implemented an analytical method that minimizes uncertainty and facilitates quantitative evaluation by utilizing images of color reaction outcomes from the newly developed strip-type drug test kit. Specifically, we developed an automatic discrimination algorithm using artificial intelligence (AI) image analysis techniques. Using a collection of 7,295 kit images, we evaluated the efficacy of several deep-learning models, including Convolutional Neural Network (CNN), Vision Transformer (ViT), and CNN-GRU (a hybrid of CNN and Gated Recurrent Unit). The findings indicated that the CNN-GRU model showed superior performance across all parameters, including accuracy, precision, recall, F1-score, and Cohen's kappa, achieving a perfect score of 1.0000. The model achieved perfect accuracy, surpassing the average performance of 32 police officers in a comparative experiment. This study demonstrated that AI-based precision reading algorithms can significantly improve the automation and reliability of strip-type drug test kits, highlighting their potential applicability and effectiveness in investigative and forensic settings. + Copyright © 2026. Published by Elsevier B.V. + + + + An + Sora + S + + Department of Forensic Science, Sungkyunkwan University, Suwon 16419, the Republic of Korea. + + + + Lee + Joong + J + + Institute of AI and Big Data in Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, the Republic of Korea. + + + + Lee + Sangki + S + + Department of Forensic Science, Sungkyunkwan University, Suwon 16419, the Republic of Korea. + + + + Chung + Heesun + H + + Department of Forensic Science, Sungkyunkwan University, Suwon 16419, the Republic of Korea. Electronic address: hschung1024@gmail.com. + + + + eng + + Journal Article + + + 2026 + 02 + 18 + +
+ + England + Sci Justice + 9508563 + 1355-0306 + + + + 0 + Reagent Strips + + + IM + + + Humans + + + Image Processing, Computer-Assisted + + + Artificial Intelligence + + + Substance Abuse Detection + instrumentation + methods + + + Algorithms + + + Convolutional Neural Networks + + + Intelligent Systems + + + Deep Learning + + + Reagent Strips + + + + AI-based precision + Artificial Intelligence (AI) + Deep-learning models + Image analysis + Strip-type drug test kit + + Declaration of competing interest the authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Heesun Chung reports financial support was provided by Commercializations Promotion Agency for R&D Outcomes. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 9 + 22 + + + 2025 + 12 + 30 + + + 2026 + 2 + 11 + + + 2026 + 5 + 29 + 20 + 55 + + + ppublish + + 42215160 + 10.1016/j.scijus.2026.101408 + S1355-0306(26)00014-6 + + + + + + 42215144 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2162-9471 + + 133 + + 2026 + + + Advances in clinical chemistry + Adv Clin Chem + + Drug induced renal injury (DIRI) biomarkers. + + 1 + 44 + 1-44 + + 10.1016/bs.acc.2026.01.006 + S0065-2423(26)00020-X + + Drug-induced renal injury (DIRI) remains a substantial issue in clinical practice, contributing to high morbidity, mortality, socioeconomic and healthcare cost. Traditional markers, such as serum creatinine or blood urea nitrogen, have low specificity and sensitivity for early identification, underscoring the need for new reliable biomarkers. This chapter investigates biomarker improvements for DIRI, focusing on clinical uses, limitations, and future possibilities. The chapter examines biomarkers such as neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, cystatin C, β2-microglobulin and clusterin for diagnosing nephrotoxicity. In addition, we highlight the role of proteomics and metabolomics in facilitating the identification and development of novel biomarker candidates via high-throughput ultra-sensitive technology in investigating DIRI mechanisms. The chapter discusses validation frameworks, regulatory criteria for biomarker adoption in clinical settings, and how they are integrated into treatment monitoring and personalized medicine. Case examples demonstrate the real-world applicability in early diagnosis, risk classification, and therapy optimization. Standardization, cost, and translational gaps are discussed, as well as the potential for artificial intelligence and machine learning to improve biomarker processing and interpretation. Finally, this chapter emphasizes the transformative potential of DIRI biomarkers in providing a pathway to more accurate and timely identification of renal injury, improved patient outcomes, and reduced healthcare costs. Future research and global collaboration are required to fully realize the potential of these advances in clinical and regulatory settings. + Copyright © 2026. Published by Elsevier Inc. + + + + Sher + Emina Karahmet + EK + + School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom; International Society of Engineering Science and Technology, Nottingham, United Kingdom. Electronic address: emina.karahmet@gmail.com. + + + + Boras + Marijana Marković + MM + + Department of Laboratory Diagnostics, University Clinical Hospital Mostar, Mostar, Bosnia and Herzegovina; International Society of Engineering Science and Technology, Nottingham, United Kingdom. + + + + Fajkić + Almir + A + + Department of Pathophysiology, Faculty of Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina. + + + + Begović + Faris + F + + International Society of Engineering Science and Technology, Nottingham, United Kingdom. + + + + eng + + Journal Article + Review + + + 2026 + 02 + 23 + +
+ + United States + Adv Clin Chem + 2985173R + 0065-2423 + + + + 0 + Biomarkers + + + 0 + Hepatitis A Virus Cellular Receptor 1 + + + 0 + Cystatin C + + + IM + + + Humans + + + Biomarkers + analysis + blood + + + Acute Kidney Injury + diagnosis + chemically induced + + + Proteomics + + + Metabolomics + + + Hepatitis A Virus Cellular Receptor 1 + + + Cystatin C + + + + Clinical chemistry + Cystatin c + Drug-induced renal injury biomarkers + Early diagnosis + Kidney injury molecule-1 + Metabolomics + Nephrotoxicity + Neutrophil gelatinase-associated lipocalin + Personalized medicine + Proteomics + + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 20 + 55 + + + ppublish + + 42215144 + 10.1016/bs.acc.2026.01.006 + S0065-2423(26)00020-X + + + + + + 42215142 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + A hybrid chemometric and deep learning model for monitoring quality loss in thermally processed edible oils. + + 119497 + 119497 + + 10.1016/j.foodres.2026.119497 + S0963-9969(26)01180-4 + + Edible oils are routinely exposed to high temperatures during domestic and commercial cooking, leading to chemical transformations that degrade the quality of oils and pose potential health risks. This study investigated the thermal degradation of groundnut, sesame, and restaurant-collected oils, with particular emphasis on polymerization, oxidation, and hydrolysis processes. Fourier-transform infrared (FTIR) spectroscopy and dielectric measurements were employed to monitor the molecular changes induced by heating the oils, whereas quantitative assessments of oil deterioration were performed using key chemical parameters and dielectric indices. Advanced chemometric and deep learning models like CARS-PLS, RF-PLS, and 1D-CNN were used to find the most important spectral regions and key chemical and dielectric features linked to oil degradation. Among these, the 1D-CNN model exhibited the highest predictive accuracy for both acid value (AV) and dielectric constant (DC), achieving training R2values of 0.9984 and 0.9931, and an external test R2values of 0.9904 and 0.9898, respectively, with the lowest RMSE. Heating oil shows the results with significant alterations in the FTIR bands, an increased dielectric constant, and elevated AV, corresponding to the accumulation of oxidized triglycerides, free fatty acids, and high-molecular-weight polymeric residues. Furthermore, explainable artificial intelligence (XAI) using SHAP analysis revealed key spectral contributions corresponding to ester groups, carbonyl stretching of triglycerides, free fatty acids, and CH₂/CH₃ vibrations. This study demonstrates that integrating spectroscopic and dielectric analyses with predictive modeling provides a sensitive, non-destructive laboratory-based proof-of-concept approach for evaluating the oil degradation during thermal processing. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Varma + Shruti O + SO + + Department of Physics, The Institute of Science, Dr. Homi Bhabha State University, Mumbai 400032, Maharashtra, India. + + + + Vishwakarma + Ajay L + AL + + Department of Physics, The Institute of Science, Dr. Homi Bhabha State University, Mumbai 400032, Maharashtra, India. + + + + Sonawane + M R + MR + + Department of Physics, The Institute of Science, Dr. Homi Bhabha State University, Mumbai 400032, Maharashtra, India. Electronic address: smahadev123@gmail.com. + + + + Garad + Nitin P + NP + + School of Physical Sciences, Swami Ramanand Teerth Marathwada University, Nanded 431606, India. + + + + Kumbharkhane + A C + AC + + School of Physical Sciences, Swami Ramanand Teerth Marathwada University, Nanded 431606, India. + + + + eng + + Journal Article + + + 2026 + 05 + 20 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + + + 0 + Plant Oils + + + IM + + + Hot Temperature + + + Spectroscopy, Fourier Transform Infrared + + + Deep Learning + + + Cooking + methods + + + Plant Oils + chemistry + analysis + + + Food, Processed + + + Oxidation-Reduction + + + Hydrolysis + + + + Chemical properties + Chemometrics + Deep learning + Dielectric properties + Explainable artificial intelligence (XAI) + Thermal degradation + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 25 + + + 2026 + 5 + 17 + + + 2026 + 5 + 19 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215142 + 10.1016/j.foodres.2026.119497 + S0963-9969(26)01180-4 + + + + + + 42215140 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + Multimodal analysis of spontaneous speech for predicting food liking: Integrating linguistic and prosodic features with machine learning. + + 119483 + 119483 + + 10.1016/j.foodres.2026.119483 + S0963-9969(26)01166-X + + Spontaneous speech offers a promising yet underexplored window into consumers' emotional experiences during food evaluation. Unlike traditional self-reported measures, vocal expression captures both explicit linguistic content and implicit affective cues, potentially providing a richer account of hedonic perception. This study investigates whether linguistic and prosodic cues embedded in natural spoken responses can predict product liking. Ninety participants evaluated three chocolates and three plant-based beverages, providing hedonic ratings and open-ended spoken descriptions. Speech was analysed using psycholinguistic, prosodic, and representation-based features. Multiple machine learning algorithms were applied for both classification (low/medium vs high liking) and regression (continuous liking prediction). Feature-level comparisons showed that chocolates elicited richer emotional language, higher vocal intensity, and greater acoustic variability than plant-based drinks (p < 0.005). Across the full dataset, linguistic markers were the strongest predictors of liking (F1-score = 0.695; mean absolute error (MAE) = 1.530), while prosodic features provided complementary information related to arousal and engagement. Speech embeddings showed limited generalisation (area under the curve (AUC) < 0.518; MAE > 1.602). Product-specific analyses revealed higher predictive accuracy for chocolates (F1-score = 0.825; MAE = 1.213) than for plant-based beverages (F1-score = 0.559; MAE = 1.619), indicating that the strength of affective cues in speech depends on the emotional resonance of the product being evaluated. These findings demonstrate that spontaneous speech contains measurable emotional signals related to food liking. Interpretable linguistic and prosodic features can partially predict hedonic responses, highlighting the potential of multimodal, speech-based emotion analysis as a complementary tool in sensory and consumer research. + Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved. + + + + Sousa + Pedro M + PM + + GreenUPorto/Inov4Agro, DGAOT, Faculty of Sciences, University of Porto, Portugal. + + + + Lima + Rui C + RC + + Sense Test, Lda, Vila Nova de Gaia, Portugal. + + + + García-Segovia + Purificación + P + + i-Food, IUIA-FoodUPV, Universitat Politècnica de València, Spain. + + + + Alcañiz + Mariano + M + + LabLENI, HumanTech/LabLENI, Universitat Politècnica de València, València, Spain. + + + + Cunha + Luís M + LM + + GreenUPorto/Inov4Agro, DGAOT, Faculty of Sciences, University of Porto, Portugal. Electronic address: lmcunha@fc.up.pt. + + + + eng + + Journal Article + + + 2026 + 05 + 18 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + IM + + + Humans + + + Machine Learning + + + Speech + + + Food Preferences + psychology + + + Female + + + Emotions + + + Male + + + Young Adult + + + Cues + + + Adult + + + Linguistics + + + Consumer Behavior + + + + Acoustic features + Affective computing + Sensory analysis + Speech emotion + Supervised learning + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 1 + 4 + + + 2026 + 4 + 28 + + + 2026 + 5 + 16 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215140 + 10.1016/j.foodres.2026.119483 + S0963-9969(26)01166-X + + + + + + 42215133 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + Translating Food Biosensing into Practice: Smart Packaging, On-Site Analytics, and AI Integration. + + 119473 + 119473 + + 10.1016/j.foodres.2026.119473 + S0963-9969(26)01156-7 + + The global food system encounters increasing challenges from contamination threats, necessitating innovative monitoring technologies to guarantee safety and quality. This review investigates novel precision monitoring methodologies, emphasizing sophisticated biosensing technologies, intelligent packaging systems, molecular docking, and AI-integrated platforms. Biosensors utilize electrochemical, optical, and thermal transducers for the swift, on-site identification of contaminants, toxins, and allergens, surpassing conventional methods in both speed and accessibility. The incorporation of intelligent packaging improves real-time quality assurance, while molecular docking determines contaminant interactions to improve sensor design. Advancements in AI and machine learning enhance predictive analytics and traceability, meeting intricate supply chain requirements. Nonetheless, obstacles such as matrix interference, sensor stability, and regulatory deficiencies remain. Future directions highlight nanotechnology, biodegradable materials, and defined standards to improve scalability and sustainability. These breakthroughs will continue to transform food safety, minimize waste, and enhance global health equity, thereby revolutionizing the food industry for a sustainable future. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Seyedhosseini-Ghaheh + Hooria + H + + Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. Electronic address: h.hosseini@nutr.mui.ac.ir. + + + + Rafieian + Fatemeh + F + + Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. + + + + Rezaei + Atefe + A + + School of Life Sciences, Plant Proteins and Nutrition, Technical University of Munich, Freising 85354, Germany. + + + + Askari + Gholamreza + G + + Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. + + + + Zarrabi + Ali + A + + Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul 34396, Türkiye; Graduate School of Biotechnology and Bioengineering, Yuan Ze University, Taoyuan 320315, Taiwan. Electronic address: ali.zarrabi@istinye.edu.tr. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 16 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + IM + + + Biosensing Techniques + methods + + + Artificial Intelligence + + + Food Packaging + methods + + + Food Safety + methods + + + Food Contamination + analysis + + + Molecular Docking Simulation + + + Intelligent Systems + + + Humans + + + + Artificial Intelligence + Food biosensors + Molecular Docking + Smart Packaging + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 23 + + + 2026 + 4 + 24 + + + 2026 + 5 + 15 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215133 + 10.1016/j.foodres.2026.119473 + S0963-9969(26)01156-7 + + + + + + 42215117 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + Artificial intelligence integrated in non-destructive technologies for monitoring food freshness: A review of next generation approach. + + 119450 + 119450 + + 10.1016/j.foodres.2026.119450 + S0963-9969(26)01132-4 + + Food freshness is a significant quality and safety attribute, which contributes in consumers' health and their food choices. The adoption of rapid, accurate, and reliable food freshness detection novel approaches is of great significance. Non-destructive testing (NDT) offers precise, fast and efficient detection of food freshness, quality and safety but, still confronts multiple issues in data preprocessing, accuracy, reliability and adaptability. However, artificial intelligence (AI) integration in NDT upholds significant promise to overcome these issues. This review, spotlights the significance of integrating the AI in NDT (spectroscopy, imaging and other technologies) for boosting the performance of NDT. It further probes the merits, and demerits, and the principles of AI assisted NDT technologies. Moving forward, integrating AI in NDT technologies have significantly boosted the reliable and accurate freshness detection in complex food matrices, plant and animal derived foods. In spite of recent advancements in this field of research, there are still several existing challenges of compatibility, precision, and adaptability associated with the NDT approaches coupled with AI. Therefore, this review is comprehensively emphasized on the promising transformative prospects of AI integrated in NDT approaches for the non-destructive, rapid, reliable and accurate detection of food freshness based on its quality indicators accompanied with highlighting the future challenges, innovations and directions. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Waseem + Muhammad + M + + College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China; Department of Food Science and Technology, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, 63100, Pakistan. + + + + Javed + Muhammad Rizwan + MR + + Department of Food Science and Technology, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, 63100, Pakistan. + + + + Manzoor + Muhammad Faisal + MF + + Guangdong Provincial Key Laboratory of Intelligent Food Manufacturing, School of Food Science and Engineering, Foshan University, Foshan, China. + + + + Xia + Xiuqi + X + + College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China. + + + + Huang + Yilei + Y + + College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China. + + + + Chen + Xiaomei + X + + College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China. Electronic address: xmchen@jmu.edu.cn. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 14 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + IM + + + Artificial Intelligence + + + Food Analysis + methods + + + Humans + + + Food Quality + + + Animals + + + Intelligent Systems + + + + Artificial intelligence + Deep learning + Freshness + Machine learning + Rapid monitoring + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 12 + 22 + + + 2026 + 5 + 6 + + + 2026 + 5 + 11 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215117 + 10.1016/j.foodres.2026.119450 + S0963-9969(26)01132-4 + + + + + + 42215102 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + Towards sustainable serum-free media development from alternative sources for cultivated meat. + + 119434 + 119434 + + 10.1016/j.foodres.2026.119434 + S0963-9969(26)01116-6 + + The economic feasibility and scalability of cultivated meat (CM) is critically hindered by the high manufacturing costs, particularly the cost associated with cell culture media. Therefore, numerous studies have proposed the use of sustainable alternatives to replace the expensive cell culture media components, especially animal serum. In this review, we comprehensively surveyed such developments and interpreted the opportunities and limitations of animal serum-reduction or replacement methodologies in designing culture media for CM production. Among the various alternative sources investigated, plant-derived sources and recombinant proteins have been the most extensively studied. We also highlight the critical gaps in existing studies such as the lack of demonstration of scalability, cost-effectiveness and long-term applicability of proposed alternative sources. Subsequently, we provide a roadmap for future studies on how to demonstrate the proposed alternative does not have adverse effects on cellular long-term stability, as well as comply with food safety and regulatory standards while being cost-effective which are critical for large scale CM manufacturing. We further propose a two-pronged framework based on high-throughput screening, systems biology, and Machine Learning (ML) tools to optimize serum-free CM media formulations using alternative sources in a rapid and scalable manner. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Sivakumar + Sanjay + S + + Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India. + + + + Manayankath + Aparna + A + + Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India. + + + + Hong + Yi Fan + YF + + Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Department of Biomedical Engineering, National University of Singapore, Singapore. + + + + Thrivikraman + Greeshma + G + + Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India. + + + + Tekkatte + Chandana + C + + The Good Food Institute India, Bengaluru, India. + + + + Choudhury + Deepak + D + + Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Department of Food Science and Technology, National University of Singapore, Singapore; Bezos Centre for Sustainable Protein at the National University of Singapore, Singapore. Electronic address: Deepak_Choudhury@a-star.edu.sg. + + + + Pang + Kuin Tian + KT + + Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Department of Biomedical Engineering, National University of Singapore, Singapore; Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore; School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore. Electronic address: zach_pang@a-star.edu.sg. + + + + Lakshmanan + Meiyappan + M + + Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India; Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Wadhwani School of Data Science and AI (WSAI), Indian Institute of Technology Madras, Chennai, India; Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology Madras, Chennai, India. Electronic address: meiyappan@iitm.ac.in. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 13 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + + + 0 + Culture Media, Serum-Free + + + IM + + + In Vitro Meat + + + Animals + + + Culture Media, Serum-Free + chemistry + + + Meat Substitutes + + + + Alternative proteins + Animal serum alternatives + Cultivated meat + Serum-free media + Sustainability + Systems biology + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 12 + 11 + + + 2026 + 3 + 21 + + + 2026 + 5 + 9 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215102 + 10.1016/j.foodres.2026.119434 + S0963-9969(26)01116-6 + + + + + + 42214983 + + 2026 + 05 + 29 + +
+ + 1879-3363 + + 231 + + 2026 + May + 29 + + + Marine pollution bulletin + Mar Pollut Bull + + Deep learning-based prediction of trace silver concentrations in marine sediments from mid-infrared spectroscopy. + + 119930 + 119930 + + 10.1016/j.marpolbul.2026.119930 + S0025-326X(26)00717-4 + + Silver (Ag) is a biologically toxic element, and the rapid detection of its concentrations in environmental samples is thus of great importance. This study investigates mid-infrared (MIR) spectra of low-Ag sediments from the South China Sea (SCS), and successfully predicts Ag concentrations by three machine learning models, including Kennard Stone-competitive adaptive reweighted sampling-partial least squares regression (KS-CARS-PLSR), Kennard Stone-random forest (KS-RF), and random selecting-squeeze excitation attention-residual network (RS-SE-ResNet-1D). Among the three techniques, the RS-SE-ResNet-1D model performs best in terms of Ag prediction, achieving an R2 of 0.82, an RMSE of 0.03, and an RPD of 2.38. A common feature wavenumber band in the range of 1408.3-1408.7 cm-1, corresponding to the v3 symmetric stretching absorption band of carbonate ions (CO3 + 2-), is identified from all three models. This suggests that marine productivity is possibly the dominant factor controlling Ag levels in marine sediments of the SCS. The machine learning models based on MIR spectral data developed in this study provide a new analytical approach for determining Ag concentrations in low-Ag marine sediments, and offer new insights into the study of biogeochemical cycling of Ag. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Yu + Boyuan + B + + School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, Anhui, China. + + + + Xu + Liqiang + L + + School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, Anhui, China. Electronic address: xlq@hfut.edu.cn. + + + + Ning + Yu + Y + + Instrumental Analysis Center, Hefei University of Technology, Hefei, Anhui, China. + + + + Xu + Fang + F + + School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui, China. + + + + Han + Qingli + Q + + School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Mar Pollut Bull + 0260231 + 0025-326X + + IM + + Machine learning + Marine sediments + Mid-infrared spectra + Productivity + Silver + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 28 + + + 2026 + 5 + 23 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 19 + 52 + + + aheadofprint + + 42214983 + 10.1016/j.marpolbul.2026.119930 + S0025-326X(26)00717-4 + + + + + + 42214978 + + 2026 + 05 + 29 + +
+ + 1878-0539 + + 118 + + 2026 + May + 14 + + + Journal of plastic, reconstructive & aesthetic surgery : JPRAS + J Plast Reconstr Aesthet Surg + + Development and nested cross-validation of machine learning models for predicting transfusion requirements in pediatric craniosynostosis surgery. + + 209 + 220 + 209-220 + + 10.1016/j.bjps.2026.05.033 + S1748-6815(26)00288-3 + + Blood transfusion is common during pediatric craniosynostosis surgery; however, transfusion volumes and use of cell salvage systems can vary considerably. To support preoperative transfusion planning in patients undergoing craniectomy for craniosynostosis, we developed and internally validated machine learning (ML) models to predict perioperative transfusion risks and transfusion volumes in this patient population. Data from eligible patients ≤36 months of age from the NSQIP® Pediatric dataset were included for modeling. A classifier pipeline was developed to predict probabilities associated with transfusion risk, and a probabilistic/quantile regression pipeline was developed to predict the median transfusion volume (in mL/kg) with interquartile range. Performance of the modeling pipelines was evaluated using nested cross-validation with 5-fold outer loops. In total, 8045 patients were included with 4091 (50.9%) receiving perioperative transfusion. For the classification pipeline, the optimal model configuration was a LightGBM classifier. The modeling pipeline achieved a mean area under the receiver operating characteristic curve (AUC-ROC) of 0.77 (95% confidence interval [CI] 0.75-0.78) and mean Brier score of 0.20 (95% CI 0.19-0.20) across nested cross-validation. For the regression stage, the optimal model configuration was a probabilistic NGBoost regressor. The modeling pipeline achieved an average median absolute error of 6.86 mL/kg (95% CI 6.61-7.11), with a mean IQR coverage of 53% (95% CI 52-53%). These models may support individualized blood product preparation and selective use of cell salvage systems. External validation and further real-world testing are needed prior to the clinical implementation of this tool. + Copyright © 2026 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved. + + + + Deng + Jiawen + J + + Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada. + + + + Gharavi + Aidin + A + + Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, MN, USA. + + + + Heybati + Kiyan + K + + Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA. + + + + Haile + Dawit + D + + Department of Anesthesiology, Mayo Clinic, Rochester, MN, USA. + + + + Ahn + Edward S + ES + + Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA. + + + + Mardini + Samir + S + + Division of Plastic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN, USA. + + + + Gibreel + Waleed + W + + Division of Plastic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN, USA; Department of Pediatric and Adolescents Medicine, Mayo Clinic, Rochester, MN, USA. Electronic address: Gibreel.Waleed@mayo.edu. + + + + eng + + Journal Article + + + 2026 + 05 + 14 + +
+ + Netherlands + J Plast Reconstr Aesthet Surg + 101264239 + 1748-6815 + + IM + + Blood transfusion + Craniectomy + Craniosynostoses + Machine learning + Operative blood salvage + + Declaration of Competing Interest The authors declare no conflict of interest. The ACS-NSQIP® and participating hospitals are the source of this data; they have not verified nor are they responsible for the statistical validity of the data analysis or the conclusions derived by the authors. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 12 + 9 + + + 2026 + 4 + 20 + + + 2026 + 5 + 12 + + + 2026 + 5 + 29 + 19 + 52 + + + aheadofprint + + 42214978 + 10.1016/j.bjps.2026.05.033 + S1748-6815(26)00288-3 + + + + + + 42214919 + + 2026 + 05 + 29 + +
+ + 1090-2104 + + 827 + + 2026 + May + 25 + + + Biochemical and biophysical research communications + Biochem Biophys Res Commun + + Employing network toxicology, molecular docking, machine learning, and single-cell analysis to analyze BPA exposure-induced ccRCC. + + 154037 + 154037 + + 10.1016/j.bbrc.2026.154037 + S0006-291X(26)00801-6 + + This study investigates the molecular mechanisms underlying bisphenol A (BPA)-induced clear cell renal cell carcinoma (ccRCC). We integrated transcriptomic data from multiple GEO datasets and performed differential expression analysis and WGCNA to identify BPA-associated candidate genes. Enrichment analyses implicated pathways including cell adhesion, lipid metabolism, arachidonic acid signaling, and innate immune response. Using twelve machine learning algorithms, we identified four core genes (ITGB2, TBXAS1, LIPA, and CLEC7A), all upregulated in ccRCC. Molecular docking suggested stable BPA-protein interactions with favorable binding energies. Single-cell analysis showed predominant expression of these genes in monocytes and macrophages. LIPA was elevated in kidney cancer tissues and associated with clinical outcomes. In vitro experiments confirmed that BPA exposure promoted ccRCC cell progression by regulating core gene expression. This integrated approach offers new insights into the molecular mechanisms of BPA-induced ccRCC and identifies potential biomarkers for environmental risk assessment. + Copyright © 2026 Elsevier Inc. All rights reserved. + + + + Chen + Jianyu + J + + Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. + + + + Wu + Lianquan + L + + Department of Urology, Longgang People's Hospital, Wenzhou, 325000, China. + + + + Jin + Haoqi + H + + Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. + + + + Li + Yeping + Y + + Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China; Institute of Urology, Wenzhou Medical University, Wenzhou, 325000, China. Electronic address: liypwz@126.com. + + + + eng + + Journal Article + + + 2026 + 05 + 25 + +
+ + United States + Biochem Biophys Res Commun + 0372516 + 0006-291X + + IM + + Bisphenol A + Clear cell renal cell carcinoma + Machine learning + Molecular docking + Single-cell analysis + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 22 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 19 + 51 + + + aheadofprint + + 42214919 + 10.1016/j.bbrc.2026.154037 + S0006-291X(26)00801-6 + + + + + + 42214908 + + 2026 + 05 + 29 + +
+ + 1876-035X + + 19 + 7 + + 2026 + May + 25 + + + Journal of infection and public health + J Infect Public Health + + The Italian hand hygiene project: A joint-commission Italian network analysis. + + 103268 + 103268 + + 10.1016/j.jiph.2026.103268 + S1876-0341(26)00140-1 + + Hand hygiene (HH) is a straightforward yet highly effective preventive measure against healthcare-associated infections; however, global compliance remains suboptimal. This study analyzed a substantial Italian dataset, focusing on adherence to HH guidelines among healthcare professionals, and identified key factors influencing compliance. + Direct multi- wave cross-sectional observational data on hand hygiene from over 749,000 opportunities were collected between 2017 and 2024 across 30 Italian hospitals participating in the Joint Commission International Hand Hygiene Project. Traditional statistical analyses and supervised machine learning (ML) models were employed to evaluate compliance rates and to examine their association with the year of observation, clinical area, professional role, and patient interaction moment (opportunity). Additionally, ML models were implemented to support proactive, risk- based surveillance within hospital settings by identifying high- risk contexts and facilitating more targeted monitoring strategies. + Overall hand hygiene compliance increased from 73.5% in 2017-82% in 2024, surpassing the World Health Organization's (WHO) target of 80% from 2020 onwards. Nursing staff demonstrated the highest adherence. High-risk opportunities, such as pre-aseptic procedures and post-exposure to bodily fluids, exhibited higher compliance than other moments. Emergency departments recorded the lowest compliance rates (76.3%). ML models confirmed that the year, patient interaction moment, and professional role are the most significant predictors of adherence. The most effective ML model achieved an accuracy of 64.6% in classifying hand hygiene actions. + This analysis underscores an improving trend in hand hygiene practices within Italian hospitals and concurrently identifies specific contexts-such as emergency departments-that require targeted interventions. The integration of observational monitoring with predictive artificial intelligence models could facilitate proactive, risk-based surveillance and focused improvement strategies. Accreditation-driven, multi-modal initiatives like this project highlight the vital role of measurement in fostering a culture of hand hygiene and advancing patient safety. + Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved. + + + + Buja + A + A + + Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua I-35131, Italy. Electronic address: alessandra.buja@unipd.it. + + + + Damiani + G + G + + Center of Research in Precision Medicine and Chronic Inflammation, Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan 20122, Italy; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan 20122, Italy. + + + + Tangherloni + A + A + + Center of Research in Precision Medicine and Chronic Inflammation, Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan 20122, Italy; Department of Computing Sciences, Bocconi University, Milan 20136, Italy. + + + + Manfredi + M + M + + Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua I-35131, Italy. + + + + Lanza + G + G + + Network Joint Commission Italia, Italy. + + + + Palmeri + C + C + + Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua I-35131, Italy. + + + + eng + + Journal Article + + + 2026 + 05 + 25 + +
+ + England + J Infect Public Health + 101487384 + 1876-0341 + + IM + + Hand hygiene + Health Care Associated Infection + Hospital infection + Nosocomial infection + Prevention and control + + Declaration of Competing Interest The authors declare that there are no conflicts of interest regarding the publication of this manuscript. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 1 + 13 + + + 2026 + 5 + 11 + + + 2026 + 5 + 19 + + + 2026 + 5 + 29 + 19 + 50 + + + aheadofprint + + 42214908 + 10.1016/j.jiph.2026.103268 + S1876-0341(26)00140-1 + + + + + + 42214885 + + 2026 + 05 + 29 + +
+ + 1879-0771 + + 132 + + 2026 + May + 24 + + + Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society + Comput Med Imaging Graph + + An interpretable multimodal model integrating clinical, spectral CT imaging, and deep learning analysis of intra- and peritumoral regions for preoperative prediction of perineural invasion in gastric cancer: A prospective, multicenter study. + + 102776 + 102776 + + 10.1016/j.compmedimag.2026.102776 + S0895-6111(26)00079-0 + + Perineural invasion (PNI) is crucial for risk stratification and treatment planning. This study aims to develop a novel multimodal model for preoperative prediction of PNI in gastric cancer. + A total of 250 patients with pathologically confirmed gastric cancer from multiple centers were enrolled and divided into a training cohort (n = 138), an internal validation cohort (n = 59), and an external validation cohort (n = 53). Spectral CT parameters from both intra- and peritumoral regions were acquired using the GSI viewer software, while deep learning features were extracted via a ResNet-50 architecture. Clinical features were screened through univariate and multivariate regression analyses. Based on machine learning, we developed a spectral parameter model, a deep learning feature model, a clinical prediction model, and a multimodal fusion model (MFM). The predictive performance of the models was evaluated using calibration curves, receiver operating characteristic curves, and decision curve analysis (DCA). Model interpretability was achieved by employing Shapley Additive exPlanations (SHAP). + Compared with individual models, MFM demonstrated the best predictive performance, with area under the curve (AUC) values of 0.926, 0.885, and 0.871 in the training, internal validation, and external validation cohorts, respectively. DCA confirmed that MFM provided a higher net benefit over a wide range of threshold probability. Calibration curve showed MFM had better predictive consistency, and the Hosmer-Lemeshow test indicated good fit across all cohorts (all p-values > 0.05). SHAP effectively interpreted the decision-making process of the model. + The novel multimodal fusion model, which integrates clinical, spectral CT parameters, and deep learning features from intra- and peritumoral regions, demonstrated outstanding performance in preoperatively predicting PNI in gastric cancer. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Zhao + Yuncong + Y + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China. + + + + Chen + Lihong + L + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China; Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou 350001, China. + + + + Wang + Lili + L + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China; Clinical Research Center for Radiology and Radiotherapy of Fujian Province (Digestive, Hematological and Breast Malignancies), Fuzhou 350001, China. + + + + Yue + Wenjing + W + + The Fourth Clinical College, Henan Medical University, Xinxiang, 453003, China. + + + + Zeng + Deye + D + + Department of Pathology, Fujian Medical University Union Hospital, Fuzhou 350001, China. + + + + You + Yaru + Y + + Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou 450052, China; Henan Key Laboratory of CT Imaging, Zhengzhou 450052, China. + + + + Weng + Caiming + C + + Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou 350001, China. + + + + Li + Weixing + W + + Department of Radiology, Xinxiang Central Hospital, The Fourth Clinical College, Henan Medical University, Xinxiang, 453003, China. + + + + Chen + Yilin + Y + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China. + + + + Lin + Lin + L + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China; Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou 350001, China. Electronic address: linlin@fjmu.edu.cn. + + + + Xu + Haiping + H + + School of Computer and Data Science, Minjiang University, Fuzhou 350108, China. Electronic address: haiping@mju.edu.cn. + + + + Xue + Yunjing + Y + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China; Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou 350001, China. Electronic address: xueyunjing@fjmu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 24 + +
+ + United States + Comput Med Imaging Graph + 8806104 + 0895-6111 + + IM + + Deep learning + Gastric cancer + Peripheral nerve invasion + SHAP + Spectral CT + + Declaration of Competing Interest The authors have no relevant financial or non-financial interests to disclose. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 10 + 15 + + + 2026 + 4 + 27 + + + 2026 + 5 + 13 + + + 2026 + 5 + 29 + 19 + 50 + + + aheadofprint + + 42214885 + 10.1016/j.compmedimag.2026.102776 + S0895-6111(26)00079-0 + + + + + + 42214863 + + 2026 + 05 + 29 + +
+ + 1873-3336 + + 513 + + 2026 + May + 28 + + + Journal of hazardous materials + J Hazard Mater + + Advances in multispectral and hyperspectral inversion for soil heavy metal contamination: Mechanisms, machine learning algorithms, and future perspectives. + + 142542 + 142542 + + 10.1016/j.jhazmat.2026.142542 + S0304-3894(26)01520-7 + + Heavy metal (HM) contamination in soil exhibits insidious and cumulative effects, posing long-term risks to ecosystems and human health. Traditional field sampling and laboratory analysis are increasingly insufficient for large-scale continuous monitoring, driving the adoption of multispectral (MS) and hyperspectral (HS) remote sensing. Bibliometric analysis reveals clear research trends: target elements are primarily copper, lead, and zinc, while data acquisition has progressively shifted from laboratory spectroscopy to portable devices and satellite platforms, reflecting an expansion from local to regional scales. This has led to increasing data complexity and greater demands on model robustness and generalization. However, expanding the spatial scale and transitioning to satellite observations introduce fundamental challenges. Mixed pixels and moisture-induced spectral distortions reduce signal purity, while the indirect spectral response of HMs further complicates quantitative inversion. Sample scarcity and spatial heterogeneity also limit cross-regional generalization, constraining model robustness and stability. In response, models have evolved from traditional linear regression to ensemble learning methods such as Extreme Gradient Boosting (XGBoost), and further to deep learning frameworks, including Convolutional Neural Networks (CNN) and Transformers, enabling hierarchical feature extraction and task-oriented structural design. This paper reviews the key technical bottlenecks in soil HM spectral inversion, integrating bibliometric insights with methodological advances to provide a comprehensive framework for understanding current progress and guiding future developments in large-scale, high-precision inversion. + Copyright © 2026 Elsevier B.V. All rights reserved. + + + + Huang + Minghao + M + + School of Geographic Sciences, School of Resources and Environmental Science, Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China. + + + + Wu + Xiang + X + + School of Geographic Sciences, School of Resources and Environmental Science, Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China; Key Laboratory of Danjiangkou Reservoir Area's Aquatic Eco-Environment and Health, Shiyan City (Hanjiang Normal University), Shiyan 442000, China. + + + + Peng + Qian + Q + + School of Geographic Sciences, School of Resources and Environmental Science, Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China; Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources (Ministry of Education), Sichuan Normal University, Chengdu 610068, China. Electronic address: qianpeng@hubu.edu.cn. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 28 + +
+ + Netherlands + J Hazard Mater + 9422688 + 0304-3894 + + IM + + Hyperspectral + Indirect Inversion Mechanisms + Machine learning + Multispectral + Soil Heavy metals + Spectral Feature Selection + + Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 2 + 21 + + + 2026 + 5 + 25 + + + 2026 + 5 + 26 + + + 2026 + 5 + 29 + 19 + 49 + + + aheadofprint + + 42214863 + 10.1016/j.jhazmat.2026.142542 + S0304-3894(26)01520-7 + + + + + + 42214786 + + 2026 + 05 + 29 + +
+ + 1876-4754 + + + 2026 + May + 28 + + + Brain stimulation + Brain Stimul + + EEG Biomarkers of Accelerated Prolonged Intermittent Theta-Burst Stimulation in Depression: Machine Learning with a Minimal-Electrode Montage. + + 103135 + 103135 + + 10.1016/j.brs.2026.103135 + S1935-861X(26)00112-9 + + + Chang + Chun-Hung + CH + + An Nan Hospital, China Medical University, Tainan, Taiwan; Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan; Shu-Zen Junior College of Medicine and Management, Tainan, Taiwan. + + + + Sack + Alexander T + AT + + Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Brain+Nerve Centre, Maastricht University Medical Centre (MUMC), The Netherlands. + + + + Chu + Che-Sheng + CS + + Department of Psychiatry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Non-Invasive Neuromodulation Consortium for Mental Disorders, Society of Psychophysiology, Taipei, Taiwan; Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan. Electronic address: youngtzuchi@hotmail.com. + + + + Chang + Hsin-An + HA + + Non-Invasive Neuromodulation Consortium for Mental Disorders, Society of Psychophysiology, Taipei, Taiwan; Department of Psychiatry, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan. Electronic address: chang.ha@mail.ndmctsgh.edu.tw. + + + + eng + + Letter + + + 2026 + 05 + 28 + +
+ + United States + Brain Stimul + 101465726 + 1876-4754 + + IM + + EEG biomarkers + accelerated piTBS + brain stimulation + machine learning + major depressive disorder + resting-state electroencephalography + + Declaration of Competing Interest ☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: ATS is director of the Academy of Brain Stimulation (www.brainstimulation-academy.com) and the International Clinical TMS Certification Course (www.tmscourse.eu), receiving equipment support from MagVenture, MagStim, Deymed, Yingchi, Brainsway. He also serves as scientific advisor for Platoscince Medical and Alpha Brain Technologies. Other authors declare that they have no competing financial interests or personal relationships that could be perceived to have influenced the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 23 + + + 2026 + 5 + 25 + + + 2026 + 5 + 26 + + + 2026 + 5 + 29 + 19 + 35 + + + aheadofprint + + 42214786 + 10.1016/j.brs.2026.103135 + S1935-861X(26)00112-9 + + + + + + 42214688 + + 2026 + 05 + 29 + +
+ + 1878-1632 + + + 2026 + May + 28 + + + The spine journal : official journal of the North American Spine Society + Spine J + + From Three to Eight Clusters: A Machine Learning-Based Classification of Operated Adult Spinal Deformity Patients. + S1529-9430(26)00157-9 + 10.1016/j.spinee.2026.05.003 + + Adult spinal deformity (ASD) is a heterogeneous condition encompassing diverse etiologies, clinical presentations, and surgical challenges. While previous unsupervised learning models have stratified ASD patients into three broad phenotypes, these often lack clinical granularity for surgical decision-making. + To refine the classification of operated ASD patients using unsupervised machine learning and assess whether expanding from 3 to 8 clusters improves clinical discrimination and predictive robustness. + Retrospective multicenter study using machine learning clustering. + 471 adult patients who underwent surgery for ASD across six specialized spine centers. + ODI, SRS-22, spinal alignment metrics, surgical strategy, and postoperative complications. + A k-means clustering algorithm was applied to a selected set of 12 demographic, radiographic, and functional variables (C12). Clustering solutions with 3 and 8 groups were compared. Each cluster was analyzed for age, etiology, disability scores (ODI, SRS-22), spinal alignment, surgical strategy, and complication rates. Predictive models using LDA and KNN assessed classification accuracy for new patient assignment. + The 8-cluster model identified clinically distinct phenotypes, including subgroups of young patients with idiopathic scoliosis, structural hyperkyphosis, and early sagittal decompensation. In elderly patients, clusters differentiated pain profiles, alignment, and frailty. The most severe clusters distinguished coronal-dominant versus sagittal collapse deformities, with differing risks and outcomes. LDA maintained robust accuracy (91.4%) in predicting cluster assignment across 50 testing splits, outperforming KNN. + To our knowledge, this is among the most granular unsupervised ML-based phenotypic classifications of operated ASD patients reported to date. This refined 8-cluster model enhances clinical phenotyping in ASD surgery, offering more precise subgroup stratification than traditional 3-cluster models. It supports the integration of unsupervised learning into personalized surgical planning, risk stratification, and multicenter outcome trials. + Copyright © 2026 Elsevier Inc. All rights reserved. + + + + Roscop + Cecile + C + + ELSAN, Polyclinique Jean Villar, Bruges Cedex, France. Electronic address: cecile.roscop@gmail.com. + + + + Bourghli + Anouar + A + + Spine Surgery Department, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia. + + + + Baroncini + Alice + A + + Casa di Cura Humanitas San Pio X, Milano, Italy. + + + + Alanay + Ahmet + A + + Spine Center, Acibadem University School of Medicine, Istanbul, Turkey. + + + + Pellise + Ferran + F + + Spine Surgery Unit, Vall D'Hebron Hospital, Barcelona, Spain. + + + + Kleinstueck + Frank + F + + Schulthess Klinik, Zürich, Switzerland. + + + + Pizones + Javier + J + + Spine Surgery Unit, Hospital Universitario La Paz, Madrid, Spain. + + + + Charles + Yann-Philippe + YP + + Hôpitaux Universitaires de Strasbourg, France. + + + + Larrieu + Daniel + D + + ELSAN, Polyclinique Jean Villar, Bruges Cedex, France. + + + + Boissière + Louis + L + + ELSAN, Polyclinique Jean Villar, Bruges Cedex, France. + + + + Obeid + Ibrahim + I + + ELSAN, Polyclinique Jean Villar, Bruges Cedex, France. + + + + European Spine Study Group (ESSG) + + No institution. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Spine J + 101130732 + 1529-9430 + + IM + + Adult spinal deformity + Clustering + K-means algorithm + Machine learning + Phenotyping + Predictive modeling + Surgical planning + + Declaration of competing interest The authors declare no competing interests. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 12 + 11 + + + 2026 + 5 + 15 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 19 + 34 + + + aheadofprint + + 42214688 + 10.1016/j.spinee.2026.05.003 + S1529-9430(26)00157-9 + + + + + + 42214660 + + 2026 + 05 + 29 + +
+ + 1878-3511 + + + 2026 + May + 28 + + + International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases + Int J Infect Dis + + Investigating Infection and Clinical Data to Predict Effective Antibiotic Therapy and Monitor Resistance Trends. + + 108842 + 108842 + + 10.1016/j.ijid.2026.108842 + S1201-9712(26)00477-7 + + Data-driven approaches to effectively select antibiotics are crucial to improving patient outcomes and reducing antibiotic resistance. This study aimed to determine whether routinely collected clinical and microbiological data can be used to train machine learning (ML) models to predict antibiotic resistance in patients with bacterial infections. + We conducted a retrospective observational study on clinical data from two separate Italian hospitals, analyzing 15,581 bacterial isolates collected from 9,966 patients between 2018 and 2024 at a multi-department hospital located in Rome and secondary clinic located in Capranica, about 70 Km away from Rome. Multiple ML models were trained, using both unbalanced and SMOTE-balanced datasets. Performance was assessed using cross-validation and independent test sets, comprising 2023-2024 isolates. Performance was measured using the Area Under the Receiver Operating Characteristic curve (AUROC), F1 score, accuracy, precision, and recall. + XGBoost consistently outperformed other trained models, achieving AUROCs of 0.882 and 0.878 for Gram-positive and Gram-negative datasets, respectively. Species-specific models further improved discrimination, reaching AUROC scores up to 0.946 for P. aeruginosa, 0.941 for K. pneumoniae, 0.938 for E. faecalis, 0.919 for E. coli, 0.894 for P. mirabilis, and 0.891 for S. aureus. + These results demonstrate the utility of ML models in accurately predicting antibiotic susceptibility from routine clinical data, thus facilitating rapid initiation of targeted therapy. Such an approach can potentially reduce treatment delays by up to 48 hours compared to traditional diagnostic methods, presenting a useful tool to manage patients in critical conditions and combat antibiotic resistance in clinical practice. + Copyright © 2026. Published by Elsevier Ltd. + + + + Benedetto + Marco + M + + Department of Management and Innovation Systems, University of Salerno, Fisciano (SA) 84084, Italy; Kelyon S.r.l., via Benedetto Brin, 59 C5/C6, 80100 Naples, Italy. + + + + Piccinni + Giuseppe + G + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + De Leo + Cristina + C + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + Cirielli + Corrado + C + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + Facchiano + Francesco + F + + Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy. + + + + Facchiano + Angelo + A + + Laboratory of Bioinformatics and Computational Biology, Institute of Food Science, CNR, via Roma 52 A/C, 83100 Avellino, Italy. + + + + Panebianco + Annarita + A + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + Tagliaferri + Stefano + S + + Kelyon S.r.l., via Benedetto Brin, 59 C5/C6, 80100 Naples, Italy. + + + + La Rocca + Michele + M + + Department of Economics and Statistics, University of Salerno, Fisciano (SA) 84084, Italy. + + + + Marchetti + Paolo + P + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + Tagliaferri + Roberto + R + + Department of Management and Innovation Systems, University of Salerno, Fisciano (SA) 84084, Italy. + + + + Facchiano + Antonio + A + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy; Department of Life Sciences, Health and Health Professions, Link University, 00165 Rome, Italy. Electronic address: a.facchiano@idi.it. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + Canada + Int J Infect Dis + 9610933 + 1201-9712 + + IM + + Antimicrobial resistance + antibiotic susceptibility prediction + antimicrobial stewardship + machine learning + precision medicine + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2025 + 11 + 28 + + + 2026 + 4 + 17 + + + 2026 + 5 + 26 + + + 2026 + 5 + 29 + 19 + 33 + + + aheadofprint + + 42214660 + 10.1016/j.ijid.2026.108842 + S1201-9712(26)00477-7 + + + + + + 42214547 + + 2026 + 05 + 29 + +
+ + 1873-6424 + + + 2026 + May + 28 + + + Environmental pollution (Barking, Essex : 1987) + Environ Pollut + + Machine-learning-based estimation of ground-level NO2 concentrations across Southeastern Europe using multi-source satellite, reanalysis, and emission data. + + 128461 + 128461 + + 10.1016/j.envpol.2026.128461 + S0269-7491(26)00831-6 + + Reliable estimation of ground-level nitrogen dioxide (NO2) remains challenging due to limited monitoring coverage and complex interactions among emissions, meteorology, and land-use factors. This study develops a machine-learning framework to estimate surface NO2 concentrations across Southeastern Europe, covering thirteen countries with diverse emission sources and sparse monitoring networks. Daily NO2 observations from 317 monitoring stations were integrated with multiple satellite and ancillary datasets, including tropospheric NO2 from Sentinel-5P TROPOMI, vegetation indices from MODIS, and nighttime light data from VIIRS. Additional inputs included meteorological variables from ERA5, anthropogenic emissions from EDGAR v8.1, land-use data from Copernicus Corine Land Cover, elevation from the Copernicus Digital Elevation Model, and population density from the Global Human Settlement Layer. All datasets were processed and harmonized using Google Earth Engine. Three Gradient Boosting Decision Tree models (XGBoost, LGBM, and CatBoost) were trained and optimized with Optuna. Model performance was assessed using 10-fold cross-validation and independent test sets with R2, MAE, and RMSE metrics. All models performed consistently, with LGBM achieving the best results (R2 = 0.86; MAE = 5.34 μg/m3; RMSE = 8.32 μg/m3). SHapley Additive exPlanations (SHAP) identified key predictors and improved interpretability. Seasonal analysis showed higher accuracy in summer and autumn than in winter and spring. These results demonstrate that integrating multi-source data with advanced machine learning enables reliable surface NO2 estimation in regions with limited monitoring, supporting air quality assessment, exposure analysis, and evidence-based policymaking in Southeastern Europe. + Copyright © 2026. Published by Elsevier Ltd. + + + + Bilgiç + Efem + E + + Department of Environmental Engineering, Faculty of Engineering, Dokuz Eylul University, Izmir, Türkiye. Electronic address: efem.bilgic@deu.edu.tr. + + + + Elbir + Tolga + T + + Department of Environmental Engineering, Faculty of Engineering, Dokuz Eylul University, Izmir, Türkiye; Environmental Research and Application Center (CEVMER), Dokuz Eylul University, Izmir, Türkiye. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Environ Pollut + 8804476 + 0269-7491 + + IM + + Nitrogen dioxide + Southeastern Europe + air quality modeling + machine learning + satellite data integration + + Declaration of Competing Interest ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 1 + 26 + + + 2026 + 5 + 24 + + + 2026 + 5 + 28 + + + 2026 + 5 + 29 + 19 + 32 + + + aheadofprint + + 42214547 + 10.1016/j.envpol.2026.128461 + S0269-7491(26)00831-6 + + + + + + 42214540 + + 2026 + 05 + 29 + +
+ + 1873-6424 + + + 2026 + May + 28 + + + Environmental pollution (Barking, Essex : 1987) + Environ Pollut + + Nonlinear meteorological controls on coastal air quality along the Indian East Coast: Insights from a multi-city interpretable machine learning framework. + + 128448 + 128448 + + 10.1016/j.envpol.2026.128448 + S0269-7491(26)00818-3 + + This study investigates the role of meteorological variability in shaping ambient air quality across five coastal cities along the Indian East Coast (Haldia, Balasore, Visakhapatnam, Chennai, and Puducherry). Despite their shared coastal setting, the cities exhibit pronounced meteorological heterogeneity, leading to a clear latitudinal gradient in air pollution, with northern cities (Haldia and Balasore) experiencing substantially higher particulate matter concentrations and more frequent exceedances than southern cities (Chennai and Puducherry). Across all cities, PM10 is the dominant contributor to variability in Air Quality Index (AQI) variability. A Random Forest model reveals that meteorological parameters explain a substantial fraction of particulate matter variability (R2 up to ∼0.6), whereas gaseous pollutants show lower predictability (R2 < 0.35). Using Shapley Additive exPlanations (SHAP) analysis, we identify critical nonlinear thresholds and binary regime switches governing pollutant accumulation. A consistent regional specific humidity (SH) threshold of ∼15 ± 2 g/kg is identified as a key regime-switching threshold, with values below this level consistently associated with PM10 accumulation across all cities. In southern cities, a Sea Breeze Component (SBC) > 3 m/s is the threshold for coastal ventilation; below this level, pollutants remain trapped. Furthermore, we uncover a monsoon break thermal trap, in which temperatures exceeding 32 °C flip the expected seasonal dispersion into an accumulation regime. These findings demonstrate that coastal air quality is governed by discrete meteorological regime transitions rather than linear responses. The identified thresholds provide a physically interpretable framework for improving localized air quality forecasting and mitigation strategies in complex coastal environments. + Copyright © 2026. Published by Elsevier Ltd. + + + + Varaprasad + V + V + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. + + + + Madhavan + B L + BL + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. + + + + Jain + Chaithanya D + CD + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. + + + + Krishnaveni + A Sai + AS + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. + + + + Ratnam + M Venkat + MV + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. Electronic address: vratnam@narl.gov.in. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Environ Pollut + 8804476 + 0269-7491 + + IM + + Coastal air quality + RF model + SHAP + boundary layer dynamics + meteorological variability + nonlinear interactions + + Declaration of Competing Interest ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 8 + + + 2026 + 5 + 22 + + + 2026 + 5 + 27 + + + 2026 + 5 + 29 + 19 + 32 + + + aheadofprint + + 42214540 + 10.1016/j.envpol.2026.128448 + S0269-7491(26)00818-3 + + + + + + 42214280 + + 2026 + 05 + 29 + +
+ + 1532-8708 + + 53 + 4 + + 2026 + Apr + 07 + + + Seminars in oncology + Semin Oncol + + Physics-informed machine learning for tumor microenvironment-responsive nanomedicine: Recent updates. + + 152498 + 152498 + + 10.1016/j.seminoncol.2026.152498 + S0093-7754(26)00045-X + + Physics-informed machine learning (PIML) is rapidly emerging as a transformative paradigm for designing tumor microenvironment (TME)-responsive nanomedicines. While existing reviews have surveyed PIML in broader biomedical contexts, this work provides a focused, critical synthesis specifically at the intersection of PIML, multiscale TME biophysics, and clinically actionable nanomedicine design. We argue that the unique value of PIML lies not merely in combining physics with data, but in its capacity to resolve the "personalization paradox" in oncology: the tension between the need for patient-specific models and the scarcity of patient-specific data. By embedding governing physical laws-such as Darcy's flow, reaction-diffusion kinetics, and Navier-Stokes equations-as soft constraints, PIML models can generate physically plausible, patient-tailored predictions even with sparse clinical inputs. This review uniquely articulates a translational roadmap that systematically links fundamental PIML methodologies to concrete nanomedicine optimization tasks: predicting nanoparticle transport in heterogeneous TMEs, designing stimulus-responsive nanocarriers, and integrating multi-omics/imaging for personalized therapy. We further introduce a novel comparative framework evaluating PIML against purely physics-based and purely data-driven approaches, highlighting its superior data efficiency and interpretability for TME applications. However, significant challenges remain, including data standardization, computational scalability, and regulatory adaptation. Looking forward, we identify under-explored yet high-impact frontiers, such as quantum-informed PIML for molecular-scale nanocarrier design, real-time adaptive nanomedicine guided by patient digital twins, and the ethical-regulatory frameworks needed for clinical deployment. By synthesizing cross-disciplinary insights and proposing a clear path from bench to bedside, this review aims to not only summarize the state-of-the-art but also to catalyze the next generation of intelligent, patient-centric cancer nanotherapeutics. + Copyright © 2026 Elsevier Inc. All rights reserved. + + + + Hasannia + Maliheh + M + + Cancer Research Center, Semnan University of Medical Sciences, Semnan, Iran. + + + + Abounoori + Ali + A + + Department of Microbial Biotechnology, Faculty of Biotechnology, Amol University of Special Modern Technologies, Amol, Iran. + + + + Abounoori + Mahdi + M + + Cancer Research Center, Semnan University of Medical Sciences, Semnan, Iran. + + + + Shirzad + Maryam + M + + Nanotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran. + + + + Rahdar + Abbas + A + + Department of Physics, University of Zabol, Zabol, Iran. Electronic address: a.rahdar@uoz.ac.ir. + + + + Fathi-Karkan + Sonia + S + + Natural Products and Medicinal Plants Research Center, North Khorasan University of Medical Sciences, Bojnurd, Iran; Department of Advanced Sciences and Technologies in Medicine, School of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran; Food and Drug Research Center, Food and Drug Administration, Ministry of Health and Medical Education, Tehran, Iran. Electronic address: Soniafathi92@gmail.com. + + + + Romanholo Ferreira + Luiz Fernando + LF + + Graduate Program in Genomic Sciences and Biotechnology, Catholic University of Brasília, Brasília, 71966-700, Brazil. Electronic address: luiz.romanholo@p.ucb.br. + + + + eng + + Journal Article + Review + + + 2026 + 04 + 07 + +
+ + United States + Semin Oncol + 0420432 + 0093-7754 + + IM + + Multi-omics integration + Nanomedicine + Nanoparticle transport + Physics-informed machine learning + Tumor microenvironment + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2025 + 11 + 14 + + + 2026 + 2 + 12 + + + 2026 + 4 + 5 + + + 2026 + 5 + 29 + 18 + 10 + + + aheadofprint + + 42214280 + 10.1016/j.seminoncol.2026.152498 + S0093-7754(26)00045-X + + + + + + 42214151 + + 2026 + 05 + 29 + +
+ + 1873-3573 + + 309 + + 2026 + May + 28 + + + Talanta + Talanta + + Machine learning assisted transparent bending-insensitive SERS substrate for reliable quantitative analysis of thiram residues on fruit surfaces. + + 130059 + 130059 + + 10.1016/j.talanta.2026.130059 + S0039-9140(26)00715-0 + + Transparent and flexible surface-enhanced Raman scattering (SERS) substrates are highly suitable for rapid pesticide detection on fruit surfaces, owing to their capability for in situ detection on curved surfaces. However, achieving reproducible and stable SERS signals from flexible SERS substrates under bending conditions remains a major challenge. Additionally, integrating machine learning into Raman spectral analysis can further enhance the practicality of SERS technology. Herein, we develop a novel sensing platform that combines a transparent, bending-insensitive SERS substrate with machine learning for the rapid quantitative determination of thiram residues on fruit surfaces. The flexible SERS substrate was constructed by depositing silver nanoparticles (AgNPs) onto a polydimethylsiloxane (PDMS) twisted nanowrinkle array. Endowed with high SERS activity, exceptional transparency, and bending insensitivity, this substrate enabled reliable and consistent detection of thiram residues on apple and cherry tomato peels, with a sensitivity as low as 10-7 M. Furthermore, a back propagation neural network (BPNN)-based machine learning model was developed to predict thiram concentrations, achieving remarkable accuracy with a coefficient of determination (R2) of 0.9954 and a root mean square error (RMSE) of 0.0985. Thus, the proposed sensing platform provides a rapid, accurate, and reliable approach for the identification and quantification of thiram in fruits. + Copyright © 2026 Elsevier B.V. All rights reserved. + + + + Wang + Yue + Y + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Huang + Lili + L + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Wang + Jie + J + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Li + Zetong + Z + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Bian + Ziyang + Z + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Yang + Zhenshan + Z + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Li + Hefu + H + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. Electronic address: lihefu@lcu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + Netherlands + Talanta + 2984816R + 0039-9140 + + IM + + BPNN + Bending insensitivity + Quantitative analysis + SERS + Thiram + + Declaration of competing interest The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this study. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 9 + + + 2026 + 5 + 23 + + + 2026 + 5 + 27 + + + 2026 + 5 + 29 + 18 + 7 + + + aheadofprint + + 42214151 + 10.1016/j.talanta.2026.130059 + S0039-9140(26)00715-0 + + + + + + 42214146 + + 2026 + 05 + 29 + +
+ + 1879-0534 + + 212 + + 2026 + May + 29 + + + Computers in biology and medicine + Comput Biol Med + + A recurrent interferon, stress, and survival axis identifies a rare malignant programme across glioblastoma single-cell, spatial, and longitudinal cohorts. + + 111780 + 111780 + + 10.1016/j.compbiomed.2026.111780 + S0010-4825(26)00344-6 + + Glioblastoma exhibits profound intratumoral heterogeneity and rapid adaptation under stress, complicating durable therapeutic control. Here, a biologically grounded transcriptional axis is defined by three complementary modules capturing type I interferon signalling, immediate-early stress response, and pro-survival signalling enriched in unfolded protein response components. Using this equal-weighted axis, a rare axis-high programme was identified in a glioblastoma training cohort (GSE173278; 75,075 cells after filtering; 5.95% prevalence) and mapped to external cohorts through marker-anchored cohort-specific clusters. To enable reproducible detection, an interpretable 40-gene regularised logistic regression classifier was trained using fold-internal panel selection and Platt calibration fitted strictly on out-of-fold predictions. The conservative out-of-fold training evaluation showed informative discrimination and good probability calibration (AUC 0.767, AP 0.116, Brier 0.055, ECE 0.011). When the final model was applied to marker-anchored operational labels, ranking performance was high in an independent glioblastoma cohort (GSE131928; AUC 0.950, AP 0.351) and in an exploratory pan-cancer transfer setting (GSE203612; AUC 0.944, AP 0.343), although calibration slopes indicated dataset-dependent probability-scale shift. Longitudinal CARE glioblastoma samples showed that recalibration on T1 samples improved probability-scale reliability on later T2/T3 samples without changing rank discrimination. Spatial stratification based on available sample annotations linked higher detector probabilities to necrotic, bulk, and tumour-region samples. Irradiation feature-count contrasts supported positive shifts of the 40-gene panel and axis-related modules, while cell-level treated/control summaries were interpreted as context-specific perturbational evidence. Together, these results define a rare axis-high glioblastoma programme and provide a recalibratable gene-panel detector for probability-guided experimental triage and mechanistic follow-up. + Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved. + + + + Pastore + Emmanuel Pio + EP + + Department of Biology, Ecology and Earth Science, University of Calabria, 87036 Rende, Italy. + + + + De Rango + Francesco + F + + Department of Biology, Ecology and Earth Science, University of Calabria, 87036 Rende, Italy. Electronic address: francesco.derango@unical.it. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + Comput Biol Med + 1250250 + 0010-4825 + + IM + + Glioblastoma + Interferon signaling + Machine learning + Protein response + Reproducible bioinformatics + Single-cell RNA-seq + Stress response + Unfolded + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 3 + 4 + + + 2026 + 5 + 11 + + + 2026 + 5 + 25 + + + 2026 + 5 + 29 + 18 + 7 + + + aheadofprint + + 42214146 + 10.1016/j.compbiomed.2026.111780 + S0010-4825(26)00344-6 + + + + + + 42214095 + + 2026 + 05 + 29 + +
+ + 1933-0693 + + + 2026 + May + 29 + + + Journal of neurosurgery + J Neurosurg + + Assessment system for short-term lower cranial nerve dysfunction following medulla oblongata glioma surgery: risk stratification and optimal surgical strategy. + + 1 + 12 + 1-12 + + 10.3171/2025.12.JNS251593 + + Medullary gliomas pose significant surgical risks, particularly the risk of postoperative lower cranial nerve (LCN) dysfunction, which profoundly affects quality of life. The lack of standardized risk assessment hinders optimal surgical planning. The aim of this study was to develop and validate an individualized predictive model for short-term postoperative LCN impairment integrating clinical and imaging data and to estimate individual risk across a range of resection extent to optimize surgical planning. + A retrospective cohort (n = 111, January 2020-February 2023) was used for model development, with prospective validation (n = 45, February 2023-December 2024). The primary outcome was postoperative LCN dysfunction (inability to be extubated within 14 days or requiring tracheotomy with persistent ventilation). Predictive modeling was performed using logistic regression, incorporating multistage feature selection, hyperparameter optimization, and bootstrapped validation. Model performance was evaluated using metrics such as area under the curve (AUC), Brier score, calibration, and decision curve analysis (DCA) in the prospective validation set. Shapley Additive Explanations (SHAP) analysis was used to interpret feature contributions, and a nomogram was constructed for clinical implementation. Optimal extent of resection (EOR) thresholds were explored to balance functional preservation and tumor clearance. + Four independent predictors of LCN dysfunction were identified: EOR (OR 1.84, 95% CI 1.07-3.16), infiltrative growth (OR 10.46 [95% CI 2.70-40.52]), preoperative LCN impairment (OR 6.79 [95% CI 2.54-18.16]), and cervical cord involvement (OR 4.64 [95% CI 1.55-13.91]). The model demonstrated strong discrimination (training AUC 0.85 [95% CI 0.76-0.92], testing AUC 0.89 [95% CI 0.79-0.97]) and good calibration (Brier score 0.12). High-risk patients, defined as those with a model predicted risk probability > 0.471 based on Youden's index, had significantly higher rates of pneumonia, tracheostomy, and prolonged mechanical ventilation. Stratified resection plans showed that low-risk patients benefited from gross-total resection (EOR 88.4%, [95% CI 75.7%-100.0%]), while high-risk patients achieved optimal functional outcomes with limited resection (EOR 40.8%, [95% CI 38.2%-50.5%]). SHAP and nomogram analyses provided transparent, patient-specific information for risk consultations. + This study presents the first predictive model tailored to short-term postoperative LCN outcomes following medullary glioma surgery, proposing a dynamic resection paradigm based on individualized risk stratification. By guiding surgical planning and intraoperative decision-making, this model facilitates a balance between maximal tumor control and functional preservation. + + + + Zhang + Mingxin + M + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + 2Chinese Institutes for Medical Research, Beijing. + + + + Xiao + Xiong + X + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Jia + Heyuan + H + + 5School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing. + + + + Liu + Yinan + Y + + 6Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing; and. + + + + Jiang + Wenju + W + + 7Beijing Arion Cancer Center, Beijing, China. + + + + Xie + Luyang + L + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + 2Chinese Institutes for Medical Research, Beijing. + + + + Pan + Changcun + C + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Wu + Zhen + Z + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Zhang + Junting + J + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Zhang + Liwei + L + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + 2Chinese Institutes for Medical Research, Beijing. + + + 3Beijing Neurosurgical Institute, Capital Medical University, Beijing. + + + 4China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Zhang + Peng + P + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + J Neurosurg + 0253357 + 0022-3085 + + IM + + lower cranial nerve dysfunction + machine learning + medullary glioma + oncology + predictive model + risk stratification + skull base + surgical decision-making + tumor + + + + + + 2025 + 6 + 21 + + + 2025 + 12 + 19 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 17 + 52 + + + aheadofprint + + 42214095 + 10.3171/2025.12.JNS251593 + + + + + + 42214078 + + 2026 + 05 + 29 + +
+ + 1520-5215 + + + 2026 + May + 29 + + + The journal of physical chemistry. A + J Phys Chem A + + IPSBrain: A Unified Intelligent Data Analysis Platform for Multimodal Experimental Characterization at Advanced Photon Source. + 10.1021/acs.jpca.6c01154 + + Advanced photon sources deliver X-ray with unprecedented brilliance and coherence, facilitating sophisticated multimodal characterization of diverse materials while simultaneously generating data volumes that far exceed the capacity of traditional, expert-driven analytical workflows. This growing gap between data acquisition and interpretation has become a critical bottleneck for timely scientific discovery, which creates a critical need for high-throughput and automated data analysis tools. Recent advances in artificial intelligence (AI) offer a transformative potential to address this challenge, enabling the direct mapping of complex experimental observations into scientific insights. However, existing AI-driven tools are remain largely fragmented and task-specific, limiting their adoption of modern multimodal experiments at the facility scale. Here we present IPSBrain, a unified AI-powered platform that serves as Intelligent Photon Source Brain for automated analysis of multimodal synchrotron experimental data. The platform integrated a series of novel, self-developed machine-learning-based models that enable automated data analysis from multiple experimental techniques, including diffraction, scattering, X-ray absorption, and tomograph. The platform is accessible through an intuitive web interface, which significantly lowers the technical barrier to advanced scientific data interpretation. By enabling end-to-end intelligent workflows from data acquisition to quantitative physical insight, IPSBrain supports high-throughput, reproducible, and scalable analysis aligned with the operational demands of modern synchrotron radiation sources. IPSBrain not only demonstrates a practical and extensible framework for AI-driven synchrotron data analysis but also establishes a transferable paradigm for intelligent data analysis of other large-scale research infrastructures, such as neutron sources and free-electron lasers. + + + + Pang + Yihe + Y + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + Spallation Neutron Source Science Center, Dongguan 523800, China. + + + + Li + Lin + L + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China. + + + + Deng + Xiangwen + X + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + University of Chinese Academy of Sciences, Beijing 100049, China. + + + + Xi + Zuoxin + Z + 0000-0003-1237-7780 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Information Engineering, Minzu University of China, Beijing 100081, China. + + + + Li + Jing + J + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Information Engineering, Minzu University of China, Beijing 100081, China. + + + + Han + Mengdan + M + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Information Engineering, Minzu University of China, Beijing 100081, China. + + + + Sun + Yaping + Y + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Science, China University of Geosciences Beijing, Beijing 100083, China. + + + + Yao + Haodong + H + 0000-0001-5959-8594 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + University of Chinese Academy of Sciences, Beijing 100049, China. + + + + Wu + Liyuan + L + 0000-0002-4890-2770 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + + Yuan + Qin + Q + 0009-0001-7123-6155 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + + Zhao + Lina + L + 0000-0002-9796-0221 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + University of Chinese Academy of Sciences, Beijing 100049, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + J Phys Chem A + 9890903 + 1089-5639 + + IM + + + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 17 + 12 + + + aheadofprint + + 42214078 + 10.1021/acs.jpca.6c01154 + + + + + + 42214077 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2561-3278 + + 11 + + 2026 + May + 29 + + + JMIR biomedical engineering + JMIR Biomed Eng + + Psychosocial Stress in the Chinese Community: Speech Analytics Through Linguistic and Acoustic Fusion Using Machine Learning. + + e91138 + e91138 + + 10.2196/91138 + + Family caregivers experience significant stress due to intensive caregiving activities, making them highly susceptible to adverse psychosocial health conditions. Early detection of this stress is crucial for timely interventions to prevent disease progression and long-term disability. + This study aimed to develop and validate the Linguistic and Acoustic Speech Analytics Program, a novel machine learning approach capable of providing a fusion analysis of linguistic and acoustic speech features to enhance the effectiveness of psychosocial stress assessment. + This quantitative study analyzed speech data collected from 100 Chinese family caregivers. Participants responded to 12 open-ended questions, and their voices were recorded for linguistic and acoustic feature extraction. Various machine learning classifiers, including support vector machine, were developed to process speech data. A key methodological step was the application of an orthogonalization procedure to decorrelate acoustic features from linguistic features before fusion analysis. The classifiers were then trained to evaluate psychosocial stress levels based on the processed and fused linguistic and acoustic speech features. Model performance was measured using receiver operating characteristic-area under the curve, F1-score, and accuracy. + The linear support vector machine model emerged as the top performer, achieving a receiver operating characteristic-area under the curve of 78.28%, an F1-score of 75.27%, and an accuracy of 73%. These results demonstrate the model's strong capability in identifying stressed participants based on their speech. Critically, the fusion of linguistic and acoustic features significantly outperformed models using either feature type alone. Furthermore, the orthogonalization procedure proved essential, as decorrelating features before fusion markedly enhanced classification accuracy compared to using non-orthogonalized features. + This study demonstrates that fusion analysis of linguistic and acoustic features effectively identifies psychosocial stress among family caregivers. It also emphasizes the importance of proper feature processing when combining multiple features extracted from the same audio sample. These findings provide valuable insights for developing machine learning models for psychosocial stress assessment and addressing various psychosocial conditions in different contexts, supporting population mental health management. + © Amanda M Y Chu, Benson S Y Lam, Jenny T Y Tsang, Agnes Tiwari, Jacky N L Chan, Mike K P So. Originally published in JMIR Biomedical Engineering (http://biomsedeng.jmir.org). + + + + Chu + Amanda M Y + AMY + 0000-0002-9543-747X + + Department of Social Sciences and Policy Studies, The Education University of Hong Kong, Tai Po, Hong Kong, China. + + + + Lam + Benson S Y + BSY + 0000-0002-0836-4162 + + Department of Mathematics, Statistics and Insurance, The Hang Seng University of Hong Kong, Shatin, Hong Kong, China. + + + + Tsang + Jenny T Y + JTY + 0000-0003-4558-9384 + + School of Nursing, Tung Wah College, Homantin, Hong Kong, China. + + + + Tiwari + Agnes + A + 0000-0002-3993-8552 + + School of Nursing, Hong Kong Sanatorium and Hospital, Hong Kong, China. + + + + Chan + Jacky N L + JNL + 0000-0003-1704-4221 + + Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China, 852 2358 7726. + + + + So + Mike K P + MKP + 0000-0003-0781-8166 + + Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China, 852 2358 7726. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Canada + JMIR Biomed Eng + 101771351 + 2561-3278 + + + caregivers + digital health + psychosocial health + speech analytics + text analytics + + + + + + 2026 + 1 + 11 + + + 2026 + 3 + 23 + + + 2026 + 3 + 24 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 17 + 12 + + + epublish + + 42214077 + 10.2196/91138 + v11i1e91138 + + + + + + 42214073 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1929-0748 + + 15 + + 2026 + May + 29 + + + JMIR research protocols + JMIR Res Protoc + + Creation of an mHealth Infrastructure to Support the Development and Delivery of mHealth Interventions: Protocol for Demonstration Projects Addressing Smoking Cessation in Cancer Care. + + e92288 + e92288 + + 10.2196/92288 + + Cancer remains a leading cause of morbidity worldwide. To reduce this burden, scalable, effective approaches are needed to address modifiable risk factors for cancer and support behavioral self-management. With smartphone ownership now nearly ubiquitous, mobile health (mHealth) interventions offer a powerful means to extend the reach, accessibility, and sustainability of evidence-based treatments for a variety of modifiable risk factors (eg, excessive alcohol use, physical inactivity, poor diet, and smoking). Moreover, the flexibility of mHealth platforms enables efficient delivery of novel interventions, supports innovative study designs, and facilitates real-time data collection to advance public health research. + Despite the great potential of mHealth interventions, developing high-quality mHealth tools is complex, time-consuming, and resource-intensive. To address these challenges, we are developing a coordinated, accessible, research-grade infrastructure for mHealth app development, testing, and dissemination. + The mHealth Florida infrastructure (mFLi) will provide a comprehensive, low-code software platform that enables researchers to build apps compatible with major mobile operating systems, namely, Apple iOS and Google Android. Through a modular interface, users will select from a menu of prebuilt features to tailor functionality to specific study needs. The platform will include 3 integrated environments (development, testing, and production), allowing researchers to prototype, evaluate, and deploy mHealth interventions. This infrastructure will be developed and maintained by a multidisciplinary team, ensuring that the platform is technically robust and usable and adheres to institutional and regulatory standards. To demonstrate the platform's functionality, utility, and adaptability, a multisite study comprising three initial projects focused on smoking cessation among patients with cancer is being conducted: (1) participant screening and enrollment, (2) randomization and treatment delivery, and (3) data processing using machine learning methods with on-device and cloud-based approaches. + This study was funded in May 2023, and ethics approval was obtained from all involved sites' institutional review boards between February 2024 and October 2025. Recruitment began in March 2025 and enrollment is ongoing. As of January 2026, 41% (37/90) of the target sample have been enrolled and 21% (19/90) have completed their 6-month assessment. Data collection will be completed once the final participant completes their 6-month assessment (expected May 2027), with analyses commencing thereafter. Study findings are anticipated to be published in a peer-reviewed journal in 2027. + Collectively, these projects will illustrate how mFLi can streamline app development, facilitate rapid translation of research into practice, and reduce barriers for researchers and developers. Ultimately, mFLi is designed to accelerate innovation in mHealth research, enhance access to behavioral interventions, and improve health outcomes among diverse populations. + ClinicalTrials.gov NCT06909357; https://clinicaltrials.gov/study/NCT06909357. + PRR1-10.2196/92288. + ©Lindsey S Sparrock, Jennifer I Vidrine, Christine E Vinci, Issam M El Naqa, Steven K Sutton, Ramzi G Salloum, Jesse Dallery, Tracy E Crane, Frank J Penedo, Samuel J Brockway, Sarah R Jones, Charles E Hoogland, Richard R Reich, Guillermo Gonzalez-Calderon, Vani N Simmons, Damon J Vidrine. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 29.05.2026. + + + + Sparrock + Lindsey S + LS + 0000-0003-3230-7886 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + + Vidrine + Jennifer I + JI + 0000-0001-7159-7252 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Psychology, College of Arts and Sciences, University of South Florida, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + + Vinci + Christine E + CE + 0000-0001-8883-0989 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Psychology, College of Arts and Sciences, University of South Florida, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + + El Naqa + Issam M + IM + 0000-0001-6023-1132 + + Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, FL, United States. + + + + Sutton + Steven K + SK + 0000-0002-2504-7601 + + Department of Psychology, College of Arts and Sciences, University of South Florida, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, United States. + + + + Salloum + Ramzi G + RG + 0000-0002-8139-2418 + + Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States. + + + Health Cancer Center, University of Florida, Gainesville, FL, United States. + + + + Dallery + Jesse + J + 0000-0002-2882-1105 + + Department of Psychology, University of Florida, Gainesville, FL, United States. + + + + Crane + Tracy E + TE + 0000-0002-4288-134X + + Department of Medicine, Miller School of Medicine, University of Miami, Miami, FL, United States. + + + + Penedo + Frank J + FJ + 0000-0002-2780-0417 + + Department of Medicine, Miller School of Medicine, University of Miami, Miami, FL, United States. + + + Department of Psychology, University of Miami, Coral Gables, FL, United States. + + + + Brockway + Samuel J + SJ + 0009-0000-4761-768X + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + + Jones + Sarah R + SR + 0000-0001-5513-2527 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + + Hoogland + Charles E + CE + 0000-0001-7735-1931 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + + Reich + Richard R + RR + 0009-0009-9559-6858 + + Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, United States. + + + + Gonzalez-Calderon + Guillermo + G + 0000-0002-4075-8946 + + Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, United States. + + + + Simmons + Vani N + VN + 0000-0002-4381-5336 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Psychology, College of Arts and Sciences, University of South Florida, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + + Vidrine + Damon J + DJ + 0000-0002-8711-796X + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + + eng + + + ClinicalTrials.gov + + NCT06909357 + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Canada + JMIR Res Protoc + 101599504 + 1929-0748 + + IM + + + Humans + + + Smoking Cessation + methods + + + Telemedicine + + + Neoplasms + therapy + + + Mobile Applications + + + Digital Health + + + + cancer + feasibility + mHealth + randomized controlled trial + smoking + tobacco cessation + + + + + + 2026 + 2 + 9 + + + 2026 + 3 + 18 + + + 2026 + 3 + 17 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 16 + 53 + + + epublish + + 42214073 + 10.2196/92288 + v15i1e92288 + + + + + + 42214056 + + 2026 + 05 + 29 + +
+ + 2688-1535 + + + 2026 + May + 29 + + + JCO oncology practice + JCO Oncol Pract + + Development and External Validation of a Machine Learning-Based Risk Classification Score for Prostate Cancer-Specific Cardiovascular Risk. + + OP2600131 + OP2600131 + + 10.1200/OP-26-00131 + + Patients with prostate cancer (PC) face elevated cardiovascular (CV) risk. Conventional CV risk scores are often poorly calibrated for PC and require laboratory tests that are not routinely available in PC clinics, limiting oncology use. + The development cohort (DC) included men with PC (age ≥18 years) diagnosed in 2010-2014. Validation cohort 1 (V1) included men age 45 years and older within 1 year of PC diagnosis or near initiation of androgen deprivation therapy. Validation cohort 2 (V2) included men with PC diagnosed in 2006-2019. Outcomes were cardiovascular disease (CVD), atherosclerotic CVD (ASCVD), and heart failure (HF). Predictors were selected with XGBoost and LASSO, modeled with Fine-Gray and penalized Cox regression, and scaled to additive point scores (low/intermediate/high risk). Performance was assessed using time-dependent AUC (TDAUC), which quantifies the model's ability to correctly rank patients by risk at each time point, and compared with conventional scores. + The DC included 1,815 patients, and V1 and V2 included 4,022 and 1,729 patients, respectively (median follow-up 10.3 years). At 10 years, cumulative incidence was 15.0% for composite CVD, 9.4% for ASCVD, and 10.1% for HF. The composite CVD score (age, race, smoking history, high-risk Gleason score, and household members) achieved a 10-year TDAUC of 0.71 (95% CI, 0.64 to 0.78) in the DC and 0.59 (95% CI, 0.52 to 0.65) in V2, with a 2-year TDAUC of 0.66 (95% CI, 0.56 to 0.75) in V1. In the DC, ASCVD and HF scores achieved 10-year TDAUCs of 0.66 (95% CI, 0.58 to 0.75) and 0.70 (95% CI, 0.62 to 0.79), respectively, and retained discrimination in validation. + To our knowledge, these are the first PC-specific CV risk scores derived from oncology-available variables, supporting pragmatic CV risk stratification in PC clinics and motivating further validation and implementation studies. + + + + Stabellini + Nickolas + N + 0000-0002-3135-7980 + + Case Western Reserve University, Cleveland, OH. + + + Department of Hematology-Oncology, University Hospitals Seidman Cancer Center, Cleveland, OH. + + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Nahle + Tarek + T + 0009-0004-4584-3308 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Shah + Viraj + V + 0009-0003-5850-9862 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Kollapaneni + Sai S + SS + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Makram + Omar M + OM + 0000-0001-8361-9068 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Cullen + Jennifer + J + + Cancer Prevention and Control Program, Houston Methodist Neal Cancer Center, Houston, TX. + + + + Weintraub + Neal L + NL + 0000-0002-5138-3705 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + Vascular Biology Center, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Kunhiraman + Harikrishnan H + HH + 0000-0002-6395-7956 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Swami + Umang + U + 0000-0003-3518-0411 + + Division of Oncology, Department of Internal Medicine, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT. + + + + Mahmoudi + Morteza + M + 0000-0002-2575-9684 + + Department of Radiology and Precision Health Program, Michigan State University, East Lansing, MI. + + + + Fillmore + Nathanael + N + 0000-0002-8058-3423 + + Department of Medicine, Harvard Medical School, Boston, MA. + + + + Garraway + Isla P + IP + + Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, CA. + + + Department of Surgical and Perioperative Care, VA Greater Los Angeles, Los Angeles, CA. + + + + Leong + Darryl + D + 0000-0002-1968-169X + + Population Health Research Institute and Department of Medicine, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada. + + + + Pinthus + Jehonathan + J + + Division of Urology, Department of Surgery, McMaster University, Hamilton, ON, Canada. + + + + Shanahan + John + J + + Cancer Informatics, Seidman Cancer Center at University Hospitals of Cleveland, Cleveland, OH. + + + + Montero + Alberto J + AJ + 0000-0002-8221-8120 + + Case Western Reserve University, Cleveland, OH. + + + Department of Hematology-Oncology, University Hospitals Seidman Cancer Center, Cleveland, OH. + + + + Agarwal + Neeraj + N + 0000-0003-1076-0428 + + Huntsman Cancer Institute, University of Utah, Salt Lake City, UT. + + + + Guha + Avirup + A + 0000-0003-0253-1174 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + Cardio-Oncology Program, Medical College of Georgia at Augusta University, Augusta, GA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + JCO Oncol Pract + 101758685 + 2688-1527 + + IM + + + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 16 + 12 + + + aheadofprint + + 42214056 + 10.1200/OP-26-00131 + + + + + + 42213920 + + 2026 + 05 + 29 + +
+ + 1079-7114 + + 136 + 19 + + 2026 + May + 15 + + + Physical review letters + Phys Rev Lett + + Hierarchical and Ultrametric Barriers in the Energy Landscape of Jammed Granular Matter. + + 198202 + 198202 + + 10.1103/mtn5-s26m + + According to the mean-field glass theory, the (free) energy landscape of disordered systems is hierarchical and ultrametric if they belong to the full-replica-symmetry-breaking universality class. However, examining this theoretical picture in three-dimensional systems remains challenging, where the energy barriers become finite. Here, we numerically explore the energy landscape of granular models near the jamming transition using a saddle dynamics algorithm to locate both local energy minima and saddles. The multiscale distances and energy barriers between minima are characterized by two metrics, both of which exhibit signatures of an ultrametric space. The scale-free distributions of energy barriers confirm the landscape's hierarchical organization and align with the distribution of elastic avalanches under athermal quasistatic shear. + + + + Wu + Shuonan + S + + Peking University, School of Mathematical Sciences, Beijing 100871, China. + https://ror.org/02v51f717 + + + + Xie + Yuchen + Y + + Peking University, School of Mathematical Sciences, Beijing 100871, China. + https://ror.org/02v51f717 + + + + Pan + Deng + D + + Chinese Academy of Sciences, Institute of Theoretical Physics, Beijing 100190, China. + https://ror.org/034t30j35 + + + + Zhang + Lei + L + + Peking University, School of Mathematical Sciences, Beijing 100871, China. + https://ror.org/02v51f717 + + + Peking University, Beijing International Center for Mathematical Research, Beijing, 100871, China. + https://ror.org/02v51f717 + + + Peking University, Center for Quantitative Biology, Beijing, 100871, China. + https://ror.org/02v51f717 + + + Peking University, Center for Machine Learning Research, Beijing, 100871, China. + https://ror.org/02v51f717 + + + + Jin + Yuliang + Y + + Chinese Academy of Sciences, Institute of Theoretical Physics, Beijing 100190, China. + https://ror.org/034t30j35 + + + University of Chinese Academy of Sciences, School of Physical Sciences, Beijing 100049, China. + https://ror.org/05qbk4x57 + + + University of Chinese Academy of Sciences, Center for Theoretical Interdisciplinary Sciences, Wenzhou Institute, Wenzhou, Zhejiang 325001, China. + https://ror.org/05qbk4x57 + + + + eng + + Journal Article + +
+ + United States + Phys Rev Lett + 0401141 + 0031-9007 + + IM + + + + + 2025 + 12 + 22 + + + 2026 + 2 + 10 + + + 2026 + 4 + 21 + + + 2026 + 5 + 29 + 18 + 35 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 15 + 12 + + + ppublish + + 42213920 + 10.1103/mtn5-s26m + + + + + + 42213819 + + 2026 + 05 + 29 + +
+ + 1535-1386 + + + 2026 + May + 29 + + + The Journal of bone and joint surgery. American volume + J Bone Joint Surg Am + + Compliance with the CONSORT AI Extension in Orthopaedic Randomized Controlled Trials. + 10.2106/JBJS.25.01439 + + The utilization of artificial intelligence (AI), such as machine learning, in health care has risen exponentially, underscoring the necessity of transparency and traceability in the reporting of AI in randomized controlled trials (RCTs) that involve AI. In 2020, a CONSORT (Consolidated Standards of Reporting Trials) AI extension was published that included 14 additional items that should be reported when AI is used in RCTs. This study aimed to evaluate compliance with these additional guidelines. + A systematic approach was used to identify all RCTs in orthopaedics that involved AI since the extension was published. Three databases were searched (PubMed, Embase, Cochrane) using the search terms ([orthopaedic] OR [orthopedic]) AND ([artificial intelligence] OR [AI] OR [machine learning] OR [deep learning]). + One hundred and seventy-four articles were identified, and 11 articles satisfied the inclusion and exclusion criteria and were assessed for compliance. The average number of CONSORT AI-extension items met per study was 11.4 ± 1.6 (81.2% ± 12.1%). Eight items (1a [i], 1b [ii], 2a [i], 4a [i], 5 [ii], 5 [v], 5 [vi], 19) that were related to standard reporting of AI utilization were reported in all articles. The items with the lowest reporting rates were related to data transparency and included 4a (ii), 5 (iii), and 25, with only 9.1%, 36.4%, and 45.5% of all studies complying, respectively. + Orthopaedic RCTs involving AI showed high compliance with respect to standard trial reporting and broad AI descriptors but low compliance with respect to detailed data transparency, data-quality handling, and code accessibility. Addressing these weaker areas is essential to improve reproducibility, reduce bias, and strengthen trust in AI-driven clinical research. + Improved compliance with CONSORT-AI reporting standards may help orthopaedic clinicians to critically evaluate AI-based randomized trials, reproduce findings, and determine whether AI interventions are reliable and safe for clinical use. + Copyright © 2026 by The Journal of Bone and Joint Surgery, Incorporated. + + + + Strasser + Thomas + T + 0000-0003-4456-8178 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + + Baker + Christopher + C + 0009-0009-9822-6633 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + + Cheminant + Jenna + J + 0009-0001-2747-5263 + + College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, California. + + + + Han + Blake + B + 0009-0002-9570-1944 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, California. + + + + Canumay + Sallie + S + 0009-0009-1627-8828 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + + Schlechter + John + J + 0000-0001-8389-8407 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + Rady Children's Health Orange County, Orange, California, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + J Bone Joint Surg Am + 0014030 + 0021-9355 + + IM + Disclosure: No external funding was received for this work. The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJS/J301). + + + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 14 + 2 + + + aheadofprint + + 42213819 + 10.2106/JBJS.25.01439 + 00004623-990000000-01850 + + + + Schulz KF, Altman DG, Moher D; CONSORT Group. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010 Mar 23;340:c332. + + + Moher D, Hopewell S, Schulz KF, Montori V, Gøtzsche PC, Devereaux PJ, Elbourne D, Egger M, Altman DG. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ. 2010 Mar 23;340:c869. + + + Ioannidis JP. How to make more published research true. PLoS Med. 2014 Oct 21;11(10):e1001747. + + + Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020 Sep;26(9):1364-74. + + + Fan X, Qiao X, Wang Z, Jiang L, Liu Y, Sun Q. Artificial Intelligence-Based CT Imaging on Diagnosis of Patients with Lumbar Disc Herniation by Scalpel Treatment. Comput Intell Neurosci. 2022 May 27;2022:3688630. + + + Anderson PG, Baum GL, Keathley N, Sicular S, Venkatesh S, Sharma A, Daluiski A, Potter H, Hotchkiss R, Lindsey RV, Jones RM. Deep learning assistance closes the accuracy gap in fracture detection across clinician types. Clin Orthop Relat Res. 2023 Mar 1;481(3):580-8. + + + Gauci MO, Olmos M, Cointat C, Chammas PE, Urvoy M, Murienne A, Bronsard N, Gonzalez JF. Validation of the shoulder range of motion software for measurement of shoulder ranges of motion in consultation: coupling a red/green/blue-depth video camera to artificial intelligence. Int Orthop. 2023 Feb;47(2):299-307. + + + Zhu Y, Wu T, Wang W, Cai C, Zhu B, Lin W, Xu H, Liang Q, Wang Y. Artificial intelligence algorithm–based lumbar and spinal MRI for evaluation of efficacy of Chinkuei Shin Chewan decoction on lumbar spinal stenosis. Contrast Media Mol Imaging. 2021 Dec 29;2021:2700452. + + + Zhang H, He Q, Liu Q, He Y, Wang T, Feng Z, Zhang Q, He S. Comparative analysis of artificial intelligence and two-dimensional digital template preoperative planning–assisted total hip arthroplasty to predict prosthesis size. Chin J Tissue Eng Res. 2023;27(29):4620-7. + + + Zheng H, Feng E, Xiao Y, Liu X, Lai T, Xu Z, Chen J, Xie S, Lin F, Zhang Y. Is AI 3D-printed PSI an accurate option for patients with developmental dysplasia of the hip undergoing THA? BMC Musculoskelet Disord. 2024 Apr 22;25(1):308. + + + Gao L, Xing B. Bone cement reinforcement improves the therapeutic effects of screws in elderly patients with pelvic fragility factures. J Orthop Surg Res. 2024 Mar 18;19(1):191. + + + Baker HP, Dwyer E, Kalidoss S, Hynes K, Wolf J, Strelzow JA. ChatGPT’s Ability to Assist with Clinical Documentation: A Randomized Controlled Trial. J Am Acad Orthop Surg. 2024 Feb 1;32(3):123-9. + + + Bates NA, Huffman A, Goodyear E, Nagai T, Rigamonti L, Breuer L, Holmes BD, Schilaty ND. Physical clinical care and artificial-intelligence-guided core resistance training improve endurance and patient-reported outcomes in subjects with lower back pain. Clin Biomech (Bristol). 2023 Mar;103:105902. + + + Zhang Y, Hui Z, Qi W, Zhang J, Wang M, Zhu D. Clinical study on the safety and feasibility of AiWalker-K for lower limbs exercise rehabilitation in children with cerebral palsy. PLoS One. 2024 May 22;19(5):e0303517. + + + Thiengwittayaporn S, Wattanapreechanon P, Sakon P, Peethong A, Ratisoontorn N, Charoenphandhu N, Charoensiriwath S. Development of a mobile application to improve exercise accuracy and quality of life in knee osteoarthritis patients: a randomized controlled trial. Arch Orthop Trauma Surg. 2023 Feb;143(2):729-38. + + + Jayakumar P, Moore MG, Furlough KA, Uhler LM, Andrawis JP, Koenig KM, Aksan N, Rathouz PJ, Bozic KJ. Comparison of an artificial intelligence–enabled patient decision aid vs educational material on decision quality, shared decision-making, patient experience, and functional outcomes in adults with knee osteoarthritis: a randomized clinical trial. JAMA Netw Open. 2021 Feb 1;4(2):e2037107. + + + Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 Mar 29;372:n71. + + + Sataloff RT, Bush ML, Chandra R, Chepeha D, Rotenberg B, Fisher EW, Goldenberg D, Hanna EY, Kerschner JE, Kraus DH, Krouse JH, Li D, Link M, Lustig LR, Selesnick SH, Sindwani R, Smith RJ, Tysome J, Weber PC, Welling DB. Systematic and other reviews: criteria and complexities. Ear Nose Throat J. 2021 Jul;100(6):403-6. + + + Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ. 2020 Sep 9;370:m3164. + + + Anderson JM, Howard C, Staggs J, Steele R, Strasser T, Small T, Vassar M, Checketts JX. Inadequate reporting of complications in randomized controlled trials cited as supporting evidence underpinning AAOS CPG recommendations for hip and knee osteoarthritis: application of the CONSORT Harms checklist. J Arthroplasty. 2022 Dec;37(12):2466-2472.e2. + + + Marcus E, Teuwen J. Artificial intelligence and explanation: How, why, and when to explain black boxes. Eur J Radiol. 2024 Apr;173:111393. + + + Wyles CC, Saniei S, Mulford KL, Girod MM, Taunton MJ. Reporting Guidelines for Artificial Intelligence Use in Orthopaedic Surgery Research. J Arthroplasty. 2025 Oct;40(10):2737-2743.e1. + + + Martindale APL, Llewellyn CD, de Visser RO, Ng B, Ngai V, Kale AU, Ferrante di Ruffano L, Golub RM, Collins GS, Moher D, McCradden MD, Oakden-Rayner L, Cruz Rivera S, Calvert M, Kelly CJ, Lee CS, Yau C, Chan AW, Keane PA, Beam AL, Denniston AK, Liu X. Concordance of randomized controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines. Nature Communications. 2024;15(1):1619. + + + Shahzad R, Ayub B, Siddiqui MAR. Quality of reporting of randomised controlled trials of artificial intelligence in healthcare: a systematic review. BMJ Open. 2022 Sep 5;12(9):e061519. + + + Misir A, Yuce A. AI in Orthopedic Research: A Comprehensive Review. J Orthop Res. 2025 Aug;43(8):1508-27. + + + + + + + 42213782 + + 2026 + 05 + 29 + +
+ + 2211-1247 + + 45 + 6 + + 2026 + May + 28 + + + Cell reports + Cell Rep + + Non-centromeric CENP-A regulates epithelial-mesenchymal plasticity and heterogeneity in human cells. + + 117405 + 117405 + + 10.1016/j.celrep.2026.117405 + S2211-1247(26)00483-3 + + Centromere protein A (CENP-A), a centromeric histone H3 variant highly expressed in aggressive cancers, promotes epithelial-mesenchymal transition (EMT), yet its underlying mechanisms remain unresolved. Here, we used a reversible high-CENP-A expression system in human cells to follow EMT-state trajectories and CENP-A localization over time. Sustained CENP-A elevation shifted hybrid populations toward mesenchymal states and increased both centromeric loading and ectopic chromatin incorporation. Chromatin immunoprecipitation revealed ectopic CENP-A enrichment at EMT-associated loci. Single-nucleus multi-omics further resolved two EMT programs engaged at distinct cell cycle stages: CENP-A strengthened a pre-existing inflammatory program and triggered a developmental program. Importantly, restoring basal CENP-A levels erased these transcriptional programs and eliminated ectopic incorporation, consistent with a reversible, non-genetic mechanism. Together, our findings uncover a non-centromeric function for CENP-A in shaping epithelial-mesenchymal plasticity and cellular heterogeneity. + Copyright © 2026 The Author(s). Published by Elsevier Inc. All rights reserved. + + + + Renaud-Pageot + Charlène + C + + Institut Curie, Université PSL, Sorbonne Université, CNRS, UMR 3664 Nuclear Dynamics, Chromatin Dynamics Lab, Equipe Labellisée Ligue Contre le Cancer, 75005 Paris, France. + + + + Capocefalo + Daniele + D + + Institut Pasteur, Université Paris Cité, CNRS, UMR 3738, Machine Learning for Integrative Genomics Lab, 75015 Paris, France. + + + + Lemaire + Sébastien + S + + Institut Curie, Université PSL, Sorbonne Université, CNRS, UMR 3664 Nuclear Dynamics, Chromatin Dynamics Lab, Equipe Labellisée Ligue Contre le Cancer, 75005 Paris, France. + + + + Forest + Audrey + A + + Institut Curie, Université PSL, Sorbonne Université, CNRS, UMR 3664 Nuclear Dynamics, Chromatin Dynamics Lab, Equipe Labellisée Ligue Contre le Cancer, 75005 Paris, France. + + + + Cantini + Laura + L + + Institut Pasteur, Université Paris Cité, CNRS, UMR 3738, Machine Learning for Integrative Genomics Lab, 75015 Paris, France. + + + + Almouzni + Geneviève + G + + Institut Curie, Université PSL, Sorbonne Université, CNRS, UMR 3664 Nuclear Dynamics, Chromatin Dynamics Lab, Equipe Labellisée Ligue Contre le Cancer, 75005 Paris, France. Electronic address: genevieve.almouzni@curie.fr. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Cell Rep + 101573691 + + IM + + CENP-A + CP: developmental biology + CP: molecular biology + cell cycle + centromere + chromatin + epigenetics + epithelial-mesenchymal transition + single-nucleus multi-omics + + Declaration of interests The authors declare no competing interests. + + + + + 2025 + 10 + 1 + + + 2026 + 2 + 10 + + + 2026 + 4 + 27 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 13 + 52 + + + aheadofprint + + 42213782 + 10.1016/j.celrep.2026.117405 + S2211-1247(26)00483-3 + + + + + + 42213739 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1932-6203 + + 21 + 5 + + 2026 + + + PloS one + PLoS One + + TransGrid-CostOpt: A hybrid transformer framework for cost prediction and optimization of distribution network assets. + + e0350026 + e0350026 + + 10.1371/journal.pone.0350026 + + The prediction and optimization of distribution network asset costs is a complex problem in the power industry, involving the optimization of multiple objectives and the response to dynamic demands. Traditional methods often struggle to effectively address fluctuations in power load and the uncertainties in the supply chain, limiting their effectiveness in complex environments. To solve this issue, we propose the TransGrid-CostOpt model, an intelligent cost optimization model that integrates deep learning, multi-objective optimization, time-series forecasting, and optimization decision-making modules. TransGrid-CostOpt optimizes load forecasting and cost allocation for the distribution network by combining multi-source data, time-series load forecasting, and reinforcement learning decision strategies, reducing operational costs, improving load forecasting accuracy, and enhancing decision adaptability. Experimental results show that TransGrid-CostOpt outperforms traditional models and other advanced methods on the BuildingsBench and PJM Hourly Load Data datasets, exhibiting higher accuracy and efficiency in forecasting, cost optimization, and multi-objective balancing. Compared to classical baseline models and cutting-edge approaches, TransGrid-CostOpt demonstrates a 15% to 30% overall performance improvement. Ablation experiments confirm the critical role of each module, especially the time-series forecasting module and optimization decision-making module, in significantly enhancing the model's performance. TransGrid-CostOpt strengthens the cost management capability of the distribution network and shows strong adaptability in dynamic electricity market environments, with broad application potential. + Copyright: © 2026 Xiong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. + + + + Xiong + Wei + W + + State Grid Hubei Electric Power Co., Ltd, Wuhan, Hubei, China. + + + + Xia + Jie + J + + State Grid Hubei Electric Power Co., Ltd. Jingzhou Power Supply Company, Jingzhou, Hubei, China. + + + + Yu + YiBo + Y + + Wuhan Anju Supply Chain Co., Ltd, Wuhan, Hubei, China. + + + + Xiong + SanMing + S + + International Education College of North China Electric Power University (Baoding), Baoding, Hebei, China. + + + + Hu + HaiYang + H + + State Grid Hubei Electric Power Co., Ltd. Jingzhou Power Supply Company, Jingzhou, Hubei, China. + + + + Wan + Peng + P + 0009-0006-8464-084X + + Hubei Power Grid Design Co., Ltd., Wuhan, Hubei, China. + + + + Li + Dan + D + + Yunxia Electric Energy (Yunnan) Co., Ltd, Kunming, Yunnan, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + PLoS One + 101285081 + 1932-6203 + + IM + + + Reinforcement Machine Learning + + + Prediction Algorithms + + + Forecasting + methods + + + Predictive Learning Models + + + The authors declare that they have no competing interests. There is no financial, personal, or professional conflict that could be perceived as influencing the research or the writing of this manuscript. + + + + + 2025 + 12 + 18 + + + 2026 + 5 + 7 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 13 + 44 + + + epublish + + 42213739 + 10.1371/journal.pone.0350026 + PONE-D-25-67221 + + + + + + 42213730 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1553-7358 + + 22 + 5 + + 2026 + May + + + PLoS computational biology + PLoS Comput Biol + + A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and facilitating drug repurposing. + + e1014323 + e1014323 + + 10.1371/journal.pcbi.1014323 + + Many genetic loci were identified as associated with neuropsychiatric disorders and neurodegenerative disorders by Genome-wide association studies (GWAS). How these loci impact these diseases is unclear. Advances in deep-learning approaches and multi-omics data have the potential to link GWAS findings with disease mechanisms. Here, we proposed the Multi-omics Graph Transformer Network (MOGT), a semi-supervised graph neural network that leverages graph representation learning to model biological networks derived from multi-omics data to predict disease-associated genes. MOGT outperforms the current approaches in disease gene prediction for two psychiatric disorders and three neurodegenerative/neurological diseases. High-risk genes (HRGs) for Parkinson's disease (PD) predicted by MOGT were used to drug discovery by integrating with the CMAP database. Finally, 10 drugs were identified as potential candidates. Among them, the effect of drug UK-356618 was experimentally verified in a primary neuron model, showing that UK-356618 reversed the abnormal expression of PD-associated genes and improved the cell-level phenotypes of PD. Together, these results indicate that MOGT can be used to identify HRGs for brain disorders, and these predicted HRGs provide high-level insights into the mechanisms and treatments of brain disorders. + Copyright: © 2026 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. + + + + Li + Jiafang + J + 0009-0006-5493-5305 + + Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China. + + + Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, Guangdong, China. + + + + Li + Yifei + Y + + Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China. + + + Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, Guangdong, China. + + + + Lin + Siying + S + + Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China. + + + School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China. + + + + Rao + Jiahua + J + + School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China. + + + + Zhao + Huiying + H + 0000-0001-9134-536X + + Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China. + + + Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, Guangdong, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + PLoS Comput Biol + 101238922 + 1553-734X + + IM + + + Graph Neural Networks + + + Drug Repositioning + methods + + + Genome-Wide Association Study + + + Humans + + + Computational Biology + methods + + + Brain Diseases + genetics + drug therapy + + + Representation Machine Learning + + + Genetic Predisposition to Disease + genetics + + + Parkinson Disease + genetics + + + Animals + + + The authors have declared that no competing interests exist. + + + + + 2025 + 12 + 1 + + + 2026 + 5 + 11 + + + 2026 + 5 + 29 + 18 + 35 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 13 + 43 + + + epublish + + 42213730 + 10.1371/journal.pcbi.1014323 + PCOMPBIOL-D-25-02551 + + + + + + 42213726 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1932-6203 + + 21 + 5 + + 2026 + + + PloS one + PLoS One + + Machine learning models based on magnetic resonance imaging for predicting Lymphovascular Invasion in Invasive Breast Cancer. + + e0350085 + e0350085 + + 10.1371/journal.pone.0350085 + + Treatment strategies for invasive breast cancer require accurate lymphovascular invasion (LVI) predictions. This study aimed to investigate the feasibility and effectiveness of delta radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and radiomics signature based on T2-weighted fat suppressed imaging(T2FS) for assessing LVI in invasive breast cancer. + A total of 166 patients with resectable invasive breast cancer who underwent preoperative DCE-MRI and T2FS from July 10, 2020 to December 31, 2023 were enrolled. Radiomics features were extracted from pre-contrast phase (RFpre), second post-contrast phase (RFpost), and Delta radiomics features (RFDelta) calculated as RFpost minus RFpre. Then four radiomics signatures (RST2, RSpre, RSpost, RSDelta) were further developed based on the Random Forest model for RFT2, RFpre, RFpost and RFDelta, respectively. The predictive performance of all signatures was evaluated by receiver operating characteristic (ROC) analysis, with accuracy and area under the curve (AUC) as the main quantitative metrics. + In the test set, RSDelta (10 features) achieved the highest accuracy of 0.717 and an AUC of 0.764; RSpost (8 features) had an accuracy of 0.565 and an AUC of 0.610; RSpre (7 features) and RST2 (6 features) both showed an accuracy of 0.565 with AUCs of 0.535 and 0.662, respectively. Statistical differences were observed in predictive performance between RSDelta and RSpre, RSpost (both p < 0.05), while no significant difference was found between RSDelta and RST2 (p = 0.239). + RST2, RSpre, RSpost and RSDelta are all feasible for predicting LVI in invasive breast cancer, and RSDelta outperforms the other three radiomics signatures, which can serve as a potential non-invasive imaging biomarker for LVI prediction in clinical practice. + Copyright: © 2026 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. + + + + Li + Hong + H + 0000-0002-4531-7541 + + Department of Radiology, the Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China. + + + + Huang + Jieling + J + + Department of Radiology, Guangzhou Geriatric Hospital, Guangzhou, Guangdong, China. + + + + Hou + Jianning + J + + Department of Radiology, Guangzhou Women And Children's Medical Center, Guangzhou, China. + + + + Chen + Xinxin + X + + Department of Breast surgery, the Second Affiliated Hospital, Guangzhou Medical University,‌‌ Guangzhou, Guangdong, China. + + + + Zhi + Cheng + C + + Department of Pathology, the Second Affiliated Hospital, Guangzhou Medical University, ‌‌Guangzhou, Guangdong, China. + + + + Li + Zhiming + Z + + Department of Radiology, the Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + PLoS One + 101285081 + 1932-6203 + + IM + + + Humans + + + Breast Neoplasms + diagnostic imaging + pathology + + + Female + + + Radiomics + + + Dynamic Contrast Enhanced Magnetic Resonance Imaging + + + Machine Learning + + + Neoplasm Invasiveness + + + Magnetic Resonance Imaging + methods + + + Middle Aged + + + Predictive Learning Models + + + Lymphatic Metastasis + diagnostic imaging + + + Random Forest + + + ROC Curve + + + The authors have declared that no competing interests exist. + + + + + 2025 + 7 + 28 + + + 2026 + 5 + 8 + + + 2026 + 5 + 29 + 18 + 35 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 13 + 43 + + + epublish + + 42213726 + 10.1371/journal.pone.0350085 + PONE-D-25-38600 + + + + + + 42213702 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2767-3170 + + 5 + 5 + + 2026 + May + + + PLOS digital health + PLOS Digit Health + + Video-based detection of Delirium in hospitalized adults. + + e0001462 + e0001462 + + 10.1371/journal.pdig.0001462 + + Delirium, a dynamic neuropsychiatric condition associated with morbidity and mortality, remains underdiagnosed due to reliance on subjective, intermittent screening tools. Objective and potentially continuous identification is needed to improve clinical care. We developed and validated an analytic framework for delirium classification based on automatically extracted video features. In this prospective cohort study, patients (≥ 18 years) admitted to the inpatient medical or neurological ward of a tertiary academic center between August 2020 and March 2022 with an expected stay longer than one night were enrolled. Daily structured delirium assessments and brief video recordings were performed in consenting patients. Videos were analyzed using deep learning pose estimation to extract keypoints and calculate behavioral features based on eye, face, and limb postures and movements. Four machine learning models (logistic regression, gradient boosting, support vector machines, and random forests) were trained to predict delirium status from extracted features. Model performance was evaluated on 20 repetitions of three-fold cross-validation using the area under the curve of the receiver operating characteristics curve (AUC ROC). The cohort included 109 videos from 25 male and 25 female participants (median age: 72, IQR: 63.25-78). Twenty videos (18%) were from patients with delirium. Keypoints for this dataset were more accurately extracted using a customized ResNet-101 model developed with DeepLabCut (sensitivity 0.94, specificity 0.89, compared to human-labeled gold standards) than using off-the-shelf models. Keypoints were then used to generate behavioral features summarizing movement and postures throughout the video. A support vector machine model achieved an average delirium classification AUC ROC of 0.79 (SD ± 0.09), sensitivity of 0.71 (SD ± 0.16), and specificity of 0.78 (SD ± 0.07). This study demonstrates the feasibility of identifying delirium using brief videos in clinically heterogeneous cohorts and reveals novel features for objective identification. + Copyright: © 2026 Mendu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. + + + + Mendu + Maanasa + M + 0000-0002-0409-1769 + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Tesh + Ryan A + RA + 0000-0002-6154-6248 + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America. + + + + Pellerin + Kyle + K + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Steward + Grace E + GE + 0000-0002-0226-0880 + + Department of Neurology, Northwestern University, Chicago, Illinois, United States of America. + + + + Cerda + Ivo H + IH + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Williams + Marta + M + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Colman + Mia + M + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Shah + Simran + S + 0000-0001-6298-7558 + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Lam + Alice D + AD + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Cash + Sydney S + SS + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + + Westover + M Brandon + MB + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America. + + + + Kimchi + Eyal Y + EY + + Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America. + + + Department of Neurology, Northwestern University, Chicago, Illinois, United States of America. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + PLOS Digit Health + 9918335064206676 + 2767-3170 + + I have read the journal’s policy and the authors of this manuscript have the following competing interests: Dr. Cash and Dr. Westover report being a cofounder of, serving as a scientific advisor and consultant to, and having a personal equity interest in Beacon Biosignals outside the submitted work. Beacon Biosignals had no role in the design, data analysis, interpretation, or writing of the manuscript. + + + + + 2025 + 10 + 23 + + + 2026 + 5 + 11 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 13 + 34 + + + epublish + + 42213702 + 10.1371/journal.pdig.0001462 + PDIG-D-25-00896 + + + + + + 42213697 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1932-6203 + + 21 + 5 + + 2026 + + + PloS one + PLoS One + + A hybrid BiLSTM-transformer-GCN architecture with API fusion for adaptive transportation resource analytics. + + e0349787 + e0349787 + + 10.1371/journal.pone.0349787 + + Intelligent Transportation Systems (ITS) generate large volumes of heterogeneous spatiotemporal data from connected vehicles, road infrastructure, and external contextual services. Effectively discovering, classifying, and managing these transportation resources in real time remains challenging due to dynamic traffic conditions and nonlinear dependencies. This paper proposes a novel hybrid deep learning framework that integrates Bidirectional Long Short-Term Memory (BiLSTM), Transformer encoders, and Graph Convolutional Networks (GCN) with API-based contextual feature fusion for adaptive resource discovery and classification. A multi-objective learning strategy jointly optimizes supervised classification and unsupervised anomaly discovery through reconstruction-based latent feature refinement. Experiments conducted on the Barcelona Accident dataset and the Metro Interstate Traffic Volume dataset demonstrate the superiority of the proposed method. On the combined dataset, the framework achieves an accuracy of 0.882, F1-score of 0.864, and AUC of 0.911, outperforming traditional baselines such as Random Forest (accuracy 0.839) and deep CNN-LSTM (accuracy 0.857). For anomaly discovery, it obtains an AUROC of 0.836 and AUPRC of 0.784 on the Barcelona dataset, and AUROC of 0.819 on the Metro dataset, while achieving the lowest reconstruction error of 0.198. The ablation study shows performance degradation when API fusion (-4.1% accuracy) or GCN modeling (-2.8% accuracy) is removed, confirming their contribution. Inference latency analysis further demonstrates practical deployability with a competitive accuracy-latency trade-off. + Copyright: © 2026 Javeed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. + + + + Javeed + Mohammed Saad + MS + + Information Science, Trine University, Michigan,‌‌ Allen Park, United States of America‌. + + + + Biswas + Arindam Kishor + AK + + University of the Cumberlands, Williamsburg, Kentucky, United States of America. + + + + Hasan + Md Nahid + MN + + University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, United States of America. + + + + Maua + Jannatul + J + + Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh. + + + + Islam + Rahomotul + R + 0009-0007-1948-0322 + + Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + PLoS One + 101285081 + 1932-6203 + + IM + + + Long Short Term Memory + + + Graph Neural Networks + + + Transportation + + + Data Analytics + + + Deep Learning + + + Intelligent Systems + + + Algorithms + + + The authors have declared that no competing interests exist. + + + + + 2025 + 10 + 26 + + + 2026 + 5 + 5 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 13 + 34 + + + epublish + + 42213697 + 10.1371/journal.pone.0349787 + PONE-D-25-57885 + + + + + + 42213657 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1932-6203 + + 21 + 5 + + 2026 + + + PloS one + PLoS One + + Towards intelligent railway monitoring: A novel hybrid deep learning architecture for railway obstacle detection. + + e0349562 + e0349562 + + 10.1371/journal.pone.0349562 + + Railways are among the most efficient modes of transportation, capable of moving large quantities of goods and passengers over long distances at relatively low cost. However, accidents frequently occur due to objects or individuals present on the tracks, as trains are unable to swerve and require long braking distances. While the localization of objects within the track bed is a well-explored topic, the reliable and high-performance classification of such obstacles across all relevant categories remains an unresolved challenge. This study proposes an innovative hybrid architecture that leverages the specific visual characteristics of track bed imagery, setting a new benchmark in this domain. The hybrid design effectively leverages the strengths of ResNet50 and Swin Transformer V2, allowing the model to capture both local and global features. In addition, an Efficient Attention Module is integrated to further emphasize the most relevant features for robust obstacle classification. Using stratified five-fold cross-validation on a dataset of 2,003 images across six classes (iron bar, boulder, person, branch, canister, and barrel), the model achieved an average balanced accuracy of 99.46%. The results have implications for accident prevention, improving operational efficiency, and modernizing railway safety systems, thereby enabling the future application of automatic railway surveillance systems to ultimately enhance operational security. + Copyright: © 2026 Mai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. + + + + Mai + Christopher + C + + Chair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Hamburg, Germany. + + + + Eisentraut + Luca + L + + Chair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Hamburg, Germany. + + + + Schadt + Merlin + M + + Chair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Hamburg, Germany. + + + + Buettner + Ricardo + R + 0000-0003-2263-6408 + + Chair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Hamburg, Germany. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + PLoS One + 101285081 + 1932-6203 + + IM + + + Railroads + + + Deep Learning + + + Humans + + + Detection Algorithms + + + Convolutional Neural Networks + + + Image Processing, Computer-Assisted + methods + + + The authors have declared that no competing interests exist. + + + + + 2025 + 11 + 7 + + + 2026 + 5 + 1 + + + 2026 + 5 + 29 + 18 + 35 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 13 + 33 + + + epublish + + 42213657 + 10.1371/journal.pone.0349562 + PONE-D-25-60054 + + + + + + 42213596 + + 2026 + 05 + 29 + +
+ + 1944-8252 + + + 2026 + May + 29 + + + ACS applied materials & interfaces + ACS Appl Mater Interfaces + + 3D-Printed Conductive Aerogel Humidity Sensor for Advanced Wearable Sleep and Health Monitoring. + 10.1021/acsami.6c01464 + + Recent advances in personalized sleep medicine and home-based health monitoring have grown rapidly, yet progress remains limited by the lack of comfortable, reliable, and durable sensors for long-term respiration tracking. Here, we present a flexible humidity sensor fabricated through a freeze-drying-assisted direct-ink-writing (DIW) 3D printing strategy. This sensor is constructed from a poly(vinyl alcohol)/nanocellulose/graphene/multiwalled carbon nanotubes (PVA/CNF/Gr/MWCNTs, PCGM) composite aerogel, with a hierarchically porous conductive architecture. This aerogel was stabilized by a biocompatible PVA matrix reinforced by nanocellulose, while graphene ensures high conductivity, and it was further enhanced by carbon nanotubes in the 3D structure. Moreover, the considerable hydrogen bonding and π-π conjugation within this hybrid material contribute to exceptional interfacial stability, water-adsorption capacity, and electrical conductivity, resulting in a remarkable humidity-sensing performance with high sensitivity, fast response/recovery times, and excellent stability across a broad humidity range. Remarkably, the sensor can accurately differentiate diverse sleep postures and respiratory patterns, including normal, snoring, and coughing. Furthermore, we further enabled a 600-sample-trained deep convolutional neural network and achieved a high-precision pattern recognition, with 100% accuracy in respiratory state classification and 97% accuracy for spoken-word recognition classification. Our work provides an integrated strategy for next-generation high-performance wearable health monitoring and human-machine interaction systems. + + + + Chen + Xiaojun + X + + School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China. + + + Department of Food Science and Technology, National University of Singapore, 2 Science Drive 2, Singapore 117542, Singapore. + + + + Lin + Xitong + X + + School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China. + + + + Huang + Yuanyu + Y + + School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China. + + + + Li + Yihuan + Y + + School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China. + + + + Lian + Haishan + H + + School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China. + + + + Zhao + Zhihao + Z + + Department of Food Science and Technology, National University of Singapore, 2 Science Drive 2, Singapore 117542, Singapore. + + + + Zhou + Jiangtao + J + 0000-0003-4248-2207 + + Department of Food Science and Technology, National University of Singapore, 2 Science Drive 2, Singapore 117542, Singapore. + + + Bezos Centre for Sustainable Protein at the National University of Singapore, 2 Science Drive 2, Singapore 117542, Singapore. + + + National University of Singapore (Suzhou) Research Institute, 377 Linquan Street, Suzhou Industrial Park, Jiangsu 215123, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + ACS Appl Mater Interfaces + 101504991 + 1944-8244 + + IM + + 3D printing + Composite aerogel + Deep learning + Flexible humidity sensor + Respiration monitoring + + + + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 13 + 12 + + + aheadofprint + + 42213596 + 10.1021/acsami.6c01464 + + + + + + 42213487 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2564-1891 + + 6 + + 2026 + May + 29 + + + JMIR infodemiology + JMIR Infodemiology + + Early Depression Detection in Social Media: Monitoring of Individual Nighttime Dynamics and Large Language Model Analysis. + + e87138 + e87138 + + 10.2196/87138 + + Depression has become a major global public health challenge, and early intervention is critical for improving patient outcomes. Current depression detection techniques based on social media data (traditional risk detection) rely heavily on users' complete historical information, which cannot meet the timeliness requirements of early intervention. This underscores the need for early risk detection (ERD) methods emphasizing early-stage, real-time warning. However, existing ERD studies have notable limitations such as (1) they overlook temporal activity patterns hidden in posting time stamps, missing vital warning signals; and (2) they depend on static templates or resource-intensive sequence models, resulting in limited interpretability and inefficient use of early data, ultimately constraining their clinical applicability for early intervention. + This study aims to develop an efficient, reliable, and interpretable ERD model. The core objectives are to extract temporal activity patterns features from posting time stamps, thereby enriching feature dimensions for risk detection; to leverage large language models (LLMs) for improved text filtering precision and depression-related factor analysis; and to achieve accurate early detection of depression to support clinical intervention. + We propose the Monitoring of Individual Nighttime Dynamics (MIND) and LLM analysis model, which integrates two key innovations: (1) circadian activity dynamics: posting time stamps are transformed into temporal activity patterns, analyzing fluctuations in posting frequency and timing to derive sleep-related features, thereby compensating for the limitations of text-only approaches, and (2) LLM depression profiler: LLMs are used for dynamic text filtering, automatically removing irrelevant noise and focusing on potential depression-related cues. Based on LLM semantic understanding, latent depression risk factors are identified, enhancing interpretability for clinical treatment and robustness to noise. + Experiments on the eRisk2017 (data source: Reddit [Reddit Inc]) benchmark dataset demonstrated that MIND significantly outperformed existing baseline models in early detection sensitivity, specificity, and accuracy. By combining sleep-related features with text analysis, the model achieved interpretable, traceable predictions that can support clinical treatment. ALL relevant experimental code is publicly available. + The MIND model combines temporal activity pattern features with LLM-based text analysis, addressing the challenges of poor interpretability and inefficient use of early-stage data in existing ERD methods. It significantly enhances early detection performance, offering a new paradigm for applying social media data in ERD, thereby enabling earlier intervention and reducing the public health burden of depression. + ©Bicheng Yu, Zhichang Zhang, Lulu Ma, Jiongfu Cai, Yuanyuan Zhang. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 29.05.2026. + + + + Yu + Bicheng + B + 0009-0009-8280-6414 + + College of Artificial Intelligence and Computer Science, Northwest Normal University, Lanzhou, Gansu, China. + + + + Zhang + Zhichang + Z + 0000-0003-3306-8493 + + College of Artificial Intelligence and Computer Science, Northwest Normal University, Lanzhou, Gansu, China. + + + + Ma + Lulu + L + 0009-0009-1573-3534 + + College of Artificial Intelligence and Computer Science, Northwest Normal University, Lanzhou, Gansu, China. + + + + Cai + Jiongfu + J + 0009-0006-0608-6097 + + College of Artificial Intelligence and Computer Science, Northwest Normal University, Lanzhou, Gansu, China. + + + + Zhang + Yuanyuan + Y + 0009-0002-0645-3409 + + College of Artificial Intelligence and Computer Science, Northwest Normal University, Lanzhou, Gansu, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Canada + JMIR Infodemiology + 9918249014806676 + 2564-1891 + + IM + + + Humans + + + Large Language Models + + + Social Media + statistics & numerical data + + + Depression + diagnosis + + + Early Diagnosis + + + + circadian rhythm + depression + early risk detection + large language model + sleep analysis + + + + + + 2025 + 11 + 5 + + + 2026 + 4 + 29 + + + 2026 + 3 + 30 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 11 + 53 + + + epublish + + 42213487 + 10.2196/87138 + v6i1e87138 + + + + + + 42213480 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2369-3762 + + 12 + + 2026 + May + 29 + + + JMIR medical education + JMIR Med Educ + + Performance of Large Language Models on the Brazilian National Medical Education Examination: Comparative Benchmark Study. + + e89839 + e89839 + + 10.2196/89839 + + Large language models (LLMs) are rapidly incorporated into medical education and examination preparation; yet, most benchmarking evidence is derived from English-language material. Whether frontier commercial models and Brazilian Portuguese domain-specialized systems perform equivalently on high-stakes Brazilian medical examinations remains unclear. + This study aims to quantify and compare the performance of 9 frontier commercial LLMs and 1 Brazilian Portuguese domain-specialized system (Charcot, Voa Health) on 2026 Brazilian National Medical Education Examination (Exame Nacional de Avaliação da Formação Médica [ENAMED] 2026) and to describe the patterns of systematic between-model error as complementary quality signal. + All 100 items of ENAMED 2026 (99 valid after annulment) were administered to 10 frontier-panel models across 5 independent runs under identical Portuguese prompts (temperature=0; top-p=.95). Commercial models were accessed through a unified OpenRouter client layer (DeepSeek provider-pinned). The primary outcome was mean accuracy against the preliminary key; the secondary outcomes were convergence error (CE), normalized mean response time (NMRT), and intermodel agreement. Accuracy was analyzed with Shapiro-Wilk, Levene, Kruskal-Wallis (ε2), Dunn-Holm post hoc, and a binomial generalized linear mixed model with question and run random intercepts. NMRT excluded Charcot (different stack) and Grok 4 (latency outlier). A total of 7 open-weight and small language models were assessed as a small language model (SLM) substudy. + Frontier-panel accuracy ranged from 73.74% (365/495) for GPT-4o-mini to 96.97% (480/495) for Charcot. Accuracy was nonnormal (Shapiro-Wilk, W=0.82; P<.001) with homogeneous variance (Levene P=.26). Kruskal-Wallis showed large between-model differences (H9=47.65; P<.001; ε2=0.97). Dunn-Holm flagged 8 of 45 pairs: Charcot was separable from GPT-4o-mini, DeepSeek v3.2-exp, and Grok 4, but not from the top frontier cluster. The generalized linear mixed model preserved the ranking (all comparators odds ratio<1 vs Charcot; upper CI<1 except GPT-5). A total of 9 items met the default CE criterion; item 77 showed 10-of-10 convergence, later confirmed by Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira rectification, and sensitivity analysis preserved the CE set (5-20 items). Intermodel agreement was high (Fleiss κ=0.852; Krippendorff α=.852). Among 8 retained commercial models, NMRT correlated positively with accuracy (Spearman ρ=0.74; P=.04); these results are interpreted as descriptive of an orchestration-level latency-accuracy law. SLM accuracy ranged from 47.47% (Gemma 3 4B) to 82.22% (GPT-OSS 120B), with lower agreement (Fleiss κ=0.508) and 17 CE items. The best SLM-panel model lagged every frontier-panel model except GPT-4o-mini, with a 12-15 percentage-point gap against the top cluster. + On ENAMED 2026, a Brazilian Portuguese domain-specialized system ranked first, indistinguishable from frontier commercial cluster and above subfrontier and open-weight systems. Charcot's architecture is not publicly disclosed; these findings should be interpreted as comparative black-box evidence of performance and not as mechanistic evidence of specialization. CE was stable, and it prospectively flagged 1 rectified item, supporting its use as a quality assurance screen. + ©Francys de Luca Fernandes da Silva, Eduardo Augusto Roeder, João Victor Bruneti Severino, Matheus Nespolo Berger, Pedro Angelo Basei de Paula, Davi Ferreira, Maria Han Veiga, Thyago Proença de Moraes, Gustavo Lenci Marques. Originally published in JMIR Medical Education (https://mededu.jmir.org), 29.05.2026. + + + + Fernandes da Silva + Francys de Luca + FL + 0000-0001-9214-691X + + R. Imac. Conceição, 1155 - Prado Velho, Pontifícia Universidade Católica do Paraná, Curitiba, Paraná, Brazil. + + + + Roeder + Eduardo Augusto + EA + 0000-0002-1016-3319 + + Universidade Federal do Paraná, Curitiba, Paraná, Brazil. + + + + Bruneti Severino + João Victor + JV + 0000-0002-8649-6494 + + R. Imac. Conceição, 1155 - Prado Velho, Pontifícia Universidade Católica do Paraná, Curitiba, Paraná, Brazil. + + + Universidade Federal do Paraná, Curitiba, Paraná, Brazil. + + + + Nespolo Berger + Matheus + M + 0009-0000-0815-3288 + + Universidade Federal do Paraná, Curitiba, Paraná, Brazil. + + + + Basei de Paula + Pedro Angelo + PA + 0009-0000-6271-6862 + + Universidade Federal do Paraná, Curitiba, Paraná, Brazil. + + + + Ferreira + Davi + D + 0000-0003-1151-9652 + + Instituto Tecnológico de Aeronáutica, São José dos Campos, São Paulo, Brazil. + + + + Han Veiga + Maria + M + 0009-0008-8261-5258 + + The Ohio State University, Columbus, OH, United States. + + + + de Moraes + Thyago Proença + TP + 0000-0002-2983-3968 + + R. Imac. Conceição, 1155 - Prado Velho, Pontifícia Universidade Católica do Paraná, Curitiba, Paraná, Brazil. + + + + Lenci Marques + Gustavo + G + 0000-0002-6057-0350 + + R. Imac. Conceição, 1155 - Prado Velho, Pontifícia Universidade Católica do Paraná, Curitiba, Paraná, Brazil. + + + Universidade Federal do Paraná, Curitiba, Paraná, Brazil. + + + + eng + + Journal Article + Comparative Study + + + 2026 + 05 + 29 + +
+ + Canada + JMIR Med Educ + 101684518 + 2369-3762 + + IM + + + Large Language Models + + + Brazil + + + Humans + + + Benchmarking + methods + + + Educational Measurement + methods + standards + statistics & numerical data + + + Education, Medical + standards + methods + + + + Brazilian Portuguese + ENAMED + Exame Nacional de Avaliação da Formação Médica + artificial intelligence + benchmarking + clinical reasoning + large language models + medical education + + + + + + 2025 + 12 + 20 + + + 2026 + 5 + 8 + + + 2026 + 4 + 28 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 11 + 52 + + + epublish + + 42213480 + 10.2196/89839 + v12i1e89839 + + + + + + 42213376 + + 2026 + 05 + 29 + +
+ + 1573-501X + + + 2026 + May + 29 + + + Molecular diversity + Mol Divers + + M-Tune: imbalanced data handling in machine learning by tuning the decision threshold. + 10.1007/s11030-026-11600-8 + + Overprediction of the majority class and poor predictive performance are the major issues when training machine learning classifiers with imbalanced data. This eventually leads to a larger misclassification rate and bias towards the majority class. This is a real-world problem, especially when dealing with biological datasets. There are several popular methods, including Random Under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE), and Generalized tHreshOld ShifTing (GHOST). However, these methods either articulate the no. of samples or are biased towards the majority class. In this study, we developed a novel strategy, "M-Tune", a simple yet powerful technique for addressing the class imbalance problem. This procedure employs an ensemble approach that incorporates threshold shifting and majority voting. This new method can effectively predict the minority class. The developed method was rigorously tested with 138 drug discovery datasets. We further rigorously evaluate the performance of the M-Tune method for two classifiers with eleven different fingerprints and find that classifiers benefit from M-Tune in effectively classifying the minority class. The M-Tune method outperformed the other strategies, including the GHOST method and SMOTE and achieved comparable performance with RUS in identifying minority classes. In conclusion, we demonstrated a new threshold procedure that can be effectively applied to real-world imbalanced datasets, i.e., drug discovery data sets where the class datasets are highly imbalanced. This method aids in identifying the minority class in highly imbalanced datasets, in general, and in drug discovery datasets, in particular. Although M-Tune increases false positive predictions relative to conservative thresholding strategies, it provides a practical advantage in applications where maximizing active compound recovery is prioritized. + © 2026. The Author(s), under exclusive licence to Springer Nature Switzerland AG. + + + + Dutta + Tapos + T + + School of Computing Science, The Assam Kaziranga University, Jorhat, 785006, India. + + + + Chutia + Hillul + H + + Advanced Computation and Data Sciences Division, CSIR-North East Institute of Science and Technology, Jorhat, 785006, India. + + + + Nagamani + Selvaraman + S + + Advanced Computation and Data Sciences Division, CSIR-North East Institute of Science and Technology, Jorhat, 785006, India. nagamaniselvaraman@gmail.com. + + + Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India. nagamaniselvaraman@gmail.com. + + + + eng + + + BT/PR40188/BTIS/137/27/2021 + Department of Biotechnology, Ministry of Science and Technology, India + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Netherlands + Mol Divers + 9516534 + 1381-1991 + + IM + + Bioactive datasets + Class imbalance problem + Data handling + Machine learning + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2025 + 7 + 18 + + + 2026 + 5 + 21 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 11 + 20 + + + aheadofprint + + 42213376 + 10.1007/s11030-026-11600-8 + 10.1007/s11030-026-11600-8 + + + + Abd Elrahman SM, Abraham A (2013) A review of class imbalance problem. J Netw Innovative Comput 1:332–340 + + + Haixiang G, Yijing L, Shassng J, Mingyun G, Yuanyue H, Bing G (2017) Learning from class-imbalanced data: review of methods and applications. Expert Syst Appl 73:220–239 + + 10.1016/j.eswa.2016.12.035 + + + + Tong X, Feng Y, Li JJ (2018) Neyman-Pearson classification algorithms and NP receiver operating characteristics. Sci Adv 4:1–10 + + 10.1126/sciadv.aao1659 + + + + Kaur H, Pannu HS, Malhi AK (2019) A systematic review on imbalanced data challenges in machine learning: applications and solutions. ACM Comput Surv 52:1–36 + + + Weiss GM (2004) Mining with rarity: a unifying framework. SIGKDD Explor Newsl 6:7–19 + + 10.1145/1007730.1007734 + + + + Provost F, Fawcett T (2001) Robust classification for imprecise environments. Mach Learn 42:203–231 + + 10.1023/A:1007601015854 + + + + Japkowicz N, Stephen S (2002) The class imbalance problem: a systematic study. Intell Data Anal 6:429–449 + + 10.3233/IDA-2002-6504 + + + + López V, Fernández A, García S, Palade V, Herrera F (2013) An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci 250:113–141 + + 10.1016/j.ins.2013.07.007 + + + + Wellintz J, Jain S, Hochuli JE, Maxfield T, Muratov EN, Tropsha A, Zakharov AV (2025) One size does not fit all: revising traditional paradigms for assessing accuracy of QSAR models used for virtual screening. J Cheminform 17:7 + + 10.1186/s13321-025-00948-y + + + + Seal S, Mahale M, Ortegon MG (2025) Machine learning for toxicity prediction using chemical structures: pillars for success in the real world. Chem Res Toxicol 38:759–807 + + + Sun Y, Wong AK, Kamel MS (2009) Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell 23:687–719 + + 10.1142/S0218001409007326 + + + + V Ganganwar (2012) An overview of classification algorithms for imbalanced datasets. Int J Emerg Technol Adv Eng 2:42–47 + + + Estabrooks A, Jo T, Japkowicz N (2004) A multiple resampling method for learning from imbalanced data sets. 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+ + 1880-344X + + + 2026 + May + 29 + + + Journal of echocardiography + J Echocardiogr + + Explainable machine learning for estimation of elevated left ventricular filling pressure: a multicenter validation. + 10.1007/s12574-026-00738-x + + Guideline-recommended algorithms (GL-algorithm) often results in indeterminate left ventricular filling pressure (LVFP). Despite high accuracy, machine learning (ML) methods lack interpretability, which necessitates the development of explainable ML models for clinical use. + To develop an explainable ML model for estimating LVFP, providing patient-level interpretation using gold-standard right heart catheterization (RHC) data. + We retrospectively enrolled 956 patients who underwent echocardiography and RHC at three hospitals within a median of 3 days. Two extreme gradient boosting models were trained using data from two hospitals (n = 621) to estimate elevated pulmonary artery wedge pressure (PAWP ≥ 18 mmHg) as a surrogate for elevated LVFP. Model 1 used variables from GL-algorithm, while Model 2 used variables selected based on Shapley additive explanations (SHAP) values. Models' area under the receiver-operating characteristic curve (AUROC) for elevated LVFP were compared using external test data from the other hospital (n = 335). + Overall, 31.0% had elevated PAWP, and 42.7% were classified as indeterminate LVFP by GL-algorithm, whereas the ML models classified all patients. AUROCs of Model 1 (0.82, 95% CI 0.73-0.92) and Model 2 (0.83, 95% CI 0.75-0.91) in classifiable cases by GL-algorithm significantly outperformed that of GL-algorithm (0.72, 95% CI 0.60-0.83, p = 0.020 and 0.016, respectively), Model 2 performed equally well for indeterminate cases. SHAP force plots visualized each variable's contribution to the ML model's decision for each patient. + Explainable ML outperformed GL-algorithm in estimating LVFP, providing a user-friendly tool for clinicians with patient-level interpretability. + © 2026. Japanese Society of Echocardiography. + + + + Nakamura + Yutaka + Y + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Kagiyama + Nobuyuki + N + 0000-0002-2305-0049 + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. kgnb_27_hot@yahoo.co.jp. + + + Data Science Course, Juntendo University, Tokyo, Japan. kgnb_27_hot@yahoo.co.jp. + + + Department of Cardiovascular Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0021, Japan. kgnb_27_hot@yahoo.co.jp. + + + + Shrestha + Sirish + S + + Center for Creative Leadership, Greensboro, USA. + + + + Akama + Yuka + Y + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Kito + Shusuke + S + + Data Science Course, Juntendo University, Tokyo, Japan. + + + + Takamatsu + Sachiko + S + + Department of Nursing, The Sakakibara Heart Institute of Okayama, Okayama, Japan. + + + + Toki + Misako + M + + Department of Clinical Laboratory, The Sakakibara Heart Institute of Okayama, Okayama, Japan. + + + + Kitai + Takeshi + T + + Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan. + + + Department of Heart Failure, National Cerebral and Cardiovascular Center, Suita, Japan. + + + + Okada + Taiji + T + + Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan. + + + Division of Cardiology, Shimane University Faculty of Medicine, Izumo, Japan. + + + + Murata + Azusa + A + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Kaneko + Tomohiro + T + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Matsue + Yuya + Y + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Miyazaki + Sakiko + S + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Okazaki + Shinya + S + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Minamino + Tohru + T + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + eng + + + 25K19371 + Japan Society for the Promotion of Scienc + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Japan + J Echocardiogr + 101263153 + 1349-0222 + + IM + + Explainable AI + Heart failure + Left ventricular filling pressure + Machine learning + SHAP + + + + + + 2025 + 12 + 21 + + + 2026 + 5 + 12 + + + 2026 + 5 + 2 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 11 + 18 + + + aheadofprint + + 42213288 + 10.1007/s12574-026-00738-x + 10.1007/s12574-026-00738-x + + + + GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories. Lancet. 2018;392:1789–858. + + + Bozkurt B, Ahmad T, Alexander KM, Baker WL, Bosak K, Breathett K, et al. Heart failure epidemiology and outcomes statistics: a report of the Heart Failure Society of America. J Card Fail. 2023;29:1412–51. + + 10.1016/j.cardfail.2023.07.006 + 37797885 + 10864030 + + + + Shimokawa H, Miura M, Nochioka K, Sakata Y. Heart failure as a general pandemic in Asia. Eur J Heart Fail. 2015;17:884–92. + + 10.1002/ejhf.319 + 26222508 + + + + Savarese G, Becher PM, Lund LH, Seferovic P, Rosano GMC, Coats AJS. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res. 2023;118:3272–87. + + 10.1093/cvr/cvac013 + 35150240 + + + + Nagueh SF, Smiseth OA, Appleton CP, Byrd BF 3rd, Dokainish H, Edvardsen T, et al. Recommendations for the evaluation of Left ventricular diastolic function by echocardiography: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2016;29:277–314. + + 10.1016/j.echo.2016.01.011 + 27037982 + + + + Andersen OS, Smiseth OA, Dokainish H, Abudiab MM, Schutt RC, Kumar A, et al. Estimating left ventricular filling pressure by echocardiography. J Am Coll Cardiol. 2017;69:1937–48. + + 10.1016/j.jacc.2017.01.058 + 28408024 + + + + Lancellotti P, Galderisi M, Edvardsen T, Donal E, Goliasch G, Cardim N, et al. Echo-Doppler estimation of left ventricular filling pressure: results of the multicentre EACVI Euro-Filling study. Eur Heart J Cardiovasc Imaging. 2017;18:961–8. + + 10.1093/ehjci/jex067 + 28444160 + + + + Sato K, Grant ADM, Negishi K, Cremer PC, Negishi T, Kumar A, et al. Reliability of updated left ventricular diastolic function recommendations in predicting elevated left ventricular filling pressure and prognosis. Am Heart J. 2017;189:28–39. + + 10.1016/j.ahj.2017.03.022 + 28625379 + + + + Playford D, Strange G, Celermajer DS, Evans G, Scalia GM, Stewart S, et al. Diastolic dysfunction and mortality in 436 360 men and women: the National Echo Database Australia (NEDA). Eur Heart J Cardiovasc Imaging. 2021;22:505–15. + + 10.1093/ehjci/jeaa253 + 33245344 + 8081414 + + + + Kagiyama N, Shrestha S, Farjo PD, Sengupta PP. Artificial intelligence: practical primer for clinical research in cardiovascular disease. J Am Heart Assoc. 2019;8:e012788. + + 10.1161/JAHA.119.012788 + 31450991 + 6755846 + + + + Tamura Y, Nomura A, Kagiyama N, Mizuno A, Node K. Digitalomics, digital intervention, and designing future: the next frontier in cardiology. J Cardiol. 2024;83:318–22. + + 10.1016/j.jjcc.2023.12.002 + 38135148 + + + + Farjo PD, Yanamala N, Kagiyama N, Patel HB, Casaclang-Verzosa G, Nezarat N, et al. Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study. Eur Heart J Digit Health. 2020;1:51–61. + + 10.1093/ehjdh/ztaa008 + 37056293 + 10087019 + + + + Misumi K, Matsue Y, Nogi K, Fujimoto Y, Kagiyama N, Kasai T, et al. Derivation and validation of a machine learning-based risk prediction model in patients with acute heart failure. J Cardiol. 2023;81:531–6. + + 10.1016/j.jjcc.2023.02.006 + 36858175 + + + + Pandey A, Kagiyama N, Yanamala N, Segar MW, Cho JS, Tokodi M, et al. Deep-learning models for the echocardiographic assessment of diastolic dysfunction. JACC Cardiovasc Imaging. 2021;14:1887–900. + + 10.1016/j.jcmg.2021.04.010 + 34023263 + + + + Lancaster MC, Salem Omar AM, Narula S, Kulkarni H, Narula J, Sengupta PP. Phenotypic clustering of left ventricular diastolic function parameters: patterns and prognostic relevance. JACC Cardiovasc Imaging. 2019;12:1149–61. + + 10.1016/j.jcmg.2018.02.005 + 29680357 + + + + Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2015;28:1-39.e14. + + 10.1016/j.echo.2014.10.003 + 25559473 + + + + Nagueh SF, Sanborn DY, Oh JK, Anderson B, Billick K, Derumeaux G, et al. Recommendations for the evaluation of left ventricular diastolic function by echocardiography and for heart failure with preserved ejection fraction diagnosis: an update from the American Society of Echocardiography. J Am Soc Echocardiogr. 2025;38:537–69. + + 10.1016/j.echo.2025.03.011 + 40617625 + + + + Heidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, Colvin MM, et al. 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Joint Committee on clinical practice guidelines. J Am Coll Cardiol. 2022;79:e263–421. + + 10.1016/j.jacc.2021.12.012 + 35379503 + + + + McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: developed by the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) With the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J. 2021;42:3599–726. + + 10.1093/eurheartj/ehab368 + 34447992 + + + + Forrester JS, Diamond GA, Swan HJ. Correlative classification of clinical and hemodynamic function after acute myocardial infarction. Am J Cardiol. 1977;39:137–45. + + 10.1016/S0002-9149(77)80182-3 + 835473 + + + + Chen T, Guestrin C. XGBoost a scalable tree boosting system. Knowl discovery data Min. 2016. https://doi.org/10.48550/arXiv.1603.02754 + + 10.48550/arXiv.1603.02754 + + + + Lundberg S, Lee S-I. A unified approach to interpreting model predictions. Neural information processing systems. 2017. https://doi.org/10.48550/arXiv.1705.07874 + + + van de Bovenkamp AA, Enait V, de Man FS, Oosterveer FTP, Bogaard HJ, Vonk Noordegraaf A, et al. Validation of the 2016 ASE/EACVI guideline for diastolic dysfunction in patients with unexplained dyspnea and a preserved left ventricular ejection fraction. J Am Heart Assoc. 2021;10:e021165. + + 10.1161/JAHA.121.021165 + 34476984 + 8649534 + + + + Almeida JG, Fontes-Carvalho R, Sampaio F, Ribeiro J, Bettencourt P, Flachskampf FA, et al. Impact of the 2016 ASE/EACVI recommendations on the prevalence of diastolic dysfunction in the general population. Eur Heart J Cardiovasc Imaging. 2018;19:380–6. + + 10.1093/ehjci/jex252 + 29236978 + + + + Luke P, Eggett C, Spyridopoulos I, Irvine T. A comparative analysis of British and American Society of Echocardiography recommendations for the assessment of left ventricular diastolic function. 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BMC Med Inform Decis Mak. 2021;21:237. + + 10.1186/s12911-021-01591-x + 34362354 + 8344327 + + + + Hou N, Li M, He L, Xie B, Wang L, Zhang R, et al. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med. 2020;18:462. + + 10.1186/s12967-020-02620-5 + 33287854 + 7720497 + + + + Wu TT, Lin XQ, Mu Y, Li H, Guo YS. Machine learning for early prediction of in-hospital cardiac arrest in patients with acute coronary syndromes. Clin Cardiol. 2021;44:349–56. + + 10.1002/clc.23541 + 33586214 + 7943901 + + + + Hirata Y, Tsuji T, Kotoku J, Sata M, Kusunose K. Echocardiographic artificial intelligence for pulmonary hypertension classification. Heart. 2024;110:586–93. + + 10.1136/heartjnl-2023-323320 + 38296266 + + + + Saito Y, Omae Y, Mizobuchi S, Fujito H, Miyagawa M, Kitano D, et al. Prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure. ESC Heart Fail. 2023;10:1103–13. + + 10.1002/ehf2.14282 + 36583242 + + + + Hummel YM, Liu LCY, Lam CSP, Fonseca-Munoz DF, Damman K, Rienstra M, et al. Echocardiographic estimation of left ventricular and pulmonary pressures in patients with heart failure and preserved ejection fraction: a study utilizing simultaneous echocardiography and invasive measurements. Eur J Heart Fail. 2017;19:1651–60. + + 10.1002/ejhf.957 + 28984057 + + + + + + + + 42213198 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1573-4951 + + 40 + 1 + + 2026 + May + 29 + + + Journal of computer-aided molecular design + J Comput Aided Mol Des + + Target-aware molecule SMILES generation using a large language model with retrieval-augmented generation, multi-turn memory, and a predictive model. + 134 + 10.1007/s10822-026-00834-1 + + Given the vastness of chemical space and the cost and time requirements of high-throughput screening, resource-efficient computational strategies are needed to prioritize candidate drug molecules. Here, we evaluated whether an open-weight large language model (LLM) augmented with retrieval-augmented generation (RAG), multi-turn memory, and a pretrained drug-target interaction predictor can generate target-aware molecules without task-specific retraining. Protein-ligand-pKi examples retrieved from BindingDB, Davis, and KIBA were used as contextual guidance, while DeepPurpose provided the optimization signal during iterative SMILES refinement. Our approach produced a statistically significant increase in predicted pKi across successive multi-turn memory iterations, indicating that retrieval- and memory-guided refinement may improve target-conditioned molecular generation. The proposed framework also showed favorable molecular generation characteristics, with novelty reaching 100%, diversity up to 0.882, and uniqueness up to 1.0, suggesting that retrieval did not reduce the process to simple reproduction of known ligands but instead supported exploration of new regions of chemical space. In addition, the approach yielded supportive docking results consistent with the generation of chemically relevant candidate ligands. However, the increase in predicted affinity was accompanied by trade-offs in molecular quality, including reduced validity and drug-likeness in later iterations. Taken together, these findings suggest that this framework provides a flexible and comparatively resource-efficient strategy for target-aware de novo molecular design, while further multi-objective optimization and independent validation remain necessary. + © 2026. The Author(s). + + + + Karabowicz + Piotr + P + 0000-0002-8072-2104 + + Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269, Bialystok, Poland. piotr.karabowicz@umb.edu.pl. + + + + Charkiewicz + Radosław + R + + Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269, Bialystok, Poland. + + + Center of Experimental Medicine, Medical University of Bialystok, 15-369, Bialystok, Poland. + + + + Charkiewicz + Alicja + A + + Department of Analysis and Bioanalysis of Medicines, Medical University of Bialystok, 15-089, Bialystok, Poland. + + + + Nikliński + Jacek + J + + Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269, Bialystok, Poland. + + + + eng + + + B.SUB.26.512 + Uniwersytet Medyczny w Bialymstoku + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Netherlands + J Comput Aided Mol Des + 8710425 + 0920-654X + + + + 0 + Ligands + + + 0 + Proteins + + + 0 + Small Molecule Libraries + + + IM + + + Large Language Models + + + Ligands + + + Proteins + chemistry + + + Drug Discovery + methods + + + Small Molecule Libraries + chemistry + + + Drug Design + + + Humans + + + + Drug discovery + Large language models + Multi-turn memory + Retrieval-augmented generation + SMILES generation + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 1 + 8 + + + 2026 + 5 + 9 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 11 + 15 + + + epublish + + 42213198 + 10.1007/s10822-026-00834-1 + 10.1007/s10822-026-00834-1 + + + + Skinnider MA (2024) Invalid SMILES are beneficial rather than detrimental to chemical language models. Nat Mach Intell 6:437–448. https://doi.org/10.1038/s42256-024-00821-x + + 10.1038/s42256-024-00821-x + + + + Zheng Y, Koh HY, Ju J et al (2025) Large language models for drug discovery and development. Patterns 6:101346. https://doi.org/10.1016/j.patter.2025.101346 + + 10.1016/j.patter.2025.101346 + 41142906 + 12546459 + + + + Mienye ID, Jere N, Obaido G et al (2025) Large language models: an overview of foundational architectures, recent trends, and a new taxonomy. Discov Appl Sci 7:1027. https://doi.org/10.1007/s42452-025-07668-w + + 10.1007/s42452-025-07668-w + + + + Umer MS, Nabeel M, Athar U et al (2025) Large language models meet molecules: a systematic review of advances and challenges in AI-driven cheminformatics. Arch Computat Methods Eng. https://doi.org/10.1007/s11831-025-10437-y + + 10.1007/s11831-025-10437-y + + + + Bagal V, Aggarwal R, Vinod PK, Priyakumar UD (2022) MolGPT: molecular generation using a transformer-decoder model. J Chem Inf Model 62:2064–2076. https://doi.org/10.1021/acs.jcim.1c00600 + + 10.1021/acs.jcim.1c00600 + 34694798 + + + + Sheikholeslami M, Mazrouei N, Gheisari Y et al (2025) DrugGen enhances drug discovery with large language models and reinforcement learning. Sci Rep 15:13445. https://doi.org/10.1038/s41598-025-98629-1 + + 10.1038/s41598-025-98629-1 + 40251288 + 12008224 + + + + Song B, Li X, Chao X et al (2025) Advancements in large language models ( LLMS ): empowering drug discovery. WIREs Comput Mol Sci 15:e70054. https://doi.org/10.1002/wcms.70054 + + 10.1002/wcms.70054 + + + + Ma J, Liu J, Xu D et al (2025) Application and prospects of large language models in small-molecule drug discovery. Anal Chem 97:27453–27477. https://doi.org/10.1021/acs.analchem.5c04083 + + 10.1021/acs.analchem.5c04083 + 41366732 + 12750412 + + + + Öztürk H, Özgür A, Ozkirimli E (2018) DeepDTA: deep drug–target binding affinity prediction. Bioinformatics 34:i821–i829. https://doi.org/10.1093/bioinformatics/bty593 + + 10.1093/bioinformatics/bty593 + 30423097 + 6129291 + + + + Mswahili ME, Jeong Y-S (2024) Transformer-based models for chemical SMILES representation: a comprehensive literature review. Heliyon 10:e39038. https://doi.org/10.1016/j.heliyon.2024.e39038 + + 10.1016/j.heliyon.2024.e39038 + 39640612 + 11620068 + + + + Gao Y, Xiong Y, Gao X, et al (2023) Retrieval-augmented generation for large language models: a survey + + + Zhang P, Peng X, Han R et al (2025) Rag2Mol: structure-based drug design based on retrieval augmented generation. Briefings Bioinform 26:bbaf265. https://doi.org/10.1093/bib/bbaf265 + + 10.1093/bib/bbaf265 + + + + Hu Y, Wang Y, McAuley J (2025) Evaluating memory in LLM agents via incremental multi-turn interactions + + + OpenAI, Agarwal S, Ahmad L, et al (2025) gpt-oss-120b &amp; gpt-oss-20b Model Card + + + Grattafiori A, Dubey A, Jauhri A, et al (2024) The Llama 3 Herd of Models + + + Liu T, Hwang L, Burley SK et al (2025) BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data. Nucleic Acids Res 53:D1633–D1644. https://doi.org/10.1093/nar/gkae1075 + + 10.1093/nar/gkae1075 + 39574417 + 11701568 + + + + Huang K, Fu T, Glass LM et al (2021) DeepPurpose: a deep learning library for drug–target interaction prediction. Bioinformatics 36:5545–5547. https://doi.org/10.1093/bioinformatics/btaa1005 + + 10.1093/bioinformatics/btaa1005 + 33275143 + 8016467 + + + + Eberhardt J, Santos-Martins D, Tillack AF, Forli S (2021) AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings. J Chem Inf Model 61:3891–3898. https://doi.org/10.1021/acs.jcim.1c00203 + + 10.1021/acs.jcim.1c00203 + 34278794 + 10683950 + + + + Fleming J, Magana P, Nair S et al (2025) AlphaFold Protein Structure Database and 3D-Beacons: new data and capabilities. J Mol Biol 437:168967. https://doi.org/10.1016/j.jmb.2025.168967 + + 10.1016/j.jmb.2025.168967 + 40133787 + + + + Laban P, Hayashi H, Zhou Y, Neville J (2025) LLMs Get Lost In Multi-Turn Conversation + + + + + + + 42213197 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1438-7948 + + 26 + 1 + + 2026 + May + 29 + + + Functional & integrative genomics + Funct Integr Genomics + + An integrated computational-experimental approach identifies Malate Synthase G as therapeutic target to disrupt carbon metabolism in chronic Pseudomonas aeruginosa infection. + 116 + 10.1007/s10142-026-01889-x + + Pseudomonas aeruginosa is a leading cause of nosocomial infections, particularly in individuals with a compromised immune system. Due to its strong adaptive ability, P. aeruginosa tends to develop antibiotic resistance and establish chronic infection, making its eradication through traditional antibiotics challenging. Thus, the development of novel therapeutic targets and corresponding inhibitors is urgently required. We conducted bioinformatics analyses of gene-chip datasets (GSE10362, GSE21966) of P. aeruginosa from patients with cystic fibrosis (CF) in the Gene Expression Omnibus (GEO) database, ranging from early to late stages of infection. Differentially expressed gene (DEG) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and Weighted Gene Co-expression Network Analysis (WGCNA) were performed to identify key functional DEGs. Among these genes, candidate genes were refined using three machine learning algorithms: LASSO regression, random forest (RF), and support vector machine (SVM). Using this integrated approach and published reports, target gene was identified consequently. With reported inhibitors, a structure-based virtual screening model against the target protein was established to screen inhibitors from an FDA-approved drug library. Screened hits were experimentally validated through in vitro MIC and checkerboard broth microdilution assays to determine their antibacterial activity and the synergistic effect of tobramycin (TOB) and amikacin (AMK). Enzyme activity was measured to evaluate target protein inhibition. Transcriptome sequencing analysis was performed to explain the effect of target inhibition and reveal the possible mechanism of inhibition. We identified 210 upregulated genes in the late stage of infection. KEGG functional enrichment demonstrated that the up-regulated genes were mainly enriched in metabolic pathways. Eight metabolism-related key DEGs were obtained by the intersection of genes from metabolic pathways and genes from the key module of WGCNA. Machine learning algorithms (LASSO, RF, and SVM) and literature investigation results identified glcB, which encodes malate synthase G (MS), as a therapeutic target for developing inhibitors. In virtual screening, we formulated four rules for a screening model based on the docking results, and finally identified three FDA-approved drugs prednisolone, dienogest and carbinoxamine maleate salt (CAR) as candidates. In vitro MIC assay and checkerboard assay suggested that CAR had an antibacterial effect with a concentration-dependent trend and could enhance the effect of tobramycin and amikacin. Consistent with MIC assay, the inhibition rate in MS activity assay showed a concentration-dependent trend. Transcriptome sequencing analysis showed that carbon metabolism was remodeled when MS was inhibited, thus impairing the growth of P. aeruginosa, and the synergistic effect with TOB and AMK may be explained by the impaired ability to combat oxidative stress. This study identified and validated drug target glcB (encodes MS) and potential inhibitor CAR in treating chronic P. aeruginosa infection based on an integrated method of bioinformatics, virtual screening, and in vitro experiments. + © 2026. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. + + + + Chen + Geping + G + + Department of Pharmacy, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China. + + + + Tan + Qiutong + Q + + College of Pharmacy, Jinan University, Guangzhou, 510632, China. + + + + Zhou + Haochuan + H + + College of Pharmacy, Jinan University, Guangzhou, 510632, China. + + + + Wang + Rui + R + + Department of Pharmacy, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China. + + + + Mo + Jingyi + J + + Department of Pharmacy, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China. + + + + Xu + Jun + J + + College of Pharmacy, Jinan University, Guangzhou, 510632, China. xujun@jnu.edu.cn. + + + + Chen + Wenying + W + + Department of Pharmacy, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China. chenwenying2016@163.com. + + + + eng + + + CPA: 2023ZYS09 + Guangdong Pharmaceutical Association + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Germany + Funct Integr Genomics + 100939343 + 1438-793X + + + + EC 2.3.3.9 + Malate Synthase + + + 0 + Anti-Bacterial Agents + + + 7440-44-0 + Carbon + + + 0 + Bacterial Proteins + + + IM + + + Pseudomonas aeruginosa + drug effects + genetics + pathogenicity + metabolism + + + Humans + + + Malate Synthase + antagonists & inhibitors + genetics + metabolism + chemistry + + + Pseudomonas Infections + drug therapy + genetics + microbiology + metabolism + + + Anti-Bacterial Agents + pharmacology + + + Carbon + metabolism + + + Bacterial Proteins + genetics + metabolism + antagonists & inhibitors + + + Cystic Fibrosis + microbiology + + + Computational Biology + + + Molecular Docking Simulation + + + + Pseudomonas aeruginosa + Bioinformatics + Carbon metabolism + Malate synthase + Virtual screening + + Declarations. 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Nucleic Acids Res 52:967–976. https://doi.org/10.1093/nar/gkad1201 + + 10.1093/nar/gkad1201 + 38096062 + 10810197 + + + + Xia Y, Wang D, Pan X et al (2020) TpiA is a Key Metabolic Enzyme That Affects Virulence and Resistance to Aminoglycoside Antibiotics through CrcZ in Pseudomonas aeruginosa. mBio 11:e02079–e02019. https://doi.org/10.1128/mBio.02079-19 + + 10.1128/mBio.02079-19 + 31911486 + 6946797 + + + + Yang H, Zhou Y, Luo Q et al (2023) L-leucine increases the sensitivity of drug-resistant Salmonella to sarafloxacin by stimulating central carbon metabolism and increasing intracellular reactive oxygen species level. Front Microbiol 14:1186841. https://doi.org/10.3389/fmicb.2023.1186841 + + 10.3389/fmicb.2023.1186841 + 37250042 + 10213264 + + + + Yang J, Xu J-F, Liang S (2025) Antibiotic resistance in Pseudomonas aeruginosa: mechanisms and emerging treatment. Crit Rev Microbiol 51:841–859. https://doi.org/10.1080/1040841X.2024.2429599 + + 10.1080/1040841X.2024.2429599 + 39556143 + + + + + + + + 42213195 + + 2026 + 05 + 29 + +
+ + 1432-0584 + + + 2026 + May + 29 + + + Annals of hematology + Ann Hematol + + A macrophage-related efferocytosis-based two-gene prognostic model for acute myeloid leukemia identified by multi-omics and machine learning. + 10.1007/s00277-026-07044-7 + + Background Acute myeloid leukemia (AML) remains a lethal hematologic malignancy with high heterogeneity. Macrophage-mediated efferocytosis in the tumor microenvironment is implicated in immune suppression and disease progression. Methods We integrated single-cell and bulk transcriptomic data from public cohorts to identify genes associated with macrophages and efferocytosis in AML. Candidate genes were screened for prognosis using univariate Cox regression. A comprehensive machine learning framework, evaluating 117 algorithm combinations, was employed to construct a robust prognostic model. The optimal LASSO and random survival forest approach identified CD52 and S100A4 as core prognostic genes. The resulting two-gene model was rigorously validated using Kaplan-Meier analysis, time-dependent ROC curves, and calibration plots across multiple independent cohorts. The associations of the risk score with the immune microenvironment and drug sensitivity were further analyzed. SHapley Additive exPlanations (SHAP) analysis was applied to interpret the model's decision-making. Results The two-gene signature demonstrated stable and powerful predictive performance for overall survival in both training and external validation sets. The risk score was an independent prognostic factor and showed significant correlations with immune cell infiltration patterns and response to chemotherapeutic agents. SHAP analysis confirmed the consistent and biologically plausible contributions of CD52 and S100A4. Single-cell resolution analysis revealed their specific enrichment in distinct AML-associated macrophage subpopulations. Conclusions We developed a novel macrophage efferocytosis-based prognostic model using a multi-omics and machine learning approach. This model provides valuable insights into the immune microenvironment of AML and offers a potential tool for risk stratification and therapeutic guidance. + © 2026. The Author(s). + + + + Li + Xiaoning + X + + Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China. + + + Guangdong Engineering Research Center of Precision Immune Cell Therapy Technology, Guangzhou, Guangdong, China. + + + + Zhang + Yujie + Y + + Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China. + + + Guangdong Engineering Research Center of Precision Immune Cell Therapy Technology, Guangzhou, Guangdong, China. + + + + Wei + Xiaoying + X + + Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China. + + + Guangdong Engineering Research Center of Precision Immune Cell Therapy Technology, Guangzhou, Guangdong, China. + + + + Huang + Rui + R + + Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China. rachelchn@163.com. + + + Guangdong Engineering Research Center of Precision Immune Cell Therapy Technology, Guangzhou, Guangdong, China. rachelchn@163.com. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Germany + Ann Hematol + 9107334 + 0939-5555 + + IM + + Acute myeloid leukemia + Biomarkers + Efferocytosis + Machine learning + Prognostic model + + Declarations. Consent for publication: All authors have read and approved the final version of the manuscript and agree to the publication of this research in the journal. Ethics and consent to participate declarations: Not applicable.  Competing interests: The authors declare no competing interests. + + + + + 2025 + 12 + 2 + + + 2026 + 4 + 26 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 11 + 15 + + + aheadofprint + + 42213195 + 10.1007/s00277-026-07044-7 + 10.1007/s00277-026-07044-7 + + + + + + 42213145 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2561-326X + + 10 + + 2026 + May + 29 + + + JMIR formative research + JMIR Form Res + + Machine Learning-Based Prediction Model for 30-Day Emergency Department Revisits in a Medically Underserved Tertiary Hospital: Formative Retrospective Cohort Study. + + e87289 + e87289 + + 10.2196/87289 + + Emergency department (ED) revisits are critical quality indicators, particularly in medically underserved areas, where traditional prediction tools show limited performance. Machine learning (ML) approaches may offer improved predictive performance for identifying high-risk patients. + This formative study aimed to develop and validate an ML-based model for predicting 30-day ED revisits using electronic health records from a tertiary hospital serving a medically underserved area in South Korea and to evaluate its clinical utility through interpretability analysis and risk stratification. + This retrospective cohort study analyzed 36,230 adult patients visiting the Gangneung Asan Hospital ED in 2023. We developed and compared 3 ML models (extreme gradient boosting [XGBoost], random forest, and ElasticNet) using electronic health records. Model interpretability was ensured through Shapley additive explanations (SHAP) analysis, and clinical utility was evaluated through 5-tier risk stratification. + Among 36,230 patients, 798 (2.2%) revisited within 30 days. XGBoost achieved superior performance with an area under the receiver operating characteristic curve of 0.90 (95% CI 0.88-0.92), a sensitivity of 0.94, and a specificity of 0.69. The SHAP analysis identified ED length of stay, oxygen saturation, systolic blood pressure, computed tomography performance, antibiotic use, and liver disease as key predictors. Risk stratification demonstrated a 25-fold difference in the actual revisit rates between the lowest (152/8450, 1.8%) and the highest (686/1500, 45.7%) risk groups. + The XGBoost model demonstrated excellent predictive performance with high interpretability for 30-day ED revisit predictions. The implementation of this model could enable risk-stratified interventions and more efficient resource allocation in medically underserved settings, potentially reducing unnecessary revisits and improving patient outcomes. This formative study establishes feasibility and provides a foundation for future multicenter validation studies in similar medically underserved settings. + © Kyongmin Sun. Originally published in JMIR Formative Research (https://formative.jmir.org). + + + + Sun + Kyongmin + K + 0000-0002-2977-0880 + + Department of Emergency Medicine, College of Medicine, Gangneung Asan Hospital, Gangneung, Gangwon-do, Republic of Korea. + + + Department of Health Administration, Graduate School, Yonsei University Mirae Campus, 1 Yonsedae-gil, Heungeop-myeon, Wonju, Gangwon-do, 26493, Republic of Korea, 82 337602418. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Canada + JMIR Form Res + 101726394 + 2561-326X + + IM + + + Retrospective Studies + + + Humans + + + Tertiary Care Centers + statistics & numerical data + organization & administration + + + Emergency Room Visits + statistics & numerical data + + + Emergency Service, Hospital + statistics & numerical data + organization & administration + + + Machine Learning + + + Republic of Korea + + + Medically Underserved Area + + + Female + + + Predictive Learning Models + + + Male + + + Middle Aged + + + Adult + + + Aged + + + Boosting Machine Learning Algorithms + + + Cohort Studies + + + + SHAP + Shapley additive explanations + XGBoost + emergency department + extreme gradient boosting + machine learning + medically underserved area + prediction model + revisit + + + + + + 2025 + 11 + 6 + + + 2026 + 4 + 9 + + + 2026 + 4 + 28 + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 11 + 13 + + + epublish + + 42213145 + 10.2196/87289 + v10i1e87289 + + + + + + 42213103 + + 2026 + 05 + 29 + +
+ + 1432-5241 + + + 2026 + May + 29 + + + Aesthetic plastic surgery + Aesthetic Plast Surg + + Does Patient History Influence Capsular Contracture? An Exploratory Analysis with Machine Learning. + 10.1007/s00266-026-05919-8 + + Capsular contracture (CC) is a frequent and distressing complication of breast augmentation and reconstruction. Although numerous patient-, surgical-, and implant-related risk factors have been proposed, reliable population-level predictors remain inconsistent across studies. This study evaluates whether administrative medical history, as encoded by ICD and CPT codes, contains sufficient predictive signal to identify patients at risk for CC using machine learning. + Patients were queried from the MerativeTM MarketScan® Research Databases from 2003 to 2017 with CPT codes for implant-based breast reconstruction and augmentation. ICD codes were then used to identify all events and conditions of a patient's history. Hyperparameter-tuned random forest models were combined with multivariable logistic regression models to validate risk factors for the development of CC. + A total of 112,489 patients were included, and the rate of capsular contracture was 9.55%. In total, 4,825 common conditions and procedures were included as features in the model. The random forest's error rate was 9.54%. Prior CC was the most important variable in the model. When removed, model accuracy decreased by 0.0011% (p <0.001). Removing prior irradiation as a feature decreased model accuracy by 0.00014% (p < 0.001). In line with the previous literature, prior CC (OR = 2.47, p <0.001) and irradiation (OR = 1.16, p <0.001) were significantly associated with CC development in a multivariable logistic regression model. + Administrative medical history alone demonstrated limited predictive utility for CC development. Although established associations such as prior CC and irradiation were confirmed, their incremental contribution to predictive performance was negligible. These associations do not meaningfully enhance a clinician's ability to reliably predict which patients are at increased risk for CC. + This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 . + © 2026. The Author(s). + + + + Johnstone + Thomas M + TM + + Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA, USA. + + + + Najafali + Daniel + D + 0000-0003-1509-9229 + + Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA. danieln6@illinois.edu. + + + + Shah + Jennifer K + JK + + Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA, USA. + + + + Camacho + Justin M + JM + + Drexel University College of Medicine, Philadelphia, PA, USA. + + + + Johnstone + Chancellor + C + + Air Force Institute of Technology, Wright-Patterson AFB, Dayton, OH, USA. + + + + Nazerali + Rahim S + RS + + Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA, USA. + + + + Lee + Gordon K + GK + + Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA, USA. gordokl1@hs.uci.edu. + + + Department of Plastic Surgery, University of California, Irvine, Orange, CA, USA. gordokl1@hs.uci.edu. + + + + eng + + + UL1TR003142 + TR + NCATS NIH HHS + United States + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + Aesthetic Plast Surg + 7701756 + 0364-216X + + IM + + Alloplastic reconstruction + Artificial intelligence + Breast + Breast implants + Breast reconstruction + Capsular contracture + Complications + Contracture formation + Implant + Implant capsular contracture + Implant-based reconstruction + Machine learning + Mammaplasty + Outcomes prediction + Postoperative complications + Supervised machine learning + + Declarations. Conflict of interest: The authors declare that they have no conflicts of interest to disclose. Human and Animal Rights: Statement of human and animal rights, or ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. Data for this project were accessed using the Stanford Center for Population Health Sciences Data Core. The PHS Data Core is supported by a National Institutes of Health National Center for Advancing Translational Science Clinical and Translational Science Award (UL1TR003142) and from Internal Stanford funding. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Informed Consent: For this type of study, informed consent is not required. + + + + + 2025 + 1 + 24 + + + 2026 + 4 + 17 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 11 + 12 + + + aheadofprint + + 42213103 + 10.1007/s00266-026-05919-8 + 10.1007/s00266-026-05919-8 + + + + ISAPS international survey on aesthetic/cosmetic procedures performed in 2019. 2020. https://www.isaps.org/discover/about-isaps/global-statistics/global-survey-2019-full-report-and-press-releases-english/ . Accessed 2024. + + + Pasca A, Bonci EA, Chiuzan C, et al. 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+ + 1613-6829 + + + 2026 + May + 29 + + + Small (Weinheim an der Bergstrasse, Germany) + Small + + Harnessing and Suppressing Electron Spin-State Transitions: From Decoding to Rational Design of High-Performance Cathodes for Alkali-Ion Batteries. + + e73936 + e73936 + + 10.1002/smll.73936 + + The electron spin state of transition metal ions represents a fundamental quantum property that is increasingly recognized as a pivotal design dimension for tuning the performance of cathode materials in Li/Na/K‑ion batteries. This review begins by consolidating the foundational principles through which spin states govern electrochemical properties, establishing a robust theoretical framework that bridges atomic-scale coordination environments with macroscopic electrode behavior. It further discusses advanced experimental and computational techniques for probing complex spin states and, critically, for establishing clear structure-spin-performance relationships. A central focus is placed on the rational design of spin configurations, whether via proactive engineering or suppression of unfavorable transitions, to optimize key electrochemical processes: modulating cationic vs. anionic redox competition, enhancing structural stability by mitigating Jahn-Teller distortions and magnetic frustration, and improving charge and ion transport. The review also highlights the emerging role of spin‑sensitive machine learning as an accelerated pathway for discovering superior cathode materials. By integrating theoretical insights, methodological advances, and application‑oriented studies, this work provides a comprehensive mechanistic framework and practical guidelines for the design of next‑generation high‑performance cathodes through deliberate spin‑state control. + © 2026 Wiley‐VCH GmbH. + + + + Peng + Tao + T + 0000-0002-0234-0477 + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + Ma + Mengzhen + M + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + Liu + Shaonan + S + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + He + Junbao + J + + Henan International Joint Laboratory of MXene Materials Microstructure, Collaborative Innovation Center of Intelligent Explosion-Proof Equipment of Henan Province, College of Physics and Electronic Engineering, Nanyang Normal University, Nanyang, China. + + + + Wang + Yuanyuan + Y + + College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China. + + + + Yang + Ya + Y + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + Lu + Yang + Y + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + Luo + Yongsong + Y + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + Henan International Joint Laboratory of MXene Materials Microstructure, Collaborative Innovation Center of Intelligent Explosion-Proof Equipment of Henan Province, College of Physics and Electronic Engineering, Nanyang Normal University, Nanyang, China. + + + + Sun + Weiwei + W + 0000-0003-4079-4063 + + College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China. + + + + eng + + + 52272219 + National Natural Science Foundation of China + + + + 52472254 + National Natural Science Foundation of China + + + + 262300421381 + Natural Science Foundation of Henan Province + + + + 242300421191 + Natural Science Foundation of Henan Province + + + + 24ZDYF016 + Key Research and Development Program of Nanyang City + + + + Nanhu Scholars Program for Young Scholars of Xinyang Normal University + + + + 2026JJ50116 + the Natural Science Foundation of Hunan Province + + + + 2026JJ50116 + the Natural Science Foundation of Hunan Province + + + + + Journal Article + Review + + + 2026 + 05 + 29 + +
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+ + 1876-7591 + + + 2026 + May + 28 + + + JACC. Cardiovascular imaging + JACC Cardiovasc Imaging + + Machine Learning Model Using Pre-Cancer Therapy Cardiac Magnetic Resonance Images to Predict Cancer Therapy-Related Cardiac Dysfunction. + S1936-878X(26)00229-9 + 10.1016/j.jcmg.2026.04.007 + + Predicting risk of cancer therapy-related cardiac dysfunction (CTRCD) remains challenging. + The purpose of this study was to assess if deep learning (DL) approaches using cardiac magnetic resonance (CMR) images before cancer therapy can predict subsequent CTRCD and compare them with clinical and conventional imaging models. + Women with HER2+ breast cancer receiving anthracyclines and trastuzumab from 3 prospective studies (Toronto, Canada: EMBRACE-MRI, SPARE-HF; and Calgary, Canada: CIROC) were included. Patients were assessed before cancer therapy, after anthracycline, and trimonthly with repeated echocardiography and CMR. CTRCD was defined according to CMR. We calculated the HFA-ICOS risk score and measured CMR and echocardiography volumetric and functional parameters. Deep convolutional neural network architectures were used with pre-cancer therapy CMR short-axis cine images to develop image-based DL models to predict CTRCD. The Toronto patients were used for model derivation and internal validation; the Calgary patients were used for external validation. + A total of 229 patients (mean age 50.4 ± 9.7 years) were included: 176 in the internal (52 CTRCD events) development cohort and 53 in the external data set (14 CTRCD events). Our pre-cancer therapy CMR DL model demonstrated an AUC of 0.85 (95% CI: 0.69-0.97) and F1 score 0.69 (95% CI: 0.47-0.86) to predict CTRCD. On external validation, the DL model had an AUC of 0.80 (95% CI: 0.58-0.86) and F1 score of 0.55 (95% CI: 0.32-0.69). In comparison, the best-performing models using HFA-ICOS risk score, CMR, and echocardiographic parameters demonstrated, respectively, AUCs of 0.66 (95% CI: 0.53-0.75), 0.59 (95% CI: 0.41-0.73), 0.62 (95% CI: 0.44-0.78) and F1 scores of 0.56 (95% CI: 0.47-0.61), 0.20 (95% CI: 0.00-0.42) and 0.36 (95% CI: 0.12-0.62) to predict CTRCD. + A DL model using pre-cancer therapy CMR short-axis cines can predict future CTRCD risk better than clinical or manually quantified imaging models. + Copyright © 2026 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved. + + + + Yu + Christopher + C + + Department of Medicine, Division of Cardiology, Ted Rogers Program in Cardiotoxicity Prevention, Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada. + + + + Peikari + Mohammad + M + + Ted Rogers Computational Program, Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada. + + + + Labib + Dina + D + + Stephenson Cardiac Imaging Centre, Libin Cardiovascular Institute of Alberta, University of Calgary, Alberta, Canada. + + + + Houbois + Christian P + CP + + Sunnybrook Health Science Centre, University of Toronto, Toronto, Ontario, Canada. + + + + Steve Fan + Chun-Po + CP + + Ted Rogers Computational Program, Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada. + + + + White + James A + JA + + Stephenson Cardiac Imaging Centre, Libin Cardiovascular Institute of Alberta, University of Calgary, Alberta, Canada. + + + + Amir + Eitan + E + + Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada. + + + + Hanneman + Kate + K + + Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada. + + + + J Wintersperger + Bernd + B + + Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany. + + + + Abdel-Qadir + Husam + H + + Department of Medicine, Division of Cardiology, Ted Rogers Program in Cardiotoxicity Prevention, Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; Women's College Hospital, Toronto, Ontario, Canada. + + + + McIntosh + Chris + C + + Ted Rogers Computational Program, Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada; Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; Princess Margaret Cancer Centre, Toronto, Ontario, Canada. + + + + Thavendiranathan + Paaladinesh + P + + Department of Medicine, Division of Cardiology, Ted Rogers Program in Cardiotoxicity Prevention, Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada. Electronic address: dinesh.thavendiranathan@uhn.ca. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + JACC Cardiovasc Imaging + 101467978 + 1876-7591 + + IM + + cardio-oncology + cardiotoxicity cancer therapy–related cardiac dysfunction + heart failure + machine learning + + Funding Support and Author Disclosures This study was funded by operating grants 137132, 142456, and 147814 from the Canadian Institutes of Health Research, an Ontario Early Research Award, a Grant Miller Research grant from the University of Toronto, and AHSC AFP Innovation Funds. Dr Thavendiranathan (147814) is supported by a Canada Research Chair in Cardiooncology (CRC-2019-00097) and the Canadian Cancer Society/Canadian Institutes of Health Research W. David Hargraft Grant. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. + + + + + 2025 + 10 + 2 + + + 2026 + 4 + 7 + + + 2026 + 4 + 10 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 10 + 31 + + + aheadofprint + + 42212994 + 10.1016/j.jcmg.2026.04.007 + S1936-878X(26)00229-9 + + + + + + 42212964 + + 2026 + 05 + 29 + +
+ + 2198-3844 + + + 2026 + May + 29 + + + Advanced science (Weinheim, Baden-Wurttemberg, Germany) + Adv Sci (Weinh) + + Machine Learning Accelerated Non-Adiabatic Molecular Dynamics Elucidates Local Polarization Effects on Non-radiative Recombination in Halide Perovskites. + + e75903 + e75903 + + 10.1002/advs.75903 + + Non-radiative recombination is a critical factor limiting the optoelectronic performance of halide perovskites, yet how local polarization induced by charge redistribution regulates this process remains unclear. To gain deeper insight while reducing the high computational cost of conventional non-adiabatic molecular dynamics (NAMD), we developed Hefei-NAMD-S, a machine learning (ML) framework constructed using stacked models. The relative errors of the ML predicted non-adiabatic coupling and pure-dephasing time, calculated with respect to the first-principles values, remain below 1.10%, while the total computational time is reduced by approximately 78%, demonstrating the accuracy and efficiency of the proposed framework. NAMD simulations further reveal that the B-site local polarization is involved in regulating the non-radiative recombination process. The rubidium-substitution-doped system (FARb) and the cesium interstitial doped system exhibit recombination times of about 280 ns, nearly 2.8 times that of the pristine system, and the enhanced performance of FARb has been supported by previous experimental evidence. These results identify B-site local polarization as one of the important factors in suppressing non-radiative recombination and provide a theoretical foundation for designing perovskite materials through local polarization modulation. + © 2026 The Author(s). Advanced Science published by Wiley‐VCH GmbH. + + + + Yang + Bing + B + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Wei + Xiaoli + X + + School of Biological Science and Medical Engineering, Southeast University, Nanjing, China. + + + + Cai + Bo + B + 0000-0002-6605-5931 + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, UK. + + + + Yang + Yan + Y + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Zhu + Xinghai + X + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Liu + Yi + Y + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Xia + Junmin + J + 0000-0002-9805-5397 + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Liu + Lihui + L + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Cao + Kun + K + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Shen + Wei + W + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Xia + Pengfei + P + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Chen + Shufen + S + 0000-0003-0333-958X + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Chen + Siyu + S + + Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, UK. + + + TCM Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK. + + + + Zhao + Jin + J + + Department of Physics and ICQD/Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, China. + + + Hefei National Laboratory, University of Science and Technology of China, Hefei, China. + + + Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, USA. + + + + eng + + + 62404107 + National Natural Science Foundation of China + + + + 62304111 + National Natural Science Foundation of China + + + + 62474097 + National Natural Science Foundation of China + + + + BK20220396 + Natural Science Foundation of Jiangsu Province + + + + GDX2022010009 + Project of the State Key Laboratory of Flexible Electronics + + + + GZR2023010041 + Project of the State Key Laboratory of Flexible Electronics + + + + BZ2023059 + Science and Technology Cooperation Project of Hong Kong, Macao, and Taiwan + + + + 20KJA510005 + Natural Science Fund for Colleges and Universities in Jiangsu Province + + + + NY223076 + Natural Science Foundation of Nanjing University of Posts and Telecommunications + + + + NY222069 + Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications + + + + CSC202308320240 + China Scholarship Council + + + + KYCX23_1091 + Ministry of Education, as well as the Postgraduate Research & Practice Innovation Program of Jiangsu Province + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Germany + Adv Sci (Weinh) + 101664569 + 2198-3844 + + IM + + excited‐state dynamics + local polarization + machine learning + non‐adiabatic molecular dynamics + + + + + + 2026 + 5 + 19 + + + 2026 + 3 + 6 + + + 2026 + 5 + 21 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 10 + 22 + + + aheadofprint + + 42212964 + 10.1002/advs.75903 + + + + G. E. Eperon, S. D. Stranks, C. Menelaou, M. B. Johnston, L. M. Herz, and H. J. Snaith, “Formamidinium Lead Trihalide: A Broadly Tunable Perovskite for Efficient Planar Heterojunction Solar Cells,” Energy & Environmental Science 7 (2014): 982, https://doi.org/10.1039/c3ee43822h. + + + A. A. Zhumekenov, M. I. Saidaminov, M. A. Haque, et al., “Formamidinium Lead Halide Perovskite Crystals With Unprecedented Long Carrier Dynamics and Diffusion Length,” ACS Energy Letters 1 (2016): 32–37, https://doi.org/10.1021/acsenergylett.6b00002. + + + A. Amat, E. Mosconi, E. 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+ + 1746-0921 + + + 2026 + May + 29 + + + Future microbiology + Future Microbiol + + AI-microbial hybrid biosensors: the next generation of intelligent detection systems. + + 1 + 21 + 1-21 + + 10.1080/17460913.2026.2678101 + + The convergence of artificial intelligence (AI) and microbial biosensor technology is transforming pathogen detection, environmental surveillance, antimicrobial resistance (AMR) profiling, and precision diagnostics. Microbial biosensors exploit the specificity of living microorganisms, but signal variability, scalability limits, and interpretive challenges have constrained clinical adoption. Integration of machine learning (ML) and deep neural networks (DNNs) now enables adaptive, high-performance sensing systems. Applied to multi-sensor datasets-such as electrochemical impedance, Raman spectroscopy, and hyperspectral microscopy-convolutional neural networks (CNNs) and ensemble models achieve bacterial classification accuracies of 95-99%, while markedly reducing diagnostic turnaround times and enabling continuous surveillance. Despite rapid progress, the field remains fragmented, lacking a unified synthesis of system architectures, computational strategies, translational barriers, and regulatory considerations. This narrative review provides an integrative analysis of AI-microbial hybrid biosensors, covering biorecognition principles, AI integration approaches, system designs, clinical and environmental applications, performance metrics, and key challenges. It also highlights emerging directions, including synthetic biology, CRISPR-enabled sensing, and edge computing. By consolidating these dimensions, this review positions AI-microbial hybrid biosensors as a next-generation platform for real-time pathogen detection and adaptive biosurveillance. Literature was identified through systematic searches of Google Scholar, PubMed, Web of Science, Scopus, and IEEE Xplore (2000-2026), supplemented by manual reference screening. + + + + Hama Soor + Taib A + TA + + Medical Laboratory Department, Technical College of Health and Medical Technology, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq. + + + + Mustafa + Ayman M + AM + 0009-0005-4254-4182 + + Medical Laboratory Department, Technical College of Health and Medical Technology, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq. + + + Scientific Affairs Department, Smart Health Tower, Sulaymaniyah, Kurdistan Region, Iraq. + + + Medical Laboratory Science Department, College of Health Sciences, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq. + + + Kscien Organization for Scientific Research (Middle East Office), Sulaymaniyah, Kurdistan Region, Iraq. + + + + Abdulkarim + Meer M + MM + + Scientific Affairs Department, Smart Health Tower, Sulaymaniyah, Kurdistan Region, Iraq. + + + + Abdullah + Hiwa O + HO + + Scientific Affairs Department, Smart Health Tower, Sulaymaniyah, Kurdistan Region, Iraq. + + + Kscien Organization for Scientific Research (Middle East Office), Sulaymaniyah, Kurdistan Region, Iraq. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 29 + +
+ + England + Future Microbiol + 101278120 + 1746-0913 + + IM + + Artificial intelligence + antimicrobial resistance + deep learning + machine learning + microbial biosensors + pathogen detection + synthetic biology + + + + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 10 + 3 + + + aheadofprint + + 42212947 + 10.1080/17460913.2026.2678101 + + + + \ No newline at end of file diff --git a/data/raw/pubmed_20260530_124620.xml b/data/raw/pubmed_20260530_124620.xml new file mode 100644 index 0000000..73ebacc --- /dev/null +++ b/data/raw/pubmed_20260530_124620.xml @@ -0,0 +1,22257 @@ + + + + + 42216673 + + 2026 + 05 + 30 + +
+ + 1875-8908 + + + 2026 + May + 30 + + + Journal of Alzheimer's disease : JAD + J Alzheimers Dis + + Multimodal machine learning for early risk stratification of post-stroke cognitive impairment. + + 13872877261454538 + 13872877261454538 + + 10.1177/13872877261454538 + + BackgroundPost-stroke cognitive impairment (PSCI) is a major vascular contributor to dementia, significantly impacting long-term recovery and quality of life. Developing accurate prediction models are essential for early identification and timely intervention in high-risk individuals.ObjectiveTo develop and validate a stacking-based multimodal machine learning model integrating clinical, demographic, and neuroimaging features for early PSCI prediction in acute ischemic stroke (AIS) patients.MethodsIn this retrospective cohort study, 1070 AIS patients admitted to Lianyungang First People's Hospital from January 2020 to August 2023 were included. Demographic, clinical, and neuroimaging data were collected, and cognitive function was assessed 3-6 months post-stroke. PSCI was defined as a z-score ≤ -2.0 in at least one of four cognitive domains. A stacking ensemble model was developed, combining six base algorithms: XGBoost, Gradient Boosting Decision Trees, CatBoost, Support Vector Machine, Logistic Regression, and LightGBM. The final prediction was generated by a meta-model trained on base model outputs.ResultsOf the 1070 patients (mean age 67.4 ± 9.3 years, 61.5% male), 37.2% developed PSCI. The stacking model achieved 98.13% accuracy, 0.9972 AUC, and 0.9744 F1-score in internal validation. External validation showed 81.00% accuracy, 0.9049 AUC, and 0.8780 recall. Key predictors of PSCI included infarct volume, cortical lesions, medial temporal lobe atrophy, and baseline NIHSS score.ConclusionsThis stacking-based multimodal machine learning model demonstrates robust predictive performance for PSCI risk in AIS patients, serving as a reliable tool for early detection that may inform personalized intervention strategies to prevent progression to post-stroke dementia. + + + + Zheng + Xingyongpei + X + 0009-0006-6281-8870 + + Department of Neurology, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China. + 117961 + + + + Zhao + Panpan + P + 0000-0002-2494-3148 + + Department of Neurosurgery, Institute of Neuroscience, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China. + 117961 + + + + Wang + Na + N + 0009-0007-4274-7726 + + Department of Neurology, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China. + 117961 + + + + Wang + Xinyu + X + 0009-0007-9290-2464 + + Department of Neurology, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, China. + + + + Dong + Ziyi + Z + 0009-0006-9227-6208 + + Department of Neurology, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China. + 117961 + + + + Gu + Caihong + C + 0009-0009-8206-8685 + + Department of Critical Care Medicine, The First Affiliated Hospital of Kangda College of Nanjing Medical University, Lianyungang, China. + + + + Sun + Yong + Y + 0009-0008-0281-5875 + + Department of Neurosurgery, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China. + 117961 + + + + Gu + Xinru + X + 0000-0003-4282-1918 + + Department of Neurology, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China. + 117961 + + + Department of Neurology, The First Affiliated Hospital of Kangda College of Nanjing Medical University, Lianyungang, China. + + + Department of Neurology, Lianyungang Medical Education Innovation and Research Centre of Nanjing Medical University, Lianyungang, China. + + + + Zhou + Xinyu + X + 0000-0002-9647-1100 + + Department of Neurology, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China. + 117961 + + + Department of Neurology, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, China. + + + Department of Neurology, The First Affiliated Hospital of Kangda College of Nanjing Medical University, Lianyungang, China. + + + Department of Neurology, Lianyungang Medical Education Innovation and Research Centre of Nanjing Medical University, Lianyungang, China. + + + + eng + + Journal Article + + + 2026 + 05 + 30 + +
+ + United States + J Alzheimers Dis + 9814863 + 1387-2877 + + IM + + Alzheimer's disease + machine learning + magnetic resonance imaging + post-stroke cognitive impairment + predictors + vascular dementia + + + + + + 2026 + 5 + 30 + 6 + 35 + + + 2026 + 5 + 30 + 6 + 35 + + + 2026 + 5 + 30 + 5 + 3 + + + aheadofprint + + 42216673 + 10.1177/13872877261454538 + + + + + + 42216643 + + 2026 + 05 + 30 + + + 2026 + 05 + 30 + +
+ + 0219-6352 + + 25 + 5 + + 2026 + May + 12 + + + Journal of integrative neuroscience + J Integr Neurosci + + Single-Cell and Integrative Analyses Uncover Therapeutic Potential of RFX1-Mediated Cuproptosis in PitNETs. + + 49669 + 49669 + + 10.31083/JIN49669 + + Pituitary neuroendocrine tumors (PitNETs) have different cell types and levels of growth. We do not fully understand why they grow so much. Cuproptosis is a new way that copper kills cells and it is important in cancer. But, we do not know how it works in PitNETs yet. + We used several methods like bulk and single-cell sequencing with machine learning to investigate cuproptosis in PitNETs. We used Weighted Gene Co-expression Network Analysis (WGCNA) and random forest to find important genes. Then, we did tests in the lab to see how these genes work. + Analysis of cuproptosis-related genes (CRGs) revealed significant differences in expression and immune landscapes between invasive and non-invasive PitNETs. We established CRG-based molecular subtypes and a high-performance predictive model for tumor invasiveness. Notably, regulatory factor X1 (RFX1) was identified and validated as a key regulator that suppresses tumor growth and sensitizes cells to cuproptosis. These results highlight the clinical relevance of cuproptosis in PitNET progression and suggest RFX1 as a potential therapeutic target. + This study establishes a single-cell-based molecular landscape of PitNETs and uncovers RFX1-mediated cuproptosis as a key suppressive mechanism of tumor progression. These findings not only deepen the understanding of PitNET heterogeneity but also propose RFX1 as a promising therapeutic target for PitNETs. + © 2026 The Author(s). Published by IMR Press. + + + + Yang + Shuangjian + S + + Department of Neurosurgery, China Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 100730 Beijing, China. + + + + Pu + Changqin + C + + Department of Neurosurgery, China Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 100730 Beijing, China. + + + + Deng + Congcong + C + + Department of Neurosurgery, China Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 100730 Beijing, China. + + + + Bai + Xuexue + X + + Department of Neurosurgery, China Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 100730 Beijing, China. + + + + Wu + Si + S + + Department of Neurosurgery, China Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 100730 Beijing, China. + + + + Tian + Chenxin + C + + Department of Neurosurgery, China Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 100730 Beijing, China. + + + + Xu + Xueyou + X + + Department of Neurosurgery, The Affiliated Hospital of Guizhou Medical University, 550004 Guiyang, Guizhou, China. + + + + Wang + Renzhi + R + + Department of Neurosurgery, China Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 100730 Beijing, China. + + + + Chang + Mengqi + M + + Department of Neurosurgery, China Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 100730 Beijing, China. + + + Institute of Clinical Medicine, National Infrastructures for Translational Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100007 Beijing, China. + + + + Feng + Ming + M + + Department of Neurosurgery, China Pituitary Disease Registry Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, 100730 Beijing, China. + + + + eng + + + Qiankehe Support [2021] General 452 + Guizhou Provincial Science and Technology Support Program + + + + CIFMS 2021-I2M-1-003 + CAMS Innovation Fund for Medical Sciences + + + + 2022-PUMCH-C-012 + National High Level Hospital Clinical Research Funding + + + + 2020-JKCS-021 + Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences + + + + + Journal Article + +
+ + Singapore + J Integr Neurosci + 101156357 + 0219-6352 + + + + 0 + Regulatory Factor X1 + + + IM + + + Cuproptosis + + + Humans + + + Neuroendocrine Tumors + metabolism + genetics + + + Regulatory Factor X1 + metabolism + genetics + + + Pituitary Neoplasms + metabolism + genetics + + + Single-Cell Analysis + + + Single-Cell Gene Expression Analysis + + + Animals + + + + cell death + machine learning + molecular targeted therapy + pituitary neoplasms + single-cell analysis + + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 1 + 3 + + + 2026 + 2 + 11 + + + 2026 + 3 + 10 + + + 2026 + 5 + 30 + 4 + 32 + + + ppublish + + 42216643 + 10.31083/JIN49669 + S0219-6352(26)01107-1 + + + + + + 42216514 + + 2026 + 05 + 30 + +
+ + 1552-5732 + + + 2026 + May + 30 + + + Journal of human lactation : official journal of International Lactation Consultant Association + J Hum Lact + + Prevalence and Predictors of Non-Exclusive Breastfeeding at Hospital Discharge in Uruguay. + + 8903344261446162 + 8903344261446162 + + 10.1177/08903344261446162 + + The prescription of infant formula during postpartum hospitalization is one of several factors that influence breastfeeding. + To analyze the prevalence of non-exclusive breastfeeding at hospital discharge in Uruguay, a Latin American country, and to identify the predictors of non-exclusive breastfeeding at hospital discharge. + This cross-sectional study draws on data from all births between 2008 and 2020 registered in the Uruguayan perinatal information system. XGBoost, a machine-learning algorithm, was used to identify the predictors of infant formula at hospital discharge. A binary variable indicating non-exclusive breastfeeding (1 = yes, 0 = no) was used as dependent variable, and another 35 variables were used as predictors, including mother, child, and hospital characteristics. + The prevalence of non-exclusive breastfeeding at hospital discharge descriptively increased in the period, from 7.0% in 2008 to 27.9% in 2020. The model had a moderately adequate prediction performance (ROC AUC = .781, 95% CI [.778, .785], Brier score = .188, 95% CI [.187, .189], PR AUC = .424, 95% CI [.187, .431]). Hospital type was the most important variable in predicting non-exclusive breastfeeding, followed by birth weight and type of birth. Predicted probability of non-exclusive breastfeeding was highest in public hospitals in the capital (Montevideo), for infants with birth weight lower than 3000 g and higher than 3900 g and born through Cesarean delivery. + Results suggest the need to address the structural and procedural factors that may contribute to the prescription of infant formula to improve breastfeeding practices in the country. Additional studies are needed to identify the specific maternity practices that promote the prescription of infant formula at hospital discharge. + + + + Vidal + Leticia + L + 0000-0001-6781-9852 + + Sensometrics & Consumer Science, Instituto Polo Tecnológico de Pando, Facultad de Química, Universidad de la República, Uruguay. + + + + de León + Carolina + C + + División Salud, Intendencia de Montevideo, Uruguay. + + + + Ares + Gastón + G + 0000-0002-0565-8835 + + Sensometrics & Consumer Science, Instituto Polo Tecnológico de Pando, Facultad de Química, Universidad de la República, Uruguay. + + + + Rodríguez + Raquel + R + + Escuela de Nutrición, Universidad de la República, Uruguay. + + + + Brunet + Gerónimo + G + + Espacio Interdisciplinario, Universidad de la República, Uruguay. + + + + Girona + Alejandra + A + + Escuela de Nutrición, Universidad de la República, Uruguay. + + + + eng + + Journal Article + + + 2026 + 05 + 30 + +
+ + United States + J Hum Lact + 8709498 + 0890-3344 + + IM + + breastfeeding + cross-sectional study + exclusive breastfeeding + feeding patterns + in-hospital breastfeeding + infant formula + newborn feeding + statistical modeling + + + + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 5 + 30 + 3 + 2 + + + aheadofprint + + 42216514 + 10.1177/08903344261446162 + + + + + + 42216443 + + 2026 + 05 + 30 + +
+ + 1365-2648 + + + 2026 + May + 29 + + + Journal of advanced nursing + J Adv Nurs + + Shift-Specific Patterns of Nursing Workloads in the Emergency Department: AI Powered Analysis. + 10.1111/jan.70648 + + To identify and differentiate workload patterns across shifts and to provide evidence for optimizing nursing workforce allocation in emergency departments: DESIGN: A cross-sectional study. + Real time data were collected from an emergency department in a general hospital in Seoul, South Korea, between October 30, 2023 to October 24, 2024. Smartphones, beacons, and smartwatches were used to capture nursing time, physical activity, work-related characteristics, and location transitions across 238 shifts. A multiclass eXtreme Gradient Boosting model was developed and evaluated to classify working shifts (day, evening, night). Shapely Additive exPlanations were applied to identify key contributing features, and shift-specific differences were examined using analysis of variance with post hoc tests. + The model demonstrated strong performance in distinguishing shifts. Key features included the number of admissions, discharges, assigned patients, and both direct and indirect nursing time, all of which varied across shifts. In contrast, location transition patterns were relatively consistent. + Shift-specific nursing workloads in emergency departments can be effectively identified using multidimensional, real-world nursing activity data. + Findings support the development of staffing strategies that account for variation in workload across shifts, with potential to improve efficiency and maintain quality of care. + This study addresses the lack of objective evidence for shift-specific workload differences in emergency nursing. It demonstrates that multidimensional activity data can distinguish workload patterns across shifts. The findings may inform staffing decisions for emergency department nurses and support improvements in workforce management and patient care. + This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines. + No patients or members of the public were involved in the design, conduct, analysis, or reporting of this study. + © 2026 The Author(s). Journal of Advanced Nursing published by John Wiley & Sons Ltd. + + + + Kang + Younhee + Y + 0000-0002-7964-5674 + + College of Nursing, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea. + + + + Park + Hyunggon + H + 0000-0002-5079-1504 + + Smart Factory Multidisciplinary Program, Department of Electronic and Electrical Engineering, College of Engineering, Ewha Womans University, Seoul, Republic of Korea. + + + + Park + Inkyung + I + 0009-0006-7180-9917 + + College of Nursing, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea. + + + + Choi + Dayoung + D + 0009-0008-9270-0528 + + Smart Factory Multidisciplinary Program, Department of Electronic and Electrical Engineering, College of Engineering, Ewha Womans University, Seoul, Republic of Korea. + + + + Kim + Suemin + S + 0009-0009-3125-4808 + + College of Nursing, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea. + + + + eng + + + 2021R1A2C2006359 + National Research Foundation of Korea (NRF) + + + + NRF-5199990614253 + BK21 FOUR (Fostering Outstanding Universities for Research) + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + J Adv Nurs + 7609811 + 0309-2402 + + IM + + SHAP + emergency department + explainable artificial intelligence + machine learning + nursing workloads + shift work + + + + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 5 + 8 + + + 2026 + 3 + 25 + + + 2026 + 5 + 19 + + + 2026 + 5 + 30 + 1 + 33 + + + aheadofprint + + 42216443 + 10.1111/jan.70648 + + + + Alomari, A. H., J. Collison, L. Hunt, and N. J. Wilson. 2021. “Stressors for Emergency Department Nurses: Insights From a Cross‐Sectional Survey.” Journal of Clinical Nursing 30, no. 7–8: 975–985. https://doi.org/10.1111/jocn.15641. + + + American Nurses Association. 2020. ANA's Principles for Nurse Staffing. 3rd ed. https://cdn2.hubspot.net/hubfs/4850206/PNS3E_ePDF.pdf. + + + Bakhoum, N., C. Gerhart, E. Schremp, et al. 2021. “A Time and Motion Analysis of Nursing Workload and Electronic Health Record Use in the Emergency Department.” Journal of Emergency Nursing 47, no. 5: 733–741. https://doi.org/10.1016/j.jen.2021.03.007. + + + Brassington, S., C. Thomas‐Hawkins, M. L. Johansen, P. B. De Cordova, J. P. Cimiotti, and P. Zha. 2025. “Impact of Registered Nurse Staffing, Workload, and the Practice Environment on Burnout in Emergency Nurses.” Journal of Emergency Nursing 51, no. 4: 684–691. https://doi.org/10.1016/j.jen.2025.01.007. + + + British Columbia Ministry of Health. 2024. Minimum Nurse‐to‐Patient Ratio: Emergency Department. https://www2.gov.bc.ca/assets/gov/health/practitioner‐pro/minimum‐nurse‐to‐patient‐ratios/mnpr_emergency_department.pdf. + + + Chang, H. E. 2024. “Relationships Among Meal Time, Break Time and Workplace Characteristics of Nurses Who Work Day, Evening and Night Shifts: A Cross‐Sectional Study.” International Journal of Occupational Safety and Ergonomics 30, no. 1: 312–318. https://doi.org/10.1080/10803548.2023.2298116. + + + Choi, H., J. S. Ok, and S. Y. An. 2019. “Evaluation of Validity of the Korean Triage and Acuity Scale.” Journal of Korean Academy of Nursing 49, no. 1: 26–35. https://doi.org/10.4040/jkan.2019.49.1.26. + + + Clopton, E. L., and E. K. Hyrkäs. 2020. “Modeling Emergency Department Nursing Workload in Real Time: An Exploratory Study.” International Emergency Nursing 48: 100793. https://doi.org/10.1016/j.ienj.2019.100793. + + + Hao, X., Y. Dai, S. Jia, S. Liu, C. Zhao, and X. Liu. 2025. “Latent Profile Analysis of Mental Workload Among Emergency Department Nurses: A Cross‐Sectional Study.” BMC Nursing 24, no. 1: 332. https://doi.org/10.1186/s12912‐025‐02976‐3. + + + Hetherington, D., N. J. Wilson, K. Dixon, and G. Murphy. 2024. “Emergency Department Nurses’ Narratives of Burnout: Changing Roles and Boundaries.” International Emergency Nursing 74: 101439. https://doi.org/10.1016/j.ienj.2024.101439. + + + Hosmer, D. W., Jr., S. Lemeshow, and R. X. Sturdivant. 2013. Applied Logistic Regression. John Wiley & Sons. + + + Hospital Nurses Association. 2024. Survey on hospital nurse staffing status. Hospital Nurses Association. + + + Iordache, S., M. Elseviers, R. De Cock, and B. Van Rompaey. 2020. “Development and Validation of an Assessment Tool for Nursing Workload in Emergency Departments.” Journal of Clinical Nursing 29, no. 5–6: 794–809. https://doi.org/10.1111/jocn.15106. + + + Janhunen, K., P. Kankkunen, and T. Kvist. 2020. “Nurse Staffing and Care Process Factors in Paediatric Emergency Department—An Administrative Data Study.” Journal of Clinical Nursing 29, no. 23–24: 4554–4560. https://doi.org/10.1111/jocn.15482. + + + Kang, Y., K. Lee, and Y. Hur. 2024. “Development of a Scale for Measuring Nurses’ Stress.” Journal of Korean Clinical Nursing Research 30, no. 1: 11–23. https://doi.org/10.22650/JKCNR.2024.30.1.11. + + + Kaya, A., and A. İşler Dalgıç. 2022. “Evaluating Workload and Manpower Planning Among Pediatric Emergency Department Nurses in Turkey During COVID‐19: A Cross‐Sectional, Multicenter Study.” Journal of Pediatric Nursing 65: 69–74. https://doi.org/10.1016/j.pedn.2022.03.014. + + + Lundberg, S. M., and S.‐I. Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems 30. https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767‐Abstract.html. + + + Mehra, M., M. Robin, and D. Agrawal. 2024. “The Relationship Between Patient Load and Nursing Staffing Strength in Various Shifts of the Day in Emergency Department of a Major Tertiary Care Hospital in India.” Indian Journal of Neurotrauma 21, no. 1: 38–42. https://doi.org/10.1055/s‐0043‐1761938. + + + Park, H., and Y. Kang. 2025. “System Development for Measuring Nursing Workload Based on the Real‐Time Location Information.” Journal of Nursing Management 2025, no. 1: 2188150. https://doi.org/10.1155/jonm/2188150. + + + Park, S., J. Yoo, Y. Lee, et al. 2024. “Quantifying Emergency Department Nursing Workload at the Task Level Using NASA‐TLX: An Exploratory Descriptive Study.” International Emergency Nursing 74: 101424. https://doi.org/10.1016/j.ienj.2024.101424. + + + Pearce, S., T. Marchand, T. Shannon, H. Ganshorn, and E. Lang. 2023. “Emergency Department Crowding: An Overview of Reviews Describing Measures Causes, and Harms.” Internal and Emergency Medicine 18, no. 4: 1137–1158. https://doi.org/10.1007/s11739‐023‐03239‐2. + + + Peta, D., and J. Y. Bradford. 2025. Staffing and Productivity in the Emergency Setting [Position Statement]. Emergency Nurses Association. + + + Pourmand, A., A. Caggiula, J. Barnett, M. Ghassemi, and R. Shesser. 2023. “Rethinking Traditional Emergency Department Care Models in a Post‐Coronavirus Disease‐2019 World.” Journal of Emergency Nursing 49, no. 4: 520–529.e2. https://doi.org/10.1016/j.jen.2023.02.008. + + + Pryce, A., M. Unwin, L. Kinsman, and D. McCann. 2021. “Delayed Flow Is a Risk to Patient Safety: A Mixed Method Analysis of Emergency Department Patient Flow.” International Emergency Nursing 54: 100956. https://doi.org/10.1016/j.ienj.2020.100956. + + + Python Software Foundation. 2020. “Python (Version 3.9) [Computer Software].” https://www.python.org. + + + Saaiman, T., C. J. Filmalter, and T. Heyns. 2021. “Important Factors for Planning Nurse Staffing in the Emergency Department: A Consensus Study.” International Emergency Nursing 56: 100979. https://doi.org/10.1016/j.ienj.2021.100979. + + + Sartini, M., A. Carbone, A. Demartini, et al. 2022. “Overcrowding in Emergency Department: Causes, Consequences, and Solutions—A Narrative Review.” Healthcare (Basel) 10, no. 9: 1625. https://doi.org/10.3390/healthcare10091625. + + + Udod, S., M. MacPhee, J. I. J. Wagner, L. Berry, G. Perchie, and A. Conway. 2021. “Nurse Perspectives in the Emergency Department: The Synergy Tool in Workload Management and Work Engagement.” Journal of Nursing Management 29, no. 6: 1763–1770. https://doi.org/10.1111/jonm.13320. + + + von Elm, E., D. G. Altman, M. Egger, et al. 2007. “The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies.” PLoS Medicine 4, no. 10: e296. https://doi.org/10.1371/journal.pmed.0040296. + + + + + + + 42216430 + + 2026 + 05 + 30 + +
+ + 1464-5491 + + + 2026 + May + 29 + + + Diabetic medicine : a journal of the British Diabetic Association + Diabet Med + + Application of construction and validation of logistic regression model in risk prediction of non-proliferative diabetic retinopathy in type 2 diabetes mellitus. + + e70374 + e70374 + + 10.1111/dme.70374 + + Diabetic retinopathy (DR) is a leading cause of vision impairment in type 2 diabetes mellitus (T2DM), with non-proliferative DR (NPDR) representing its most prevalent form. Early identification of high-risk individuals remains challenging due to the complexity and poor interpretability of existing machine learning models. This study aims to develop a clinically interpretable Logistic regression model for NPDR risk prediction using routinely available clinical indicators. + A retrospective cohort of 421 T2DM patients from a single centre was divided into training (n = 295) and validation (n = 126) sets. Demographic, glycemic (fasting glucose, HbA1c), renal (UACR) and ophthalmologic (macular oedema) data were collected. Univariate and multivariate Logistic regression with stepwise selection identified independent predictors. Model performance was evaluated using area under the ROC curve (AUC), sensitivity, specificity and Hosmer-Lemeshow goodness-of-fit. Internal validation was performed via bootstrapping (1000 replicates), and external validation used an independent cohort. + Four independent predictors were identified: macular oedema (OR = 3.247), fasting glucose (OR = 2.194), HbA1c (OR = 2.799) and UACR (OR = 1.153). The model demonstrated excellent discrimination in the training set (AUC = 0.949, sensitivity = 86.4%, specificity = 95.5%) and good calibration (H-L test, p = 0.358). Bootstrap validation confirmed stability of HbA1c and UACR. External validation yielded an AUC of 0.918, with a positive predictive value of 91.1% and maintained calibration (p = 0.282). + The constructed Logistic regression model accurately predicts NPDR risk using four readily available clinical variables, offering high discriminative power, interpretability and clinical utility for stratifying high-risk T2DM patients in primary care settings. + © 2026 Diabetes UK. + + + + Zhang + Jiaoyan + J + + Wuxi Medical Center, Ophthalmology Department, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Nanjing Medical University, Wuxi People's Hospital, Wuxi, Jiangsu, China. + + + + Cao + Xiaodong + X + + Wuxi Medical Center, Nursing Department, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Nanjing Medical University, Wuxi People's Hospital, Wuxi, Jiangsu, China. + + + + Yu + Xin + X + + Wuxi Medical Center, Surgical Department, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Nanjing Medical University, Wuxi People's Hospital, Wuxi, Jiangsu, China. + + + + Jiang + Minfeng + M + + Wuxi Medical Center, Ophthalmology Department, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Nanjing Medical University, Wuxi People's Hospital, Wuxi, Jiangsu, China. + + + + Xie + Tianhua + T + + Wuxi Medical Center, Ophthalmology Department, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Nanjing Medical University, Wuxi People's Hospital, Wuxi, Jiangsu, China. + + + + Wang + Yangningzhi + Y + + Wuxi Medical Center, Ophthalmology Department, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Nanjing Medical University, Wuxi People's Hospital, Wuxi, Jiangsu, China. + + + + Tang + Zhengyuan + Z + 0009-0002-4161-9187 + + Wuxi Medical Center, Deanery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Nanjing Medical University, Wuxi People's Hospital, Wuxi, Jiangsu, China. + + + + eng + + + Q202301 + Youth Project of Wuxi Nursing Association + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Diabet Med + 8500858 + 0742-3071 + + IM + + Hosmer–Lemeshow fitting + ROC curve + logistic regression model + non‐proliferative diabetic retinopathy + type 2 diabetes mellitus + + + + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 5 + 12 + + + 2025 + 10 + 13 + + + 2026 + 5 + 14 + + + 2026 + 5 + 30 + 1 + 22 + + + aheadofprint + + 42216430 + 10.1111/dme.70374 + + + + Zaman M, Zajner C, Xie J, et al. Association between sociodemographic factors and self‐reported diabetic retinopathy: a cross‐sectional, population‐based analysis. Am J Ophthalmol. 2025;271:138‐148. + + + Ingle P, Hamden NA, Soh WK, Loh R, Hatware K. Development and novel therapeutics in diabetic retinopathy. Curr Diabetes Rev. 2026;22(3):e15733998324556. + + + Thomas RL, Halim S, Gurudas S, Sivaprasad S, Owens DR. IDF diabetes atlas: a review of studies utilising retinal photography on the global prevalence of diabetes related retinopathy between 2015 and 2018. Diabetes Res Clin Pract. 2019;157:107840. + + + Yang QH, Zhang Y, Zhang XM, Li XR. Prevalence of diabetic retinopathy, proliferative diabetic retinopathy and non‐proliferative diabetic retinopathy in Asian T2DM patients: a systematic review and meta‐analysis. Int J Ophthalmol. 2019;12(2):302‐311. + + + Hou X, Wang L, Zhu D, et al. Prevalence of diabetic retinopathy and vision‐threatening diabetic retinopathy in adults with diabetes in China. Nat Commun. 2023;14(1):4296. + + + Stokholm L, Pedersen FN, Andersen N, et al. Presence and development of diabetic retinopathy in 16999 patients with type 1 diabetes in the Danish Registry of Diabetic Retinopathy. Acta Ophthalmol. 2024;102(7):805‐812. + + + Tran EM, Gregori NZ, Rachitskaya A, Nandan A, Pershing S, Goldberg JL. Systemic predictors of diabetic retinopathy and diabetic macular edema in an adult veteran population. Clin Ophthalmol. 2025;19:101‐110. + + + Markle J, Shaia JK, Araich H, Sharma N, Talcott KE, Singh RP. Longitudinal trends and disparities in diabetic retinopathy within an aggregate health care network. JAMA Ophthalmol. 2024;142(7):599‐606. + + + Agapitou C, Sergentanis TN, Papageorgiou EG, et al. Investigation of the potential association between atherosclerotic cardiovascular disease risk score and diabetic retinopathy in patients with type 2 diabetes: a cross‐sectional study. Biomedicine. 2025;13(3):633. + + + Irodi A, Zhu Z, Grzybowski A, et al. The evolution of diabetic retinopathy screening. Eye (Lond). 2025;39(6):1040‐1046. + + + Yao L, Cao CY, Yu GX, Shu XP, Fan XN, Zhang YF. Screening and evaluation of diabetic retinopathy via a deep learning network model: a prospective study. World J Diabetes. 2024;15(12):2302‐2310. + + + Bora A, Balasubramanian S, Babenko B, et al. Predicting the risk of developing diabetic retinopathy using deep learning. Lancet Digit Health. 2021;3(1):e10‐e19. + + + Wan X, Zhang R, Wang Y, et al. Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms. Eur J Med Res. 2025;30(1):183. + + + Lim WX, Chen Z, Ahmed A. The adoption of deep learning interpretability techniques on diabetic retinopathy analysis: a review. Med Biol Eng Comput. 2022;60(3):633‐642. + + + Saini M, Susan S. Diabetic retinopathy screening using deep learning for multi‐class imbalanced datasets. Comput Biol Med. 2022;149:105989. + + + Scanlon PH, Aldington SJ, Stratton IM. Delay in diabetic retinopathy screening increases the rate of detection of referable diabetic retinopathy. Diabet Med. 2014;31(4):439‐442. + + + Scanlon PH. Screening intervals for diabetic retinopathy and implications for care. Curr Diab Rep. 2017;17(10):96. + + + Niroomand M, Afsar J, Hosseinpanah F, Afrakhteh M, Farzaneh F, Serahati S. Comparison of the International Association of Diabetes in pregnancy study group criteria with the old American Diabetes Association criteria for diagnosis of gestational diabetes mellitus. Int J Endocrinol Metab. 2019;17(4):e88343. + + + Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604‐612. + + + Dehghani Firouzabadi F, Poopak A, Samimi S, et al. Glycemic profile variability as an independent predictor of diabetic retinopathy in patients with type 2 diabetes: a prospective cohort study. Front Endocrinol (Lausanne). 2024;15:1383345. + + + Arnqvist HJ, Westerlund MC, Fredrikson M, Ludvigsson J, Nordwall M. Impact of HbA1c followed 32 years from diagnosis of type 1 diabetes on development of severe retinopathy and nephropathy: the VISS study. Diabetes Care. 2022;45(11):2675‐2682. + + + Basiony AI, Mohamed Gad Marey H, Ezzat Abdel Fattah AM, Aly Zaky M. Predictive value of optical coherence tomography angiography in management of diabetic macular edema. BMC Ophthalmol. 2024;24(1):429. + + + Suzuki Y, Kiyosawa M. Relationship between diabetic nephropathy and development of diabetic macular edema in addition to diabetic retinopathy. Biomedicine. 2023;11(5):1502. + + + Song JJ, Han XF, Chen JF, Liu KM. Correlation between glycated hemoglobin A1c, urinary microalbumin, urinary creatinine, β2 microglobulin, retinol binding protein and diabetic retinopathy. World J Diabetes. 2023;14(7):1103‐1111. + + + Ajoy Mohan VK, Nithyanandam S, Idiculla J. Microalbuminuria and low hemoglobin as risk factors for the occurrence and increasing severity of diabetic retinopathy. Indian J Ophthalmol. 2011;59(3):207‐210. + + + + + + + 42216394 + + 2026 + 05 + 30 + + + 2026 + 05 + 30 + +
+ + 1536-5964 + + 105 + 22 + + 2026 + May + 29 + + + Medicine + Medicine (Baltimore) + + The heart's fibrous web: A bibliometric analysis of cardiac fibrosis in Asia and Oceania. + + e49017 + e49017 + + 10.1097/MD.0000000000049017 + + Cardiac fibrosis is a pathophysiologic condition in heart diseases. It refers to an excess deposition of extracellular matrix, which will cause hardening and stiffness in heart tissues, leading to heart failure. By considering the high prevalence of cardiac fibrosis globally, a bibliometric analysis is conducted to elucidate the research trend involving the unique perspectives in both the Asia and Oceania regions. + Data were retrieved from the Scopus database without restrictions on publication year. Documents related to cardiac fibrosis affiliated with countries in Asia and Oceania were identified using the United Nations geoscheme classifications. The dataset was curated using biblioMagika and OpenRefine. Then, bibliometric mapping was conducted using VOSviewer to analyze co-authorship and keyword co-occurrence networks. + A total of 983 publications were identified from 1985 to 2025, with a marked increase in research output from 2010 onwards. China emerged as the leading contributor in terms of productivity and total citations, while Australia showed the highest citation impact per publication. High-impact institutions and authors were identified, and core themes included molecular mechanisms such as transforming growth factor beta signaling, extracellular matrix remodeling, and noncoding RNAs. + This study is the first to systematically examine cardiac fibrosis research output across Asia and Oceania using bibliometric methods. It contributes novel insights into the regional dynamics, intellectual structure, and emerging priorities within the field. By identifying key actors, collaboration networks, and knowledge gaps, this research provides valuable guidance for scholars, funding agencies, and policymakers to advance cardiac fibrosis research in regional and global contexts. However, reliance on citation-based metrics may introduce temporal bias, as older publications tend to accumulate more citations over time. Future studies should employ multi-database strategies, altmetric indicators, and machine learning approaches to address these limitations and capture a broader spectrum of research impact. + Copyright © 2026 the Author(s). Published by Wolters Kluwer Health, Inc. + + + + Ahmad Damahuri + Arifah + A + 0000-0002-8488-6966 + + Laboratory Animal Care Unit, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia. + + + Institute of Medical Molecular Biotechnology, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia. + + + + Mohamad + Maslinawati + M + + Accounting Research Institute, University Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia. + + + + Abdul Rahman + Thuhairah Hasrah + TH + + Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia. + + + Cardiovascular Advancement Research Excellence Institute (CARE-i), Universiti Teknologi MARA, Malaysia. + + + + Rmt Balasubramaniam + Vimala + V + + Nutrition, Metabolism & Cardiovascular Research Centre, Institute for Medical Research, Ministry of Health Malaysia, Setia Alam, Selangor, Malaysia. + + + + Nasrudin + Nurdiyana + N + + Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia. + + + + Azme + Nasibah + N + 0000-0003-0044-2477 + + Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia. + + + + eng + + Journal Article + +
+ + United States + Medicine (Baltimore) + 2985248R + 0025-7974 + + IM + + + Bibliometrics + + + Humans + + + Oceania + epidemiology + + + Asia + epidemiology + + + Fibrosis + + + Heart Diseases + + + Myocardium + pathology + + + + Asia + Oceania + OpenRefine + VOSviewer + cardiac fibrosis + heart fibrosis + + The authors have no funding and conflicts of interest to disclose. + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2024 + 8 + 29 + + + 2026 + 5 + 4 + + + 2026 + 5 + 30 + 1 + 1 + + + ppublish + + 42216394 + 10.1097/MD.0000000000049017 + 00005792-202605290-00080 + + + + Hinderer S, Schenke-Layland K. Cardiac fibrosis – a short review of causes and therapeutic strategies. Adv Drug Deliv Rev. 2019;146:77–82. + + + Taeuber I. Asia’s increasing population. Ann Am Acad Pol Soc Sci. 1958;318:1–7. + + + Westhuysen A. Country profile: China. Carbon Clim Law Rev. 2011;5:384–9. + + + Kc S, Lutz W. The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100. Glob Environ Change. 2017;42:181–92. + + + Lippi G, Sanchis-Gomar F. Global epidemiology and future trends of heart failure. AME Med J. 2020;5. + + + Surucu M, Isler Y, Perc M, Kara R. Convolutional neural networks predict the onset of paroxysmal atrial fibrillation: theory and applications. Chaos. 2021;31:113119. + + + Isler Y, Narin A, Ozer M, Perc M. Multi-stage classification of congestive heart failure based on short-term heart rate variability. Chaos Solitons Fractals. 2019;118:145–51. + + + Handra J, James H, Mbilinyi A, et al. The role of machine learning in the detection of cardiac fibrosis in electrocardiograms: scoping review. JMIR Cardio. 2024;8:e60697. + + + Nelson AR, Christiansen SL, Naegle KM, Saucerman JJ. Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts. Proc Natl Acad Sci U S A. 2024;121:e2303513121. + + + Gosak M, Milojević M, Duh M, Skok K, Perc M. Networks behind the morphology and structural design of living systems. Phys Life Rev. 2022;41:1–21. + + + Moral-Muñoz J, Herrera-Viedma E, Santisteban-Espejo A, Cobo M. Software tools for conducting bibliometric analysis in science: an up-to-date review. Prof Inf. 2020;29:e290103. + + + Oliveira O, Silva F, Juliani F, Barbosa L, Nunhes T. Bibliometric method for mapping the state-of-the-art and identifying research gaps and trends in literature: An essential instrument to support the development of scientific projects. In: Scientometrics Recent Advances. 2019. + + + Solheim WG. Eastern Asia and Oceania. Asian Perspect. 1963;8:1–9. + + + United Nations Statistics Division. Standard country or area codes for statistical use (M49). United Nations. Available from: https://unstats.un.org/unsd/methodology/m49/. Accessed August 5, 2025. + + + Ahmi A. biblioMagika. 2024. Available from: https://bibliomagika.com. Accessed August 5, 2025. + + + Van Eck NJ, Waltman L. Visualizing bibliometric networks. In: Meas Scholarly Impact. Springer International Publishing; 2014:285–320. + + + Ahmi A. OpenRefine: an approachable tool for cleaning and harmonizing bibliographical data. 11th International Conference on Applied Science and Technology 2022 (11th ICAST 2022). AIP Conf Proc. 2023;2894:030006. + + + Tamura N, Ogawa Y, Chusho H, et al. Cardiac fibrosis in mice lacking brain natriuretic peptide. Proc Natl Acad Sci U S A. 2000;97:4239–44. + + + Young M, Fullerton M, Dilley R, Funder J. Mineralocorticoids, hypertension, and cardiac fibrosis. J Clin Invest. 1994;93:2578–83. + + + Lee TM, Chang NC, Lin SZ. Dapagliflozin, a selective SGLT2 Inhibitor, attenuated cardiac fibrosis by regulating the macrophage polarization via STAT3 signaling in infarcted rat hearts. Free Radic Biol Med. 2017;104:298–310. + + + Pan Z, Sun X, Shan H, et al. MicroRNA-101 inhibited postinfarct cardiac fibrosis and improved left ventricular compliance via the FBJ osteosarcoma oncogene/transforming growth factor-β1 pathway. Circulation. 2012;126:840–50. + + + Ma ZG, Yuan YP, Wu HM, Zhang X, Tang QZ. Cardiac fibrosis: new insights into the pathogenesis. Int J Biol Sci. 2018;14:1645–57. + + + Zhang Y, Huang XR, Wei LH, Chung AC, Yu C-M, Lan H-Y. RETRACTED: miR-29b as a therapeutic agent for angiotensin II-induced cardiac fibrosis by targeting TGF-β/Smad3 signaling. Mol Ther. 2014;22:974–85. + + + Rickard AJ, Morgan J, Tesch G, Funder JW, Fuller PJ, Young MJ. Deletion of mineralocorticoid receptors from macrophages protects against deoxycorticosterone/salt-induced cardiac fibrosis and increased blood pressure. Hypertension. 2009;54:537–43. + + + Wang J, Huang W, Xu R, et al. MicroRNA-24 regulates cardiac fibrosis after myocardial infarction. J Cell Mol Med. 2012;16:2150–60. + + + Ma F, Li Y, Jia L, et al. Macrophage-stimulated cardiac fibroblast production of IL-6 is essential for TGF β/Smad activation and cardiac fibrosis induced by angiotensin II. PLoS One. 2012;7:e35144. + + + Young MJ, Head GE, Funder JW. Determinants of cardiac fibrosis in experimental hypermineralocorticoid states. Am J Physiol. 1995;269:E657–62. + + + Xiao H, Ma X, Feng W, et al. Metformin attenuates cardiac fibrosis by inhibiting the TGFbeta1-Smad3 signalling pathway. Cardiovasc Res. 2010;87:504–13. + + + Li Z, Zhu S, Liu Q, et al. Polystyrene microplastics cause cardiac fibrosis by activating Wnt/β-catenin signaling pathway and promoting cardiomyocyte apoptosis in rats. Environ Pollut. 2020;265(Pt A):115025. + + + Yue Y, Meng K, Pu Y, Zhang X. Transforming growth factor beta (TGF-β) mediates cardiac fibrosis and induces diabetic cardiomyopathy. Diabetes Res Clin Pract. 2017;133:124–30. + + + Tzanidis A, Hannan RD, Thomas WG, et al. Direct actions of urotensin II on the heart: implications for cardiac fibrosis and hypertrophy. Circ Res. 2003;93:246–53. + + + Qu X, Du Y, Shu Y, et al. MIAT is a pro-fibrotic long non-coding RNA governing cardiac fibrosis in post-infarct myocardium. Sci Rep. 2017;7:42657. + + + Matsumoto T, Wada A, Tsutamoto T, Ohnishi M, Isono T, Kinoshita M. Chymase inhibition prevents cardiac fibrosis and improves diastolic dysfunction in the progression of heart failure. Circulation. 2003;107:2555–8. + + + Yuan J, Chen H, Ge D, et al. Mir-21 promotes cardiac fibrosis after myocardial infarction via targeting Smad7. Cell Physiol Biochem. 2017;42:2207–19. + + + Tomita H, Egashira K, Ohara Y, et al. Early induction of transforming growth factor-beta via angiotensin II type 1 receptors contributes to cardiac fibrosis induced by long-term blockade of nitric oxide synthesis in rats. Hypertension. 1998;32:273–9. + + + Fujita K, Maeda N, Sonoda M, et al. Adiponectin protects against angiotensin II-induced cardiac fibrosis through activation of PPAR-alpha. Arterioscler Thromb Vasc Biol. 2008;28:863–70. + + + Qin W, Cao L, Massey IY. Role of PI3K/Akt signaling pathway in cardiac fibrosis. Mol Cell Biochem. 2021;476:4045–59. + + + Hua M, Fan J, Dong H, Sherer R. Integrating traditional Chinese medicine into Chinese medical education reform: issues and challenges. Int J Med Educ. 2017;8:126–7. + + + Jin YH, Wang YP, Xie YL, et al. Research on the development methodology for clinical practice guidelines for organic integration of traditional Chinese and Western medicine. Mil Med Res. 2023;10:45. + + + Mao Y, Fu Q, Su F, et al. Trends in worldwide research on cardiac fibrosis over the period 1989-2022: a bibliometric study. Front Cardiovasc Med. 2023;10:1182606. + + + Burke RM, Dirkx RA Jr, Quijada P, et al. Prevention of fibrosis and pathological cardiac remodeling by salinomycin. Circ Res. 2021;128:1663–78. + + + McKinsey TA, Foo R, Anene-Nzelu CG, et al. Emerging epigenetic therapies of cardiac fibrosis and remodelling in heart failure: from basic mechanisms to early clinical development. Cardiovasc Res. 2023;118:3482–98. + + + Perrucci GL, Rurali E, Pompilio G. Cardiac fibrosis in regenerative medicine: destroy to rebuild. J Thorac Dis. 2018;10(Suppl 20):S2376–89. + + + + + + + 42216384 + + 2026 + 05 + 30 + + + 2026 + 05 + 30 + +
+ + 1536-5964 + + 105 + 22 + + 2026 + May + 29 + + + Medicine + Medicine (Baltimore) + + Common biomarkers of Alzheimer disease and postoperative delirium. + + e48921 + e48921 + + 10.1097/MD.0000000000048921 + + Alzheimer disease (AD) and Postoperative delirium (POD) may share a common mechanism, but their shared genes and potential novel therapeutic targets remains unknown. We selected datasets GSE48350, GSE4226, and GSE16759 containing AD and control samples from the GEO database for AD model development, and use the GSE63061 dataset for AD model validation. The GSE163943 dataset containing PODs and controls was used to identify common genes. Differentially expressed genes (DEGs) associated with POD and AD respectively, as well as common DEGs between 2 diseases were analyzed. Shared DEGs were assessed using gene ontology and KEGG enrichment and PPI interaction networks. Machine learning algorithms, nomogram and decision tree analyses were used to interpret top 5 genes and visualize the models. 259 DEGs in POD with 148 being up-regulated and 111 down-regulated, and 199 DEGs in AD with 25 up-regulated and 174 down-regulated were found, of which 23 were common to POD and AD. Gene ontology analysis indicated that DEGs were primarily concentrated in 9 distinct biological processes, while KEGG results revealed 3 major metabolic pathways. Five hub genes emerged as potential diagnostic biomarkers for both diseases, Bruton tyrosine kinase (BTK), NCF2, CRH, FCGR3A, and SERPINA3. The LASSO model determined that the 23 common genes could serve as biomarkers for both POD and AD patients, and the Random Forest model is the the most suitable model. POD or AD decision trees showed BTK and NCF2 were sufficient to distinguish between POD or AD patients and healthy controls. BTK and NCF2 were identified common biomarkers of POD and AD. Further investigations are required to verify correlations between gene expression levels and pathological characteristics. + Copyright © 2026 the Author(s). Published by Wolters Kluwer Health, Inc. + + + + Fang + Yiqi + Y + + Department of Anesthesiology, Chun'an First People's Hospital, Zhejiang Provincial People's Hospital Chun'an Branch, Hangzhou Medical College Affiliated Chun'an Hospital, Hangzhou City, Zhejiang Province, China. + + + + Zhang + Xiao + X + + Department of Anesthesiology, Chun'an First People's Hospital, Zhejiang Provincial People's Hospital Chun'an Branch, Hangzhou Medical College Affiliated Chun'an Hospital, Hangzhou City, Zhejiang Province, China. + + + + Zhan + Gaofeng + G + 0009-0004-6379-9873 + + Department of Anesthesiology, Chun'an Second People's Hospital, Zhejiang Provincial People's Hospital Chun'an Branch, Hangzhou Medical College Affiliated Chun'an Hospital, Hangzhou City, Zhejiang Province, China. + + + + eng + + + 2024CAYY004 + Key Projects of the Medical and Health Science and Technology Program of Chun'an County + + + + + Journal Article + +
+ + United States + Medicine (Baltimore) + 2985248R + 0025-7974 + + + + 0 + Biomarkers + + + IM + + + Alzheimer Disease + genetics + diagnosis + metabolism + + + Humans + + + Biomarkers + metabolism + + + Delirium + genetics + diagnosis + + + Postoperative Complications + genetics + diagnosis + + + Gene Expression Profiling + + + Machine Learning + + + Gene Ontology + + + Protein Interaction Maps + genetics + + + Databases, Genetic + + + + Alzheimer disease + biomarker + decision tree + machine learning + postoperative delirium + + The authors have no conflicts of interest to disclose. + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2025 + 12 + 2 + + + 2026 + 4 + 30 + + + 2026 + 5 + 30 + 1 + 1 + + + ppublish + + 42216384 + 10.1097/MD.0000000000048921 + 00005792-202605290-00070 + + + + Thakur S, Dhapola R, Sarma P, Medhi B, Reddy DH. Neuroinflammation in alzheimer’s disease: current progress in molecular signaling and therapeutics. Inflammation. 2023;46:1–17. + + + Andrade-Guerrero J, Santiago-Balmaseda A, Jeronimo-Aguilar P, et al. Alzheimer’s disease: an updated overview of its genetics. Int J Mol Sci. 2023;24:3754. + + + Ren QW, Katherine Teng TH, Tse YK, et al. Incidence, clinical correlates, and prognostic impact of dementia in heart failure: a population-based cohort study. JACC Asia. 2023;3:108–19. + + + Sun Y, Ji M, Leng M, Li X, Zhang X, Wang Z. Comparative efficacy of 11 non-pharmacological interventions on depression, anxiety, quality of life, and caregiver burden for informal caregivers of people with dementia: a systematic review and network meta-analysis. Int J Nurs Stud. 2022;129:104204. + + + Shi M, Chu F, Zhu F, Zhu J. Impact of anti-amyloid-β monoclonal antibodies on the pathology and clinical profile of alzheimer’s disease: a focus on aducanumab and lecanemab. Front Aging Neurosci. 2022;14:870517. + + + Martin FE, Hilton JA, Martin FC, Nath R, Partridge JSL, Dhesi JK. The functional trajectories of older women having surgery for gynaeoncology cancer: a single site prospective observational study. J Geriatr Oncol. 2024;15:101678. + + + Deiner SG, Marcantonio ER, Trivedi S, et al. Comparison of the frailty index and frailty phenotype and their associations with postoperative delirium incidence and severity. J Am Geriatr Soc. 2024;72:1781–92. + + + Choi S, Jung I, Yoo B, Lee S, Kim MC. Risk factors for postoperative delirium in elderly patients after spinal fusion surgery. Anesth Pain Med (Seoul). 2020;15:275–82. + + + Reekes TH, Devinney MJ, Berger M. Amyloid beta and postoperative delirium: partners in crime or strangers in the dark? Br J Anaesth. 2023;131:205–8. + + + Wu X, Chi F, Wang B, et al. Relationship between preoperative neutrophil-to-lymphocyte ratio and postoperative delirium: the PNDABLE and the PNDRFAP cohort studies. Brain Behav. 2023;13:e3281. + + + Liang F, Baldyga K, Quan Q, et al. Preoperative plasma Tau-PT217 and Tau-PT181 are associated with postoperative delirium. Ann Surg. 2023;277:e1232–8. + + + Hshieh TT, Fong TG, Schmitt EM, et al.; BASIL Study Group. Does alzheimer’s disease and related dementias modify delirium severity and hospital outcomes? J Am Geriatr Soc. 2020;68:1722–30. + + + Gomez EB, Ebata K, Randeria HS, et al. Preclinical characterization of pirtobrutinib, a highly selective, noncovalent (reversible) BTK inhibitor. Blood. 2023;142:62–72. + + + Rozkiewicz D, Hermanowicz JM, Kwiatkowska I, Krupa A, Pawlak D. Bruton’s tyrosine kinase inhibitors (BTKIs): review of preclinical studies and evaluation of clinical trials. Molecules. 2023;28:2400. + + + Burger JA. Bruton tyrosine kinase inhibitors: present and future. Cancer J. 2019;25:386–93. + + + Mansour HM. The interference between SARS-COV-2 and Alzheimer’s disease: potential immunological and neurobiological crosstalk from a kinase perspective reveals a delayed pandemic. Ageing Res Rev. 2024;94:102195. + + + Li M, Xin S, Gu R, et al. Novel diagnostic biomarkers related to oxidative stress and macrophage ferroptosis in atherosclerosis. Oxid Med Cell Longev. 2022;2022:8917947. + + + Li HM, Huang Q, Tang F, Zhang TP. Altered NCF2, NOX2 mRNA expression levels in peripheral blood mononuclear cells of pulmonary tuberculosis patients. Int J Gen Med. 2021;14:9203–9. + + + Wang B, Fu C, Wei Y, et al. Ferroptosis-related biomarkers for Alzheimer’s disease: identification by bioinformatic analysis in hippocampus. Front Cell Neurosci. 2022;16:1023947. + + + Greve HJ, Mumaw CL, Messenger EJ, et al. Diesel exhaust impairs TREM2 to dysregulate neuroinflammation. J Neuroinflammation. 2020;17:351. + + + + + + + 42216376 + + 2026 + 05 + 30 + + + 2026 + 05 + 30 + +
+ + 1536-5964 + + 105 + 22 + + 2026 + May + 29 + + + Medicine + Medicine (Baltimore) + + Identification of senescence-related biomarker for aortic dissection based on bioinformatics and machine learning algorithms. + + e48873 + e48873 + + 10.1097/MD.0000000000048873 + + Aortic dissection (AD) is a vascular surgical disease that seriously threatens human health. Due to a high misdiagnosis rate and unclear pathogenesis, it brings greater challenges to AD patients and vascular surgeons. This study aimed to explore sensitive diagnostic markers and potential therapeutic targets of AD from the perspective of cellular senescence, which has been our long-term concern. We downloaded the expression matrix of AD and control samples from the Gene Expression Omnibus database and obtained the senescence-related gene set. Differentially expressed genes (DEGs) in AD and control groups were analyzed by the "limma" package. Gene Ontology, Disease Ontology, and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were carried out to demonstrate the enrichment function of DEGs. Two sensitive screening diagnostic marker algorithms, the least absolute shrinkage and selection operator and support vector machine-recursive feature elimination, were used to identify target genes in combination with senescence-related genes. Receiver operating characteristic curves for targeted genes were plotted to assess diagnostic efficacy. Gene set enrichment analysis was performed to preliminarily explore the pathways enriched in the 2 groups. CIBERSORT was used to look for differentially infiltrating immune cells, and Spearman analysis was carried out to explore the association between targeted genes and infiltrating immune cells. A total of 111 DEGs were identified, which were closely related to regulation of leukocyte migration, cellular transition metal ion homeostasis, transition metal ion homeostasis, and detoxification of copper ion from Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Disease Ontology analysis found that these DEGs were mainly involved in acute myocardial infarction, vasculitis, and coronary artery disease. Hexokinase-3 (HK3) was identified as an overlapping gene of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and senescence. HK3 showed good diagnostic efficacy (area under the receiver operating characteristic curve = 0.924 in the integrated cohort and 0.914 in the validation cohort). Correlation analysis results showed that HK3 was positively correlated with monocytes, neutrophils, and natural killer cells resting, while B cells naive, mast cells resting, and macrophages M1 were negatively correlated. HK3 was a potential biomarker for AD diagnosis and a target for precision therapy, and HK3 is also closely related to immune infiltration. + Copyright © 2026 the Author(s). Published by Wolters Kluwer Health, Inc. + + + + Zhong + Jiabao + J + + Department of Cardiac Surgery, The First Hospital of China Medical University, Shenyang, Liaoning Province, China. + + + + Luo + Hongli + H + 0009-0006-7054-665 + + + eng + + Journal Article + +
+ + United States + Medicine (Baltimore) + 2985248R + 0025-7974 + + + + 0 + Biomarkers + + + IM + + + Humans + + + Aortic Dissection + genetics + diagnosis + + + Machine Learning + + + Computational Biology + methods + + + Biomarkers + metabolism + + + Cellular Senescence + genetics + + + Gene Expression Profiling + + + Algorithms + + + ROC Curve + + + Gene Ontology + + + + aortic dissection + diagnosis and treatment + infiltrating immune cells + machine learning + senescence + + The authors have no funding and conflicts of interest to disclose. + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2025 + 6 + 26 + + + 2026 + 4 + 29 + + + 2026 + 5 + 30 + 1 + 1 + + + ppublish + + 42216376 + 10.1097/MD.0000000000048873 + 00005792-202605290-00062 + + + + Lombardi JV, Hughes GC, Appoo JJ, et al. Society for Vascular Surgery (SVS) and Society of Thoracic Surgeons (STS) reporting standards for type B aortic dissections. J Vasc Surg. 2020;71:723–47. + + + Rombouts KB, van Merrienboer TAR, Ket JCF, Bogunovic N, van der Velden J, Yeung KK. The role of vascular smooth muscle cells in the development of aortic aneurysms and dissections. Eur J Clin Invest. 2022;52:e13697. + + + Kamman AV, Yang B, Kim KM, Williams DM, Michael Deeb G, Patel HJ. Visceral malperfusion in aortic dissection: the Michigan experience. Semin Thorac Cardiovasc Surg. 2017;29:173–8. + + + Evangelista A, Isselbacher EM, Bossone E, et al. Insights from the International Registry of Acute Aortic Dissection: a 20-year experience of collaborative clinical research. Circulation. 2018;137:1846–60. + + + Nienaber CA, Clough RE, Sakalihasan N, et al. Aortic dissection. Nat Rev Dis Primers. 2016;2:16053. + + + Olsson C, Thelin S, Ståhle E, Ekbom A, Granath F. Thoracic aortic aneurysm and dissection: increasing prevalence and improved outcomes reported in a nationwide population-based study of more than 14,000 cases from 1987 to 2002. Circulation. 2006;114:2611–8. + + + Nienaber CA, Clough RE. Management of acute aortic dissection. Lancet. 2015;385:800–11. + + + Salmasi MY, Al-Saadi N, Hartley P, et al. The risk of misdiagnosis in acute thoracic aortic dissection: a review of current guidelines. Heart. 2020;106:885–91. + + + Joshi A, Rienks M, Theofilatos K, Mayr M. Systems biology in cardiovascular disease: a multiomics approach. Nat Rev Cardiol. 2021;18:313–30. + + + Xu J, Yang Y. Integrated gene expression profiling analysis reveals potential molecular mechanisms and candidate biomarkers for early risk stratification and prediction of STEMI and post-STEMI heart failure patients. Front Cardiovasc Med. 2021;8:736497. + + + Xie H, Zha E, Zhang Y. Identification of featured metabolism-related genes in patients with acute myocardial infarction. Dis Markers. 2020;2020:8880004. + + + Yang Y, Yi X, Cai Y, Zhang Y, Xu Z. Immune-associated gene signatures and subtypes to predict the progression of atherosclerotic plaques based on machine learning. Front Pharmacol. 2022;13:865624. + + + Xia L, Sun C, Zhu H, et al. Melatonin protects against thoracic aortic aneurysm and dissection through SIRT1-dependent regulation of oxidative stress and vascular smooth muscle cell loss. J Pineal Res. 2020;69:e12661. + + + Ma D, Zheng B, Liu H-L, et al. Klf5 down-regulation induces vascular senescence through eIF5a depletion and mitochondrial fission. PLoS Biol. 2020;18:e3000808. + + + Owens WA, Walaszczyk A, Spyridopoulos I, Dookun E, Richardson GD. Senescence and senolytics in cardiovascular disease: promise and potential pitfalls. Mech Ageing Dev. 2021;198:111540. + + + Rufini A, Tucci P, Celardo I, Melino G. Senescence and aging: the critical roles of p53. Oncogene. 2013;32:5129–43. + + + Salama R, Sadaie M, Hoare M, Narita M. Cellular senescence and its effector programs. Genes Dev. 2014;28:99–114. + + + Ménégaut L, Jalil A, Pilot T, et al. Regulation of glycolytic genes in human macrophages by oxysterols: a potential role for liver X receptors. Br J Pharmacol. 2021;178:3124–39. + + + Aging Atlas Consortium. Aging Atlas: a multi-omics database for aging biology. Nucleic Acids Res. 2021;49:D825–30. + + + Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1–22. + + + Huang M-L, Hung Y-H, Lee WM, Li RK, Jiang B-R. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier. ScientificWorldJournal. 2014;2014:795624. + + + Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–50. + + + Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12:453–7. + + + Wang W, Liu Q, Wang Y, et al. Verification of hub genes in the expression profile of aortic dissection. PLoS One. 2019;14:e0224922. + + + Cheng M, Yang Y, Xin H, et al. Non-coding RNAs in aortic dissection: from biomarkers to therapeutic targets. J Cell Mol Med. 2020;24:11622–37. + + + Wilson JE. Isozymes of mammalian hexokinase: structure, subcellular localization and metabolic function. J Exp Biol. 2003;206:2049–57. + + + Qin Y, Sun M, You L, et al. ESR1, HK3 and BRSK1 gene variants are associated with both age at natural menopause and premature ovarian failure. Orphanet J Rare Dis. 2012;7:5. + + + Geng Z, Liu J, Hu J, et al. Crucial transcripts predict response to initial immunoglobulin treatment in acute Kawasaki disease. Sci Rep. 2020;10:17860. + + + Liu C, Zhou Y, Zhao D, et al. Identification and validation of differentially expressed chromatin regulators for diagnosis of aortic dissection using integrated bioinformatics analysis and machine-learning algorithms. Front Genet. 2022;13:950613. + + + Ye J, Fu Y, Ke Y, et al. Lactylation associated biomarkers and immune infiltration in aortic dissection. Sci Rep. 2025;15:21536. + + + Kany S, Friedman SF, Al-Alusi M, et al. Electrocardiogram-based artificial intelligence to identify coronary artery disease. JACC Adv. 2025;4:102041. + + + Al-Zaiti SS, Martin-Gill C, Zègre-Hemsey JK, et al. Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nat Med. 2023;29:1804–13. + + + Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019;394:861–7. + + + Hayiroğlu MI, Altay S. The role of artificial intelligence in coronary artery disease and atrial fibrillation. Balkan Med J. 2023;40:151–2. + + + Yilmaz A, Hayiroğlu MI, Salturk S, et al. Machine learning approach on high risk treadmill exercise test to predict obstructive coronary artery disease by using P, QRS, and T waves’ features. Curr Probl Cardiol. 2023;48:101482. + + + Cicek V, Babaoglu M, Saylik F, et al. A new risk prediction model for the assessment of myocardial injury in elderly patients undergoing non-elective surgery. J Cardiovasc Dev Dis. 2024;12:6. + + + Liu T, Krentz A, Lu L, Curcin V. Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis. Eur Heart J Digit Health. 2025;6:7–22. + + + Liu Y, Zou L, Tang H, et al. Single-cell sequencing of immune cells in human aortic dissection tissue provides insights into immune cell heterogeneity. Front Cardiovasc Med. 2022;9:791875. + + + Chen F, Han J, Tang B. Patterns of immune infiltration and the key immune-related genes in acute type A aortic dissection in bioinformatics analyses. Int J Gen Med. 2021;14:2857–69. + + + Tuo Z, Zheng X, Zong Y, et al. HK3 is correlated with immune infiltrates and predicts response to immunotherapy in non-small cell lung cancer. Clin Transl Med. 2020;10:319–30. + + + Xu W, Liu W-R, Xu Y, et al. Hexokinase 3 dysfunction promotes tumorigenesis and immune escape by upregulating monocyte/macrophage infiltration into the clear cell renal cell carcinoma microenvironment. Int J Biol Sci. 2021;17:2205–22. + + + + + + + 42216361 + + 2026 + 05 + 30 + + + 2026 + 05 + 30 + +
+ + 1536-5964 + + 105 + 22 + + 2026 + May + 29 + + + Medicine + Medicine (Baltimore) + + Association between the development of sepsis and the BAR index in patients with acute pancreatitis: A retrospective investigation based on MIMIC-IV database. + + e49043 + e49043 + + 10.1097/MD.0000000000049043 + + Sepsis, as a severe complication of acute pancreatitis (AP), needs to be identified and treated as early as possible. The blood urea nitrogen-to-albumin ratio (BAR) index has shown good predictive ability in many studies. However, its effectiveness in predicting AP complicated by sepsis has not been further verified. Therefore, this study mainly discusses the correlation between BAR and the risk of sepsis, aiming to provide a simple and early warning tool for sepsis risk stratification in AP patients. This retrospective cohort study analyzed the data of 779 AP patients from the Medical Information Mart for Intensive Care database 3.1. Critically ill participants admitted between 2008 and 2022 at the Beth Israel Deaconess Medical Center in Boston were included. According to the Sepsis 3.0 diagnostic criteria, the AP patients included in the study were further divided into a sepsis group and a non-sepsis group. The Boruta algorithm, combined with the extreme gradient boosting model, was used to screen variables beneficial to the outcome. Univariate and multivariate logistic regression were used to evaluate the relationship between the BAR index and the occurrence of sepsis after admission. The results showed that the BAR index was significantly associated with the occurrence of sepsis, and the risk of sepsis increased with the increase in the BAR index (all P values for trend < .001). Subgroup analysis showed that the BAR index was positively correlated with the probability of sepsis in patients of different ages, genders, and those with hypertension and diabetes (all P for interaction > .05). The area under the curve of the combined use of the BAR index and clinical indicators was 0.704 (0.667-0.741), which was significantly higher than that of using the BAR index alone, 0.611 (0.572-0.651), with a statistically significant difference (P < .001). In conclusion, the study results suggest that the BAR index may help identify which AP patients are at a higher risk of developing sepsis. + Copyright © 2026 the Author(s). Published by Wolters Kluwer Health, Inc. + + + + Wang + Jinhai + J + + The First Hospital of Jilin University, Changchun, China. + + + + Zheng + Fuhong + F + + + Zhu + Lei + L + + + Liu + Haibo + H + 0009-0001-3334-8092 + + + eng + + Journal Article + +
+ + United States + Medicine (Baltimore) + 2985248R + 0025-7974 + + + + 0 + Serum Albumin + + + IM + + + Humans + + + Sepsis + etiology + diagnosis + epidemiology + blood + + + Pancreatitis + complications + blood + + + Retrospective Studies + + + Female + + + Male + + + Blood Urea Nitrogen + + + Middle Aged + + + Serum Albumin + analysis + + + Aged + + + Databases, Factual + + + Risk Factors + + + Acute Disease + + + Logistic Models + + + Adult + + + + MIMIC-IV database + Sepsis + acute pancreatitis + blood urea nitrogen-to-albumin ratio + machine learning + + The authors have no funding and conflicts of interest to disclose. + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2025 + 11 + 2 + + + 2026 + 5 + 10 + + + 2026 + 5 + 30 + 1 + 1 + + + ppublish + + 42216361 + 10.1097/MD.0000000000049043 + 00005792-202605290-00047 + + + + Iannuzzi JP, King JA, Leong JH, et al. Global incidence of acute pancreatitis is increasing over time: a systematic review and meta-analysis. Gastroenterology. 2022;162:122–34. + + + Beij A, Verdonk RC, van Santvoort HC, de-Madaria E, Voermans RP. Acute pancreatitis: an update of evidence-based management and recent trends in treatment strategies. United European Gastroenterol J. 2025;13:97–106. + + + Pavlidis ET, Pavlidis TE. Management of infected acute necrotizing pancreatitis. World J Clin Cases. 2023;11:482–6. + + + Bellomo R, Kellum JA, Ronco C, et al. Acute kidney injury in sepsis. Intensive Care Med. 2017;43:816–28. + + + Kamar C, Ali A, Altun D, et al. Evaluation of risk factors and development of acute kidney injury in aneurysmal subarachnoid hemorrhage, head injury, and severe sepsis/septic shock patients during ICU treatment. Ulus Travma Acil Cerrahi Derg. 2017;23:39–45. + + + Haines RW, Zolfaghari P, Wan Y, Pearse RM, Puthucheary Z, Prowle JR. Elevated urea-to-creatinine ratio provides a biochemical signature of muscle catabolism and persistent critical illness after major trauma. 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Prognostic value of blood urea nitrogen to serum albumin ratio in intensive care unit patients with lung cancer. Int J Gen Med. 2021;14:7349–59. + + + Degenhardt F, Seifert S, Szymczak S. Evaluation of variable selection methods for random forests and omics data sets. Brief Bioinform. 2019;20:492–503. + + + Lundberg SM, Erion G, Chen H, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2:56–67. + + + Zhang P, Zhang X, Zheng K, He L, Lv M. Association of the stress hyperglycemia ratio (SHR) with 90-day and 180-day all-cause mortality in patients undergoing coronary artery bypass grafting (CABG) for acute myocardial infarction (AMI): a retrospective cohort study using the MIMIC-IV database. Medicine (Baltimore). 2025;104:e45022. + + + Li X, Li T, Wang J, et al. Higher blood urea nitrogen level is independently linked with the presence and severity of neonatal sepsis. Ann Med. 2021;53:2192–8. + + + Li X, Zheng R, Zhang T, Zeng Z, Li H, Liu J. Association between blood urea nitrogen and 30-day mortality in patients with sepsis: a retrospective analysis. Ann Palliat Med. 2021;10:11653–63. + + + Bou Chebl R, Geha M, Assaf M, et al. The prognostic value of the lactate/albumin ratio for predicting mortality in septic patients presenting to the emergency department: a prospective study. Ann Med. 2021;53:2268–77. + + + Peters E, Antonelli M, Wittebole X, et al. A worldwide multicentre evaluation of the influence of deterioration or improvement of acute kidney injury on clinical outcome in critically ill patients with and without sepsis at ICU admission: results from the Intensive Care Over Nations audit. Crit Care. 2018;22:188. + + + Ergin B, Kapucu A, Demirci-Tansel C, Ince C. The renal microcirculation in sepsis. Nephrol Dial Transplant. 2015;30:169–77. + + + Omiya K, Sato H, Sato T, et al. Albumin and fibrinogen kinetics in sepsis: a prospective observational study. Crit Care. 2021;25:436. + + + Caraceni P, Domenicali M, Tovoli A, et al. Clinical indications for the albumin use: still a controversial issue. Eur J Intern Med. 2013;24:721–8. + + + Fang CJ, Saadat GH, Butler BA, Bokhari F. The geriatric nutritional risk index is an independent predictor of adverse outcomes for total joint arthroplasty patients. J Arthroplasty. 2022;37:S836–41. + + + Gao Q, Cheng Y, Li Z, et al. Association between nutritional risk screening score and prognosis of patients with sepsis. Infect Drug Resist. 2021;14:3817–25. + + + Yoon SH, Choi B, Eun S, Bae GE, Koo CM, Kim MK. Using the lactate-to-albumin ratio to predict mortality in patients with sepsis or septic shock: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci. 2022;26:1743–52. + + + Chen X, Zhou X, Zhao H, et al. Clinical value of the lactate/albumin ratio and lactate/albumin ratio × age score in the assessment of prognosis in patients with sepsis. Front Med (Lausanne). 2021;8:732410. + + + Akahane J, Ushiki A, Kosaka M, et al. Blood urea nitrogen-to-serum albumin ratio and A-DROP are useful in assessing the severity of Pneumocystis pneumonia in patients without human immunodeficiency virus infection. J Infect Chemother. 2021;27:707–14. + + + Lin Z, Zhao Y, Xiao L, Qi C, Chen Q, Li Y. Blood urea nitrogen to serum albumin ratio as a new prognostic indicator in critical patients with chronic heart failure. ESC Heart Fail. 2022;9:1360–9. + + + Ryu S, Oh SK, Cho SU, et al. Utility of the blood urea nitrogen to serum albumin ratio as a prognostic factor of mortality in aspiration pneumonia patients. Am J Emerg Med. 2021;43:175–9. + + + Fang J, Xu B. Blood urea nitrogen to serum albumin ratio independently predicts mortality in critically ill patients with acute pulmonary embolism. Clin Appl Thromb Hemost. 2021;27:10760296211010241. + + + Zou XL, Feng D-Y, Wu W-B, Yang H-L, Zhang T-T. Blood urea nitrogen to serum albumin ratio independently predicts 30-day mortality and severity in patients with Escherichia coli bacteraemia. Med Clin (Barc). 2021;157:219–25. + + + Lee DW, Cho CM. Predicting severity of acute pancreatitis. Medicina (Kaunas). 2022;58:787. + + + Li G, Li S, Cao L, et al. Nomogram development and validation for predicting minimally invasive step-up approach failure in infected necrotizing pancreatitis patients: a retrospective cohort study. Int J Surg. 2023;109:1677–87. + + + Tao S, Yu L, Li J, et al. Prognostic value of triglyceride-glucose index in patients with chronic coronary syndrome undergoing percutaneous coronary intervention. Cardiovasc Diabetol. 2023;22:322. + + + + + + + 42216358 + + 2026 + 05 + 30 + + + 2026 + 05 + 30 + +
+ + 1536-5964 + + 105 + 22 + + 2026 + May + 29 + + + Medicine + Medicine (Baltimore) + + Bioinformatics analysis identifies biomarkers associated with T-cell depletion in systemic lupus erythematosus. + + e49016 + e49016 + + 10.1097/MD.0000000000049016 + + Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects multiple organ systems. In SLE, T-cell subsets are closely associated with autoimmunity and organ inflammation. Detecting T-cell exhaustion (TEX)-related biomarkers is crucial for early SLE diagnosis and offers new insights into disease mechanisms. Transcriptomic data for SLE were collected from public databases, and TEX-related genes were obtained from the literature. Biomarkers were identified using analyses such as differential expression analysis and machine learning algorithms. The potential mechanisms of action of these biomarkers were further investigated, including nomogram construction, gene set enrichment analysis, immune microenvironment analysis, molecular regulatory network analysis, molecular docking, and reverse transcription-quantitative polymerase chain reaction validation. IFIT3, IFIH1, CXCL10, and STAT2 were identified as biomarkers. The nomogram demonstrated good predictive performance. These biomarkers were mainly involved in biological processes such as ribosome biogenesis and negative regulation of viral genome replication. Four significantly different immune cell types were identified, including monocytes and resting memory CD4 T cells. The biomarkers were found to be regulated by several transcription factors and microRNAs. Furthermore, the 4 biomarkers exhibited high binding affinity for several drugs; in particular, the binding energy of STAT2 with bisphenol A was -7.0 kcal/mol. Finally, reverse transcription-quantitative polymerase chain reaction results showed that all 4 biomarkers were significantly upregulated in SLE patients. In summary, this study identified TEX-related biomarkers in SLE, which may have important clinical implications for the diagnosis and treatment of SLE. + Copyright © 2026 the Author(s). Published by Wolters Kluwer Health, Inc. + + + + Yang + Lanqing + L + 0009-0004-0217-3084 + + Department of Rheumatology and Immunology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning Province, China. + + + + Gao + Wei + W + + Department of Rheumatology and Immunology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning Province, China. + + + + Yang + Lulu + L + + Department of Pediatrics, Shiqian County Maternal and Child Health Hospital, Tongren, Guizhou Province, China. + + + + Liu + Xingyu + X + + Department of Pediatric Orthopedics, Shanghai Children's Medical Center Guizhou Hospital, Shanghai Jiao Tong University School of Medicine, Guiyang, Guizhou Province, China. + + + + eng + + Journal Article + +
+ + United States + Medicine (Baltimore) + 2985248R + 0025-7974 + + + + 0 + Biomarkers + + + 0 + STAT2 Transcription Factor + + + 0 + IFIT3 protein, human + + + 0 + Chemokine CXCL10 + + + 0 + CXCL10 protein, human + + + 0 + MicroRNAs + + + 0 + Intracellular Signaling Peptides and Proteins + + + IM + + + Humans + + + Lupus Erythematosus, Systemic + immunology + genetics + + + Biomarkers + metabolism + + + Computational Biology + methods + + + STAT2 Transcription Factor + genetics + metabolism + + + Chemokine CXCL10 + genetics + metabolism + + + T-Cell Exhaustion + + + Gene Expression Profiling + + + MicroRNAs + genetics + metabolism + + + T-Lymphocytes + + + Transcriptome + + + Intracellular Signaling Peptides and Proteins + + + + bioinformatics + enrichment analysis + immune microenvironment + + The authors have no funding and conflicts of interest to disclose. + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2025 + 3 + 10 + + + 2026 + 5 + 7 + + + 2026 + 5 + 30 + 1 + 1 + + + ppublish + + 42216358 + 10.1097/MD.0000000000049016 + 00005792-202605290-00044 + + + + Hoi A, Igel T, Mok CC, Arnaud L. 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+ + 1536-5964 + + 105 + 22 + + 2026 + May + 29 + + + Medicine + Medicine (Baltimore) + + Environmental toxicant glyphosate induces cardiotoxicity: New insights from network toxicology, integrated machine learning, molecular modeling and multidimensional bioinformatics analysis. + + e48974 + e48974 + + 10.1097/MD.0000000000048974 + + Glyphosate is one of the world's most widely used herbicide, yet the causal relationship between glyphosate and cardiotoxicity remains inadequate. To elucidate the cardiotoxic mechanisms of glyphosate, an integrative strategy was developed in this work, which combines computational toxicometry, predictive machine learning, and atomic-level molecular modeling. Initially, glyphosate's systematic toxicological profile was evaluated using ProTox-3.0 and ADMETlab 3.0. Candidate genes associated with glyphosate were obtained from 3 separate databases. Target genes associated with cardiotoxicity were integrated from 2 databases. Intersection analysis identified 113 core candidate genes associated with glyphosate and cardiotoxicity. Functional enrichment-related analyses were employed and revealed that the target genes were significantly involved in key biological processes such as ERBB3 signaling pathways. In order to refine the candidate genes to a more precise gene pool, net topology analyses were employed and identified 16 key feature genes. We subsequently integrated machine learning algorithms and validated the results using the GEO database, ultimately identifying 4 hub genes, AKT1, IL1B, BRCA1, and PTGS2. Finally, we applied molecular docking analysis and revealed strong binding affinities, which suggested direct interaction mechanisms in glyphosate-induced cardiotoxicity. In summary, our study establishes a comprehensive mechanistic framework for glyphosate-induced cardiotoxicity. + Copyright © 2026 the Author(s). Published by Wolters Kluwer Health, Inc. + + + + Sun + Mingze + M + 0009-0008-8144-6261 + + State Key Laboratory for Systems Medicine for Cancer, Division of Cardiology, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. + + + + Wang + Yankai + Y + + State Key Laboratory for Systems Medicine for Cancer, Division of Cardiology, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. + + + + Tong + Renyang + R + + Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China. + + + + Yan + Ruiming + R + + Department of Trauma Center, School of Medicine, Tongji Hospital, Tongji University, Shanghai, China. + + + + Pu + Jun + J + 0000-0003-2240-5108 + + State Key Laboratory for Systems Medicine for Cancer, Division of Cardiology, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. + + + + eng + + + 2023ZD0503204 + Noncommunicable Chronic Diseases-National Science and Technology Major Project + + + + GZC20241101 + Postdoctoral Fellowship Program of CPSF + + + + Ggyfz202515 + Reform and Development Program of Beijing Institute of Respiratory Medicine + + + + + Journal Article + +
+ + United States + Medicine (Baltimore) + 2985248R + 0025-7974 + + + + 4632WW1X5A + Glyphosate + + + TE7660XO1C + Glycine + + + 0 + Herbicides + + + IM + + + Glyphosate + + + Glycine + analogs & derivatives + toxicity + + + Machine Learning + + + Cardiotoxicity + genetics + etiology + + + Computational Biology + methods + + + Herbicides + toxicity + + + Humans + + + Models, Molecular + + + + cardiotoxicity + glyphosate + hub genes + machine learning + molecular docking + network toxicology + + The authors have no conflicts of interest to disclose. + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 3 + 17 + + + 2026 + 5 + 6 + + + 2026 + 5 + 30 + 1 + 1 + + + ppublish + + 42216354 + 10.1097/MD.0000000000048974 + 00005792-202605290-00040 + + + + Diaz-Martin RD, Carvajal-Peraza A, Yanez-Rivera B, Betancourt-Lozano M. Short exposure to glyphosate induces locomotor, craniofacial, and bone disorders in zebrafish (Danio rerio) embryos. 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+ + 1536-5964 + + 105 + 22 + + 2026 + May + 29 + + + Medicine + Medicine (Baltimore) + + Predicting depressive symptoms in older adults with hearing impairment development and validation of NHANES-based machine-learning models: A cross-sectional study. + + e49004 + e49004 + + 10.1097/MD.0000000000049004 + + Depressive symptoms frequently co-occur with hearing impairment in older adults, yet available prediction tools for this high-risk subgroup remain limited in practicality and applicability. We developed and internally validated prediction models for clinically relevant depressive symptoms among older adults with hearing impairment. We analyzed cross-sectional data from 5 National Health and Nutrition Examination Survey cycles (2005-2006, 2009-2010, 2011-2012, 2015-2016, and 2017-2018). Adults aged ≥60 years with hearing impairment were identified using the 2021 World Health Organization criterion (better-ear pure-tone average at 0.5/1/2/4 kHz ≥20 dB). Depressive symptoms were defined as a Patient Health Questionnaire-9 score ≥10. Candidate predictors included sociodemographic factors, comorbidities, lifestyle behaviors, and sensory/functional limitations; missing covariates were handled using multiple imputation. Participants were randomly split into training and validation sets (7:3). Predictors were selected using the Boruta algorithm followed by least absolute shrinkage and selection operator regression, yielding 6 predictors (age, poverty-income ratio, self-rated health, sleep disorder, memory difficulty, and physical functional limitation). Logistic regression and 6 machine-learning algorithms were developed. Model performance was assessed by discrimination (area under the receiver operating characteristic curve [AUC]), calibration (calibration curves and Brier score), and clinical utility (decision-curve analysis). The analytic sample included 2123 participants, of whom 154 (7.25%) had depressive symptoms. In the validation set, AUCs across models ranged from 0.799 to 0.862. Logistic regression achieved the highest discrimination (AUC 0.862, 95% CI 0.800-0.911) with good calibration (Brier score 0.053) and favorable net benefit across clinically relevant thresholds. A nomogram, a dynamic nomogram, and an online calculator were developed to facilitate individualized risk estimation. Sensitivity analyses showed broadly consistent results, except for attenuated discrimination in the severe hearing-loss subgroup. A parsimonious 6-variable model demonstrated strong discrimination, good calibration, and potential clinical utility for risk stratification and targeted screening among older adults with hearing impairment. External validation and prospective evaluation are warranted before clinical implementation. + Copyright © 2026 the Author(s). Published by Wolters Kluwer Health, Inc. + + + + Shang + Lunyu + L + 0009-0005-3585-5927 + + College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China. + + + + Jiang + Shuaiyu + S + + Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + + Dai + Qi + Q + + College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China. + + + + eng + + Journal Article + Validation Study + +
+ + United States + Medicine (Baltimore) + 2985248R + 0025-7974 + + IM + + + Humans + + + Cross-Sectional Studies + + + Hearing Loss + psychology + epidemiology + complications + + + Female + + + Aged + + + Nutrition Surveys + + + Male + + + Depression + epidemiology + diagnosis + + + Machine Learning + + + Predictive Learning Models + + + Middle Aged + + + Logistic Models + + + Prediction Algorithms + + + United States + epidemiology + + + + NHANES + depressive symptoms + hearing loss + machine learning + older adults + + The authors have no conflicts of interest to disclose. + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 1 + 1 + + + 2026 + 5 + 7 + + + 2026 + 5 + 30 + 1 + 1 + + + ppublish + + 42216348 + 10.1097/MD.0000000000049004 + 00005792-202605290-00034 + + + + Dalton DS, Cruickshanks KJ, Klein BEK, Klein R, Wiley TL, Nondahl DM. The impact of hearing loss on quality of life in older adults. Gerontologist. 2003;43:661–8. + + + Lohi V, Ohtonen P, Sorri M, Mäki-Torkko E, Hannula S. Prevalence and incidence of hearing impairment among adults: a 13-year follow-up study. Int J Audiol. 2021;60:687–94. + + + Bainbridge KE, Wallhagen MI. Hearing loss in an aging American population: extent, impact, and management. Annu Rev Public Health. 2014;35:139–52. + + + Zheng Q, Xu Z, Li N, Wang Y, Zhang T, Jing J. Age-related hearing loss in older adults: etiology and rehabilitation strategies. Front Neurosci. 2024;18:1428564. + + + Britt CJ, Storey E, Woods RL, et al.; ASPREE Investigators. Age-related hearing loss: a cross-sectional study of healthy older Australians. Gerontology. 2024;71:1–12. + + + Lee KY. Pathophysiology of age-related hearing loss (peripheral and central). Korean J Audiol. 2013;17:45–9. + + + Haile LM, Kamenov K, Briant PS, et al. Hearing loss prevalence and years lived with disability, 1990–2019: findings from the Global Burden of Disease Study 2019. Lancet. 2021;397:996–1009. + + + Chang HP, Chou P. Presbycusis among older Chinese people in Taipei, Taiwan: a community-based study. Int J Audiol. 2007;46:738–45. + + + Zhang Y, He X, Liu Y, Tian C, Yang H, Zhang L. The association between hearing loss and depression in the China health and retirement longitudinal study. Sci Rep. 2025;15:20537. + + + Arlinger S. Negative consequences of uncorrected hearing loss—a review. Int J Audiol. 2003;42:17–20. + + + Hu T, Zhao X, Wu M, et al. Prevalence of depression in older adults: a systematic review and meta-analysis. Psychiatry Res. 2022;311:114511. + + + Wei J, Li Y, Gui X. Association of hearing loss and risk of depression: a systematic review and meta-analysis. Front Neurol. 2024;15:1446262. + + + Cosh S, Helmer C, Delcourt C, Robins TG, Tully PJ. Depression in elderly patients with hearing loss: current perspectives. Clin Interv Aging. 2019;14:1471–80. + + + Zhang G, Dong S, Wang L. Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal study. BMC Public Health. 2025;25:1904. + + + Qin M, Qiao M, Dong Y, Wang H. Development and validation of a depression risk prediction model for middle-aged and elderly adults with sensory impairment: evidence from the China health and retirement longitudinal study. PLoS One. 2025;20:e0332907. + + + Chen J, Lin Y, Hu R, Hu C. Prediction model for depression risk in middle-aged and elderly patients with metabolic syndrome: a nomogram and interpretable machine learning approach based on CHARLS. BMC Psychiatry. 2025;25:987. + + + Jin T, Halili A. Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data. Front Artif Intell. 2025;8:1624171. + + + Hong S, Lu B, Wang S, Jiang Y. Comparison of logistic regression and machine learning methods for predicting depression risks among disabled elderly individuals: results from the China Health and Retirement Longitudinal Study. BMC Psychiatry. 2025;25:128. + + + Du J, Tao X, Zhu L, et al. A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study. Front Public Health. 2025;13:1606316. + + + World Health Organization. World report on hearing. World Health Organization; 2021. + + + Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–13. + + + Yeung A, Fung F, Yu SC, et al. Validation of the Patient Health Questionnaire-9 for depression screening among Chinese Americans. Compr Psychiatry. 2008;49:211–7. + + + Sun Y, Kong Z, Song Y, Liu J, Wang X. The validity and reliability of the PHQ-9 on screening of depression in neurology: a cross sectional study. BMC Psychiatry. 2022;22:98. + + + Levis B, Benedetti A, Thombs BD; DEPRESsion Screening Data (DEPRESSD) Collaboration. Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis. BMJ. 2019;365:l1476. + + + 全国防聋治聋技术指导组, 中华医学会耳鼻咽喉头颈外科学分会, 中华耳鼻咽喉头颈外科杂志编辑委员会, 中华医学会老年医学分会. 老年听力损失诊断与干预专家共识 (2019). 中华耳鼻咽喉头颈外科杂志. 2019;54:166–73. + + + Zhao H, Wang X, Shi Y. The effect of hearing impairment and social participation on depressive symptoms in older adults: a cross-lagged analysis. Front Comput Neurosci. 2023;17:1240587. + + + Li CM, Zhang X, Hoffman HJ, Cotch MF, Themann CL, Wilson MR. Hearing impairment associated with depression in US adults, National Health and Nutrition Examination Survey 2005-2010. JAMA Otolaryngol Head Neck Surg. 2014;140:293–302. + + + Scinicariello F, Przybyla J, Carroll Y, Eichwald J, Decker J, Breysse PN. Age and sex differences in hearing loss association with depressive symptoms: analyses of NHANES 2011-2012. Psychol Med. 2019;49:962–8. + + + Harjono Hadiwiardjo Y, Damayanti R. Biopsychosocial factors associated with depression in the elderly: a systematic literature review. F1000Res. 2024;11:1068. + + + Li E, Ai F, Tian Q, Yang H, Tang P, Guo B. Develop and validate machine learning models to predict the risk of depressive symptoms in older adults with cognitive impairment. BMC Psychiatry. 2025;25:219. + + + Aoki M, Okuda H, Ishihara H, et al. Hearing loss is associated with hippocampal atrophy and high cortisol/dehydroepiandrosterone sulphate ratio in older adults. Int J Audiol. 2021;60:293–9. + + + Li IH, Shih JH, Jhao YT, et al. Regulation of noise-induced loss of serotonin transporters with resveratrol in a rat model using 4-[18F]-ADAM/small-animal positron emission tomography. Molecules. 2019;24:1344. + + + Huber M, Reuter L, Weitgasser L, Pletzer B, Rösch S, Illg A. Hearing loss, depression, and cognition in younger and older adult CI candidates. Front Neurol. 2023;14:1272210. + + + Bainbridge KE, Ramachandran V. Hearing aid use among older U.S. adults; the national health and nutrition examination survey, 2005-2006 and 2009-2010. Ear Hear. 2014;35:289–94. + + + Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12–22. + + + + + + + 42216340 + + 2026 + 05 + 30 + + + 2026 + 05 + 30 + +
+ + 1536-5964 + + 105 + 22 + + 2026 + May + 29 + + + Medicine + Medicine (Baltimore) + + Vasoactive intestinal peptide associated 8-gene signature with prognostic and immune associations in melanoma. + + e48961 + e48961 + + 10.1097/MD.0000000000048961 + + Vasoactive intestinal peptide (VIP) plays a multifaceted role in cancer biology, yet its prognostic and immunological implications in melanoma remain underexplored. This study aimed to construct a VIP-related gene signature for risk stratification and to explore its potential associations with the tumor immune microenvironment and drug sensitivity in melanoma. RNA-sequencing data and clinical information were obtained from TCGA-SKCM (n = 477, training set) and GSE65904 (n = 209, validation set). A VIP-related prognostic signature (VIPsig) was developed using stepwise Cox regression and Gradient Boosting Machine algorithms. Patients were stratified into high- and low-risk groups based on the median risk score. Somatic mutations, immune infiltration, drug sensitivity, and functional enrichment were further analyzed. A nomogram integrating clinical factors was developed and validated. Single-cell RNA-seq data (GSE115978) were reanalyzed to characterize cellular expression patterns. A preliminary pan-cancer analysis indicated potential dysregulation of VIP pathway genes. In melanoma, we identified an 8-gene signature (CD28, CD80, CD86, CTLA4, FAS, IFNG, IL10, and IL12A) associated with survival. In the training set, high-risk patients exhibited significantly worse overall survival (OS) compared to low-risk patients (HR = 2.96, 95% CI: 2.24-3.92, P < .001). This was validated in the GSE65904 cohort (HR = 1.87, 95% CI: 1.26-2.77, P = .002). The VIPsig demonstrated discriminative ability with 1-, 3-, and 5-year AUCs of 0.760 (95% CI: 0.675-0.845, P < .001), 0.749 (95% CI: 0.692-0.807, P < .001), and 0.770 (95% CI: 0.717-0.823, P < .001) in the training set, respectively. Functionally, the signature was significantly associated with immune cell infiltration and immune checkpoint expression. In silico drug sensitivity analysis suggested potential associations with estimated IC50 values for several agents, though these findings require experimental validation. A nomogram integrating VIPsig with clinicopathological factors showed improved net benefit in decision curve analysis. The VIP-based signature demonstrates robust prognostic value for melanoma survival and reflects the tumor immune microenvironment status. Exploratory analyses suggest potential associations with in silico drug sensitivity estimates; however, its utility for treatment response prediction remains unvalidated and warrants further investigation in prospective melanoma cohorts treated with immune checkpoint inhibitors. + Copyright © 2026 the Author(s). Published by Wolters Kluwer Health, Inc. + + + + Hong + Xiaogang + X + 0009-0001-0028-4088 + + Department of Ophthalmology, People's Hospital of Kaihua, Quzhou, Zhejiang, China. + + + + Zheng + Xiuzhen + X + + + eng + + Journal Article + +
+ + United States + Medicine (Baltimore) + 2985248R + 0025-7974 + + + + 37221-79-7 + Vasoactive Intestinal Peptide + + + 0 + Biomarkers, Tumor + + + IM + + + Humans + + + Vasoactive Intestinal Peptide + genetics + metabolism + + + Melanoma + genetics + immunology + mortality + + + Prognosis + + + Tumor Microenvironment + immunology + genetics + + + Gene Expression Regulation, Neoplastic + + + Nomograms + + + Biomarkers, Tumor + genetics + + + + immune cell infiltration + machine learning + melanoma + nomogram + vasoactive intestinal peptide + + The authors have no funding and conflicts of interest to disclose. + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2025 + 8 + 31 + + + 2026 + 5 + 5 + + + 2026 + 5 + 30 + 1 + 0 + + + ppublish + + 42216340 + 10.1097/MD.0000000000048961 + 00005792-202605290-00026 + + + + Karlsson MJ, Costa SF, Tebani A, et al. Inflammation and apolipoproteins are potential biomarkers for stratification of cutaneous melanoma patients for immunotherapy and targeted therapy. Cancer Res. 2021;81:2545–55. + + + Mao R, Yang F, Zhang T, Li J. Landscape and clinical significance of immune checkpoint in cutaneous melanoma. Front Immunol. 2021;12:756282. + + + Bida M, Miya TV, Hull R, Dlamini Z. Tumor-infiltrating lymphocytes in melanoma: from prognostic assessment to therapeutic applications. Front Immunol. 2024;15:1497522. + + + Hodi FS, Wolchok JD, Schadendorf D, et al. TMB and inflammatory gene expression associated with clinical outcomes following immunotherapy in advanced melanoma. Cancer Immunol Res. 2021;9:1202–13. + + + Ascierto PA, Tang H, Dolfi S, et al. Effect of prior and first-line immunotherapy on baseline immune biomarkers and modulation of the tumor microenvironment in response to nivolumab and relatlimab combination therapy in patients with melanoma from RELATIVITY-020. 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IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest. 2017;127:2930–40. + + + Yang M, Zheng C, Miao Y, et al. BTLA promoter hypomethylation correlates with enhanced immune cell infiltration, favorable prognosis, and immunotherapy response in melanoma. J ImmunoTher Cancer. 2025;13:e009841. + + + Asano S, Ono A, Sakamoto K, et al. Vasoactive intestinal peptide receptor 2 signaling promotes breast cancer cell proliferation by enhancing the ERK pathway. Peptides. 2023;161:170940. + + + Asano S, Yamasaka M, Ozasa K, et al. Vasoactive intestinal peptide-VIPR2 signaling regulates tumor cell migration. Front Oncol. 2022;12:852358. + + + Gkonos PJ, Ashby MH, Andrade AA. Vasoactive intestinal peptide stimulates prostate-specific antigen secretion by LNCaP prostate cancer cells. Regul Pept. 1996;65:153–7. + + + Kittikulsuth W, Nakano D, Kitada K, et al. Vasoactive intestinal peptide blockade suppresses tumor growth by regulating macrophage polarization and function in CT26 tumor-bearing mice. Sci Rep. 2023;13:927. + + + Pozo D, Gonzalez-Rey E, Chorny A, Anderson P, Varela N, Delgado M. Tuning immune tolerance with vasoactive intestinal peptide: a new therapeutic approach for immune disorders. Peptides. 2007;28:1833–46. + + + Chen L, Yuan W, Chen Z, et al. Vasoactive intestinal peptide represses activation of tumor-associated macrophages in gastric cancer via regulation of TNFα, IL-6, IL-12 and iNOS. Int J Oncol. 2015;47:1361–70. + + + Li G, Wu K, Tao K, et al. Vasoactive intestinal peptide induces CD14+HLA-DR‑/low myeloid-derived suppressor cells in gastric cancer. Mol Med Rep. 2015;12:760–8. + + + Li JM, Darlak KA, Southerland L, et al. VIPhyb, an antagonist of vasoactive intestinal peptide receptor, enhances cellular antiviral immunity in murine cytomegalovirus infected mice. 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Clin Cancer Res. 2007;13(18 Pt 1):5238–42. + + + Rudy W, Gückel B, Siebels M, Lindauer M, Meuer SC, Moebius U. Differential function of CD80- and CD86-transfected human melanoma cells in the presence of IL-12 and IFN-gamma. Int Immunol. 1997;9:853–60. + + + Aytekin EC, Unal B, Bassorgun CI, Ozkan O. Clinicopathologic evaluation of CD80, CD86, and PD-L1 expressions with immunohistochemical methods in malignant melanoma patients. Turk Patoloji Derg. 2024;40:16–26. + + + Mo X, Zhang H, Preston S, et al. Interferon-γ signaling in melanocytes and melanoma cells regulates expression of CTLA-4. Cancer Res. 2018;78:436–50. + + + Váraljai R, Zimmer L, Al-Matary Y, et al. Interleukin 17 signaling supports clinical benefit of dual CTLA-4 and PD-1 checkpoint inhibition in melanoma. Nat Cancer. 2023;4:1292–308. + + + Zhang Z, Langenbach M, Sagar S, et al. Efficacy of CTLA-4 checkpoint therapy is dependent on IL-21 signaling to mediate cytotoxic reprogramming of PD-1(+)CD8(+) T cells. 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DOT1L inhibition reprograms innate immunity to potentiate immunomodulatory drug responses in multiple myeloma. Cancer Lett. 2025;631:217941. + + + Zebley CC, Abdelsamed HA, Ghoneim HE, et al. Proinflammatory cytokines promote TET2-mediated DNA demethylation during CD8 T cell effector differentiation. Cell Rep. 2021;37:109796. + + + Daly RJ, Scott AM, Klein O, Ernst M. Enhancing therapeutic anti-cancer responses by combining immune checkpoint and tyrosine kinase inhibition. Mol Cancer. 2022;21:189. + + + Lin S, Cheng L, Ye W, et al. Chimeric CTLA4-CD28-CD3z T Cells potentiate antitumor activity against CD80/CD86-Positive B cell malignancies. Front Immunol. 2021;12:642528. + + + Dugage DM, Jones RL, Trent J, Champiat S, Dumont S. Beyond the driver mutation: immunotherapies in gastrointestinal stromal tumors. Front Immunol. 2021;12:715727. + + + Sugihara E, Hashimoto N, Osuka S, et al. The Inhibitor of apoptosis protein livin confers resistance to fas-mediated immune cytotoxicity in refractory lymphoma. Cancer Res. 2020;80:4439–50. + + + Carlini V, Noonan DM, Abdalalem E, et al. The multifaceted nature of IL-10: regulation, role in immunological homeostasis and its relevance to cancer, COVID-19 and post-COVID conditions. Front Immunol. 2023;14:1161067. + + + Kureshi CT, Dougan SK. Cytokines in cancer. Cancer Cell. 2025;43:15–35. + + + Kupke P, Adenugba A, Schemmerer M, et al. Immunomodulation of natural killer cell function by ribavirin involves TYK-2 activation and subsequent increased IFN-γ secretion in the context of in vitro hepatitis E virus infection. Cells. 2023;12:453. + + + Iglesias M, Khalifian S, Oh BC, et al. A short course of tofacitinib sustains the immunoregulatory effect of CTLA4-Ig in the presence of inflammatory cytokines and promotes long-term survival of murine cardiac allografts. Am J Transplant. 2021;21:2675–87. + + + + + + + 42216334 + + 2026 + 05 + 30 + + + 2026 + 05 + 30 + +
+ + 1536-5964 + + 105 + 22 + + 2026 + May + 29 + + + Medicine + Medicine (Baltimore) + + Machine learning-based identification of multidimensional predictors of quality of life in individuals with multiple sclerosis. + + e49025 + e49025 + + 10.1097/MD.0000000000049025 + + This study aimed to identify independent predictors of quality of life in patients with multiple sclerosis (MS) using machine learning approaches. One hundred and one individuals diagnosed with MS were included in this cross-sectional study. Quality of life was assessed using the MS Quality of Life-54 (MSQoL-54). Demographic variables, clinical characteristics, disability level (Expanded Disability Status Scale [EDSS]), fatigue severity, sleep quality (Pittsburgh Sleep Quality Index), depression level (Beck Depression Inventory), and functional mobility (Timed Up and Go test) were evaluated. Multiple machine learning regression models, including Linear Regression, Lasso, Elastic Net, Support Vector Machines, Random Forest, and XGBoost, were developed and compared. Five-times repeated five-fold cross-validation was applied for internal validation. Model performance was evaluated using root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Among all models, Lasso regression demonstrated the best predictive performance (RMSE = 5.02, MAE = 4.04, R2 = 0.86). Disability level (EDSS) emerged as the strongest negative predictor of quality of life, followed by fatigue severity, sleep quality, and impaired functional mobility. Although disease duration was not significantly correlated with quality of life in univariate analyses, it showed a positive contribution in the multivariable model. Depression showed a strong bivariate association but had a relatively lower weight in the final predictive model. Quality of life in individuals with MS is strongly influenced by disability, fatigue, sleep quality, and functional mobility. Machine learning approaches provide an effective and interpretable framework for identifying key determinants of quality of life and may support personalized rehabilitation and clinical decision-making in MS. + Copyright © 2026 the Author(s). Published by Wolters Kluwer Health, Inc. + + + + Yetiş + Aysu + A + 0000-0003-2139-0848 + + Faculty of Medicine, Department of Neurology, Kirşehir Ahi Evran University, Kirşehir, Turkey. + + + + Canli + Mehmet + M + 0000-0002-8868-9599 + + School of Physical Therapy and Rehabilitation, Kirşehir Ahi Evran University, Kirşehir, Turkey. + + + + Canli + İrem + İ + 0000-0003-4478-3660 + + School of Physical Therapy and Rehabilitation, Kirşehir Ahi Evran University, Kirşehir, Turkey. + + + + Kocaman + Hikmet + H + 0000-0001-5971-7274 + + Faculty of Health Sciences, Deparment of Physiotherapy and Rehabilitation, Karamanoglu Mehmetbey University, Karaman, Turkey. + + + + Yildirim + Hasan + H + 0000-0003-4582-9018 + + Faculty of Kamil Özdağ Science, Department of Mathematics, Karamanoğlu Mehmetbey University, Karaman, Turkey. + + + + Yildiz + Nazim Tolgahan + NT + 0000-0002-2404-2884 + + Faculty of Health Sciences, Deparment of Physiotherapy and Rehabilitation, Karamanoglu Mehmetbey University, Karaman, Turkey. + + + + Duran + Selcen + S + 0000-0001-7553-2034 + + Faculty of Medicine, Department of Neurology, Kirşehir Ahi Evran University, Kirşehir, Turkey. + + + + eng + + Journal Article + +
+ + United States + Medicine (Baltimore) + 2985248R + 0025-7974 + + IM + + + Humans + + + Quality of Life + + + Multiple Sclerosis + psychology + physiopathology + + + Female + + + Machine Learning + + + Cross-Sectional Studies + + + Male + + + Adult + + + Middle Aged + + + Fatigue + + + Random Forest + + + Boosting Machine Learning Algorithms + + + Predictive Learning Models + + + Depression + + + Sleep Quality + + + Disability Evaluation + + + Severity of Illness Index + + + Prediction Algorithms + + + + disability + machine learning + multiple sclerosis + quality of life + + The authors have no funding and conflicts of interest to disclose. + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 3 + 13 + + + 2026 + 5 + 8 + + + 2026 + 5 + 30 + 1 + 0 + + + ppublish + + 42216334 + 10.1097/MD.0000000000049025 + 00005792-202605290-00020 + + + + Özüdoğru A, Canli M, Gürses A, Alkan H, Yetiş A. Determination of five times-sit-to-stand test performance in patients with multiple sclerosis: validity and reliability. Somatosens Mot Res. 2023;40:72–7. + + + Kargarfard M, Eetemadifar M, Mehrabi M, Maghzi A, Hayatbakhsh M. Fatigue, depression, and health‐related quality of life in patients with multiple sclerosis in Isfahan, Iran. Eur J Neurol. 2012;19:431–7. + + + Opara J, Jaracz K, Brola W. Burden and quality of life in caregivers of persons with multiple sclerosis. Neurol Neurochir Pol. 2012;46:472–9. + + + Kes VB, Cengić L, Cesarik M, et al. Quality of life in patients with multiple sclerosis. Acta Clin Croat. 2013;52:107–10. + + + Tan H, Yu J, Tabby D, Devries A, Singer J. Clinical and economic impact of a specialty care management program among patients with multiple sclerosis: a cohort study. Mult Scler. 2010;16:956–63. + + + Morgante L. Hope in multiple sclerosis: a nursing perspective. Int J MS Care. 2000;2:9–15. + + + Bishop M, Stenhoff DM, Shepard L. Psychosocial adaptation and quality of life in multiple sclerosis: assessment of the disability centrality model. J Rehabil. 2007;73:3–12. + + + Blanco-Rivera C, Capeans-Tome C, Otero-Cepeda X. Quality of life in patients with choroidal melanoma. Arch Soc Esp Oftalmol. 2008;83:301–6. + + + Benedict RH, Wahlig E, Bakshi R, et al. Predicting quality of life in multiple sclerosis: accounting for physical disability, fatigue, cognition, mood disorder, personality, and behavior change. J Neurol Sci. 2005;231:29–34. + + + Miller A, Dishon S. Health-related quality of life in multiple sclerosis: the impact of disability, gender and employment status. Qual Life Res. 2006;15:259–71. + + + Göksel Karatepe A, Kaya T, Günaydn R, Demirhan A, Ce P, Gedizlioğlu M. Quality of life in patients with multiple sclerosis: the impact of depression, fatigue, and disability. Int J Rehabil Res. 2011;34:290–8. + + + Domingo S, Kinzy T, Thompson N, Gales S, Stone L, Sullivan A. Factors associated with sexual dysfunction in individuals with multiple sclerosis: implications for assessment and treatment. Int J MS Care. 2018;20:191–7. + + + Amtmann D, Askew RL, Kim J, et al. Pain affects depression through anxiety, fatigue, and sleep in multiple sclerosis. Rehabil Psychol. 2015;60:81–90. + + + Zivadinov R, Sepcic J, Nasuelli D, et al. A longitudinal study of brain atrophy and cognitive disturbances in the early phase of relapsing-remitting multiple sclerosis. J Neurol Neurosurg Psychiatry. 2001;70:773–80. + + + Strober L. Quality of life and psychological well-being in the early stages of multiple sclerosis (MS): importance of adopting a biopsychosocial model. Disabil Health J. 2018;11:555–61. + + + Yildiz NT, Kocaman H, Yildirim H, Canli M. An investigation of machine learning algorithms for prediction of temporomandibular disorders by using clinical parameters. Medicine (Baltimore). 2024;103:e39912. + + + Yetiş M, Kocaman H, Canli M, Yildirim H, Yetiş A, Ceylan I. Carpal tunnel syndrome prediction with machine learning algorithms using anthropometric and strength-based measurement. PLoS One. 2024;19:e0300044. + + + Polman CH, Reingold SC, Banwell B, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011;69:292–302. + + + Cockrell JR, Folstein MF. Mini-mental state examination. In: Copeland JRM, Abou-Saleh MT, Blazer DG, eds. Principles and Practice of Geriatric Psychiatry. 2nd ed. John Wiley & Sons; 2002:140–1. + + + Idiman E, Uzunel F, Ozakbas S, et al. Cross-cultural adaptation and validation of multiple sclerosis quality of life questionnaire (MSQOL-54) in a Turkish multiple sclerosis sample. J Neurol Sci. 2006;240:77–80. + + + Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33:1444–52. + + + Armutlu K, Korkmaz NC, Keser I, et al. The validity and reliability of the Fatigue Severity Scale in Turkish multiple sclerosis patients. Int J Rehabil Res. 2007;30:81–5. + + + Buysse DJ, Reynolds CF III, Monk TH, Berman SR, Kupfer DJ. The pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193–213. + + + Agargun M, Kara H, Anlar O. The validity and reliability of the pittsburgh sleep quality index. Turk J Psychiatry. 1996;7:107–15. + + + Nilsagard Y, Lundholm C, Gunnarsson LG, Denison E. Clinical relevance using timed walk tests and ‘timed up and go’testing in persons with multiple sclerosis. Physiother Res Int. 2007;12:105–14. + + + Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatry. 1961;4:561–71. + + + Hisli N. Beck depresyon envanterinin universite ogrencileri icin gecerliligi, guvenilirligi. (A reliability and validity study of Beck Depression Inventory in a university student sample). J Psychol. 1989;7:3–13. + + + Lobentanz IS, Asenbaum S, Vass K, et al. Factors influencing quality of life in multiple sclerosis patients: disability, depressive mood, fatigue and sleep quality. Acta Neurol Scand. 2004;110:6–13. + + + Janardhan V, Bakshi R. Quality of life in patients with multiple sclerosis: the impact of fatigue and depression. J Neurol Sci. 2002;205:51–8. + + + Kamran F, Samaei A, Asghari N, et al. The associations between fatigue, disability, and mobility and the quality of life in patients with multiple sclerosis. Middle East J Rehabil Health. 2016;3:e3437. + + + Fruewald S, Loeffler‐Stastka H, Eher R, Saletu B, Baumhacki U. Depression and quality of life in multiple sclerosis. Acta Neurol Scand. 2001;104:257–61. + + + Benito‐Leon J, Morales J, Rivera‐Navarro J. Health‐related quality of life and its relationship to cognitive and emotional functioning in multiple sclerosis patients. Eur J Neurol. 2002;9:497–502. + + + Ford HL, Gerry E, Johnson MH, Tennant A. Health status and quality of life of people with multiple sclerosis. Disabil Rehabil. 2001;23:516–21. + + + + + + + 42216333 + + 2026 + 05 + 30 + + + 2026 + 05 + 30 + +
+ + 1536-5964 + + 105 + 22 + + 2026 + May + 29 + + + Medicine + Medicine (Baltimore) + + Machine learning identifies traffic-related pollutant mixtures as potential factors suggestive of association with osteoporosis in a cross-sectional analysis of NHANES. + + e48884 + e48884 + + 10.1097/MD.0000000000048884 + + The combined effect of traffic-related pollutant mixtures on osteoporosis (OP) remains unclear. This study aimed to evaluate such associations using machine learning and mixture modeling approaches. A cross-sectional analysis was conducted among 3053 participants from the National Health and Nutrition Examination Survey 2015 to 2018. Eighteen exposures were assessed, including heavy metals, polycyclic aromatic hydrocarbons, phthalates, per- and polyfluoroalkyl substances, volatile organic compounds, diesel exhaust, and behavioral factors. OP was defined by femoral neck bone mineral density T-score ≤-2.5. Weighted quantile sum regression and 5 machine learning models were applied, accounting for complex survey design. OP prevalence was 12.97%. Weighted quantile sum regression showed a positive but nonsignificant mixture odds ratio (odds ratio = 1.21; 95% confidence interval: 0.77-2.63; P = .412). The largest positive weights were daily outdoor work duration (0.240) and occupational diesel exhaust (0.136). Random forest identified female sex, age, vitamin D, blood lead, and body mass index as top predictors. Least Absolute Shrinkage and Selection Operator achieved the highest test area under the receiver operating characteristic curve (0.81) with the lowest cross-validation standard deviation (0.0167). Its coefficients highlighted occupational diesel exhaust (β = 0.40) as a notable environmental factor alongside female sex (β = 0.95) and age (β = 0.70). Traffic-related pollutant mixtures showed a cross-sectional association with OP in a nationally representative US population, with occupational diesel exhaust and prolonged outdoor work emerging as key contributors in mixture models. However, the nonsignificant overall mixture effect and potential for reverse causation warrant caution in interpretation. These findings support considering environmental and occupational factors in OP prevention, although the overall mixture effect did not reach statistical significance. + Copyright © 2026 the Author(s). Published by Wolters Kluwer Health, Inc. + + + + Wang + Yifei + Y + 0009-0009-4135-3009 + + Department of Orthopedics and Joint Surgery, The First Affiliated Hospital of Jinan University, First Clinical Medical College of Jinan University, Guangzhou, Guangdong, China. + + + + Deng + Ming + M + + Department of Orthopedics and Joint Surgery, The First Affiliated Hospital of Jinan University, First Clinical Medical College of Jinan University, Guangzhou, Guangdong, China. + + + + Xu + Yali + Y + + Department of Orthopedics and Joint Surgery, The First Affiliated Hospital of Jinan University, First Clinical Medical College of Jinan University, Guangzhou, Guangdong, China. + + + + Zha + Zhengang + Z + 0000-0003-4851-3297 + + Department of Orthopedics and Joint Surgery, The First Affiliated Hospital of Jinan University, First Clinical Medical College of Jinan University, Guangzhou, Guangdong, China. + + + + Feng + Ying + Y + + Department of Orthopedics and Joint Surgery, The First Affiliated Hospital of Jinan University, First Clinical Medical College of Jinan University, Guangzhou, Guangdong, China. + + + + Zhuang + Tengfeng + T + + Department of Orthopedics and Joint Surgery, The First Affiliated Hospital of Jinan University, First Clinical Medical College of Jinan University, Guangzhou, Guangdong, China. + + + + He + Jiaqing + J + + Department of Orthopedics and Joint Surgery, The First Affiliated Hospital of Jinan University, First Clinical Medical College of Jinan University, Guangzhou, Guangdong, China. + + + + Peng + Hui + H + + Department of Orthopedics and Joint Surgery, The First Affiliated Hospital of Jinan University, First Clinical Medical College of Jinan University, Guangzhou, Guangdong, China. + + + + Wu + Wenrui + W + + Department of Traumatic Orthopedics, The First Affiliated Hospital, Jinan University, Guangzhou, Guangdong, China. + + + Department of Orthopedics, Chaoshan Hospital of The First Affiliated Hospital of Jinan University, Chaozhou, Guangdong, China. + + + + eng + + Journal Article + +
+ + United States + Medicine (Baltimore) + 2985248R + 0025-7974 + + + + 0 + Vehicle Emissions + + + 0 + Air Pollutants + + + IM + + + Humans + + + Cross-Sectional Studies + + + Female + + + Machine Learning + + + Nutrition Surveys + + + Osteoporosis + epidemiology + etiology + chemically induced + + + Male + + + Middle Aged + + + Vehicle Emissions + analysis + toxicity + + + Adult + + + United States + epidemiology + + + Air Pollutants + adverse effects + analysis + + + Environmental Exposure + adverse effects + + + Bone Density + + + Traffic-Related Pollution + adverse effects + + + + NHANES + bone health + environmental mixtures + machine learning + occupational diesel exhaust + osteoporosis + traffic-related air pollution + + The authors have no conflicts of interest to disclose. + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 2 + 4 + + + 2026 + 4 + 29 + + + 2026 + 5 + 30 + 1 + 0 + + + ppublish + + 42216333 + 10.1097/MD.0000000000048884 + 00005792-202605290-00019 + + + + Jeddi MZ, et al. Environmental exposure to endocrine-disrupting chemicals and osteoporosis: a systematic review. Osteoporos Int. 2021;32:2397–412. + + + Compston JE, McClung MR, Leslie WD. Osteoporosis. Lancet. 2019;393:364–76. + + + Johnell O, Kanis JA. An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int. 2006;17:1726–33. + + + Wright NC, Looker AC, Saag KG, et al. The recent prevalence of osteoporosis and low bone mass in the United States based on bone mineral density at the femoral neck or lumbar spine. J Bone Miner Res. 2014;29:2520–6. + + + Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A, Tosteson A. Incidence and economic burden of osteoporosis-related fractures in the United States, 2005-2025. J Bone Miner Res. 2007;22:465–75. + + + Abrahamsen B, van Staa T, Ariely R, Olson M, Cooper C. Excess mortality following hip fracture: a systematic epidemiological review. Osteoporos Int. 2009;20:1633–50. + + + Kanis JA, McCloskey EV, Johansson H, Oden A, Ström O, Borgström F. Development and use of FRAX® in osteoporosis. Osteoporos Int. 2010;21(Suppl 2):S407–13. + + + Liu CT, Yu YH, Estrada K, et al. Metabolomics insights into osteoporosis through association with bone mineral density. J Bone Miner Res. 2021;36:245–56. + + + Mahdavi SB, Zamani S, Riahi R, et al. Exposure to environmental chemicals and human bone health: a systematic review and meta-analysis. Exposure Health. 2023;15:807–22. + + + Rizzoli R, Biver E, Brennan-Speranza TC. Nutritional intake and bone health. Lancet Diabetes Endocrinol. 2021;9:606–21. + + + Prada D, Colicino E, Power MC, et al. Influence of chemical and environmental exposures on the risk of osteoporosis: a review of the literature. Curr Environ Health Rep. 2020;7:337–48. + + + Kelly FJ, Fussell JC. Air pollution and public health: emerging hazards and improved understanding of risk. Environ Geochem Health. 2015;37:631–49. + + + Raisz LG. Pathogenesis of osteoporosis: concepts, conflicts, and prospects. J Clin Invest. 2005;115:3318–25. + + + Manolagas SC. From estrogen-centric to aging and oxidative stress: a revised perspective of the pathogenesis of osteoporosis. Endocr Rev. 2010;31:266–300. + + + Pounds JG, Long GJ, Rosen JF. Cellular and molecular toxicity of lead in bone. Environ Health Perspect. 1991;91:17–32. + + + Campbell JR, Rosier RN, Novotny L, Puzas JE. The association between environmental lead exposure and bone density in children. Environ Health Perspect. 2004;112:1200–3. + + + Engström A, Michaëlsson K, Suwazono Y, Wolk A, Vahter M, Akesson A. Long-term cadmium exposure and the association with bone mineral density and fractures in a population-based study among women. J Bone Miner Res. 2011;26:486–95. + + + Gallagher CM, Kovach JS, Meliker JR. Urinary cadmium and osteoporosis in U.S. women ≥50 years of age: NHANES 1988-1994 and 1999-2004. Environ Health Perspect. 2008;116:1338–43. + + + Prada D, Zhong J, Colicino E, et al. Association of air particulate pollution with bone loss over time and bone fracture risk: analysis of data from two independent studies. Lancet Planet Health. 2017;1:e337–47. + + + Nemmar A, Al-Salam S, Zia S, et al. Diesel engine exhaust initiates a sequence of pulmonary and cardiovascular effects in rats. J Toxicol. 2010;2010:206057. + + + Vaishnavi N, Sekaran S, Suresh Babu R, et al. Diesel exhaust particles modulate bone remodeling and worsen osteoporosis: in vitro and in vivo investigations. Nanoscale Adv. 2025;7:4577–90. + + + Braun JM, Gennings C, Hauser R, Webster TF. What can epidemiological studies tell us about the impact of chemical mixtures on human health? Environ Health Perspect. 2016;124:A6–9. + + + Carlin DJ, Rider CV, Woychik R, Birnbaum LS. Unraveling the health effects of environmental mixtures: an NIEHS priority. 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EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12142 + + + Boffetta P, Dosemeci M, Gridley G, Bath H, Moradi T, Silverman D. Occupational exposure to diesel engine emissions and risk of cancer in Swedish men and women. Cancer Causes Control. 2001;12:365–74. + + + Rappaport SM, Kupper LL. Variability of environmental exposures to volatile organic compounds. J Expo Anal Environ Epidemiol. 2004;14:92–107. + + + + + + + 42216320 + + 2026 + 05 + 30 + + + 2026 + 05 + 30 + +
+ + 1536-5964 + + 105 + 22 + + 2026 + May + 29 + + + Medicine + Medicine (Baltimore) + + Association between fat mass index and depressive symptoms in U.S. adults: A cross-sectional analysis of NHANES 2011-2016. + + e49130 + e49130 + + 10.1097/MD.0000000000049130 + + Fat mass index (FMI) reflects adiposity normalized for height and may provide information beyond body mass index. Evidence on its relationship with depressive symptoms in U.S. adults remains limited. This cross-sectional study used data from adults participating in National Health and Nutrition Examination Survey 2011 to 2016. Fat mass index was calculated from dual-energy X-ray absorptiometry-derived fat mass divided by height squared. Depressive symptoms were defined using the Patient Health Questionnaire-9. Survey-weighted logistic regression, restricted cubic spline analysis, subgroup analyses, and machine-learning models were applied to evaluate the association and predictive performance. The final analysis included 7273 participants. After full adjustment, FMI was positively associated with depressive symptoms, with an odds ratio (OR) of 1.05 for each 1-unit increment (95% confidence interval [CI]: 1.03-1.08). In the quartile-based analysis, participants in Q4 showed greater odds of depressive symptoms compared with those in Q1 (OR = 1.50, 95% CI: 1.12-2.01). Restricted cubic spline analysis suggested an approximately linear positive association. In exploratory prediction models, FMI showed a higher AUC than body mass index, although the overall discrimination remained moderate. Higher FMI was associated with a higher prevalence of depressive symptoms among U.S. adults. FMI may serve as a useful adiposity-related indicator in epidemiological research, but prospective studies are needed before clinical predictive use can be recommended. + Copyright © 2026 the Author(s). Published by Wolters Kluwer Health, Inc. + + + + Wu + Haixia + H + 0009-0004-2047-5852 + + Department of Breast Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China. + + + + Yang + Shangjin + S + + Department of Gastrointestinal Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China. + + + + Chen + Cheng + C + + Department of Breast Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China. + + + + Wang + Xin + X + + Department of Breast Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China. + + + + Chen + Wenlin + W + + The Third Department of Breast Surgery, Peking University Cancer Hospital Yunnan, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China. + + + + Ge + Fei + F + 0000-0002-1561-2785 + + Department of Breast Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China. + + + + eng + + Journal Article + +
+ + United States + Medicine (Baltimore) + 2985248R + 0025-7974 + + IM + + + Humans + + + Cross-Sectional Studies + + + Female + + + Nutrition Surveys + + + Depression + epidemiology + + + United States + epidemiology + + + Body Mass Index + + + Male + + + Adult + + + Middle Aged + + + Absorptiometry, Photon + + + Adiposity + physiology + + + Logistic Models + + + + NHANES + Random Forest + XGBoost + depression + fat mass index + + The authors have no funding and conflicts of interest to disclose. + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2025 + 10 + 26 + + + 2026 + 5 + 15 + + + 2026 + 5 + 30 + 1 + 0 + + + ppublish + + 42216320 + 10.1097/MD.0000000000049130 + 00005792-202605290-00006 + + + + Kim J-W, Suzuki K, Kavalali ET, Monteggia LM. Ketamine: mechanisms and relevance to treatment of depression. Annu Rev Med. 2024;75:129–43. + + + Herrman H, Patel V, Kieling C, et al. Time for united action on depression: a Lancet-world psychiatric association commission. 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Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: a multimodal, multisite machine learning analysis. JAMA Psychiatry. 2018;75:1156–72. + + + + + + + 42216309 + + 2026 + 05 + 30 + +
+ + 1521-4095 + + + 2026 + May + 29 + + + Advanced materials (Deerfield Beach, Fla.) + Adv Mater + + From Single Motif to Active Ensembles: Phase-Controlled Co8 Cluster Catalysis on MoS2 for Nitrogen Electroreduction. + + e73521 + e73521 + + 10.1002/adma.73521 + + Atomic-scale metal clusters, which bridge the gap between nanoparticles and single-atom catalysts, show pronounced structure-sensitive reactivity that is strongly governed by the support. Here, we reveal that the crystal phase of a two-dimensional support-metallic 1T versus semiconducting 2H MoS2-directs the structure and electrochemical nitrogen reduction (eNRR) performance of supported Co8 clusters via machine-learning-potential-accelerated multiscale simulations. On 1T-MoS2, strong metal-support interactions act as a structural converger, locking Co8 into a single dominant operando active motif. In contrast, 2H-MoS2 behaves as a structural diverger, stabilizing a thermodynamically broad ensemble of active motifs under reaction conditions. This phase-controlled structural diversity translates directly into a function: Co8N7H12/2H-MoS2 hosts a larger population of intrinsically active motifs and delivers an ammonia production rate significantly higher than that of Co8N7H14/1T-MoS2 while concurrently suppressing hydrogen evolution. Electronic structure analysis identified the total Bader charge of the cluster across different exposed active sites as a key descriptor that was exponentially correlated with the N2H2 hydrogenation barrier and turnover frequency. These findings establish a crystal phase-structure-charge-function causal chain and highlight support phase engineering-specifically, the deliberate promotion of structural diversity-as a general strategy that can be extended to other catalytic reactions to increase activity. + © 2026 Wiley‐VCH GmbH. + + + + Zhao + Jian-Wen + JW + + State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China. + + + + Wei + Wan-Yao + WY + + State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China. + + + Hefei National Laboratory, University of Science and Technology of China, Hefei, China. + + + + Li + Wei-Xue + WX + + State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China. + + + Hefei National Laboratory, University of Science and Technology of China, Hefei, China. + + + + Liu + Jin-Xun + JX + + State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China. + + + Hefei National Laboratory, University of Science and Technology of China, Hefei, China. + + + + eng + + + 2021YFA1502804 + Key Technologies R&D Program of China + + + + 22172150 + National Natural Science Foundation of China + + + + 22222306 + National Natural Science Foundation of China + + + + 22221003 + National Natural Science Foundation of China + + + + 22432004 + National Natural Science Foundation of China + + + + 2021ZD0303302 + Quantum Science and Technology-National Science and Technology Major Project + + + + + Journal Article + + + 2026 + 05 + 29 + +
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+ + 1462-2920 + + 28 + 6 + + 2026 + Jun + + + Environmental microbiology + Environ Microbiol + + Predicting Bacterial Radiation Resistance Through Identifying Novel Genomic Features. + + e70338 + e70338 + + 10.1111/1462-2920.70338 + + Radiation resistance in bacteria is a critical trait with implications for biotechnology, medicine and environmental science. Deinococcus species possess unique genomic features that enable their extraordinary survival in extreme environments. However, the diversity of radiation resistance mechanisms across bacteria remains poorly explored. Here, we analysed the genomic diversity and functional gene repertoire of Deinococcus and extended the analysis to a broader set of radiation-resistant bacteria. We found that highly resistant strains tend to have smaller genomes and harbour more genes positively associated with radiation resistance, highlighting their potential roles in sustaining cellular homeostasis under radiation-induced stress. By integrating machine learning with curated genomic datasets, we identified key resistance-associated gene signatures and developed a predictive tool to quantify bacterial radiation resistance. This study offers new insights into the genomic basis of radiation resistance and provides a practical framework for its assessment in environmental and microbial engineering applications. + © 2026 John Wiley & Sons Ltd. + + + + Liu + Jiayu + J + 0009-0002-9544-6330 + + College of Food Science and Light Industry, Nanjing Tech University, Nanjing, China. + + + College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China. + + + + Fang + Chao + C + 0009-0002-0121-0280 + + College of Food Science and Light Industry, Nanjing Tech University, Nanjing, China. + + + + Ji + Boyang + B + 0000-0002-7269-4342 + + BioInnovation Institute, Copenhagen, Denmark. + + + + Zhang + Tongrong + T + 0009-0004-0491-3824 + + College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, China. + + + + Li + Lin + L + 0009-0009-1849-3003 + + College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, China. + + + + Zhu + Liying + L + 0000-0002-2497-1624 + + College of Chemical and Molecular Engineering, Nanjing Tech University, Nanjing, China. + + + + Zhang + Zhidong + Z + 0000-0001-8511-5867 + + Xinjiang Key Laboratory of Special Environmental Microbiology, Institute of Microbiology, Xinjiang Academy of Agricultural Sciences, Urumqi, China. + + + + Zhu + Zhengming + Z + 0000-0002-4177-2416 + + College of Food Science and Light Industry, Nanjing Tech University, Nanjing, China. + + + + Jiang + Ling + L + 0000-0001-6625-5557 + + College of Food Science and Light Industry, Nanjing Tech University, Nanjing, China. + + + State Key Laboratory of Materials-Oriented Chemical Engineering, Nanjing Tech University, Nanjing, China. + + + + eng + + + 32472489 + National Natural Science Foundation of China + + + + 22578212 + National Natural Science Foundation of China + + + + U2106228 + National Natural Science Foundation of China + + + + 2024MD753948 + China Postdoctoral Science Foundation + + + + BK20233003 + Basic Research Program of Jiangsu Province + + + + + Journal Article + +
+ + England + Environ Microbiol + 100883692 + 1462-2912 + + IM + + + Deinococcus + genetics + radiation effects + + + Genome, Bacterial + + + Radiation Tolerance + genetics + + + Genomics + + + + Deinococcus + comparative genomics + genomic features + radiation resistance + + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 4 + 18 + + + 2025 + 11 + 11 + + + 2026 + 5 + 20 + + + 2026 + 5 + 30 + 0 + 53 + + + ppublish + + 42216307 + 10.1111/1462-2920.70338 + + + + Agostini, H. J., J. D. Carroll, and K. W. Minton. 1996. “Identification and Characterization of uvrA, a DNA Repair Gene of Deinococcus radiodurans.” Journal of Bacteriology 178: 6759–6765. + + + Aridhi, S., H. Sghaier, M. Zoghlami, M. Maddouri, and E. M. Nguifo. 2016. “Prediction of Ionizing Radiation Resistance in Bacteria Using a Multiple Instance Learning Model.” Journal of Computational Biology 23: 10–20. + + + Aw, A. 1956. “Studies on a Radio‐Resistant Micrococcus. I. 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+ + 1563-5279 + + + 2026 + May + 29 + + + The International journal of neuroscience + Int J Neurosci + + Proteomic and transcriptomic analyses identify the role of RBM25 in the malignant progression of glioma. + + 1 + 16 + 1-16 + + 10.1080/00207454.2026.2681886 + + Glioma is a primary tumor derived from central nervous system glial cells. RNA binding motif protein 25 (RBM25) has been implicated in glioma progression, yet its underlying molecular mechanism remains incompletely understood. In this study, glioma-related datasets were retrieved from the Proteomic Data Commons (PDC) and Gene Expression Omnibus (GEO) databases. Core glioma-associated targets were screened using machine learning algorithms. Protein expression was assessed by Western blot, while migration, and invasion, cell cycle and apoptosis were analyzed by Transwell assay and flow cytometry. Chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays were used to validate the interaction between transcription factors and genes. Proteomic analysis revealed distinct differences between glioma and control groups, with differentially expressed proteins mainly enriched in mitochondrial energy metabolism, synaptic function, and neurodegenerative disease pathways. Transcriptomic profiles also exhibited significant alterations, with RBM25 and HSPA8 showing consistent changes at both protein and RNA levels. Multiple machine learning models identified RBM25, NKTR, MAP2K6, TRAPPC3, and HSPA8 as key genes associated with glioma, with RBM25 and NKTR showing strong relevance in model-based feature selection. RBM25 was upregulated in glioma and linked to neuronal signaling pathways, whereas HSPA8 and TRAPPC3 were downregulated. RBM25 knockdown suppressed glioma cell proliferation, migration, and invasion, and induced cell cycle arrest and apoptosis. In conclusion, RBM25 contributes to glioma malignancy and may serve as a potential biomarker and therapeutic target for glioma. + + + + Xu + Xiangdi + X + + Department of Neurosurgery, Xingtai People's Hospital, Xingtai, China. + + + + Wang + Jiahao + J + + Department of Neurosurgery, Xingtai People's Hospital, Xingtai, China. + + + + Xue + Mingming + M + + Department of Neurosurgery, Xingtai People's Hospital, Xingtai, China. + + + + Feng + Longkai + L + + Department of Neurosurgery, Xingtai People's Hospital, Xingtai, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Int J Neurosci + 0270707 + 0020-7454 + + IM + + Glioma + Machine learning + Proteomic + RNA binding motif protein 25 + Transcriptomics + + + + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 5 + 30 + 0 + 52 + + + aheadofprint + + 42216302 + 10.1080/00207454.2026.2681886 + + + + + + 42216239 + + 2026 + 05 + 30 + + + 2026 + 05 + 30 + +
+ + 1582-4934 + + 30 + 11 + + 2026 + Jun + + + Journal of cellular and molecular medicine + J Cell Mol Med + + Integration of Transcriptomics With Interpretable Artificial Intelligence for Identifying Molecular Signatures of Physiological Stress in Sleep Deprivation. + + e71211 + e71211 + + 10.1111/jcmm.71211 + + Sleep deprivation induces systemic physiological stress accompanied by transcriptomic remodelling and immune dysregulation, yet objective molecular indicators for its assessment remain insufficient. This study integrated blood transcriptomic analysis with an interpretable machine learning framework to identify and validate candidate molecular signatures associated with sleep deprivation and their potential relevance to insomnia. Publicly available Gene Expression Omnibus datasets were used to construct an acute sleep deprivation training cohort, an independent sleep deprivation validation cohort, and a chronic insomnia validation cohort. Differentially expressed genes were first identified, followed by feature selection using six machine learning algorithms and Shapley additive explanations to improve model interpretability. Immune cell composition was inferred using CIBERSORT, and associations between candidate genes and immune cell subsets were further evaluated. Twenty-five differentially expressed genes were identified in the training cohort, from which eight high-priority candidate genes were selected by the interpretable machine learning framework. Among them, S100A3 showed consistent discriminatory performance across the training cohort, the independent sleep deprivation cohort, and the insomnia cohort, whereas VEGFB exhibited notable diagnostic potential, particularly in insomnia. Immune infiltration analysis indicated that sleep deprivation was associated with altered peripheral immune composition, including reduced resting natural killer cells and activated dendritic cells, together with changes in regulatory and naïve immune cell populations. Expression levels of S100A3 and VEGFB were significantly correlated with specific immune cell subsets, suggesting a link between these molecular signatures and stress-related immunomodulation. These findings identify S100A3 as a robust candidate biomarker shared by acute sleep deprivation and chronic insomnia, while VEGFB may reflect chronic metabolic or inflammatory adaptation. The proposed interpretable transcriptomic-machine learning framework provides a non-invasive strategy for discovering molecular indicators of sleep-related physiological stress and may support future risk stratification in sleep medicine. + © 2026 The Author(s). Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd. + + + + Wang + Kun + K + + The Wenzhou Third Clinical Institute Affiliated, Wenzhou Medical University, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Institute UCAS, Wenzhou, Zhejiang, China. + + + + Zong + Qiang + Q + + Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China. + + + + Wang + Chengcheng + C + + Department of Public Health and Health Management, Clinical College of, Anhui Medical University, Hefei, Anhui, China. + + + + Wang + Peng + P + + The Wenzhou Third Clinical Institute Affiliated, Wenzhou Medical University, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Institute UCAS, Wenzhou, Zhejiang, China. + + + + Shuai + Zhenhao + Z + + The Wenzhou Third Clinical Institute Affiliated, Wenzhou Medical University, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Institute UCAS, Wenzhou, Zhejiang, China. + + + Department of Computer Science and Technology, Xiamen University, Xiamen, Fujian, China. + + + + Wu + Min + M + + The Wenzhou Third Clinical Institute Affiliated, Wenzhou Medical University, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Institute UCAS, Wenzhou, Zhejiang, China. + + + + Peng + Yuming + Y + + Department of Geriatrics, Central Hospital of Karamay, Xinjiang, China. + + + + Zhou + Junying + J + 0000-0001-5060-3879 + + Sleep Medicine Centre, West China Hospital, Sichuan University, Chengdu, Sichuan, China. + + + + Shuai + Jianwei + J + 0000-0002-8712-0544 + + The Wenzhou Third Clinical Institute Affiliated, Wenzhou Medical University, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Institute UCAS, Wenzhou, Zhejiang, China. + + + + Ye + Fangfu + F + + The Wenzhou Third Clinical Institute Affiliated, Wenzhou Medical University, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Institute UCAS, Wenzhou, Zhejiang, China. + + + + Wu + Aimin + A + 0000-0002-8582-4599 + + Department of Orthopaedics, Key Laboratory of Orthopaedics of Zhejiang Province, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China. + + + + Zheng + Yanyan + Y + + The Wenzhou Third Clinical Institute Affiliated, Wenzhou Medical University, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Institute UCAS, Wenzhou, Zhejiang, China. + + + + eng + + + U24A2014 + National Natural Science Foundation of China + + + + LTGY23H090014 + Natural Science Foundation of Zhejiang Province + + + + 2020YFC2005605 + National Key Research and Development Program of China + + + + TSYC202301A076 + Xinjiang Tianshan Talents Project + + + + 2021E02080 + Xinjiang Regional Collaborative Innovation Project + + + + STI2030-Major Projects2021ZD0201900 + Ministry of Science and Technology of the People's Republic of China + + + + 2025HY1108 + General Project of Zhejiang Provincial Health Science and Technology Plan (Wenzhou) + + + + ZY2022024 + Major Science and Technology Project of Wenzhou Municipal Science and Technology Bureau + + + + + Journal Article + +
+ + England + J Cell Mol Med + 101083777 + 1582-1838 + + + + 0 + Biomarkers + + + IM + + + Sleep Deprivation + genetics + + + Humans + + + Stress, Physiological + genetics + + + Gene Expression Profiling + methods + + + Transcriptome + genetics + + + Artificial Intelligence + + + Machine Learning + + + Sleep Initiation and Maintenance Disorders + genetics + + + Biomarkers + + + + S100A3 + feature genes + immune infiltration + machine learning + sleep deprivation + + + + + + 2026 + 5 + 30 + 6 + 37 + + + 2026 + 5 + 30 + 6 + 36 + + + 2026 + 4 + 27 + + + 2026 + 3 + 12 + + + 2026 + 5 + 11 + + + 2026 + 5 + 30 + 0 + 2 + + + ppublish + + 42216239 + 10.1111/jcmm.71211 + + + + A. D. Laposky, E. Van Cauter, and A. V. Diez‐Roux, “Reducing Health Disparities: The Role of Sleep Deficiency and Sleep Disorders,” Sleep Medicine 18 (2016): 3–6, https://doi.org/10.1016/j.sleep.2015.01.007. + + + D. Riemann and U. 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Peters, R. Joehanes, L. C. Pilling, et al., “The Transcriptional Landscape of Age in Human Peripheral Blood,” Nature Communications 6 (2015): 8570, https://doi.org/10.1038/ncomms9570. + + + + + + + 42216193 + + 2026 + 05 + 29 + +
+ + 1742-4755 + + + 2026 + May + 29 + + + Reproductive health + Reprod Health + + Predicting short birth intervals in Bangladesh using stacked machine learning and SHAP explainability: evidence from BDHS 2022. + 10.1186/s12978-026-02367-0 + + Short birth interval (SBI) is a major public health concern, associated with adverse outcomes such as preterm birth, low birth weight, and maternal depletion. Identifying its influential predictors is essential for improving family planning and maternal health interventions. The study aimed to predict SBI and identify its influential predictors using stacked machine learning and SHAP explainability. + This study used data, obtained from the Bangladesh Demographic and Health Survey (BDHS), 2022. The dataset comprised 11,872 respondents, of whom 2,137 experienced SBI (18.0%). Class imbalance problem was addressed by applying class weighting during model training. Boruta and least absolute shrinkage and selection operator-based methods were applied to identify the most important predictors of SBI. Subsequently, multiple machine learning models (including logistic regression, random forest, extreme gradient boosting, categorical boosting, artificial neural network, and stacking ensemble) were used to predict SBI and evaluated their model performances using accuracy, precision, sensitivity, F1-score, and the area under the curve (AUC). Finally, SHAP explainability were employed to identify the most influential predictors of SBI. + The stacking ensemble model achieved the highest predictive performance compared to the individual models, with an accuracy of 65.8%, precision of 42.6%, sensitivity of 72.3%, F1-score of 53.6%, and modest AUC of approximately 0.667. The SHAP analysis showed that no educated respondents, higher parity, and do not intend to use contraceptive method were the most influential predictors of SBI. + Interventions could therefore focus on improving female education, expanding access to contraceptives, and promoting awareness of optimal birth spacing. Policymakers may incorporate SHAP explainability to support data-driven reproductive health strategies to reduce SBI in Bangladesh. + © 2026. The Author(s). + + + + Mia + Mostakim + M + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Akter + Shanti + S + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Sraboni + Takyeatun Nesa + TN + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Islam + Md Merajul + MM + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Tawabunnahar + Mst + M + + Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2224, Bangladesh. + + + + Maniruzamman + Md + M + + Statistics Discipline, Khulna University, Khulna, 9208, Bangladesh. + + + + Hossain + Md Golam + MG + + Health Research Group, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh. hossain95@yahoo.com. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Reprod Health + 101224380 + 1742-4755 + + IM + + Birth Spacing + Family Planning + Maternal and Child health + Model Interpretability + Predictive Modeling + + Declarations. Ethics approval and consent to participate: The survey was conducted in accordance with the Declaration of Helsinki, and the protocol for the 2022 BDHS received ethical clearance from both the ICF Institutional Review Board ethics committee and the Bangladesh Medical Research Council (BMRC). Informed written consent was obtained from each participant before commencement of the interview. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Conflict of interest: The authors declare that they have no competing interests. + + + + + 2025 + 10 + 14 + + + 2026 + 5 + 19 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 45 + + + aheadofprint + + 42216193 + 10.1186/s12978-026-02367-0 + 10.1186/s12978-026-02367-0 + + + + + + 42216185 + + 2026 + 05 + 29 + +
+ + 1758-2946 + + + 2026 + May + 29 + + + Journal of cheminformatics + J Cheminform + + Towards the design of artificial sensing materials via quantum-informed explainable AI. + 10.1186/s13321-026-01232-3 + + Computational design of sensing materials remains fundamentally challenging due to the vast configurational landscape and the absence of robust property correlations that enable efficient molecular representation. These challenges become particularly critical in healthcare applications, where reliable and interpretable molecular recognition is essential for non-invasive diagnostics. To address these challenges, we developed MORE-ML, a quantum-informed AI framework that combines electronic-structure-derived properties of e-nose molecular building blocks with machine learning (ML) methods to uncover sensing mechanisms and guide the design of new systems. Within this framework, we expanded our previous dataset, MORE-Q, to MORE-QX by sampling a larger conformational space of interactions between body odor volatilomes (BOV) molecules and mucin-derived receptors, both in the gas phase and when deposited on graphene. MORE-QX provides extensive electronic binding features (BFs) computed upon BOV adsorption. Analysis of the property space revealed weak correlations between quantum-mechanical (QM) properties of building blocks and resulting BFs. Leveraging this observation, we defined electronic descriptors of building blocks as inputs for tree-based ML models to predict BFs. Benchmarking showed CatBoost models outperform alternatives, especially in transferability to unseen compounds. Through explainable AI, we reduced the high-dimensional QM property space to a compact and physically interpretable set of descriptors, revealing the properties that most influence BF predictions. Collectively, MORE-ML combines QM insights with ML to provide mechanistic understanding and rational design principles for artificial sensing materials in BOV sensing. This approach establishes a foundation for advancing materials capable of analyzing complex odor mixtures, bridging the gap between molecular-level computations and practical e-nose applications. + © 2026. The Author(s). + + + + Chen + Li + L + + Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany. + + + + Medrano Sandonas + Leonardo + L + + Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany. leonardo.medrano@tu-dresden.de. + + + + Huang + Shirong + S + + Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany. + + + + Croy + Alexander + A + + Institute of Physical Chemistry, Friedrich Schiller University Jena, 07737, Jena, Germany. + + + + Cuniberti + Gianaurelio + G + + Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany. gianaurelio.cuniberti@tu-dresden.de. + + + Dresden Center for Computational Materials Science (DCMS), TUD Dresden University of Technology, 01062, Dresden, Germany. gianaurelio.cuniberti@tu-dresden.de. + + + Cluster of Excellence CARE, TUD Dresden University of Technology and RWTH Aachen, Dresden, Germany. gianaurelio.cuniberti@tu-dresden.de. + + + Cluster of Excellence CeTI, TUD Dresden University of Technology, Dresden, Germany. gianaurelio.cuniberti@tu-dresden.de. + + + + eng + + + 101046369 + HORIZON EUROPE European Innovation Council + + + + RTG2767 + Deutsche Forschungsgemeinschaft + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + J Cheminform + 101516718 + 1758-2946 + + + Explainable AI + Healthcare + Molecular sensing + Quantum-informed descriptors + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 2 + 17 + + + 2026 + 5 + 21 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 45 + + + aheadofprint + + 42216185 + 10.1186/s13321-026-01232-3 + 10.1186/s13321-026-01232-3 + + + + + + 42216176 + + 2026 + 05 + 29 + +
+ + 1472-6947 + + 26 + 1 + + 2026 + May + 29 + + + BMC medical informatics and decision making + BMC Med Inform Decis Mak + + Correction to: Simultaneous prediction of early and delayed mortality in burn patients: a comparative machine learning analysis of feature importance in a single-center retrospective study. + 194 + 10.1186/s12911-026-03563-5 + + + Motamedi + Mehran + M + + Nanotechnology Engineering, Babol Noshirvani University of Technology, Shariati Avenue, Babol, Mazandaran Province, 47148-71167, Iran. + + + + Moallemkolaei + Najibeh Mohseni + NM + + Health Information Management, Health Information Management Research Center, Kashan University of Medical Sciences, Ravandi Boulevard, Kashan, Isfahan Province, 87159-73449, Iran. + + + + Hesamirostami + Mohammadhossein + M + + Plastic and Reconstructive Surgery, Mazandaran University of Medical Sciences, Daneshgah Boulevard, Sari, Mazandaran Province, 48157-33971, Iran. hesami_plas@yahoo.com. + + + + Ghorbani + Mojtaba + M + + Plastic and Reconstructive Surgery, Mazandaran University of Medical Sciences, Daneshgah Boulevard, Sari, Mazandaran Province, 48157-33971, Iran. + + + + Arani + Leila Shokrizadeh + LS + + Health Information Management, Health Information Management Research Center, Kashan University of Medical Sciences, Ravandi Boulevard, Kashan, Isfahan Province, 87159-73449, Iran. + + + + eng + + Published Erratum + + + 2026 + 05 + 29 + +
+ + England + BMC Med Inform Decis Mak + 101088682 + 1472-6947 + + IM + + + BMC Med Inform Decis Mak. 2025 Dec 5;26(1):7. doi: 10.1186/s12911-025-03311-1. + 41350855 + + + + + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 44 + + + epublish + + 42216176 + 10.1186/s12911-026-03563-5 + 10.1186/s12911-026-03563-5 + + + + + + 42216170 + + 2026 + 05 + 29 + +
+ + 1475-9276 + + + 2026 + May + 29 + + + International journal for equity in health + Int J Equity Health + + Intersection of gender and class in the assessment of self-rated health in the Spanish population: a classification tree study. + 10.1186/s12939-026-02897-w + + This study assesses the influence of various specific health conditions as activity limitations and groups of chronic diseases, in estimating poor self-rated health for the Spanish population aged 30-60, stratified by the intersections of gender and social class. Moving beyond unidimensional social gradients, the research utilises decision tree analysis to identify specific configurations of SRH responses that align with the epidemiological profiles of distinct intersectional strata. + Data were drawn from the 2023 Spanish Health Survey (ESdE), focusing on a sample of 10,210 adults aged 30-60. This age bracket was selected to capture health inequality dynamics during the working life-course. Specific machine learning decision trees (J48 algorithm) were employed to assess the pathways for classifying individuals with poor SRH across six distinct intersectional groups. + Resulting decision trees differ significantly in composition, shape, and length across strata. A clear trend emerged: less skilled groups exhibited higher tree complexity, reflecting a more rapid accumulation of chronic illnesses at earlier ages. + This research contributes to the emerging field of study regarding what truly underlies individuals' general assessment of their health. The resulting decision trees illustrate the social gradient in health in a novel way that enriches the understanding of health inequalities. Instead of simply showing a linear trend across a one-dimensional variable, these trees reveal that as disadvantages accumulate through the intersection between gender and social class, the combination of relevant diseases do not only become more complex, but also distinct, in specific and unique ways for each group, as the intersectional framework predicts. + © 2026. The Author(s). + + + + Gumà-Lao + Jordi + J + + Centre for Demographic Studies, Barcelona, Spain. jguma@ced.uab.es. + + + + Barnils + Núria Pedrós + NP + + Institute for Public Health and Nursing Research, University of Bremen, Bremen, Germany. + + + Department of Epidemiology and Global Health, Umeå University, Umeå, Sweden. + + + + Parra-Casado + Daniel La + D + + Department of Sociology II, University of Alicante, Alicante, Spain. + + + + eng + + + Ramón y Cajal program (RYC2022-037781-I) + Agencia Estatal de Investigación + + + + "Generación de Conocimiento" Project [PID2022-141543OB-I00] + Agencia Estatal de Investigación + + + + 535865195 + Deutsche Forschungsgemenischaft + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Int J Equity Health + 101147692 + 1475-9276 + + IM + + Health inequity + Intersectional framework + Machine learning + Self-rated health + + Declarations. Human ethics and consent to participate: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 2 + 3 + + + 2026 + 5 + 14 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 44 + + + aheadofprint + + 42216170 + 10.1186/s12939-026-02897-w + 10.1186/s12939-026-02897-w + + + + + + 42216166 + + 2026 + 05 + 29 + +
+ + 1471-2474 + + + 2026 + May + 29 + + + BMC musculoskeletal disorders + BMC Musculoskelet Disord + + Optimizing preoperative planning for total hip arthroplasty using random forest models to predict stem size and compatibility. + 10.1186/s12891-026-10022-9 + + Preoperative planning is essential for optimal outcomes post-total hip arthroplasty (THA). Appropriate stem size and compatibility with femoral dimensions are essential in THA to prevent distal fixation. Previous studies have proposed deep learning-based models with image recognition for preoperative planning to determine the best-fit stem; however, whether size and compatibility are adequately optimized remains unclear. This study developed methods that may assist surgeons in preoperative planning to estimate the stem size and compatibility using supervised machine learning models. + Two Random Forest (RF) models were developed to estimate the best-fit stem size and stem compatibility with the femoral geometry. Femoral size information measured at 10 locations from computed tomography images of 320 hips was used to train the models. The training data included the sizes and compatibility information as the target variables for supervised learning provided by a simulation software currently used in clinical practice to determine the optimal stem. As part of feature engineering, ratios derived from combinations of the 10 measured values were used as learning data. The size estimation model was designed as a seven-class classification model, whereas the compatibility estimation model was a binary classification model, predicting whether the stem will exclusively fit distally or not. Both models were tested using data from 109 hips of patients who underwent THA. The model performances were assessed using accuracy, F1 score, precision, and recall. Each parameter's impact was determined using feature importance analysis. + The size and compatibility estimation models trained without ratio information showed accuracies of 89.0% (exact-match: 46.8%) and 85.3%, respectively. When it was included, the models' performance improved, with the size and compatibility estimation accuracies increasing to 92.7% (exact-match: 39.4%) and 87.2%, respectively. Feature importance analysis highlighted that distal medullary cavity diameter is key in size estimation, while overall femoral dimensions are vital for compatibility estimation. + This study developed models that estimate the two critical factors in stem selection: size and compatibility. These models may support preoperative planning for THA using the Accolade II stem, although further validation is required to establish applicability to other implant systems. + © 2026. The Author(s). + + + + Kaneoka + Takehiro + T + 0000-0002-2742-4183 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Imagama + Takashi + T + 0000-0001-8509-1850 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Okazaki + Tomoya + T + 0000-0002-6635-3541 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Matsuki + Yuta + Y + 0000-0002-0664-4136 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Kawakami + Takehiro + T + 0000-0001-6363-560X + + Department of Orthopaedic surgery, Yamaguchi Prefectural Grand Medical Centre, Hofu, Yamaguchi, JP, Japan. + + + + Yamazaki + Kazuhiro + K + 0000-0002-1627-7368 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Sasaki + Kei + K + 0009-0003-8535-1589 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. + + + + Asai + Yoshiyuki + Y + 0000-0001-5519-4306 + + Department of Systems Bioinformatics, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan. + + + + Sakai + Takashi + T + 0000-0001-6367-1299 + + Department of Orthopaedic Surgery, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan. cozy@yamaguchi-u.ac.jp. + + + + eng + + + JP22he0122010 + Japan Agency for Medical Research and Development (AMED) + + + + JP 23K10426 + JSPS KAKENHI + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Musculoskelet Disord + 100968565 + 1471-2474 + + IM + + femoral dimensions + machine learning + preoperative planning + random forest + total hip arthroplasty + + Declarations. Ethics approval and consent to participate: This retrospective study’s protocol was approved by the by the Ethics Committee of Yamaguchi University Graduate School of Medicine (approval number: H2021-166-2), and informed consent was obtained from all participants. The ethical standards from the 1964 Helsinki declaration and its later amendments were upheld. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2024 + 12 + 4 + + + 2026 + 5 + 22 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 44 + + + aheadofprint + + 42216166 + 10.1186/s12891-026-10022-9 + 10.1186/s12891-026-10022-9 + + + + + + 42216115 + + 2026 + 05 + 29 + +
+ + 1471-2393 + + + 2026 + May + 30 + + + BMC pregnancy and childbirth + BMC Pregnancy Childbirth + + Predicting facility delivery and its determinants among reproductive-age women in Ethiopia using machine learning algorithm: evidence from performance monitoring for action Ethiopia 2019 dataset. + 10.1186/s12884-026-09354-0 + + Facility delivery attended by skilled health professionals is a critical strategy for reducing maternal and neonatal mortality. Despite substantial efforts to expand maternal health services in Ethiopia, a large proportion of births still occur at home. This study aimed to predict facility delivery and identify its associated factors among reproductive-age women in Ethiopia using machine learning approaches. + This study used secondary data from the 2019 Performance Monitoring for Action (PMA) Ethiopia cross-sectional household and female survey. A weighted sample of 5,413 women aged 15-49 years who had a recent birth was included in the analysis. Data extraction and preprocessing were conducted using STATA version 17, while machine learning models were implemented using Python version 3.11.5. Seven supervised machine learning algorithms were developed to predict facility delivery. Model performance was evaluated using accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC). Data preparation included feature engineering, data splitting, handling missing values, addressing class imbalance, and outlier detection. SHapley Additive exPlanations (SHAP) were applied to interpret the contribution of important predictors to model predictions. + Among the evaluated models, the Random Forest algorithm demonstrated the best predictive performance, achieving 74.15% accuracy, 72.58% recall, 76.27% F1-score, and 80.36% precision on test data. The most influential predictors of facility delivery included higher household wealth status, secondary or higher maternal education, television ownership, discussion of delivery location with a partner, partner encouragement for antenatal care attendance, family planning use, maternal age between 20 and 34 years, age at first sexual intercourse of 19 years or older, awareness of emergency contacts, and completion of primary education. + Interpretable machine learning approaches can provide useful insights for identifying factors associated with facility delivery. The findings highlight key socioeconomic and behavioral characteristics that may inform targeted strategies to improve utilization of facility-based delivery services and support efforts to enhance maternal health outcomes in Ethiopia. + © 2026. The Author(s). + + + + Degefa + Siraj Muhidin + SM + + Department of Health Informatics, College of Health Science, Mattu University, Mettu, Ethiopia. sirajmuhidin6@gmail.com. + + + + Hailu + Beriso Alemu + BA + + Department of Health Informatics, College of Health Science, Mattu University, Mettu, Ethiopia. + + + + Serbessa + Naol Gonfa + NG + + Department of Health Informatics, College of Health Science, Mattu University, Mettu, Ethiopia. + + + + Tsehay + Asmamaw Ketemaw + AK + + School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia. + + + + Sedi + Tigist Tollessa + TT + + Department of Health Informatics, College of Medicine and Health Sciences, Arbaminch University, Arbaminch, Ethiopia. + + + + Woldekidan + Eden Ketema + EK + + Department of Health Informatics, College of Medicine and Health Sciences, Arbaminch University, Arbaminch, Ethiopia. + + + + Ahmed + Mohammedjud Hassen + MH + + School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia. + + + + Adem + Jibril Bashir + JB + + Department of Public Health, College of medicine and Health sciences, Arsi University, Asella, Ethiopia. + + + + Wale + Agmasie Damtew + AD + + Department of Health Informatics, School of Public Health, Asrat Woldeyes Health Science Campus, Debre Berhan University, Debre Berhan, Ethiopia. + + + + Guadie + Habtamu Alganeh + HA + + School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia. + + + + Asemahagn + Mulusew Andualem + MA + + School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia. + + + + eng + + Journal Article + + + 2026 + 05 + 30 + +
+ + England + BMC Pregnancy Childbirth + 100967799 + 1471-2393 + + IM + + Determinants + Ethiopia + Facility delivery + Women + + Declarations. Ethics approval and consent to participate: For this study, secondary anonymized data were used, and permission to access the dataset was obtained from the Performance Monitoring for Action (PMA) data repository through the official PMA data access portal: PMA Dataset Access Portal. Ethical clearance for this secondary data analysis was obtained from the Institutional Review Board (IRB) of Bahir Dar University, College of Medicine and Health Sciences. The study was conducted in accordance with the Declaration of Helsinki. Since the dataset was fully anonymized and publicly available for research purposes, informed consent from participants was not required for this secondary analysis. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2024 + 11 + 7 + + + 2026 + 5 + 22 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 42 + + + aheadofprint + + 42216115 + 10.1186/s12884-026-09354-0 + 10.1186/s12884-026-09354-0 + + + + + + 42216109 + + 2026 + 05 + 29 + +
+ + 2045-3701 + + + 2026 + May + 29 + + + Cell & bioscience + Cell Biosci + + Decoding the role of H19 in cholestatic liver injury using snRNA-seq, spatial transcriptomics, and machine learning-based disease prediction. + 10.1186/s13578-026-01590-3 + + Despite recent advances, Primary Sclerosing Cholangitis (PSC)-a chronic obstructive biliary disease-still lacks effective therapies to prevent disease progression or the need for liver transplantation. Moreover, up to 30% of transplant recipients experience recurrence. Long non-coding RNA H19 (H19) has been implicated in promoting PSC progression, yet the cellular and molecular mechanisms underlying its pathogenic role remain incompletely understood. + Liver tissues from age- and sex-matched wild type (WT), H19 knockout (H19KO), Mdr2 knockout (Mdr2KO), and double-knockout (DKO; Mdr2KO/H19KO) mice were analyzed using single-nucleus RNA sequencing (snRNAseq) and GeoMx spatial transcriptomics to define the cell type and spatially specific effects of H19 deletion in cholestatic liver injury. Machine learning models were built to develop cell-type-specific gene prediction signatures and cross-validated using the human dataset GSE243981. A disease-associated cholangiocyte subcluster that increased in Mdr2KO but was markedly reduced in DKO mice was identified. SPP1 signaling was significantly dysregulated in cholestatic liver injury and mitigated following H19 deletion. Translationally conserved healthy (Clu, Spp1) and diseased (Csmd1, Slco3a1, Cftr) cholangiocyte markers were identified. When validated in a human patient dataset (GSE243981), our machine-learning prediction models achieved AUC values > 0.869. Finally, spatial analyses demonstrated that the mitigation of disease-associated gene expression following H19 deletion was specifically restricted to hepatocytes within the bile duct region. + H19 deletion mitigates cholestatic injury by suppressing pathogenic cholangiocyte states, normalizing Spp1-mediated signaling, and shifting transcriptional programs specifically within the periductal niche. Furthermore, our machine-learning signatures demonstrate robust cross-species translation and may benefit future post-transplant analytics and disease recurrence predictions. + © 2026. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. + + + + Way + Grayson W + GW + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Jiang + Xixian + X + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Lu + Hongkun + H + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Wu + Nan + N + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Zhao + Derrick + D + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Tai + Yun-Ling + YL + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Bayatpour + Sareh + S + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Wang + Xuan + X + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. + + + + Zhou + Huiping + H + + Department of Microbiology and Immunology, Richmond VA Medical Center, Virginia Commonwealth University, 1220 East Broad Street, MMRB-5044, Richmond, VA, 23298-0678, USA. huiping.zhou@vcuhealth.org. + + + Richmond Veterans Affairs Medical Center, Richmond, VA, 23249, USA. huiping.zhou@vcuhealth.org. + + + + eng + + + 5T32AA029975 + GF + NIH HHS + United States + + + DK115377, 2R56DK115377-05A1, 5R01AA030180, R01DK139587, NIH-NCI P01CA275740 + GF + NIH HHS + United States + + + 5I01BX005730,1 IS1 BX004777-01 and 1IS1BX005517-01, IK6BX004477 + U.S. Department of Veterans Affairs + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Cell Biosci + 101561195 + 2045-3701 + + + Cholestasis + H19 + Long non-coding RNA + Machine learning + Primary sclerosing cholangitis + SPP1 + + Declarations. Conflict of interest: Huiping Zhou is the corresponding author and is also an Editor for the journal. Ethics approval and consent to participate: All animal experiments were performed following institutional guidelines for ethical animal studies and approved by the Virginia Commonwealth University and Richmond VA Medical Center Institutional Animal Care and Use Committee, Virginia, USA. All patient samples used followed Richmond VA Medical Center Institutional Review Board guidelines for de-identified consenting patients. Consent for publication: All authors have given their consent for publication. + + + + + 2025 + 12 + 11 + + + 2026 + 5 + 7 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 40 + + + aheadofprint + + 42216109 + 10.1186/s13578-026-01590-3 + 10.1186/s13578-026-01590-3 + + + + + + 42216097 + + 2026 + 05 + 29 + +
+ + 1758-9193 + + + 2026 + May + 29 + + + Alzheimer's research & therapy + Alzheimers Res Ther + + EEG biomarkers can predict early-stage Alzheimer's disease and correlate with intracerebral pathology: a multimodal machine learning study. + 10.1186/s13195-026-02096-3 + + Early recognition of Alzheimer's disease (AD) is crucial for timely intervention and delaying disease progression. Electroencephalogram (EEG) technology provides a direct reflection of the brain's dynamic activity. However, the relationship between potential EEG features and cognitive function in early-stage AD patients, as well as cerebrospinal fluid (CSF) pathological biomarkers, remains unclear. + This study included 101 patients with mild cognitive impairment (MCI) and mild AD, alongside 69 healthy controls (HC) matched for gender, age, and educational attainment. Extracting EEG power spectral density (PSD) and microstates analysis features as training features for machine learning (ML), we employed five ML algorithms-Support Vector Machines (SVM), Logistic Regression (LR), Random Forests (RF), XGBoost, and LightGBM-for training and testing. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). SHapley Additive exPlanations (SHAP) plots were employed to elucidate variable importance within the model, and sequential forward selection (SFS) was utilised to identify potential features. Correlation analysis and mediation analysis were conducted to investigate the relationships between EEG features, CSF pathological biomarkers, and cognitive function. + The LR model demonstrated the highest average predictive performance in the training set (mean AUC = 0.859 ± 0.059). The model incorporating PSD and microstates features demonstrated optimal predictive performance in the test set (AUC = 0.949, 95% CI: 0.877-1.000), outperforming any single-feature model. Based on SHAP and SFS analyses, six potential EEG indicators were identified: central region delta frequency band, central region theta frequency band, temporal region beta frequency band, microstate mean duration, microstate C duration, and the transition probability from microstate C to A. Mediation analysis revealed a significant negative correlation between central region theta frequency band and CSF Aβ₁₋₄₂ levels (r = - 0.31, p = 0.015), and the central region theta frequency band mediated the relationship between Aβ₁₋₄₂ levels and Mini-Mental State Examination (MMSE) scores (indirect effect = 0.0007, 95% CI: 0.0001-0.0013). + The combined application of EEG and ML enables efficient classification diagnosis of early-stage AD, and EEG is correlated with intracerebral pathological biomarkers and cognitive impairment. + © 2026. The Author(s). + + + + Geng + Zhi + Z + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Song + Wenqian + W + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Gao + Chen + C + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Zhu + Yun + Y + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Wu + Yue + Y + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + Department of Sleep Psychology, The Second Hospital of Anhui Medical University, Anhui Medical University, Hefei, China. + + + + Song + Bo + B + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Guo + Haihua + H + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Wu + Jiayun + J + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Fang + Miao + M + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Yan + Yibing + Y + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Zhou + Shanshan + S + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Hu + Panpan + P + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Tian + Yanghua + Y + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. + + + + Wu + Xingqi + X + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. wuxq@fy.ahmu.edu.cn. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. wuxq@fy.ahmu.edu.cn. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. wuxq@fy.ahmu.edu.cn. + + + + Wang + Kai + K + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. kwang@ahmu.edu.cn. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. kwang@ahmu.edu.cn. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. kwang@ahmu.edu.cn. + + + Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, China. kwang@ahmu.edu.cn. + + + + Wei + Ling + L + + Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China. weil@fy.ahmu.edu.cn. + + + Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China. weil@fy.ahmu.edu.cn. + + + Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China. weil@fy.ahmu.edu.cn. + + + + eng + + + 82101498 + National Natural Science Foundation of China + + + + 82371201 + National Natural Science Foundation of China + + + + 82371201 + National Natural Science Foundation of China + + + + 2021kj19 + The 2021 Youth Foundation training program of the First Affliated Hospital of Anhui Medical University + + + + 202204295107020006 + The Anhui Province Clinical Medical ResearchTransformation Special Project + + + + XZZR202402048 + The Natural Science Foundation of theXizang Autonomous Region Group Medical Aid Project to Xizang + + + + 2022zhyx-B11 + Research Fund of Anhui Institute of Translational Medicine + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Alzheimers Res Ther + 101511643 + + IM + + Alzheimer’s disease + CSF biomarkers + EEG microstates + EEG power spectral density + Machine learning + + Declarations. Ethics approval and consent to participate: The studies involving human participants were reviewed and approved by the Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University. The patients/participants provided their written informed consent to participate in this study. Consent for publication: All participants provided consent for the publication of this manuscript. Competing interests: The authors declare no competing interests. + + + + + 2026 + 1 + 12 + + + 2026 + 5 + 25 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 40 + + + aheadofprint + + 42216097 + 10.1186/s13195-026-02096-3 + 10.1186/s13195-026-02096-3 + + + + + + 42216034 + + 2026 + 05 + 29 + +
+ + 1475-2867 + + + 2026 + May + 29 + + + Cancer cell international + Cancer Cell Int + + DNA damage response signature-based prognostic genes for intrahepatic cholangiocarcinoma: a combined analysis of machine learning and biological experiments. + 10.1186/s12935-026-04355-7 + + Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive subtype of primary liver cancer with insidious onset, early metastasis, and poor prognosis. DNA damage response (DDR) dysfunction is linked to ICC tumorigenesis, progression, and drug resistance. However, the detailed mechanisms remain underexplored. + DDR activity was quantified by ssGSEA across 6 independent ICC cohorts. DDR risk model was analyzed using 10 machine learning algorithms to construct 101 combined models, and the optimal Lasso + RSF model was adopted by c-index. The core gene SFN was selected for experimental validation. Single‑cell RNA sequencing and spatial transcriptomics were used to explore cellular localization and tissue distribution of signature genes. In vitro assays were performed in ICC cell lines after SFN knockdown to assess cell proliferation and chemosensitivity. + The DDR risk model showed stable prognostic performance in training and external validation cohorts. High‑risk patients exhibited poor prognosis, immunosuppressive microenvironment and reduced sensitivity to multiple chemotherapeutic agents. SFN was specifically enriched in malignant cells. Knockdown of SFN significantly suppressed cell proliferation and colony formation, enhanced sensitivity to cisplatin and gemcitabine, and increased γ‑H2AX expression indicating elevated DNA double‑strand breaks. + We constructed a robust DDR‑based prognostic model for ICC that enables accurate risk stratification and prediction of therapeutic response. SFN serves as a critical driver of chemoresistance and a potential therapeutic target. These results offer novel biomarkers and mechanistic insights to facilitate precision medicine for ICC patients. + © 2026. The Author(s). + + + + Lu + Kai + K + + Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Li + Hanqi + H + + Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Wu + Yinying + Y + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Dong + Xuyuan + X + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Dong + Danfeng + D + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Fan + Yangwei + Y + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Li + Enxiao + E + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. + + + + Sun + Liankang + L + + Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. sunliankang@xjtu.edu.cn. + + + + Shi + Yu + Y + + Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 Yanta West Road, Xi'an, 710061, Shaanxi Province, China. shiyu@xjtu.edu.cn. + + + + eng + + + 2022JQ-846 + Natural Science Basic Research Program of Shaanxi + + + + 2021QN-01,2022QN-15 + Research Foundation of the First Affiliated Hospital of Xi'an Jiaotong University + + + + xzy012022103 + Fundamental Research Funds for the Central Universities + + + + 82303663 + National Natural Science Foundation of China + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Cancer Cell Int + 101139795 + 1475-2867 + + + DNA damage response + Experimental validation + Intrahepatic cholangiocarcinoma + Machine learning + Prognostic markers + + Declarations. Ethics approval and consent to participate: All data utilized in this study were retrieved from publicly available online databases, as specified in the manuscript. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2025 + 8 + 3 + + + 2026 + 5 + 23 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 38 + + + aheadofprint + + 42216034 + 10.1186/s12935-026-04355-7 + 10.1186/s12935-026-04355-7 + + + + + + 42216026 + + 2026 + 05 + 29 + +
+ + 1757-6512 + + + 2026 + May + 29 + + + Stem cell research & therapy + Stem Cell Res Ther + + KRT6A derived from mesenchymal stem cells as a potential biomarker and therapeutic target for alopecia areata: insights from multi-omics analysis and experimental evidence. + 10.1186/s13287-026-05077-3 + + Mesenchymal stem cells (MSCs) secretome have shown promise in the treatment of alopecia areata (AA). However, the key therapeutic genes remain unclear. This study aimed to identify potential critical therapeutic molecules using multi-omics approaches. + MSCs extracellular vesicles related genes was retrieved from ExoCarta database, and which integrated with transcriptomic and proteomic sequencing data from AA for cross-analysis to identify AA-related therapeutic genes. Mfuzz clustering, PPI networks, and machine learning algorithms were employed to potential key therapeutic factors. Functional mechanisms were explored through gene expression analysis, immune infiltration, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics. AA mice were used for experimental validation. + We identified 146 MSCs-derived genes with potential therapeutic relevance for AA. Enrichment analysis suggests that they are involved in skin development and differentiation. Through Mfuzz clustering, 13 machine learning algorithms, and combined with SHAP interpretability analysis, the KRT6A was we further identified as the most critical candidate gene. Validation analyses demonstrated that KRT6A serves as a robust diagnostic marker for AA (AUC = 0.989), a predictor of disease severity (AUC = 0.769), and a protective factor (HR = 0.52, 95%CI: 0.34-0.79). Immune infiltration analysis revealed severe immune dysregulation in AA, and KRT6A was significantly negative correlated with dysregulated immune cells. By exploring the expression source of KRT6A, we found that it was mainly enriched in skin hair follicle tissues, and single-cell subset analysis and spatial transcriptomics further mapped KRT6A to hair follicle stem-like cells from the cellular level, yet KRT6A was significantly downregulated in AA models. Primary human hair follicle mesenchymal stem cells (HFMSCs) further confirmed KRT6A localization. Furthermore, KRT6A-overexpressing HFMSCs exhibited enhanced migration, whereas KRT6A knockdown reduced migration and suppressed WNT pathway activation. Conditioned medium from KRT6A-overexpressing HFMSCs effectively alleviated alopecia and reduced apoptosis in AA mice, whereas KRT6A knockdown reversed these effects. + MSCs-derived KRT6A may serve as a potentia biomarker and therapeutic target for AA, which provide novel insights into AA pathogenesis and may help the development of targeted therapeutic strategies. + © 2026. The Author(s). + + + + Peng + Shixiong + S + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. + + + + Deng + Jia + J + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. + + + + Meng + Xiuming + X + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. + + + + Deng + Shuangjie + S + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. + + + + Yan + Wenjie + W + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. ywj716@glmu.edu.cn. + + + + Huang + Xi + X + + Department of Dermatology, the Affiliated Hospital of Guilin Medical University, Guilin Medical University, Lequn Road, Guilin, China. professorhuangxi@163.com. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Stem Cell Res Ther + 101527581 + 1757-6512 + + IM + + Alopecia areata + KRT6A + Machine learning + Mesenchymal stem cells + Multi-omics + + Declarations. Ethics approval and consent to participate: The animal research, entitled “KRT6A from Mesenchymal Stem Cells Exosomes as a Potential Biomarker and Therapeutic Target for Alopecia Areata: Insights from Multi-omics Analysis and Experimental Evidence”, was approved by the Animal Ethics Committee of Guilin Medical University (GLMU-IACUC-20251084) on August 13, 2025, and was performed in accordance with the ARRIVE 2.0 guidelines. The human-related research in this study, which was part of the project entitled “Exosome Multimodal Analysis Reveals the Mechanism by Which Mesenchymal Stem Cell Exosomes Regulate Hair Follicle Regeneration”, was approved by the Ethics Committee of the Affiliated Hospital of Guilin Medical University on February 18, 2025 (No. 2025QTLL-10), and was conducted in compliance with the principles of the Declaration of Helsinki. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2025 + 10 + 11 + + + 2026 + 5 + 22 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 37 + + + aheadofprint + + 42216026 + 10.1186/s13287-026-05077-3 + 10.1186/s13287-026-05077-3 + + + + + + 42216024 + + 2026 + 05 + 29 + +
+ + 2050-6511 + + + 2026 + May + 29 + + + BMC pharmacology & toxicology + BMC Pharmacol Toxicol + + Network toxicology and bioinformatics reveal potential molecular links between cadmium exposure and pancreatic cancer. + 10.1186/s40360-026-01156-6 + + Environmental cadmium (Cd) pollution poses a severe threat to human health due to its strong bioaccumulation and high carcinogenicity. Although Cd exposure has been linked to various cancers, its specific role in pancreatic cancer (PC) remains unclear. This study aimed to explore the molecular mechanisms underlying Cd-associated PC and identify potential diagnostic and therapeutic targets. + Transcriptomic data of PC were obtained from the GEO database to screen differentially expressed genes (DEGs), which were then intersected with Cd-related genes retrieved from the Comparative Toxicogenomics Database (CTD). Functional enrichment analyses, including GO and KEGG, were conducted to identify key biological processes and pathways. Multiple machine learning algorithms were used to construct a diagnostic model and identify hub genes. Furthermore, molecular docking and molecular dynamics simulations were performed to evaluate drug-target interactions, and in vitro experiments were used to validate the effects of Cd exposure on PC cell proliferation and invasiveness. + A total of five key genes-FN1, COL5A1, AHR, COL3A1, and TIMP1-were identified as core regulators of Cd-associated PC. Functional analyses indicated that Cd primarily affects the extracellular matrix (ECM) metabolic and collagen remodeling pathways. Experimental results confirmed that Cd exposure significantly enhanced the proliferation and invasiveness of PC cells. Molecular docking and dynamics simulations revealed that the EGFR inhibitor dacomitinib exhibited favorable predicted binding affinity to FN1, suggesting its potential as a therapeutic agent. + This study reveals that Cd may promote the malignant progression of PC by regulating ECM remodeling through key genes such as FN1. Dacomitinib shows promise as an FN1-associated therapy, offering new insights for the precise prevention and treatment of Cd-associated PC. + © 2026. The Author(s). + + + + Liu + Shuyuan + S + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. + + + + Lu + Xinyan + X + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + + Li + Desheng + D + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + + Ma + Jingyuan + J + + The First Clinical Medical College, Liaoning University of Traditional Chinese Medicine, Shenyang, China. + + + + Zhang + Yunshu + Y + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. + + + + Li + Junchen + J + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. + + + + Wan + Xing + X + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. wanx03@dmu.edu.cn. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. wanx03@dmu.edu.cn. + + + + Shang + Dong + D + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. shangdong@dmu.edu.cn. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. shangdong@dmu.edu.cn. + + + Institute of Integrative Medicine, Dalian Medical University, Dalian, China. shangdong@dmu.edu.cn. + + + + Zhang + Qingkai + Q + + Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China. zhangqk@dmu.edu.cn. + + + Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China. zhangqk@dmu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Pharmacol Toxicol + 101590449 + 2050-6511 + + IM + + Cadmium + Machine learning + Molecular dynamic simulation + Network toxicology + Pancreatic cancer + + Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2025 + 11 + 11 + + + 2026 + 5 + 20 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 37 + + + aheadofprint + + 42216024 + 10.1186/s40360-026-01156-6 + 10.1186/s40360-026-01156-6 + + + + + + 42216013 + + 2026 + 05 + 29 + +
+ + 1749-7922 + + + 2026 + May + 29 + + + World journal of emergency surgery : WJES + World J Emerg Surg + + Machine learning models for early mortality prediction in trauma patients using public data: a nationwide retrospective study. + 10.1186/s13017-026-00706-x + + Trauma is a leading cause of morbidity and mortality worldwide, particularly in younger populations. Early identification of high-risk trauma patients is critical for timely interventions and improved outcomes. Although artificial intelligence and machine learning have demonstrated promise in healthcare, their application in trauma mortality prediction has been limited. + This study developed and validated machine learning models to predict mortality in trauma patients using a large public dataset from the National Community-Based Critical Injury Survey (South Korea, 2016-2020). Overall, 207,012 cases were analyzed. Six machine learning algorithms, including logistic regression, k-nearest neighbor, decision tree, random forest (RF), extreme gradient boosting (XGB), and multi-layer perceptron, were trained and evaluated. Their performance was assessed using the areas under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC), and other metrics. Shapley additive explanations (SHAP) scores were used to interpret feature importance. + The XGB model demonstrated the highest performance (AUROC 0.985; AUPRC 0.957), followed closely by the RF model (AUROC 0.984; AUPRC 0.956). Performance remained stable during the COVID-19 period, supporting the model's temporal robustness under systemic disruption. SHAP analysis identified clinically actionable features such as out-of-hospital cardiac arrest, injury severity score, age, and time to transfusion. Unlike many prior studies based on small or single-center datasets, our model was developed using a nationally representative cohort and prioritized interpretability, scalability, and generalizability. + This study presents a high-performing, interpretable machine learning framework for early mortality risk stratification in trauma patients using nationwide registry data. The strong discrimination and temporal robustness of the model support its value as a system-level prediction tool; however, further calibration analyses, external validation, and prospective implementation studies are required before integration into clinical workflows. + © 2026. The Author(s). + + + + Baik + Seung Min + SM + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Lee + Jae Gil + JG + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Shim + Hongjin + H + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Kim + Sujung + S + + Division of Acute Care Surgery, Department of Surgery, Korea University Anam Hospital, Korea University College of Medicine, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Republic of Korea. + + + + Hong + Kyung Sook + KS + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Yi + Heejung + H + + Division of Critical Care Medicine, Department of Surgery, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea. + + + + Lee + Kyung Hyun + KH + + Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. + + + + Lee + Jae-Myeong + JM + + Division of Acute Care Surgery, Department of Surgery, Korea University Anam Hospital, Korea University College of Medicine, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Republic of Korea. ljm3225@hanmail.net. + + + + eng + + + RS-2023-00240766 + National Research Foundation of Korea + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + World J Emerg Surg + 101266603 + 1749-7922 + + IM + + Artificial intelligence + Clinical decision-making + Emergency medical services + Injury severity score + + Declarations. Ethics approval and consent to participate: The Ewha Womans University Mokdong Hospital Institutional Review Board deemed this study exempt from ethics review and waived the informed consent requirement. Consent for publication: Not applicable. Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2025 + 10 + 20 + + + 2026 + 5 + 23 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 37 + + + aheadofprint + + 42216013 + 10.1186/s13017-026-00706-x + 10.1186/s13017-026-00706-x + + + + + + 42215973 + + 2026 + 05 + 29 + +
+ + 1472-6963 + + + 2026 + May + 29 + + + BMC health services research + BMC Health Serv Res + + Why are rural hospitals closing in the U.S.? Predictors identified using explainable machine learning. + 10.1186/s12913-026-14810-w + + Rural hospital closures in the U.S. reduce access to essential healthcare services and worsen health and economic outcomes in rural communities. This study uses national longitudinal data and explainable machine learning (XML) to identify and interpret risk factors of rural hospital closures. + We conducted a retrospective longitudinal study of U.S. rural hospitals using national datasets from 2011 to 2022, integrating hospital-level financial, operational, policy, population, and community data. Longitudinal feature engineering and statistical summarization were performed, class imbalance and multicollinearity were addressed using random undersampling and variance inflation factor (VIF) analysis, and an XGBoost classifier was trained using stratified three-fold cross-validation and Optuna for hyperparameter tuning. Model explainability was assessed using SHapley Additive exPlanations (SHAP). + Among 2,683 rural hospitals, 139 (5.2%) closed during the study period. The model achieved a recall of 86%, accuracy of 75%, and an AUC of 89%. The key variables linked to closure risk are sustained financial weakness and volatility (e.g., total margin, variability in return on equity, and current ratio), operational instability (low patient volume and variability in bed-days available), policy context (e.g., years since Medicaid expansion), and community context, including sociodemographic characteristics of the service area (e.g., proportion of Black or African American residents), which may reflect underlying structural and socioeconomic conditions rather than direct predictors or causal drivers of closure. + These findings indicate that the risk of closure of rural hospitals reflects accumulated long-term financial and operational instability, as well as policy and community context, rather than short-term distress, highlighting the value of longitudinal and explainable modeling approaches. + Longitudinal data integrated into XML provides a practical approach for early warning systems that enable proactive monitoring and timely interventions, particularly for hospitals serving historically underserved communities. + Not applicable. + © 2026. The Author(s). + + + + Balakrishnan + Kiruthika + K + + National Center for Rural Health Professions, University of Illinois College of Medicine Rockford, 1601 Parkview Avenue, Rockford, IL, 61107, USA. bkiruthi@uic.edu. + + + AI.Health4All Center, University of Illinois Chicago, 1747 West Roosevelt Road, Chicago, IL, 60608, USA. bkiruthi@uic.edu. + + + + Abuhay + Tesfamariam M + TM + + Department of Medicine, University of Illinois Chicago, 840 S. Wood St., Chicago, IL, 60612, USA. + + + AI.Health4All Center, University of Illinois Chicago, 1747 West Roosevelt Road, Chicago, IL, 60608, USA. + + + + Hinkle + Hana E + HE + + National Center for Rural Health Professions, University of Illinois College of Medicine Rockford, 1601 Parkview Avenue, Rockford, IL, 61107, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Health Serv Res + 101088677 + 1472-6963 + + IM + + Early warning systems + Explainable machine learning + Hospital closure risk factors + Longitudinally derived features + Rural hospital closures + + Declarations. Ethics consent to participate: This study is a retrospective analysis of publicly available data and published literature and did not involve human participants, identifiable private information, or the collection of new data requiring ethical approval or informed consent. Consent for publication: Not applicable Competing interests: The authors declare no competing interests. + + + + + 2026 + 2 + 10 + + + 2026 + 5 + 15 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 36 + + + aheadofprint + + 42215973 + 10.1186/s12913-026-14810-w + 10.1186/s12913-026-14810-w + + + + + + 42215935 + + 2026 + 05 + 29 + +
+ + 1472-6947 + + + 2026 + May + 29 + + + BMC medical informatics and decision making + BMC Med Inform Decis Mak + + Explainable machine learning for the prediction of Alzheimer's disease-related cognitive impairment: a consensus feature selection approach. + 10.1186/s12911-026-03585-z + + Early identification of Alzheimer's disease-related cognitive impairment remains challenging, and existing machine learning (ML) models often suffer from feature instability and limited interpretability. This study developed robust and explainable ML models using cerebrospinal fluid (CSF) biomarkers by systematically comparing sparsity-based (LASSO), importance-based (Boruta), and consensus feature selection strategies. + A publicly available cohort of 333 individuals (91 cognitively impaired, 242 cognitively normal) was analyzed. Data were split into training (70%) and independent test (30%) sets. Multiple classifiers, including Elastic Net-regularized logistic regression (LR), support vector machine (SVM), random forest, XGBoost, and Naive Bayes (NB), were trained using repeated 5-fold cross-validation (10 repetitions; 10 × 5-fold cross-validation) with class weighting. Model performance was evaluated using discrimination, calibration, and clinical utility metrics, and interpretability was assessed using SHAP. + All models demonstrated strong discriminative performance on the test set (AUROC 0.861-0.958). LASSO-based models showed high specificity, Boruta-based models achieved higher sensitivity, and consensus-based models provided the most balanced performance. The consensus-LR and -SVM models achieved AUROC values of 0.954 and 0.951, respectively. Beyond discrimination, the consensus-LR model demonstrated good calibration and consistent net clinical benefit in decision curve analysis, analyses that remain relatively underreported in the Alzheimer's disease machine learning literature. SHAP analyses highlighted biologically plausible contributions from key biomarkers, including tau, Aβ42, NT-proBNP, pancreatic polypeptide, and IL-7. + In summary, stable and interpretable ML models for Alzheimer's disease-related cognitive impairment can be developed using CSF-derived biomarkers obtained through lumbar puncture. The proposed consensus-based feature selection framework improves feature stability and model transparency, facilitating the discrimination between cognitively normal and impaired individuals and providing a foundation for future external validation studies. + © 2026. The Author(s). + + + + Cantaş Türkiş + Fulden + F + 0000-0002-7018-7187 + + Division of Biostatistics, Faculty of Medicine, Muğla Sıtkı Koçman University, Muğla, 48000, Turkey. fuldencantas@mu.edu.tr. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Med Inform Decis Mak + 101088682 + 1472-6947 + + IM + + Calibration + Clinical utility + Explainability + Feature selection + Machine learning + + Declarations. Ethics approval and consent to participate: Not applicable. This study is a secondary analysis of publicly available, anonymized data obtained from an open-access repository. Ethical approval and informed consent were obtained in the original study, and no additional ethical approval was required for the present analysis. The study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication: Not applicable. The study does not include any individual-level identifiable data. Authors’ information: Fulden Cantaş Türkiş is an Assistant Professor at Muğla Sıtkı Koçman University, Faculty of Medicine, Department of Biostatistics. Her research interests include biostatistics, time-to-event data analysis, machine learning, artificial intelligence in healthcare, explainable artificial intelligence, predictive modeling, and statistical learning using real-world clinical data. She collaborates with multiple interdisciplinary research groups as the lead biostatistician and provides statistical consultancy for clinical and translational research projects. Competing interests: The authors declare no competing interests. + + + + + 2026 + 1 + 22 + + + 2026 + 5 + 20 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 35 + + + aheadofprint + + 42215935 + 10.1186/s12911-026-03585-z + 10.1186/s12911-026-03585-z + + + + + + 42215930 + + 2026 + 05 + 29 + +
+ + 1472-6947 + + + 2026 + May + 29 + + + BMC medical informatics and decision making + BMC Med Inform Decis Mak + + Beyond prediction: explainable machine learning for ferritin estimation from complete blood count. + 10.1186/s12911-026-03601-2 + + Serum ferritin is a primary marker of body iron stores; however, its diagnostic utility is limited by cost and non-specific elevation during inflammatory states. This study aimed to develop and validate explainable machine learning (ML) models to estimate ferritin status using only widely available and cost-effective complete blood count (CBC) and demographic parameters. + This prospective study analyzed 12,450 adult laboratory records. To ensure ferritin reflected true iron status, cases with inflammation (elevated white blood cell count or erythrocyte sedimentation rate) were excluded. A balanced dataset of 7,200 samples was used to train five supervised ML algorithms, including Logistic Regression, XGBoost, and Artificial Neural Network (ANN), for both binary classification and regression tasks. Model interpretability was assessed using SHapley Additive Explanations (SHAP) to evaluate global feature importance. + The ANN model achieved the best classification performance, with an area under the curve of 0.837 and an F1-score of 0.760 in discriminating low ferritin levels (< 30 ng/mL) from normal values. Although the regression model demonstrated a moderate global R² of 0.32, it showed higher predictive accuracy in lower ferritin ranges critical for diagnosing iron deficiency. SHAP analysis revealed Sex, Mean Corpuscular Hemoglobin, and Red Cell Distribution Width as the most influential predictors, consistent with iron-restricted erythropoiesis. + ML models based solely on routine hemogram data provide a reliable, zero-cost screening approach for iron deficiency. The integration of explainable AI enhances clinical credibility and supports implementation as an accessible decision support tool. + © 2026. The Author(s). + + + + Us + Ramazan Berk + RB + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. berkus62@gmail.com. + + + + Panatli + Oguzhan + O + + Department of Biomedical Engineering, Faculty of Engineering, Ankara University, Gölbaşı, Ankara, 06830, Türkiye. + + + Department of Computer Engineering, Faculty of Engineering, Ankara University, Gölbaşı, Ankara, 06830, Türkiye. + + + + Bozkürk + Mine Büşra + MB + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Öztürk + Atakan + A + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Sağlam + Suadiye + S + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Kösem + Arzu + A + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Öztürk + Alpaslan + A + + Department of Medical Biochemistry, Ankara Etlik City Hospital, Yenimahalle, Ankara, 06170, Türkiye. + + + + Bostanci + Gazi Erkan + GE + + Department of Computer Engineering, Faculty of Engineering, Ankara University, Gölbaşı, Ankara, 06830, Türkiye. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Med Inform Decis Mak + 101088682 + 1472-6947 + + IM + + Clinical decision support systems + Complete blood count + Explainable artificial intelligence + Ferritin + Iron deficiency + SHAP + + Declarations. Ethics approval and consent to participate: The study protocol was approved by the Ankara Etlik City Hospital Ethics Committee (Approval No: AEŞH-BADEK1-2025-032, Date: 02/07/2025) and was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all participants prior to inclusion in the study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 1 + 6 + + + 2026 + 5 + 25 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 35 + + + aheadofprint + + 42215930 + 10.1186/s12911-026-03601-2 + 10.1186/s12911-026-03601-2 + + + + + + 42215875 + + 2026 + 05 + 29 + +
+ + 1480-9222 + + + 2026 + May + 29 + + + Biological procedures online + Biol Proced Online + + A novel prognostic model based on MVIRGs identifies ANGPT2 as a key target driving the malignant progression of HCC. + 10.1186/s12575-026-00347-z + + Hepatocellular carcinoma (HCC) is a highly lethal malignancy with high recurrence and limited therapies. Microvascular invasion (MVI) is a critical prognostic factor, but its molecular mechanisms and precise predictive tools are lacking. This study aimed to identify key MVI-associated genes (MVIRGs), develop a prognostic model, and elucidate the role of ANGPT2 in HCC progression and therapeutic responses. + Machine learning on multi-cohort data (TCGA, ICGC) identified MVIRGs and constructed a prognostic model. ANGPT2, the core oncogenic MVIRG, was validated clinically (IHC) and functionally (in vitro and in vivo). We analyzed its association with the tumor immune microenvironment (TIME), immune checkpoint inhibitors (ICIs), and drug sensitivity. + A powerful six-MVIRG prognostic model was developed, with ANGPT2 identified as the sole oncogenic gene. Clinically, ANGPT2 was significantly overexpressed, correlating strongly with aggressive features like MVI, vascular invasion, and advanced stages, serving as a robust poor prognostic biomarker for OS and PFS. Functionally, ANGPT2 knockdown significantly inhibited HCC cell growth and migration in vitro, and inhibited tumor growth in vivo. Bioinformatics revealed ANGPT2 fosters an immunosuppressive TIME, upregulating immune checkpoints (e.g., PD-L1) to mediate resistance to ICIs. High ANGPT2 expression showed a differential drug response: resistance to targeted therapies (e.g., EGFR-TKIs) but paradoxically increased sensitivity to chemotherapies (e.g., docetaxel, paclitaxel). + This study constructed a powerful MVIRG model and identified ANGPT2 as a core oncogenic molecule. ANGPT2 drives HCC progression, mediates immune resistance, and differentially regulates drug sensitivity, making it a key biomarker for prognosis and treatment guidance. + © 2026. The Author(s). + + + + Li + Xian + X + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Tan + Zhouli + Z + + The Fifth School of Clinical Medicine, Anhui Medical University, Hefei, China. + + + Navy Clinical College, Anhui Medical University, Beijing, China. + + + + Feng + Shanru + S + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Yu + Qiang + Q + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Zhou + Jian + J + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Fan + Jia + J + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. + + + + Xu + Yang + Y + + Department of Hepatobiliary Surgery and Liver Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. xuyangdoctor@163.com. + + + + eng + + + 82073272 + National Natural Science Foundation of China + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Biol Proced Online + 100963717 + 1480-9222 + + + ANGPT2 + Hepatocellular carcinoma + MVIRGs + Microvascular invasion + Prognostic model + + Declarations. Ethics approval and consent to participate: Ethical approval was provided by the Ethics Committee of Zhongshan Hospital, and written informed consent was obtained from each patient (Approval number: No. Y2020-374). All animal experiments involved in this study were reviewed and approved by the Ethics Committee of Zhongshan Hospital, Fudan University, Xiamen Hospital (Approval number: No. Y2024-021). We confirm that all experiments were performed in accordance with the approved protocols and all relevant guidelines and regulations for the care and use of laboratory animals. Regarding the tumor burden, the maximum tumor size/burden permitted by our Ethics Committee/IRB is a tumor volume not exceeding 2000 mm3 or a longest tumor diameter not exceeding 2.0 cm. We confirm that this maximum permitted tumor size/burden was not exceeded at any point during the study. Clinical trial number: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 1 + 31 + + + 2026 + 5 + 25 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 33 + + + aheadofprint + + 42215875 + 10.1186/s12575-026-00347-z + 10.1186/s12575-026-00347-z + + + + + + 42215857 + + 2026 + 05 + 29 + +
+ + 1471-2164 + + + 2026 + May + 30 + + + BMC genomics + BMC Genomics + + PRIME: An evaluation framework for protein representation inference and generalization in viral mutation space. + 10.1186/s12864-026-12976-5 + + Protein language models (PLMs) have revolutionized protein fitness prediction, yet their application to rapidly evolving viral pathogens is often confounded by extreme sequence homology. This homology leads to "data leakage" in standard random validation splits, yielding inflated performance metrics that fail to translate into real-world biosurveillance utility. + We present Protein Representation Inference for Mutation Evaluation (PRIME), a framework that integrates domain-specific fine-tuning with a rigorous position-stratified validation protocol to evaluate viral threats. Using a dataset of 347,432 SARS-CoV-2 receptor binding domain (RBD) sequences, we demonstrate that while random training data split yields deceptive R2 values (> 0.90), they fail to generalize to novel mutational sites. By benchmarking models up to 650 M parameters, we show that domain-specific fine-tuning of the ESM-C 600 M model with correctly stratified data provides an initial demonstration of predictive signal for binding affinity and expression at unseen mutational sites of binding affinity and expression on unseen sites (R2 ~0.23), a significant advancement over base foundation models which exhibit no predictive power (R2 <0). PRIME's embedding-based clustering identified 3.03% of bat coronavirus sequences as candidates for further experimental prioritization based on their functional similarity to human-infective strains in embedding space, offering a perspective complementary to traditional phylogenetic methods. + PRIME establishes a new benchmark for the application of PLMs in pathogen surveillance. Our findings demonstrate that state-of-the-art models and fine-tuning, when paired with stratified validation, provide biologically meaningful insights into pathogen evolution and zoonotic risk. + © 2026. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. + + + + Gibson + Kaetlyn + K + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Li + Po-E + PE + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Li + Valerie + V + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Dix + Martha + M + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Hung + Li-Wei + LW + + Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Stelle + George Widgery + GW + + Applied Computer Science Group, Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Babinski + Michal + M + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Chain + Patrick + P + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. + + + + Hu + Bin + B + + Genomics and Bioanalytics Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. bhu@lanl.gov. + + + + eng + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + 20250637DI, 20250638DI, 20250639DI, 20240734DI, 20210767DI, 20200732ER + Los Alamos National Laboratory + + + + + Journal Article + + + 2026 + 05 + 30 + +
+ + England + BMC Genomics + 100965258 + 1471-2164 + + IM + + Biosurveillance + Gomology leakage + Host tropism prediction + Machine learning efficiency + Model fine-tuning + Phenotype prediction + Position-stratified validation + Protein language models + + Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 3 + 12 + + + 2026 + 5 + 18 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 32 + + + aheadofprint + + 42215857 + 10.1186/s12864-026-12976-5 + 10.1186/s12864-026-12976-5 + + + + + + 42215832 + + 2026 + 05 + 29 + +
+ + 1573-6822 + + + 2026 + May + 29 + + + Cell biology and toxicology + Cell Biol Toxicol + + Integrative multi-omics, machine learning, and experimental validation reveal that TPM1 suppresses M2 macrophage polarization and enhances chemosensitivity in acute myeloid leukemia. + 10.1007/s10565-026-10201-3 + + Acute myeloid leukemia (AML) remains a therapeutic challenge due to chemoresistance and immunosuppression. This study aimed to discover novel regulatory mechanisms in AML pathogenesis. + Integrative transcriptomic (GSE30029) and proteomic (PXD008378) analyses with machine learning (LASSO, SVM-RFE, random forest) were performed to identify core AML regulators. Functional validation was conducted in AML cell lines (HL-60, NB4), including cell proliferation and apoptosis assays, cytarabine (Ara-C) sensitivity testing, and a subcutaneous xenograft model. Macrophage polarization was evaluated in co-culture systems. Molecular mechanisms were explored via MeRIP, RIP, and mRNA stability assays. + Tropomyosin 1 (TPM1) was identified as a potential key factor in AML. TPM1 was downregulated in AML tissues and cells. TPM1 overexpression inhibited AML cell proliferation, promoted apoptosis and oxidative stress, and enhanced sensitivity to Ara-C. Furthermore, TPM1 upregulation attenuated M2 macrophage polarization. Mechanistically, the m6A eraser fat mass and obesity-associated protein (FTO) destabilized TPM1 mRNA in an m6A-dependent manner. Downregulation of TPM1 reversed the effects of FTO depletion on AML cell proliferation, Ara-C sensitivity, and M2 macrophage polarization. + This study unveils a novel FTO/m6A/TPM1 axis that coordinately governs AML cell growth, chemosensitivity, and macrophage polarization, highlighting it as a promising therapeutic target. + © 2026. The Author(s). + + + + Li + Yue + Y + + Department of Clinical Laboratory, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 222, West Huanhu 3Rd Road, Lingang New City 201306, Pudong New Area, Shanghai, China. + + + + Wei + Baosheng + B + + Department of Clinical Laboratory, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 222, West Huanhu 3Rd Road, Lingang New City 201306, Pudong New Area, Shanghai, China. + + + + Hu + Liuping + L + + Department of Clinical Laboratory, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 222, West Huanhu 3Rd Road, Lingang New City 201306, Pudong New Area, Shanghai, China. huhuliuping@163.com. + + + + Du + Yuzhen + Y + + Department of Clinical Laboratory, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 222, West Huanhu 3Rd Road, Lingang New City 201306, Pudong New Area, Shanghai, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Switzerland + Cell Biol Toxicol + 8506639 + 0742-2091 + + IM + + Acute myeloid leukemia + Chemosensitivity + M6A eraser + Macrophage polarization + Tropomyosin 1 + + Declarations. Ethics approval: The present study was approved by the ethical review committee of Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine with approval No.20250222. Written informed consent was obtained from all enrolled patients. Human study: This study was carried out in strict accordance with the relevant regulations and requirements of the Declaration of Helsinki. Written informed consents were obtained from all participants and this study was permitted by the Ethics Committee of Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. Animal study: The procedures performed on all animals complied with the standards formulated by the Experimental Animal Ethics Committee of Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and obtained the approval of the said committee. Conflicts of interest: The authors declare no competing interests. Clinical trial number: Not applicable + + + + + 2026 + 1 + 31 + + + 2026 + 4 + 23 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 26 + + + aheadofprint + + 42215832 + 10.1007/s10565-026-10201-3 + 10.1007/s10565-026-10201-3 + + + + + + 42215825 + + 2026 + 05 + 29 + +
+ + 1618-1905 + + + 2026 + May + 30 + + + International microbiology : the official journal of the Spanish Society for Microbiology + Int Microbiol + + Gut microbial alterations and functional shifts in patients with hypertriglyceridemia: insights from a northwestern Chinese metagenomic study. + 10.1007/s10123-026-00845-w + + Although hypertriglyceridemia (HTG) is a significant contributor to lipid-associated pathologies such as atherosclerotic cardiovascular disease, its regulation by host‒microbiome interactions remain insufficiently characterized. While the gut microbiota (GM) is known to influence cholesterol metabolism, its specific role in systemic triglyceride (TG) homeostasis, particularly in non-Western populations, is poorly defined. This study aimed to identify preliminary robust GM signatures associated with HTG and to assess their translational potential using integrated multiomics and explainable machine learning approaches. In a cross-sectional investigation of 50 well-phenotyped adults from Northwest China, we combined 16S rRNA sequencing, shotgun metagenomics, and ensemble machine learning (LightGBM/XGBoost) to elucidate the associations between the GM and TGs. Microbial features were rigorously linked to serum lipid profiles through dual-algorithm validation and SHAP interpretability analysis, while functional potential was assessed via KEGG pathway mapping. Subjects with HTG exhibited a distinct gut microbial configuration, marked by consistent enrichment of Faecalibacterium and Bacteroides coprocola (positively correlated with serum TG levels) and depletion of Bifidobacterium pseudocatenulatum and Lactobacillus salivarius (inversely correlated). Machine learning converged on five exploratory consensus biomarker taxa, three of which were independently confirmed by LEfSe analysis (Faecalibacterium). Functional profiling further revealed the upregulation of microbial starch and sucrose metabolism pathways in the HTG cohort. Our findings establish a preliminary gut microbial signature for HTG patients and suggest context‑dependent associations of butyrate-producing taxa such as Faecalibacterium. By integrating multiomics with explainable artificial intelligence, this work addresses key challenges in reproducibility and mechanistic inference in microbiome research. These results pave the way for novel microbiota-targeted therapeutic strategies, including precision probiotics and dietary interventions, to modulate lipid metabolism, pending further validation in expanded cohorts and functional studies. + © 2026. The Author(s). + + + + Lv + Jing + J + + Department of Clinical Laboratory, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Wang + Ji-Han + JH + + Yan'an Medical College of Yan'an University, Yan'an, Shaanxi, 716000, China. + + + + Wang + Yang-Yang + YY + + School of Physics and Electronic Information, Yan'an University, Yan'an, Shaanxi, 716000, China. + + + + Huang + Jing + J + + Department of Clinical Pharmacy, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Chen + Fen-Rong + FR + + Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710000, China. + + + + Fang + Sa + S + + Department of Gastroenterology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Wang + Xiao-Juan + XJ + + Department of Gastroenterology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Li + Zi-Tong + ZT + + Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. + + + + Shi + Yu-Peng + YP + + Department of Gastroenterology, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. xjshiyupeng@163.com. + + + + Guo + Lei + L + + Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710054, China. guolei0711@163.com. + + + + eng + + + 2025JC-YBMS-916 + Shaanxi Natural Science Foundation of China + + + + No. 81702067 and 82560411 + National Natural Science Foundation of China + + + + + Journal Article + + + 2026 + 05 + 30 + +
+ + Switzerland + Int Microbiol + 9816585 + 1139-6709 + + IM + + Biomarker + Gut microbiota + Hypertriglyceridemia + Machine learning + Triglyceride + + Declarations. Competing interests: The authors declare no competing interests. Consent to participate: Informed consent was provided by all recruited subjects prior to the study. + + + + + 2026 + 3 + 18 + + + 2026 + 5 + 22 + + + 2026 + 5 + 11 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 26 + + + aheadofprint + + 42215825 + 10.1007/s10123-026-00845-w + 10.1007/s10123-026-00845-w + + + + The Writing Committee Of The Report On Cardiovascular Health And Diseases In China (2022) Key points of Report on Cardiovascular Health and Diseases in China 2021. Chin J Cardiovasc Res, 20(7): 577–596 + + + An HM et al (2011) Antiobesity and lipid-lowering effects of Bifidobacterium spp. in high fat diet-induced obese rats. 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+ + 1550-7416 + + 28 + 4 + + 2026 + May + 29 + + + The AAPS journal + AAPS J + + New Frontiers of Drug Development Through the Use of New Approach Methodologies. + 112 + 10.1208/s12248-026-01263-6 + + On November 2025, AAPS PharmSci 360 convened a symposium that included experts in the application of various types of New Approach Methodologies (NAMs). They shared their experiences and insights on topics that included microphysiological systems (MPS), the use of 3D organoids, and in silico tools. MPS systems were explored from the perspective of their testing and qualification through an academia-industry-government tissue chip testing consortium whose mission is to perform context-of-use-based testing of MPS and conduct comparative evaluations to support the use of MPS systems in safety assessments. 3D organoids were described and insights shared on how and when they may provide an appropriate tool for evaluating drug safety and effectiveness, and their use in supporting personalized medicine. Regarding in silico tools, examples were provided to describe their growing utility in predicting drug toxicity, population variability, and its potential benefits over traditional evidence-based toxicology. These tools include in silico models (e.g., physiologically based pharmacokinetic models), Artificial Intelligence (AI), and Machine Learning (ML) which, unlike other aspects of model informed drug development, reduce reliance on predefined models and allows for the integration of diverse sources of data. Computationally, these tools can generate predictions in hours where it would otherwise have taken weeks or months (and extensive experimentation). Within this meeting report, we highlight the key issues discussed during that symposium and share additional aspects to consider when developing a NAMs-based roadmap for specific contexts of use. + © 2026. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. + + + + Martinez + M N + MN + 0000-0002-4512-853X + + Food and Drug Administration, Center for Veterinary Medicine, Office of Generic Animal Drugs, CPK1 5001 Campus Drive, College Park, MD, 20740-3835, USA. marilyn.martinez@fda.hhs.gov. + + + + Mochel + J P + JP + 0000-0002-0997-3111 + + Precision One Health Initiative and Biomedical & Translational Sciences Institute (One Health), Diane L. Wynocker Endowed Professor, Athens, GA, USA. + + + + Allenspach + K + K + 0000-0002-2168-5809 + + Precision One Health and Department of Pathology, College of Veterinary Medicine, The University of Georgia, 501 D. W. Brooks Drive, Athens, GA, 30602, USA. + + + + Zdyrski + C + C + 0000-0002-7556-5298 + + Precision One Health and College of Veterinary Medicine, The University of Georgia, 501 D. W. Brooks Drive, Athens, GA, 30602, USA. + + + + Krishna + S + S + 0000-0002-4960-2753 + + Physicians Committee for Responsible Medicine, 5100 Wisconsin Ave., NW, Suite 400, Washington, DC, 20016, USA. + + + + Rusyn + I + I + 0000-0001-9340-7384 + + Department of Veterinary Physiology and Pharmacology, Texas A&M College of Veterinary Medicine & Biomedical Sciences, 4466 TAMU, College Station, TX, 77843, USA. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 29 + +
+ + United States + AAPS J + 101223209 + 1550-7416 + + IM + + + Drug Development + methods + trends + + + Humans + + + Microphysiological Systems + + + Computer Simulation + + + Organoids + + + Animals + + + Machine Learning + + + Artificial Intelligence + + + + AI + NAMs + PBPK models + organ-on-a-chip + organoid + + Declarations. Disclaimer: The findings and conclusions in this research have not been formally disseminated by the Food and Drug Administration and should not be construed to represent any Agency endorsement, determination, or policy. The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the FDA. Animal Use and Assurances: No animals were used in the writing of this Commentary. A.I. Statement: "No, but part of the graphical abstract had assist from Google AI. This was indicated in the figure.". Conflict of interest: Ivan Rusyn has no conflicts of interest to declare. Jonathan Mochel is a co-founder of 3D Health Solutions Inc., a small biotechnology company specializing in the development of animal-derived organoids for drug testing. Karin Allenspach co-founder of 3D Health Solutions. She serves as a consultant for Ceva Animal Health, Antech Diagnostics, hills, Royal Canin, Purina, and Heel GmbH. Christopher Zdyrski is the Director of Research and Product Development at 3D Health Solutions. Shagun Krishna has no conflicts of interest to declare. Marilyn N. Martinez has no conflicts of interest to declare. + + + + + 2026 + 3 + 12 + + + 2026 + 5 + 18 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 25 + + + epublish + + 42215821 + 10.1208/s12248-026-01263-6 + 10.1208/s12248-026-01263-6 + + + + Avila AM, Bebenek I, Mendrick DL, Peretz J, Yao J, Brown PC. 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+ + 2730-6011 + + + 2026 + May + 29 + + + Discover oncology + Discov Oncol + + Exercise-related genes predicts overall survival and tumor immune microenvironment, and identifies the biological role of SLC52A2 in breast cancer. + 10.1007/s12672-026-05321-5 + + Breast cancer (BRCA) is the most common malignant tumor in women, with prognoses differing markedly by molecular subtype. Exercise is known to be related to tumor occurrence and progression, yet its role in BRCA remains understudied. This research seeks to explore the prognostic and immunomodulatory functions of exercise-related genes (ERGs) in BRCA, and their links to molecular subtypes, so as to inform targeted and precision treatment. + We obtained BRCA gene expression profiles and clinical data from the TCGA-BRCA project. Using LASSO, random forest, gradient boosting machine (GBM), stepwise Cox regression (Stepcox), and extreme gradient boosting (XGBOOST) algorithms, we screened prognostic signature genes from the intersecting ERGs, determined by differential expression analysis and weighted gene co-expression network analysis (WGCNA), established and validated a novel risk prediction model. We further analyzed biological functions, mutation profiles, immune traits, and molecular subtype associations across different risk groups. Additionally, the prognostic, biological and immunological roles of the core risk biomarker in the exercise-related signature was evaluated. + WGCNA identified 345 differentially expressed ERGs in BRCA, and five machine learning algorithms further screened 6 prognostic genes (SNX22, SLC52A2, TNFRSF18, NFE2, EZR, and EMP1). The low-risk group had significantly higher overall survival rate than the high-risk group, a difference driven by the functional roles of the two groups' differentially expressed genes, which regulated the cell cycle and epithelial-mesenchymal transition (EMT). Additionally, the high- and low-risk groups showed distinct mutation and immunomodulatory features. Consensus clustering of prognosis between the groups linked these differences to BRCA molecular subtypes. As a core candidate factor, SLC52A2 contributed to BRCA progression and poor prognosis in patients by participating in multiple signaling pathways and regulating immune cells. + ERGs-based prognostic signatures could distinguish clinical outcomes of BRCA patients and were correlated with immune regulation and mutation patterns. The ERGs-constructed risk scoring system and molecular subtypes have the potential to serve as preclinical biological and immunological markers, paving the way for clinical management and treatment of BRCA. + © 2026. The Author(s). + + + + Chen + Caiping + C + + Department of Breast Surgery, The Affiliated Hospital of Jiaxing University (The First Hospital of Jiaxing), Jiaxing, China. + + + + Han + Chao + C + + Department of Breast Surgery, The Affiliated Hospital of Jiaxing University (The First Hospital of Jiaxing), Jiaxing, China. + + + + Xue + Li + L + + Department of Breast Surgery, The Affiliated Hospital of Jiaxing University (The First Hospital of Jiaxing), Jiaxing, China. + + + + Lu + Xiang + X + + Department of Breast Surgery, The Affiliated Hospital of Jiaxing University (The First Hospital of Jiaxing), Jiaxing, China. 00187649@zjxu.edu.cn. + + + + eng + + + 2023-FC-001 + Jiaxing Key Discipiline of Medcine - -Surgery Med (Mastopathy) + + + + 2023-PYXK-001 + Jiaxing Provinces and Cities Jointly Cultivate Discipline - -General Surgery (Minimally Invasive) + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + Discov Oncol + 101775142 + 2730-6011 + + + Breast cancer + Exercise + Genetic mutation + Immune response + Molecular cluster + Prognosis + + Declarations. Ethics approval and consent to participate: The cohort data used in this article were all from online databases and therefore did not involve ethics. Consent to participate is not applicable to this study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2025 + 11 + 28 + + + 2026 + 5 + 26 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 25 + + + aheadofprint + + 42215809 + 10.1007/s12672-026-05321-5 + 10.1007/s12672-026-05321-5 + + + + + + 42215746 + + 2026 + 05 + 29 + +
+ + 1435-2451 + + + 2026 + May + 30 + + + Langenbeck's archives of surgery + Langenbecks Arch Surg + + Relationship between ileocolic arterial stump length and lymph node yield - a retrospective study using machine learning. + 10.1007/s00423-026-04048-9 + + In right-sided colon cancer surgery, ileocolic artery stump length may reflect the extent of mesenteric resection and lymph node harvest. This study evaluated the association between postoperative stump length measured on CT, lymph node yield, and recurrence or progression within 24 months. + This single-center retrospective study included 64 patients who underwent right or extended right hemicolectomy for right-sided colon adenocarcinoma between 2016 and 2024. Right-sided tumors were defined as those located in the cecum, ascending colon, hepatic flexure, or proximal transverse colon. Only primary surgical cases were included. Ileocolic artery stump length was measured on contrast-enhanced CT scans performed approximately 12 months postoperatively (± 1 month) by two independent radiologists. A Random Forest regression model using stump length as the only predictor variable was developed to estimate lymph node yield. Model performance was assessed using 10-fold cross-validation with R² and mean squared error (MSE). Exploratory recurrence/progression analysis used logistic regression with natural cubic splines. + Mean stump length was 2.03 cm (range 1.0-4.5 cm), and median lymph node yield was 17 (range 10-55). Adequate nodal evaluation (≥ 12 nodes) was achieved in 85.9% of patients. The model demonstrated a consistent inverse relationship between stump length and lymph node yield (R² = 0.78; MSE = 0.16). Exploratory analysis suggested a non-significant trend toward higher recurrence or progression with longer stumps. + Longer ileocolic artery stumps were associated with reduced lymph node yield, suggesting stump length may reflect the extent of mesenteric resection. External validation is required before clinical application. + © 2026. The Author(s). + + + + Zayas-Borquez + Rogelio + R + 0000-0003-1751-4211 + + Universidad Nacional Autónoma de México, Mexico City, Mexico. rogeliozb94@unam.edu. + + + + Arizaga-Ramírez + María Sofía + MS + 0009-0004-2981-0530 + + Department of Radiology and Imaging, National Institute of Medical Sciences and Nutrition "Salvador Zubirán", Mexico City, Mexico. + + + + Canto-Losa + Jorge + J + 0000-0003-3768-3688 + + Universidad Nacional Autónoma de México, Mexico City, Mexico. jorge.canto@incmnsz.mx. + + + + Gándara-Lois + Ana C + AC + 0009-0001-0265-1726 + + Department of Radiology and Imaging, National Institute of Medical Sciences and Nutrition "Salvador Zubirán", Mexico City, Mexico. + + + + Salgado-Nesme + Noel + N + 0000-0002-3559-5070 + + Department of Colorectal Surgery, National Institute of Medical Sciences and Nutrition "Salvador Zubirán", Vasco de Quiroga 15, Belisario Domínguez Seccion 16, Tlalpan, Mexico City, PC 14080, Mexico. + + + + Alvarez-Bautista + Francisco + F + 0000-0003-2645-832X + + Department of General and Endoscopic Surgery, "Dr. Manuel GEA González" General Hospital, Mexico City, Mexico. + + + + Santes + Oscar + O + 0000-0002-6393-4607 + + Department of Colorectal Surgery, National Institute of Medical Sciences and Nutrition "Salvador Zubirán", Vasco de Quiroga 15, Belisario Domínguez Seccion 16, Tlalpan, Mexico City, PC 14080, Mexico. + + + + eng + + Journal Article + + + 2026 + 05 + 30 + +
+ + Germany + Langenbecks Arch Surg + 9808285 + 1435-2443 + + IM + + Central vascular ligation + Complete mesocolic excision + Lymph node retrieval + Machine learning + Right hemicolectomy + + Declarations. Ethics approval: This study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board of the corresponding institution. Given the retrospective nature of the study and the use of anonymized data, the requirement for informed consent was waived. Informed consent: Informed consent was waived due to the retrospective design of the study and the use of anonymized patient data. Competing interests: The authors declare no competing interests. + + + + + 2026 + 1 + 16 + + + 2026 + 4 + 11 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 23 + + + aheadofprint + + 42215746 + 10.1007/s00423-026-04048-9 + 10.1007/s00423-026-04048-9 + + + + + + 42215700 + + 2026 + 05 + 29 + +
+ + 1437-1596 + + + 2026 + May + 30 + + + International journal of legal medicine + Int J Legal Med + + Proteomic profiling of bone for the estimation of post-mortem interval and post-mortem submersion interval: a systematic review. + 10.1007/s00414-026-03844-8 + + Accurate estimation of the Post-Mortem Interval (PMI) and Post-Mortem Submersion Interval (PMSI) remains a persistent challenge in forensic science, especially when traditional morphological and entomological methods fail due to advanced decomposition or in aquatic environments. Proteomic profiling of bone tissues has recently emerged as a promising approach, leveraging the predictable degradation patterns of bone proteins to estimate time since death more reliably. This systematic review, conducted in accordance with PRISMA guidelines, analyzed 24 peer-reviewed studies focusing on the application of proteomic techniques to bone tissue for PMI and PMSI estimation. The included studies were evaluated based on sample type, analytical techniques used, identified biomarkers, environmental conditions assessed, and the overall reliability and reproducibility of the findings. The review found that specific bone proteins, particularly collagen, osteocalcin, fetuin-A, etc. exhibited consistent degradation patterns that correlated strongly with elapsed post-mortem time. Cortical bone was identified as a more stable and informative matrix compared to trabecular bone. Mass spectrometry, especially LC-MS/MS, emerged as the predominant analytical technique due to its high sensitivity and accuracy in detecting low-abundance proteins over extended PMIs and PMSIs. However, protein degradation rates were significantly influenced by environmental variables such as temperature, humidity, soil pH, and microbial activity. This review also emphasizes the transformative role of bone proteomics in advancing forensic science while identifying key gaps that must be addressed to achieve global standardization and practical implementation in diverse forensic contexts. The integration of proteomics with other emerging technologies, such as machine learning algorithms and computational modeling, may further enhance the precision of PMI and PMSI estimation in future applications. + © 2026. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. + + + + Dalal + Jyoti + J + 0000-0002-4690-6452 + + School of Forensic Sciences, Amity University Punjab, Mohali, Punjab, India. jyotidalal417@gmail.com. + + + + Kumari + Pallavi + P + + School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India. + + + Department of Forensic Science, University Institute Allied Health Sciences, Chandigarh University, Mohali, Punjab, India. + + + + Gupta + Vinayak + V + + School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India. + + + + Chhikara + Anjali + A + + School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 30 + +
+ + Germany + Int J Legal Med + 9101456 + 0937-9827 + + IM + + Bone Biomarkers + Forensic Proteomics + Post-Mortem Interval (PMI) + Post-Mortem Submersion Interval (PMSI) + Protein degradation + Proteomic profiling + Systematic review + + Declarations. Competing Interest: None. + + + + + 2025 + 5 + 8 + + + 2026 + 5 + 17 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 22 + + + aheadofprint + + 42215700 + 10.1007/s00414-026-03844-8 + 10.1007/s00414-026-03844-8 + + + + Amendt J, Richards CS, Campobasso CP, Zehner R, Hall MJ (2011) Forensic entomology: applications and limitations. Forensic science, medicine, and pathology 7(4):379–392. https://doi.org/10.1007/s12024-010-9209-2 + + 10.1007/s12024-010-9209-2 + 21213072 + + + + Barrios M, Wolff M (2011) Initial study of arthropods succession and pig carrion decomposition in two freshwater ecosystems in the Colombian Andes. 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Rapid Commun Mass Spectrom 28(6):605–615. https://doi.org/10.1002/rcm.6821 + + 10.1002/rcm.6821 + 24519823 + 4282581 + + + + Wadsworth C, Procopio N, Anderung C, Carretero JM, Iriarte E, Valdiosera C, Elburg R, Penkman K, Buckley M (2017) Comparing ancient DNA survival and proteome content in 69 archaeological cattle tooth and bone samples from multiple European sites. J Proteom 158:1–8. https://doi.org/10.1016/j.jprot.2017.01.004 + + 10.1016/j.jprot.2017.01.004 + + + + Widya M, Moffatt C, Simmons T (2012) The formation of early stage adipocere in submerged remains: a preliminary experimental study. J Forensic Sci 57(2):328–333. https://doi.org/10.1111/j.1556-4029.2011.01980.x + + 10.1111/j.1556-4029.2011.01980.x + 22150268 + + + + Woess C, Unterberger SH, Roider C, Ritsch-Marte M, Pemberger N, Cemper-Kiesslich J, Hatzer-Grubwieser P, Parson W, Pallua JD (2017) Assessing various infrared (IR) microscopic imaging techniques for post-mortem interval evaluation of human skeletal remains. PLoS One 12(3):e0174552. https://doi.org/10.1371/journal.pone.0174552 + + 10.1371/journal.pone.0174552 + 28334006 + 5363948 + + + + Zapico SC, Adserias-Garriga J (2022) Postmortem Interval Estimation: New Approaches by the Analysis of Human Tissues and Microbial Communities’ Changes. Forensic Sci 2(1):163–174. https://doi.org/10.3390/forensicsci2010013 + + 10.3390/forensicsci2010013 + + + + Zeng WF, Zhou XX, Willems S et al (2022) AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics. Nat Commun 13:7238. https://doi.org/10.1038/s41467-022-34904-3 + + 10.1038/s41467-022-34904-3 + 36433986 + 9700817 + + + + Zerbino, D. R., Achuthan, P., Akanni, W., Amode, M. R., Barrell, D., Bhai, J., Billis,K., Cummins, C., Gall, A., Girón, C. G., Gil, L., Gordon, L., Haggerty, L., Haskell,E., Hourlier, T., Izuogu, O. G., Janacek, S. H., Juettemann, T., To, J. K., Laird,M. R., … Flicek, P. (2018). Ensembl 2018. Nucleic acids research, 46(D1), D754–D761. https://doi.org/10.1093/nar/gkx1098 + + + Zhang J, Xin L, Shan B, Chen W, Xie M, Yuen D, Zhang W, Zhang Z, Lajoie GA, Ma B (2012) PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification. Mol Cell proteomics: MCP 11(4) M111.010587. https://doi.org/10.1074/mcp.M111.010587 + + + Zhang M, Su Q, Lu Y, Zhao M, Niu B (2017) Application of Machine Learning Approaches for Protein-protein Interactions Prediction. Medicinal chemistry (Shariqah (United Arab Emirates)), 13(6), 506–514. https://doi.org/10.2174/1573406413666170522150940 + + + Zissler A, Stoiber W, Steinbacher P, Geissenberger J, Monticelli FC, Pittner S (2020) Postmortem protein degradation as a tool to estimate the PMI: a systematic review. Diagnostics (Basel Switzerland) 10(12):1014. https://doi.org/10.3390/diagnostics10121014 + + 10.3390/diagnostics10121014 + 33256203 + 7760775 + + + + + + + + 42215696 + + 2026 + 05 + 29 + +
+ + 1546-170X + + + 2026 + May + 29 + + + Nature medicine + Nat Med + + From donor lungs to digital twins. + 10.1038/d41591-026-00029-z + + + O'Leary + Karen + K + + + eng + + News + + + 2026 + 05 + 29 + +
+ + United States + Nat Med + 9502015 + 1078-8956 + + IM + + Machine Learning + Technology + Translational research + + + + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 22 + + + aheadofprint + + 42215696 + 10.1038/d41591-026-00029-z + 10.1038/d41591-026-00029-z + + + + + + 42215691 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + O²RDL-net for joint risk classification and delay forecasting in logistics systems using interaction amplified deep. + 10.1038/s41598-026-55703-6 + + Transportation and logistics systems face frequent delays and operational risks due to congestion, human factors, and rapidly changing conditions. Predicting delays and identifying risk levels in advance is therefore essential for reliable logistics planning. Most existing methods handle delay forecasting and risk classification separately and often fail to capture complex temporal and interaction-driven patterns. To overcome these limitations, this study proposes O²RDL-Net, a load-aware and interaction-amplified deep learning framework for joint risk classification and delay forecasting. The proposed model integrates higher-order operational indicators, including the Risk Accumulation Index, Delay Sensitivity Indicator, Human-Operational Interaction Score, and Logistics System Representation, to capture cumulative risk, short-term sensitivity, and system-level interactions. A spatio-temporal attention encoder with risk regime modulation is used to improve learning stability and generalization. Experimental results show that O²RDL-Net achieves superior performance compared to baseline models, with a mean absolute error of 2.05, RMSE of 3.01, and R² of 0.91 for delay forecasting. For risk classification, the model reaches an accuracy of 92%, precision of 91%, recall of 92%, and F1-score of 91%. Interpretability analysis using SHAP and local explanations confirms that the model relies on meaningful and domain-consistent features. Overall, O²RDL-Net provides an accurate, stable, and interpretable solution for real-world logistics risk and delay management. + © 2026. The Author(s). + + + + Yang + Xiao + X + + Low-altitude Management and Digital Economy College, XIHANG UNIVERSITY, Xi'an, 710077, China. jonah-y@xaau.edu.cn. + + + + eng + + + Grant No. 2025YB0214 + Research on the Coupled and Coordinated Development of Transportation and Modern Industrial System in Shaanxi Province + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Artificial intelligence + Deep Learning + Feature Construction + Forecasting + Logistics Network + Machine Learning + Risk Prediction + Supply Chain + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 2 + 9 + + + 2026 + 5 + 26 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215691 + 10.1038/s41598-026-55703-6 + 10.1038/s41598-026-55703-6 + + + + + + 42215678 + + 2026 + 05 + 29 + +
+ + 2398-6352 + + + 2026 + May + 29 + + + NPJ digital medicine + NPJ Digit Med + + Detecting dataset bias in medical AI using a generalized and modality agnostic auditing approach. + 10.1038/s41746-026-02807-y + + Despite many success stories along the path of Artificial Intelligence's (AI) rise in healthcare, there are comparably many reports of significant shortcomings and unexpected behavior of AI in deployment. A major risk is AI's reliance on association-based learning, where non-representative machine learning datasets can amplify latent bias during training and hide it during testing. To unlock new tools capable of detecting such AI bias issues, we present Generalized Attribute Utility and Detectability-Induced bias Testing (G-AUDIT). G-AUDIT is a data modality-agnostic dataset auditing approach that automatically quantifies shortcut learning risks by examining the interplay between task-level annotations, sensor-level measurements, and patient, environmental, and acquisition characteristics. We demonstrate the broad applicability of this method by analyzing a range of medical datasets across three distinct modalities (images, text, and tabular data) and machine learning tasks, successfully identifying potential shortcuts commonly overlooked by traditional qualitative methods. + © 2026. The Author(s). + + + + Drenkow + Nathan + N + + The Johns Hopkins University, Baltimore, MD, USA. nathan.drenkow@jhuapl.edu. + + + The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA. nathan.drenkow@jhuapl.edu. + + + + Pavlak + Mitchell + M + + The Johns Hopkins University, Baltimore, MD, USA. + + + + Harrigian + Keith + K + + The Johns Hopkins University, Baltimore, MD, USA. + + + + Zirikly + Ayah + A + + The Johns Hopkins University, Baltimore, MD, USA. + + + Department of Computer Science, School of Engineering and Applied Science, George Washington University, Washington, DC, USA. + + + + Subbaswamy + Adarsh + A + + Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA. + + + + Farhangi + Mohammad Mehdi + MM + + Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA. + + + + Petrick + Nicholas + N + + Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA. + + + + Unberath + Mathias + M + + The Johns Hopkins University, Baltimore, MD, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Digit Med + 101731738 + 2398-6352 + + Competing interests: The authors declare no competing interests. + + + + + 2025 + 6 + 3 + + + 2026 + 5 + 18 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215678 + 10.1038/s41746-026-02807-y + 10.1038/s41746-026-02807-y + + + + + + 42215677 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Stacked multi-fusion CNN: an adaptive attention model for privacy preserving deepfake forensics. + 10.1038/s41598-026-55325-y + + The emergence of Generative Artificial Intelligence (Gen-AI) and Generative Adversarial Network (GAN)-based deepfakes poses significant security risks in sociocultural and sociopolitical domains. This necessitates the development of advanced and effective detection methods to prevent vulnerability in social networks. Classical Machine Learning (ML) algorithms have their limitations, especially in classifying the deepfakes. To address these issues, this paper suggests a privacy-preserving Stacked Multi-Fusion (SMF) Convolutional Neural Network (CNN) approach to classify deepfakes. An improved CNN model is proposed, integrating an adaptive multi-scale attention framework with enhanced residual blocks and a Squeeze-and-Excitation (SE) mechanism. The selection of these components is backed with an ablation study to report the individual contribution to the overall optimal architecture. A hybrid lossless multilayer cryptosystem based on a chaos-based approach, Deoxyribonucleic Acid (DNA)-based computing, etc., is developed to secure images in cloud storage. The efficacy of the proposed SMF model is validated using the 140K Real and Fake Faces (RFF) image dataset. The proposed approach was found to achieve stable performance with minimal variation in various tests. It achieved a test accuracy and ROC-AUC of 97.80 and 99.79, respectively. This paper provides comprehensive relevant factors for building effective synthetic media detection systems. + © 2026. The Author(s). + + + + Rout + Jayanti + J + + P. G. Department of Computer Science, Fakir Mohan University, Balasore, 756019, Odisha, India. + + + + Mishra + Minati + M + + P. G. Department of Computer Science, Fakir Mohan University, Balasore, 756019, Odisha, India. + + + + Barik + Ram Chandra + RC + + Department of CSE, C. V. Raman Global University, Bhubaneswar, 752054, Odisha, India. + + + + Yadav + Devendra Kumar + DK + + School of Computer Science and Engineering, XIM University, Bhubaneswar, 752050, Odisha, India. devendra@xim.edu.in. + + + + Prasad + Dilip K + DK + + Department of Computer Science, UiT The Arctic University of Norway, Tromsø, 9037, Norway. + + + + Sekh + Arif Ahmed + AA + + Department of Computer Science, UiT The Arctic University of Norway, Tromsø, 9037, Norway. arif.a.sekh@uit.no. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Convolutional neural network + Deepfake detection + Squeeze-and-excitation attention + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2025 + 10 + 25 + + + 2026 + 5 + 23 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215677 + 10.1038/s41598-026-55325-y + 10.1038/s41598-026-55325-y + + + + + + 42215670 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + A hybrid AI vision model for single-camera tracking of rehabilitation exercises: a proof of concept in clinical settings. + 10.1038/s41598-026-55431-x + + Accurate measurement of range of motion (ROM) during physical rehabilitation is traditionally achieved using goniometers or multi-camera, marker-based motion capture systems. The latter, while highly accurate, require specialised laboratory infrastructure, are costly, and are unsuitable for home-based use. There is growing interest in markerless, camera-based alternatives that leverage artificial intelligence (AI) for joint angle estimation. This study presents a preliminary proof-of-concept evaluation of a hybrid AI vision system, combining the MediaPipe Pose framework (version 0.8.9.1, Full model variant) with a Mamdani-type fuzzy inference system, for joint angle estimation and exercise repetition counting using a single consumer-grade camera. Fourteen healthy adult rehabilitation staff members performed three exercises (elbow flexion, knee extension, and hip external rotation) simultaneously recorded by the proposed AI system and by a reference optical motion capture system (C-Motion Visual3D™, 14 calibrated infrared cameras, 53 retro-reflective markers). Reliability was assessed using two-way mixed-effects intraclass correlation coefficients (ICC[Formula: see text]) with absolute agreement. Individual-measure ICC values ranged from poor to good across joints and conditions (range: 0.005-0.68). The highest reliability was observed for the right hip external rotation ROM (individual ICC [Formula: see text]; average ICC [Formula: see text], 95% CI: 0.63-0.89). Several measures showed poor reliability, including the right elbow flexion ROM (individual ICC [Formula: see text]) and the minimum angle of the left hip external rotation (individual ICC [Formula: see text]). Confidence intervals were wide throughout. This proof of concept demonstrates that a single-camera AI system can capture general trends in joint angles during simple rehabilitation exercises in healthy adults under controlled conditions. However, reliability is inconsistent and frequently poor, and the system has not been evaluated in patient populations with pathological movement patterns. These findings do not support clinical deployment at this stage. Substantial further development and validation in patient cohorts are required before clinical or home-based use can be considered. + © 2026. The Author(s). + + + + Das + Utpal Chandra + UC + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. + + + School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Kelvin Grove, Brisbane, QLD, 4059, Australia. + + + Centre for Biomedical Technologies (CBT), Queensland University of Technology, Brisbane, QLD, 4000, Australia. + + + + Le + Ngoc Thien + NT + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. + + + + Vitoonpong + Timporn + T + + Department of Rehabilitation Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand. timporn.v@chula.ac.th. + + + Department of Rehabilitation Medicine, King Chulalongkorn Memorial Hospital & The Thai Red Cross Society, Bangkok, 10330, Thailand. timporn.v@chula.ac.th. + + + + Pongmala + Chatkaew + C + + Department of Radiology, University of Michigan, Ann Arbor, 48104, MI, USA. + + + Department of Neurology, University of Michigan, Ann Arbor, 48104, MI, USA. + + + + Prapinpairoj + Chalermdej + C + + Department of Rehabilitation Medicine, King Chulalongkorn Memorial Hospital & The Thai Red Cross Society, Bangkok, 10330, Thailand. + + + + Anannub + Kawee + K + + Health Service Center, Chulalongkorn University, Bangkok, 10330, Thailand. + + + + Kaewplung + Pasu + P + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. + + + + Chaitusaney + Surachai + S + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. surachai.c@chula.ac.th. + + + + Benjapolakul + Watit + W + + Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand. watit.b@chula.ac.th. + + + + eng + + + B04G640068 + National Science, Research and Innovation Fund (NSRF) via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + Competing interests: The authors declare no competing interests. Ethical approval: The research conducted with healthy human participants at Chulalongkorn University in Thailand complied with the Regulations Governing Life Science and Medical Research Involving Human Subjects. Approval was secured from the Institutional Review Board of the Faculty of Medicine, Chulalongkorn University, Thailand (COA No. 0495-2022; IRB No. 846-64), in accordance with the Declaration of Helsinki, The Belmont Report, CIOMS Guidelines, and ICH-GCP. Informed consent: All study participants provided written informed consent for participation and for the publication of identifiable information and images. Participants were fully informed of the study objectives, dissemination plans, and potential risks and benefits prior to enrolment. + + + + + 2025 + 9 + 20 + + + 2026 + 5 + 25 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215670 + 10.1038/s41598-026-55431-x + 10.1038/s41598-026-55431-x + + + + + + 42215668 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Autism spectrum disorder identification using machine learning models on MRI data. + 10.1038/s41598-026-55163-y + + Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects sensory processing, speech, behavior and identifying the condition at an early stage is necessary to provide the treatment. The existing diagnostic methods are mainly based on personalized subjective and time consuming. This study introduces an objective diagnostic method by analyzing technical noise signature producing the Quality Vector which is extracted from multimodal MRI data. This work leverages Machine Learning (ML) and Deep Learning (DL) techniques on this Quality vector for improving ASD detection. The methodology leveraged technical Quality Assessment Protocol (QAP) metrics from the ABIDE II repository, integrating structural (sMRI), functional (fMRI), and diffusion (DTI) modalities. The methodology utilized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for outlier removal and Principal Component Analysis (PCA) for dimensionality reduction. A classification pipeline was implemented using a 1D-Residual Network (1D-ResNet), Convolutional Neural Network(CNN), SVM, kNN and a proposed Voting Ensemble (SVM, KNN, and XGBoost), validated through stratified 10-fold cross-validation. In this work, the proposed ensemble emerged as the superior model, achieving a diagnostic accuracy of 95.84%. These findings indicate that technical biomarkers derived from imaging quality metrics are highly useful for ASD detection. This research demonstrates that technical quality metrics, often dismissed as noise, contain significant diagnostic value. The Quality Vector framework provides a computationally efficient and objective tool for ASD identification. The high classification performance suggests that the biomarkers obtained from MRI have a potential future scope in research on early ASD diagnosis and detection. + © 2026. The Author(s). + + + + V + Milner Paul + MP + + Department of Electrical Engineering, National Institute of Technology Manipur (NITM), Manipur, India. vithayathilmilner@gmail.com. + + + Department of Electrical and Electronics Engineering, Christ College of Engineering, Thrissur, Kerala, 680125, India. vithayathilmilner@gmail.com. + + + + Babu + Caren + C + + Department of Electronics and Communication Engineering, Christ College of Engineering, Thrissur, Kerala, 680125, India. + + + + Jose + Suni + S + + Department of Computer Science and Engineering, Christ College of Engineering, Thrissur, Kerala, 680125, India. + + + + Adhikari + Shuma + S + + Department of Electrical Engineering, National Institute of Technology Manipur (NITM), Manipur, India. + + + + Singh + Loitongbam Surajkumar + LS + + Department of Electronics and Communication Engineering, National Institute of Technology Manipur (NITM), Manipur, India. + + + + Francis + Jijo + J + + Department of Zoology, Christ College (Autonomous), 680125, Irinjalakuda, India. + + + + Parekkattil + Adarsh V + AV + + Department of Electronics and Communication, Indian Institute of Technology Roorkee (IITR), Roorkee, India. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Autism spectrum disorder + Convolutional neural network (CNN) + Early detection + Machine learning + Magnetic resonance imaging + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2025 + 7 + 8 + + + 2026 + 5 + 22 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215668 + 10.1038/s41598-026-55163-y + 10.1038/s41598-026-55163-y + + + + + + 42215666 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Blockchain-driven machine learning-enabled intrusion-resilient authenticated key agreement protocol for edge-centric IoT systems. + 10.1038/s41598-026-55266-6 + + The edge computing-based Internet of Things (IoT) system minimizes latency by processing data locally, reducing the distance it needs to travel. Processing data in proximity to its source enables rapid decision-making and real-time reactions. The edge-based IoT has several possible uses, including smart cities, smart healthcare, industrial automation & processing, smart farming, and many more. In this paper, we propose a blockchain-driven machine learning-enabled intrusion-resilient authenticated key agreement scheme for edge-centric IoT systems (in short, BMAS-EIoT), which is equipped with the features of authentication, key management, and machine learning-based intrusion detection. In BMAS-EIoT, we provide the network and threat models to enhance comprehension of the organization and deployment of devices and systems, as well as the potential threats to the system. BMAS-EIoT has been observed to possess protection against a variety of potential attacks during the security investigation. Moreover, it has been observed that BMAS-EIoT outperforms other present schemes in terms of performance comparison. A practical implementation of BMAS-EIoT is provided to evaluate the effectiveness of its key components, including intrusion detection and blockchain implementation. Furthermore, BMAS-EIoT possesses supplementary noteworthy capabilities and enhanced security attributes. + © 2026. The Author(s). + + + + Karnatak + Vijay + V + + Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India. + + + + Tripathi + Neha + N + + Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India. + + + + Wazid + Mohammad + M + + Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, Uttarakhand, India. + + + + Mittal + Saksham + S + + Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, 248002, Uttarakhand, India. + + + + Das + Ashok Kumar + AK + + Center for Security, Theory and Algorithmic Research, International Institute of Information Technology, Hyderabad, 500032, India. ashok.das@iiit.ac.in. + + + Department of Computer Science and Engineering, College of Informatics, Korea University, 145 Anam-ro, Seongbuk-gu, 02841, Seoul, South Korea. ashok.das@iiit.ac.in. + + + + K + Vivekananda Bhat + VB + + Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India. kv.bhat@manipal.edu. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Authentication + Blockchain + Edge computing + Internet of things + Intrusion detection + Security + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 3 + 23 + + + 2026 + 5 + 22 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 21 + + + aheadofprint + + 42215666 + 10.1038/s41598-026-55266-6 + 10.1038/s41598-026-55266-6 + + + + + + 42215649 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Machine learning based approaches for structure activity relationship analysis of heparanase inhibitors. + 10.1038/s41598-026-55098-4 + + Human Heparanase (HPSE), the only mammalian endo-β-D-glucuronidase, plays an important role in extracellular matrix remodeling and the release of heparin-bound growth factors. Its overexpression is strongly correlated with increased tumor growth, angiogenesis, metastasis, and inflammation, highlighting HPSE as a compelling therapeutic target for oncology and inflammatory diseases. This study aimed to develop and validate a robust computational workflow for predicting the activity class of potential HPSE inhibitors using curated data from the ChEMBL database. Bioactivity data ([Formula: see text]) for known HPSE inhibitors were extracted and put through a meticulous data curation process, which included chemical structure standardization, molecular weight filtering, and final deduplication based on standardized isomeric SMILES to ensure structural uniqueness. Continuous [Formula: see text] values (nM) were converted to [Formula: see text] and subsequently categorized into three activity classes: A ([Formula: see text] and [Formula: see text]), B ([Formula: see text] and [Formula: see text]), and C ([Formula: see text] and [Formula: see text]) for multi-class classification. Molecular representations included two-dimensional physicochemical descriptors, Morgan fingerprints, and three-dimensional descriptors derived from optimized low-energy conformers generated using ETKDGv3 and MMFF94s. Multiple machine learning classifiers were evaluated using pipelines incorporating imputation, scaling, optional Principal Component Analysis (PCA) dimensionality reduction applied to the combined feature sets, and SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance. Models were trained and optimized using randomized search cross-validation on an 80% training split, maximizing balanced accuracy. The best-performing model pipeline (RF_B, a Random Forest with PCA on 2D+Morgan Fingerprints+3D features) achieved approximately 80% accuracy and 78.5% balanced accuracy on the held-out 20% test set. The final validated model was successfully utilized to predict the activity classes of new, unseen compounds. This comprehensive pipeline provides a validated tool for classifying HPSE inhibitors derived from ChEMBL data, potentially aiding virtual screening efforts and guiding hit prioritization in drug discovery campaigns targeting HPSE. + © 2026. The Author(s). + + + + Shanbhogue + Rachana V + RV + + Manipal Institute of Technology, Manipal Academy of Higher Education, 576104, Manipal, Karnataka, India. shanbhoguerachana@gmail.com. + + + + Gandhi + Neha S + NS + + Department of Biotechnology and Chemical Engineering, Manipal University Jaipur, 303007, Rajasthan, India. neha.gandhi@jaipur.manipal.edu. + + + School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, Queensland, Brisbane, Australia. neha.gandhi@jaipur.manipal.edu. + + + + B + Shanthi P + SP + + Manipal Institute of Technology, Manipal Academy of Higher Education, 576104, Manipal, Karnataka, India. shanthi.moorkoth@manipal.edu. + + + + Navada + Sandhyalaxmi G + SG + + Manipal Institute of Technology, Manipal Academy of Higher Education, 576104, Manipal, Karnataka, India. sandhya.girish@manipal.edu. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2025 + 11 + 10 + + + 2026 + 5 + 22 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 20 + + + aheadofprint + + 42215649 + 10.1038/s41598-026-55098-4 + 10.1038/s41598-026-55098-4 + + + + + + 42215613 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + A temporal adaptive dictionary-constrained LDA and Bi-calibrated dual granularity DTM framework for dynamic topic evolution analysis in academic papers. + 10.1038/s41598-026-54348-9 + + With the explosive growth of academic achievements in artificial intelligence (AI), accurately capturing the dynamic evolution of research topics is crucial for research topic selection, policy-making, and industrial innovation. To address the limitation that traditional topic models struggle to balance static topic recognition accuracy and dynamic evolution tracking capability, this study proposes a unified framework: Temporal Adaptive Dictionary-Constrained Latent Dirichlet Allocation (LDA) combined with Bi-calibrated Dual Granularity Dynamic Topic Model (DTM). We conduct a systematic empirical analysis of dynamic topic evolution in the AI field using English AI papers retrieved from the Web of Science (WOS) Core Collection (2014-2023). Specifically, we first optimize the topic initialization process of LDA using a domain-specific dictionary to enhance the accuracy of static topic clustering. Second, we take the core topics identified by the optimized LDA as prior knowledge and input them into DTM, while optimizing the traditional single temporal granularity to an "annual + quarterly" dual granularity to accurately track the changes in topic strength, keyword composition, and cross-topic association relationships over time. Finally, we verify the performance of the proposed model using perplexity and topic coherence indicators, and reveal the coupling characteristics between different topics through correlation analysis. Results indicate that six core topic clusters have emerged in the AI field, with "generative AI", "large language models", and "multimodal learning" evolving into explosive topics after 2021, traditional machine learning shows a declining trend, with its core focus shifting toward "few-shot learning" and "edge computing adaptation". The top three topic couplings are "generative AI-large language models", "computer vision-multimodal learning" and "reinforcement learning-robotics". The proposed combined model outperforms single models, achieving a 17.3% reduction in perplexity and a 23.5% decrease in topic drift rate, thereby providing valuable decision support for researchers seeking to grasp field frontiers and management departments for optimizing resource allocation. + © 2026. The Author(s). + + + + Yin + Xueqi + X + + School of International Studies, Tianjin University of Commerce, Tianjin, 300134, China. yinxq@tjcu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Academic paper analysis + Artificial intelligence + Dynamic topic model + Latent dirichlet allocation + Text mining + Topic evolution + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 1 + 30 + + + 2026 + 5 + 18 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215613 + 10.1038/s41598-026-54348-9 + 10.1038/s41598-026-54348-9 + + + + + + 42215604 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Development and validation of a noninvasive machine learning model using urinary extracellular vesicle physical parameters for prostate cancer diagnosis. + 10.1038/s41598-026-54920-3 + + Urinary extracellular vesicles (uEVs) are promising biomarkers for prostate cancer (PCa). Although novel uEV-based biomarkers have advanced early diagnosis, their detection processes remain cumbersome and cost-prohibitive. The physical parameters of uEVs offer untapped predictive potential for PCa, providing a more convenient, rapid, and cost-effective diagnostic approach. This study aims to construct a predictive model integrating uEV physical parameters with machine learning (ML) algorithms to enhance the early diagnosis of PCa. Urine samples were collected from 222 eligible participants. uEVs were isolated using a commercial kit, and their concentration and size parameters were quantified via nanoparticle tracking analysis (NTA). Utilizing these physical parameters, five ML algorithms were trained and evaluated to identify the optimal diagnostic model for PCa. Model performance was systematically assessed using the area under the receiver operating characteristic curve (AUC), learning curves, calibration curves, and decision curve analysis (DCA). Additionally, SHapley Additive exPlanations (SHAP) were employed to visualize the contributions of key predictors. Analysis of uEV physical parameters revealed that, compared to benign controls, PCa patients exhibited a significantly higher proportion of 30-150 nm uEVs and a smaller overall particle size (p < 0.001). Among the evaluated algorithms, the eXtreme Gradient Boosting (XGBoost) model demonstrated superior performance. For discriminating PCa from benign prostatic hyperplasia (BPH), the XGBoost model achieved AUC values of 0.934 and 0.864 in the training and testing cohorts, respectively. DCA demonstrated that the XGBoost model yielded a higher clinical net benefit across a threshold probability range of 0-60%. Overall, the diagnostic efficacy and clinical utility of the XGBoost model significantly outperformed routine clinical parameters, including prostate-specific antigen (PSA) and prostate-specific antigen density (PSAD). This study successfully developed and validated a noninvasive ML-based diagnostic model utilizing the physical parameters of uEVs. This approach serves as a preliminary adjunctive tool to assist clinicians in accurately identifying prostate cancer, thereby potentially reducing the incidence of unnecessary prostate biopsies. + © 2026. The Author(s). + + + + Jiang + Kangxian + K + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + Department of Surgical Critical Care Medicine, Beijing Shijitan Hospital, Capital Medical University, No.10, Tieyi Road, Beijing, 100038, China. + + + + Pan + Dongwei + D + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Yu + Jiayin + J + + Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + + Cai + Wenbing + W + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Zhang + Bingfeng + B + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Wang + Zuheng + Z + + Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China. + + + Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + + Lu + Yuqing + Y + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + + Wang + Dianyu + D + + Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China. + + + + Li + Xiao + X + + Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China. + + + Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. + + + + Zheng + Deyong + D + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Wang + Mingda + M + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. + + + + Wang + Fubo + F + + Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China. wangfubo@gxmu.edu.cn. + + + Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, Guangxi, China. wangfubo@gxmu.edu.cn. + + + School of Life Sciences, Guangxi Medical University, Nanning, 530021, Guangxi, China. wangfubo@gxmu.edu.cn. + + + + Chen + Junyi + J + + Department of Urology, The Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Road, Licheng District, Quanzhou, 362000, Fujian Province, China. chenjunyidoctor@163.com. + + + + eng + + + 82372828, + National Natural Science Foundation of China + + + + 2023GXNSFFA026003 + Science Foundation for Distinguished Young Scholars of Guangxi + + + + AA22096030 + Science and Technology Major Project of Guangxi + + + + 2021015 + Yongjiang Program of Nanning + + + + 2021GGA038 + the Science and Technology Plan Project of Fujian Provincial Health Commission + + + + 2022C035R + High-level Talents Innovation and Entrepreneurship Project of Quanzhou Science and Technology Plan + + + + 2022J01277 + Natural Science Foundation of Fujian Province + + + + 2025CXB023 + Fujian Provincial Health Commission + + + + 2026J001703 + Fujian Provincial Natural Science Foundation of China + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Diagnosis + Machine learning + NTA + Prostate cancer + Urinary extracellular vesicles + + Declarations. Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: The studies involving human participants were reviewed and approved by the Ethics Committee of Shanghai Changhai Hospital, the Ethics Committee of Taizhou People’s Hospital, and the Ethics Committee of Zhongda Hospital. (Approval No. CHEC2013-115).Clinical trial number: not applicable. This study strictly adhered to the ethical principles outlined in the Declaration of Helsinki, ensuring that all data were derived from actual clinical cases. Informed consent: Informed consent was obtained from each patient or their legal representative before sample collection. Consent for publication: Not applicable. + + + + + 2025 + 10 + 14 + + + 2026 + 5 + 21 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215604 + 10.1038/s41598-026-54920-3 + 10.1038/s41598-026-54920-3 + + + + + + 42215600 + + 2026 + 05 + 29 + +
+ + 2399-3669 + + + 2026 + May + 29 + + + Communications chemistry + Commun Chem + + CholBindNet as an interpretable neural network for cholesterol-binding site classification. + 10.1038/s42004-026-02064-w + + Cholesterol is a key modulator of membrane protein structure and function, yet predicting cholesterol-binding sites remains challenging due to its non-druglike physicochemical properties. Here, we curated more than 800 high-resolution transmembrane protein structures containing cholesterol, and developed an interpretable, atom-based graph neural network, called CholBindNet. A positive-unlabeled (PU) training strategy was employed to address the scarcity of negative samples due to the promiscuous nature of cholesterol binding. We show that CholBindNet substantially outperforms existing machine learning models trained on general ligand-binding datasets, including AlphaFold3, P2Rank, and DiffDock. The performance and generalizability of the model on unseen membrane proteins were further demonstrated by rapidly assessing cholesterol-binding sites in the PIEZO2 ion channel against all-atom molecular dynamics (MD) simulations conducted on Anton3 supercomputer. Additionally, strong model interpretability was achieved for CholBindNet through atom-level feature encoding, Grad-CAM visualization, and attention-based scoring analysis. Overall, CholBindNet provides an efficient and scalable approach for classifying and ranking cholesterol-binding sites on membrane proteins, achieving performance comparable to computationally expensive MD simulations while offering rich biophysical insights into the atomic-level spatial patterns beyond amino-acid sequence. This work lays the foundation for future deep-learning models targeting membrane protein drug-binding sites and cholesterol-modulated therapeutics. + © 2026. The Author(s). + + + + Hernandez + Alexis + A + + Computer Science Department, California State Polytechnic University, Pomona, CA, USA. + + + Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA, USA. + + + + Bhatt + Aashish + A + + Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA, USA. + + + + Revilla + Ivan + I + + Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA, USA. + + + + Levine + Jacob Ede + JE + + Computer Science Department, California State Polytechnic University, Pomona, CA, USA. + + + + Kosaraju + Sai Chandra + SC + 0000-0002-4332-6217 + + Computer Science Department, California State Polytechnic University, Pomona, CA, USA. skosaraju@cpp.edu. + + + + Luo + Yun Lyna + YL + 0000-0003-3581-754X + + Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA, USA. luoy@westernu.edu. + + + + eng + + + GM130834 + U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Commun Chem + 101725670 + 2399-3669 + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 1 + 1 + + + 2026 + 5 + 4 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215600 + 10.1038/s42004-026-02064-w + 10.1038/s42004-026-02064-w + + + + + + 42215593 + + 2026 + 05 + 29 + +
+ + 2397-768X + + + 2026 + May + 29 + + + NPJ precision oncology + NPJ Precis Oncol + + Early detection of gastric cancer: a novel circulating microbiome DNA based liquid biopsy assay. + 10.1038/s41698-026-01532-9 + + Microorganisms play significant roles in gastric cancer (GC) progression. However, it is unknown whether circulating microbiome DNA (cmDNA) possesses GC specific features and could serve as diagnostic biomarker for GC detection. In this study, one cohort of 586 participants from Zhejiang Cancer Hospital were divided randomly into the training and testing datasets. Another cohort of 299 participants enrolled from three hospitals was used as an independent validation cohort. The cmDNA from plasma samples were analyzed by sequencing and various tools. The significant features of cmDNA were used as inputs to establish a machine learning diagnostic model (cmDNA-MLM). The cmDNA-MLM achieved the area under receiver operating characteristic curve (AUC) of 0.831 for GC across all stages in the testing cohort and 0.914 in the independent validation cohort. Notably, this model demonstrated strong performance in detecting early-stage GC, achieving an AUC of 0.792 for stage I GC in the validation cohort and exhibited favorable sensitivities across various molecular subtypes. The stage shift analysis showed a notable increase in the number of patients diagnosed at stage I. This cmDNA-MLM exhibited promising performance in early GC detection, which could be used as a clinical liquid biopsy methodology after more clinical validation studies. + © 2026. The Author(s). + + + + Chen + Yongyi + Y + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China. + + + + Han + Xinhong + X + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Luo + Miao + M + + Department of Clinical Laboratory, Chongqing Yubei District People's Hospital, Chongqing, China. + + + + Luo + Liulin + L + + Department of Clinical Laboratory, Shanghai Yangpu Hospital, Tongji University School of Medicine, Shanghai, China. + + + + Wang + Tingzhang + T + + Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou, China. + + + + Kong + Congcong + C + + Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou, China. + + + + Ye + Liuqing + L + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Jin + Jiangang + J + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Hou + Dingding + D + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Liao + Haiqi + H + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. + + + + Wang + Zhonglin + Z + + OmixScience Laboratory, OmixScience Co. Ltd., Hangzhou, China. + + + + Xue + Wei + W + + OmixScience Laboratory, OmixScience Co. Ltd., Hangzhou, China. + + + + Lin + Ziao + Z + + OmixScience Research Institute, OmixScience Co. Ltd., Shenzhen, China. + + + Liangzhu Laboratory, Zhejiang University, Hangzhou, China. + + + + Xu + Songxiao + S + + Department of Clinical Laboratory, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. xusx@zjcc.org.cn. + + + School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China. xusx@zjcc.org.cn. + + + + eng + + + 82302622 + National Natural Science Foundation of China + + + + 2022KY675 + Zhejiang Province Medical and Health Technology Project + + + + 2024KY862 + Zhejiang Province Medical and Health Technology Project + + + + 2024C03050 + "Pioneer" and "Leading Goose" R&D Program of Zhejiang + + + + 2022C03002 + "Pioneer" and "Leading Goose" R&D Program of Zhejiang + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Precis Oncol + 101708166 + 2397-768X + + Competing interests: The authors declare no competing interests. + + + + + 2025 + 9 + 16 + + + 2026 + 5 + 22 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215593 + 10.1038/s41698-026-01532-9 + 10.1038/s41698-026-01532-9 + + + + + + 42215588 + + 2026 + 05 + 29 + +
+ + 2398-6352 + + + 2026 + May + 29 + + + NPJ digital medicine + NPJ Digit Med + + Development and validation of a machine learning-based diagnostic system for 22 pediatric respiratory pathogens: a large-scale multicenter study. + 10.1038/s41746-026-02818-9 + + Respiratory tract infections (RTIs) are a significant cause of morbidity in children, caused by a wide range of pathogens. As treatment strategies depend on the causative pathogen, early and accurate diagnosis is crucial. We developed and validated an interpretable Pathogen Diagnostic System for Pediatric Respiratory Infections (Pathog-PDx), conducting a multicenter study involving 134,500 hospitalized children across three clinical centers and two databases. The model integrated 42 clinical and laboratory features from electronic health records (EHR) to enable early pathogen identification. Prospective validation was carried out on an independent cohort of 1338 children to assess the real-world applicability of the model. Pathog-PDx accurately distinguished 22 pathogen subtypes and outperformed conventional models in identifying both single and mixed infections. The model achieved high classification performance for key pathogens, such as influenza virus (AUC = 0.95; Sn: 0.88; Sp: 0.86), with mean AUCs of 0.88 for various pathogens of RTIs. The model has been deployed as a web-based decision support system, which is freely accessible at https://pathogpdx.zzu.edu.cn. Altogether, Pathog-PDx represents a potential tool for the early identification of respiratory tract pathogens in pediatric patients, which can provide actionable predictions ahead of conventional test results to guide timely and targeted therapy. + © 2026. The Author(s). + + + + Su + Dubin + D + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China. + + + + Chen + Qun + Q + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Xiamen Cell Therapy Research Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China. + + + + Xu + Ruizhi + R + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China. + + + + Chen + Qihong + Q + + Department of Pediatrics, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Department of Pediatrics, Pediatric Key Laboratory of Xiamen, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China. + + + + Ma + Chiyuan + C + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China. + + + + Chen + Xi + X + + Department of Clinical Laboratory, Children's Hospital of Fudan University (Xiamen Branch), Xiamen Children's Hospital, Xiamen, Fujian, China. + + + + Yang + Yunyun + Y + + Department of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + + Qin + Feibo + F + + Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China. + + + + Zhou + Ziwei + Z + + Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China. + + + + Li + Sixian + S + + Department of Functional Neurology, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China. + + + + Peng + Jie + J + + Department of Pediatrics, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Department of Pediatrics, Pediatric Key Laboratory of Xiamen, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China. + + + + Ge + Dandan + D + + Department of Pediatrics, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + Department of Pediatrics, Pediatric Key Laboratory of Xiamen, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China. + + + + Fan + Jiajun + J + + Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, Champaign, IL, USA. + + + + Li + Xingyu + X + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. + + + + Wang + Yanbo + Y + + Nanjing Drum Tower Hospital Center of Molecular Diagnostic and Therapy, State Key Laboratory of Pharmaceutical Biotechnology, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute of Life Sciences (NAILS), School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China. + + + + Yang + Yungang + Y + + Department of Pediatrics, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. xmyyg@sina.com. + + + Department of Pediatrics, Pediatric Key Laboratory of Xiamen, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China. xmyyg@sina.com. + + + + Guo + Yaping + Y + + Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China. guoyaping@zzu.edu.cn. + + + + Yang + Jingjing + J + + Department of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. Jingjingyang_xmu@126.com. + + + + Ning + Wanshan + W + + Institute for Clinical Medical Research, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. ningwanshan@xmu.edu.cn. + + + + eng + + + 3502Z20214001 + Project of Xiamen Cell Therapy Research Center + + + + 32570802 + National Natural Science Foundation of China + + + + 32400532 + National Natural Science Foundation of China + + + + 3502Z20254ZD1032 + the Guided Project of Medical and Health Care in Xiamen + + + + 3502Z20254ZD1004 + the Guided Project of Medical and Health Care in Xiamen + + + + 2021ZD0201300 + National Key R&D Program of China + + + + 2024GGB18 + Fujian Provincial Health Technology Project + + + + 2025D022 + Fujian Science and Technology Program Guiding Project + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Digit Med + 101731738 + 2398-6352 + + Competing interests: The authors declare no competing interests. + + + + + 2025 + 8 + 19 + + + 2026 + 5 + 15 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215588 + 10.1038/s41746-026-02818-9 + 10.1038/s41746-026-02818-9 + + + + + + 42215586 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Ethnicity-specific molecular subtypes and a machine-learning risk model in Asian patients with non-muscle-invasive bladder cancer. + 10.1038/s41598-026-55438-4 + + Non-muscle-invasive bladder cancer (NMIBC) has high rates of recurrence and progression, yet its molecular landscape across different ethnicities remains poorly defined. We assembled a meta-cohort of 202 Asian NMIBC transcriptomes and identified three novel molecular subtypes (AC0, AC1, AC2) using non-negative matrix factorization clustering. These subtypes, each associated with distinct biological pathways and clinical outcomes, were externally validated across 22 datasets. Subtype-specific biomarkers (RABL6, MYBL2, RAD54L, and FAM64A) were confirmed by immunohistochemistry in an independent Asian cohort (n = 210), enabling the development of a machine-learning-based risk stratification model. AC1, characterized by aggressive features and enrichment of cell cycle and oncogenic signaling pathways, exhibited the poorest prognosis. Notably, after adjustment for T-stage and grade, the combined risk model was independently prognostic in Asian cohorts but not in European cohorts, underscoring ethnic differences in NMIBC biology. Our findings delineate distinct molecular subtypes and introduce a clinically applicable, ethnicity-specific prognostic model for Asian patients with NMIBC. This study emphasizes the importance of incorporating ethnic diversity into precision oncology frameworks and provides a foundation for personalized therapeutic and surveillance strategies in underrepresented populations. + © 2026. The Author(s). + + + + Jung + Minsun + M + + Department of Pathology, Yonsei University College of Medicine, Seoul, Republic of Korea. + + + + Park + Sangyong + S + + Department of Statistics, Seoul National University, Seoul, Republic of Korea. + + + + Jang + Insoon + I + + Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea. + + + Blood Transfusion Research Institute, Korean Red Cross, Wonju, Gangwon-do, Republic of Korea. + + + + Han + Dohyun + D + + Department of Transdisciplinary Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul, Republic of Korea. + + + Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea. + + + + Cho + Yong Mee + YM + + Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. + + + + Kim + Kwangsoo + K + + Department of Transdisciplinary Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul, Republic of Korea. kksoo716@gmail.com. + + + Department of Medicine, Seoul National University, Seoul, Republic of Korea. kksoo716@gmail.com. + + + + Moon + Kyung Chul + KC + + Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea. blue7270@snu.ac.kr. + + + Kidney Research Institute, Medical Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea. blue7270@snu.ac.kr. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Cluster Analysis + Ethnicity + Machine Learning + Non-Muscle Invasive Bladder Neoplasms + Tumor Biomarkers + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2025 + 5 + 13 + + + 2026 + 5 + 25 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215586 + 10.1038/s41598-026-55438-4 + 10.1038/s41598-026-55438-4 + + + + + + 42215583 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Wireless charging ferrite core design with improved material efficiency for mobile devices by enhancing outer-region flux density. + 10.1038/s41598-026-55270-w + + Wireless power transfer (WPT) systems integrated with machine learning (ML) techniques have emerged as promising solutions for enhancing performance and efficiency. Material efficiency, which involves achieving high performance with minimal material use, is crucial for mobile devices to enhance user satisfaction. This paper proposes a core structure design methodology for WPT systems that enhances mutual inductance (MI) by segmenting and assembling the ferrite core, thereby improving material efficiency within limited space constraints. For genetic algorithm (GA)-based optimization, an ML estimator is implemented to replace iterative finite element method simulations during the GA optimization loop, reducing the average computation time from 203s to 86μs and significantly accelerating the GA optimization process. Furthermore, the designed core structure exhibits improved uniformity of magnetic flux density by enhancing the outer-region magnetic flux density between the coils, thereby contributing to higher MI. Experimental results show that the proposed core structure achieves a 17.44% higher normalized MI compared to conventional structures, using less material through an asymmetrical configuration, which also reduces weight and cost. Furthermore, the optimized asymmetrical structure demonstrates robustness under lateral misalignment conditions ([Formula: see text] mm), consistently maintaining higher material efficiency than the full-piece reference even under spatial offsets. These findings demonstrate the potential of ML-driven approaches for developing high-performance, computationally efficient, and cost-effective WPT systems. + © 2026. The Author(s). + + + + Park + Yohan + Y + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. + + + + Shah + Syed Ahson Ali + SAA + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. + + + + Cho + Woo-Young + WY + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. + + + + Park + Hyojun + H + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. + + + + Lee + Hyunwoong + H + + Oceansbio, Seoul, 04303, South Korea. + + + + Kim + Yun-Su + YS + + Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. yunsukim@gist.ac.kr. + + + Research Institute for Solar and Sustainable Energies, Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea. yunsukim@gist.ac.kr. + + + + eng + + + No. RS-2026-25533249 + Korea Institute of Energy Technology Evaluation and Planning + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Ferromagnetic cores design + Genetic algorithm + Inductive power transfer + Mutual inductance + Wireless power transfer + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 3 + 3 + + + 2026 + 5 + 23 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 19 + + + aheadofprint + + 42215583 + 10.1038/s41598-026-55270-w + 10.1038/s41598-026-55270-w + + + + + + 42215579 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Machine learning based detection of subacute ruminal acidosis in early lactation dairy cows using multi-sensor behavioral, physiological, and milk production data. + 10.1038/s41598-026-55162-z + + Subacute ruminal acidosis (SARA) is a common metabolic disorder in early lactation dairy cows that negatively affects rumen function, milk production, and animal welfare. Early identification remains challenging because clinical signs are often subtle and transient. The aim of this study was to evaluate whether multi sensor behavioral, physiological, and milk-production data could be used to identify cows experiencing SARA using machine-learning approaches. The study included early-lactation Holstein cows during the first 100 days in milk. The final dataset comprised 636 cow-day observations, including 134 SARA cases and 502 clinically healthy controls. Cow identification numbers were additionally used as grouping variables during cross-validation to ensure complete separation of individual animals between training and validation subsets. SARA was defined based on continuous ruminal pH measurements, where cows were classified as SARA when ruminal pH remained between 5.2 and 5.8 for at least 180 min per day. Sensor derived variables included rumination time, activity, water intake, reticulorumen temperature, milk yield, and milk composition obtained from intraruminal boluses and an in-line milk analyzer. Six supervised machine learning classifiers were developed to classify SARA status based on combined sensor data. Among the evaluated models, SVM demonstrated the highest discriminatory performance, which achieved an area under the curve (AUC) of 0.97, accuracy of 0.95, sensitivity of 0.86, and specificity of 0.98 under repeated cow-level grouped cross-validation. Random forest showed similar performance (AUC = 0.97; accuracy = 0.93 and 0.98, respectively). Across all models, specificity was consistently higher than sensitivity, indicating that healthy cows were classified more accurately than SARA cases. These results demonstrate that integrated behavioral, physiological, and milk production data obtained from automated sensor systems can support classification of cows experiencing SARA under commercial farm conditions. The findings support the potential of multi sensor monitoring systems combined with machine learning classifiers as a tool for automated detection of rumen health disturbances in precision dairy farming. + © 2026. The Author(s). + + + + Grigė + Samanta + S + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. Samanta.grige1@lsmu.lt. + + + + Girdauskaitė + Akvilė + A + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Grigas + Ovidijus + O + + Faculty of Informatics, Kaunas University of Technology, 50254, Kaunas, Lithuania. + + + + Japertas + Sigitas + S + + Practical Training and Research Center, Lithuanian University of Health Sciences, Topolių g. 6, 54310, Kaunas, Lithuania. + + + + Malašauskienė + Dovilė + D + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Televičius + Mindaugas + M + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Urbutis + Mingaudas + M + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Džermeikaitė + Karina + K + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Krištolaitytė + Justina + J + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + Antanaitis + Ramūnas + R + + Animal Clinic, Veterinary Academy, Lithuania University of Health Sciences, Tilžės Str. 18, 47181, Kaunas, Lithuania. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Biosensors + Dairy cattle + Innovations + Machine learning + + Declarations. Competing interests: The authors declare no competing interests. Ethics approval: All animal procedures were reviewed and approved by the Institutional Animal Care and Use Committee of the Lithuanian University of Health Sciences (Protocol No. G2-227, approved on 7 March 2025). + + + + + 2026 + 1 + 2 + + + 2026 + 5 + 22 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 18 + + + aheadofprint + + 42215579 + 10.1038/s41598-026-55162-z + 10.1038/s41598-026-55162-z + + + + + + 42215538 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Transition of blue space in the urban area of khulna city corporation using geospatial and machine learning techniques. + 10.1038/s41598-026-52656-8 + + Urban blue-space loss and grey-space expansion have significantly altered the surface water systems of the Khulna City Corporation, increasing the environmental degradation and hydrological vulnerability of this climate-sensitive coastal city. This study used multi-temporal Landsat imagery, including Landsat 5 TM data from 1994 to 2004 and Landsat 8 OLI data from 2014 to 2024. The analysis integrated the Normalized Difference Vegetation Index, Normalized Difference Built-up Index, and Normalized Difference Water Index with Random Forest classification and post-classification change detection to quantify long-term land-use/land-cover transitions and urban blue-space transformation. Post-classification comparison generated directional transition matrices documenting conversion pathways among land-cover categories. The results show that surface water bodies have decreased from 4.20 km2 in 1994 to 2.27 km2 in 2024, while the built-up area has increased from 25.9 to 53.3% of the area studied. The accuracy assessment resulted in an overall accuracy of 84.5 to 89.3%, with Kappa coefficients ranging from 0.78 to 0.86. Water body classifications have remained reliable throughout all periods, which has supported the use of classifiable maps for long-term migration analysis. Temporal analysis revealed two main phases: an early phase characterized by mixed land reclamation and later phases dominated by conversion of water bodies into built-up areas. Spatial analysis identified critical hotspots of canal infill, wetland loss, and riverbank modification, mainly in central and southern municipal zones. These findings highlight the need to treat urban blue spaces as critical infrastructure and to promote integrated management, natural drainage solutions and regular remote sensing monitoring. The study frames the loss of blue space as a process of urban transformation and advocates better monitoring of urban water systems in coastal secondary coastal cities. + © 2026. The Author(s). + + + + Hossan + Md Refath + MR + + Institute of Disaster Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh. + + + + Hossain + Mohammad Ismail + MI + + Institute of Disaster Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh. ihossain@idm.kuet.ac.bd. + + + + Islam + Md Wahidul + MW + + Department of Chemistry, Gopalganj Science and Technology University, Gopalganj, 8100, Bangladesh. + + + + Shaon + Zahid Hassan + ZH + + Institute of Disaster Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + GIS analysis + Land cover change + Remote sensing + Surface water decline + Urban expansion + + Declarations. Competing interests: The authors declare no competing interests. Consent to participate: This study did not involve any human participants, animal subjects, or personally identifiable. data, and therefore did not require ethical approval. + + + + + 2026 + 4 + 6 + + + 2026 + 5 + 6 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 23 + 17 + + + aheadofprint + + 42215538 + 10.1038/s41598-026-52656-8 + 10.1038/s41598-026-52656-8 + + + + + + 42215534 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + An explainable spatio-temporal deep learning framework for crop yield prediction and recommendation. + 10.1038/s41598-026-54439-7 + + Agriculture plays a critical role in ensuring global food security, yet crop yield variability driven by climate change, soil heterogeneity, and environmental fluctuations poses persistent challenges. Accurate yield forecasting and intelligent crop recommendation are essential for sustainable and efficient farm management. This study proposes a novel AI-driven framework for precise crop yield prediction and data-driven crop selection. The framework integrates systematic data preprocessing, hybrid feature selection, deep learning, and attention-based modeling to capture complex nonlinear relationships within agricultural datasets. Raw data, including soil properties, topography, climatic variables, and historical yield records, are processed using median-based imputation, normalization, and Z-score outlier detection to enhance reliability. A multi-stage hybrid feature selection approach combining Minimum Redundancy Maximum Weight, Sequential Forward Subset Selection, and Recursive Fisher Score identifies the most informative features while reducing redundancy. Yield prediction is performed using an attention-enhanced hybrid kernel Extreme Learning Machine (ELM). Crop recommendation is achieved through a Spatio-Temporal Explainable Group-Enhanced Transformer Network (STX-GTNET) optimized with the PantheraCobra metaheuristic. Model interpretability is ensured using Grad-CAM and Integrated Gradients. Experimental results demonstrate strong performance, achieving an RMSE of 281.6 and R² of 0.94 for yield prediction, and 98.4% accuracy with a 0.991 ROC-AUC for crop recommendation. + © 2026. The Author(s). + + + + Piyar S + Nifasath + N + + Department of Computer Science and Engineering, Anna University, Chennai, 600 025, India. nifasathpiyar@gmail.com. + + + + S + Dr Baulkani + DB + + Department of Electronics and Communication Engineering, Government College of Engineering, Bodinayakkanur, India. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Crop recommendation system + Crop yield prediction + Explainable artificial intelligence and precision agriculture + Spatio-temporal deep learning + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 2 + 24 + + + 2026 + 5 + 19 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 17 + + + aheadofprint + + 42215534 + 10.1038/s41598-026-54439-7 + 10.1038/s41598-026-54439-7 + + + + + + 42215508 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + The role of biopsychosocial factors in classifying pain intensity across various chronic pain conditions. + 10.1038/s41598-026-46612-9 + + Chronic pain is a multifaceted condition shaped by biological, psychological, and social factors, which challenges its diagnosis and treatment. While prior research largely focused on sensory profiles to distinguish pain mechanisms, this study adopted a biopsychosocial perspective with the key addition of experimental pain paradigms to identify factors explaining variance in chronic pain severity across diverse etiologies. The dataset comprised 101 individuals with chronic pain and 63 pain-free controls. Participants were classified into three groups (no pain, mild-to-moderate pain, and severe pain) using three models: a sensory-profile-based model including quantitative sensory testing (QST model), a biopsychosocial model incorporating QST alongside psychological and social variables (biopsychosocial model), and a model using the same biopsychosocial data but excluding QST measures (noQST model). The QST model achieved an accuracy of 0.51 and an F1-score of 0.48. In contrast, the biopsychosocial model performed best, with both accuracy and F1-score of 0.71, while the noQST model reached 0.60 for both metrics. Key predictors in the best-performing model included quality of life, loss of sensation, depressive mood, pain catastrophizing, anxiety, fatigue, and general health. Overall, biopsychosocial factors enhanced chronic pain severity classification beyond sensory profiles alone across heterogeneous pain etiologies. + © 2026. The Author(s). + + + + De Schoenmacker + Iara + I + + Biomedical Data Science Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich, 8092, Zurich, Switzerland. deschoeia@gmail.com. + + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. deschoeia@gmail.com. + + + Biomedical Data Science Lab, Swiss Federal Institute of Technology (ETH), Lengghalde 2, 8008, Zurich, Switzerland. deschoeia@gmail.com. + + + + Monzon + Maria + M + + Biomedical Data Science Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich, 8092, Zurich, Switzerland. + + + + Sirucek + Laura + L + + Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland. + + + Department of Chiropractic Medicine, Integrative Spinal Research Group, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark. + + + + Scheuren + Paulina S + PS + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada. + + + Department of Anesthesiology, Pharmacology and Therapeutics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada. + + + + Lütolf + Robin + R + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Gorrell + Lindsay M + LM + + Department of Chiropractic Medicine, Integrative Spinal Research Group, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Brunner + Florian + F + + Physical Medicine and Rheumatology, Balgrist University Hospital, Zurich, Switzerland. + + + + Curt + Armin + A + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Rosner + Jan + J + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + Danish Pain Research Center, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark. + + + + Schweinhardt + Petra + P + + Department of Chiropractic Medicine, Integrative Spinal Research Group, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Hubli + Michèle + M + + Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. + + + + Jutzeler + Catherine R + CR + + Biomedical Data Science Lab, Department of Health Sciences and Technology (D-HEST), ETH Zurich, 8092, Zurich, Switzerland. + + + Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland. + + + + eng + + + PF198F + International Foundation for Research in Paraplegia + + + + P500PB_214416 + Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung + + + + PZ00P3_186101 + Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung + + + + RT-2023-3173 + Michael Smith Health Research BC + + + + R359-2020-2620 + Lundbeck Foundation + + + + 380 + Strategic Focus Area "Personalized Health and Related Technologies (PHRT)" of the Swiss Federal Institutes of Technology + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Biopsychosocial model + Chronic pain + Machine learning + Pain classification + Quantitative sensory testing + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2025 + 5 + 21 + + + 2026 + 3 + 26 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 16 + + + aheadofprint + + 42215508 + 10.1038/s41598-026-46612-9 + 10.1038/s41598-026-46612-9 + + + + + + 42215501 + + 2026 + 05 + 29 + +
+ + 2045-2322 + + + 2026 + May + 29 + + + Scientific reports + Sci Rep + + Predictive analysis on mechanical properties, material flow and thermal analysis of friction stir welded dissimilar AA2014/AA7075 Al-alloy joints using machine learning. + 10.1038/s41598-026-48688-9 + + Aluminum alloys are the most ideal materials for aerospace applications, and joining of a light weighted materials with an eco-friendly method is encouraging ongoing research into the green welding technique. In the current work solid-state joints of 10 mm thick AA2014/AA7075 Al-alloys dissimilar material joints with hybrid pin profiles were fabricated by using Friction Stir Welding (FSW) technique. The microstructure and mechanical properties of weld joints, temperature analysis of the welds processed under various conditions is investigated. An Artificial Neural Network (ANN) model is used to predict the mechanical properties of the welded joints fabricated at different conditions. The weld joints processed with hybrid pentagonal pin profile exhibits the highest UTS and YS measuring as 294 and 267 MPa respectively. The ANN showed strong prediction using a dataset of 27 experimentally obtained results providing a reliable data-driven method for forecasting UTS and optimizing FSW parameters. The weld joints processed at 900 rpm 31. 5 mm/min 2° tilt angle 0 mm tool offset and pentagonal pin profile revealed 62% of joint efficiency, which is suitable for the suggested AWS requirements for aerospace applications must be met by 60%. ANN model provided a good agreement with experimental results (R² = 0.93). However, due to the relatively small dataset (L27 design). + © 2026. The Author(s). + + + + Rao + R Venkateswara + RV + + Department of Mechanical Engineering, Vignana Bharathi Institute of Technology, Telangana, 501301, India. + + + + Kumar + M Senthil + MS + + School of Mechanical Engineering, Vellore Institute of Technology, Chennai, 600127, India. msv305@yahoo.co.in. + + + + Vanmathi + M + M + + Department of Electronics and Communication Engineering, B S Abdurrahman Crescent Institute of Science and Technology, Chennai, 60048, India. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Dissimilar Materials + FSW Process + Mechanical Properties + Process Parameters + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2025 + 10 + 10 + + + 2026 + 4 + 9 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 16 + + + aheadofprint + + 42215501 + 10.1038/s41598-026-48688-9 + 10.1038/s41598-026-48688-9 + + + + + + 42215494 + + 2026 + 05 + 29 + +
+ + 2396-8370 + + + 2026 + May + 29 + + + NPJ science of food + NPJ Sci Food + + Novel insights into the binding mechanisms of selected aldehydes during heat-induced protein unfolding. + 10.1038/s41538-026-00874-9 + + This investigation aimed to clarify the binding mechanisms between six aldehydes and myofibrillar proteins (MPs), with a structural explanation in response to stage-heating treatments. The conformational intermediates of MPs, which form during heat processing, were systematically characterized to elucidate their role in aldehyde binding and flavor retention. Machine learning results suggested that high-temperature boiling promoted extensive protein denaturation and aggregation, while subsequent low-temperature stewing induced partial rearrangement. Thermodynamic parameters indicated that hexanal-MPs formation was primarily driven by hydrogen bonding, whereas other longer-chain and unsaturated aldehydes penetrated hydrophobic pockets. Proteomics revealed that saturated aldehydes predominantly formed Schiff bases with the lysine ε-amino group. Unsaturated aldehydes, especially (E, E)-2,4-decadienal, undergo both Schiff base reactions and Michael addition with cysteine, histidine, and tryptophan residues. The retention/release behavior of aldehydes during processing is determined by covalent and non-covalent interactions. These results provide a scientific basis for precisely controlling flavor quality in meat products. + © 2026. The Author(s). + + + + Wang + Jingfan + J + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. + + + Laboratory of Chemistry of Natural Molecules, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium. + + + Laboratory of Analytical Chemistry, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium. + + + + Wang + Tianze + T + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. + + + + Yang + Ping + P + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. + + + + Han + Dong + D + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. orange_1101@126.com. + + + + Zhang + Chunhui + C + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. dr_zch@163.com. + + + + Jia + Wei + W + + Key Laboratory of Agro-Products Processing, Ministry of Agriculture and Rural Affairs, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China. + + + Jiangsu Chaoyue Agricultural Development Co. Ltd, Taizhou, China. + + + + Purcaro + Giorgia + G + + Laboratory of Analytical Chemistry, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium. + + + + Fauconnier + Marie-Laure + ML + + Laboratory of Chemistry of Natural Molecules, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium. + + + + eng + + + No. 32472392 + the National Natural Science Foundation of China + + + + ASTIP-Y2025QC29 + the Agricultural Science and Technology Innovation Program + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Sci Food + 101739627 + 2396-8370 + + Competing interests: The authors declare no competing interests. + + + + + 2025 + 12 + 25 + + + 2026 + 4 + 28 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 15 + + + aheadofprint + + 42215494 + 10.1038/s41538-026-00874-9 + 10.1038/s41538-026-00874-9 + + + + + + 42215477 + + 2026 + 05 + 29 + +
+ + 2731-6068 + + + 2026 + May + 29 + + + npj aging + NPJ Aging + + Latent biochemical phenotypes delineate divergent health trajectories in older adults. + 10.1038/s41514-026-00415-4 + + Ageing heterogeneity hampers prevention and care. We used routine biochemical panels and unsupervised learning to identify latent phenotypes in community-dwelling older adults. In 1491 participants from the Toledo Study for Healthy Ageing (TSHA) with ~10-11 years of follow-up, 39 blood biomarkers were dimension-reduced and clustered, yielding three phenotypes: Healthy, Metabolic (subclinical dysmetabolism), and Haematological (low erythroid/renal profile). Phenotypes differed in functional capacity, frailty, and independence at baseline (all p < 0.05 after age/sex adjustment) and predicted long-term mortality (Metabolic women HR = 1.49, p = 0.016). Sex-specific analyses revealed distinct disease-trajectory patterns (e.g., hypertension in Metabolic women HR = 1.30, p = 0.005; thrombosis in Haematological men HR = 7.20, p = 0.018; syncope in Haematological women HR = 1.88, p = 0.009). Findings are partially replicated in a cohort of physically active older adults (EXERNET), supporting the generalizability of the Metabolic phenotype. Standard laboratory data, integrated through machine learning, capture ageing-relevant biology and stratify future risk without specialised assays, enabling low-cost, scalable precision prevention. + © 2026. The Author(s). + + + + González-Martos + Raquel + R + + Centro de Tecnología Biomédica (CTB), Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Madrid, Spain. + + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. + + + Nageru S.L, Madrid, Spain. + + + + Rodríguez-Gómez + Irene + I + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. + + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. + + + Geriatrics Department, Hospital Universitario de Toledo, Toledo, Spain. + + + + Galeano + Javier + J + + Grupo de Sistemas Complejos, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Madrid, Spain. + + + Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain. + + + + Ara + Ignacio + I + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. + + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. + + + + Alegre + Luis M + LM + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. + + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. + + + + Rodríguez-Mañas + Leocadio + L + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Geriatrics Department, Getafe University Hospital, Getafe, Spain. + + + + Garcia-Garcia + Francisco J + FJ + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. + + + Geriatrics Department, Hospital Universitario de Toledo, Toledo, Spain. + + + + Ramírez-Castillejo + Carmen + C + + Centro de Tecnología Biomédica (CTB), Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Madrid, Spain. + + + Departamento de Oncología, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain. + + + + Guadalupe-Grau + Amelia + A + + GENUD Toledo Research Group, Faculty of Sport Sciences, University of Castilla-La Mancha, Avda. Carlos III S/N, Toledo, Spain. amelia.guadalupe@uclm.es. + + + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain. amelia.guadalupe@uclm.es. + + + Grupo Mixto de Fragilidad y Envejecimiento Exitoso UCLM-SESCAM, Universidad de Castilla-La Mancha-Servicio de Salud de Castilla-La Mancha, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), Toledo, Spain. amelia.guadalupe@uclm.es. + + + + eng + + + IND2022/BMD-23595 + Comunidad de Madrid + + + + CD23/00236 + Instituto de Salud Carlos III + + + + PI10/01532, PI031558 + Instituto de Salud Carlos III + + + + PI2010/020 + Junta de Comunidades de Castilla-La Mancha + + + + 03031-00 + Consejería de Sanidad de Castilla-La Mancha + + + + CB16/10/00456 + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable + + + + CB16/10/00477 + Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + NPJ Aging + 9918402285106676 + 2731-6068 + + Competing interests: The authors declare no competing interests. + + + + + 2025 + 11 + 25 + + + 2026 + 5 + 18 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 15 + + + aheadofprint + + 42215477 + 10.1038/s41514-026-00415-4 + 10.1038/s41514-026-00415-4 + + + + + + 42215447 + + 2026 + 05 + 29 + +
+ + 2041-1723 + + + 2026 + May + 29 + + + Nature communications + Nat Commun + + Multifactorial sheltering in peristromal niches shapes in vivo responses of lung cancers to targeted therapies. + 10.1038/s41467-026-73454-w + + Targeted therapies induce strong clinical responses but fail to eliminate advanced cancers, as a subset of tumor cells survives within residual disease and eventually develops resistance. While numerous cell-intrinsic and microenvironmental mechanisms have been implicated in this survival, their relative contributions remain poorly defined. Using spatial histological inferences from ALK + NSCLC models, we show that peristromal niches protect tumor cells from elimination, enabling in vivo persistence. This spatially restricted sheltering provides an ecological rescue mechanism that sustains residual populations, enabling their eventual evolutionary escape. Mechanistically, this protective effect reflects an integrated action of multiple juxtacrine and paracrine signals. This complexity limits the utility of targeting individual mechanisms of protection, favoring a shift towards exploiting orthogonal collateral sensitivities of residual disease. We find that adaptive HER2 upregulation, associated with both cell-intrinsic and stroma-mediated persistence, can be exploited by the antibody-drug conjugate T-DXd to dramatically enhance therapeutic responses and suppress tumor relapse. + © 2026. The Author(s). + + + + Desai + Bina + B + 0000-0003-0454-7583 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + Cancer Biology Ph.D. Program, University of South Florida, Tampa, FL, USA. + + + + Miti + Tatiana + T + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Prabhakaran + Sandhya + S + 0000-0002-9537-507X + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Miroshnychenko + Daria + D + 0000-0001-6425-5000 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Kumar + Pragya + P + 0000-0002-5691-7016 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Henry + Menkara + M + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Marusyk + Viktoriya + V + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Siqueira + Natalia Souza Nunes + NSN + 0000-0002-7877-2655 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Gatenbee + Chandler + C + 0000-0002-9730-5964 + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Ozakinci + Hilal + H + + Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Bui + Marilyn + M + 0000-0003-4963-2255 + + Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + Department of Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Boyle + Theresa A + TA + 0000-0001-5950-3970 + + Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Scott + Jacob + J + 0000-0003-2971-7673 + + Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA. + + + + Altrock + Philipp M + PM + 0000-0001-7731-3345 + + Department of Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany. + + + + Pellini + Bruna + B + + Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + Department of Thoracic Oncology, Miami Cancer Institute, Baptist Health South Florida, Miami, FL, USA. + + + + Haura + Eric + E + + Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Anderson + Alexander R A + ARA + 0000-0002-2536-4383 + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Basanta + David + D + + Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. + + + + Marusyk + Andriy + A + 0000-0002-0087-9575 + + Department of Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. andriy.marusyk@moffitt.org. + + + Department of Molecular Medicine, University of South Florida, Tampa, FL, USA. andriy.marusyk@moffitt.org. + + + + eng + + + NIH U54 CA274507 + U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Nat Commun + 101528555 + 2041-1723 + + IM + Competing interests: The authors declare no competing interests. + + + + + 2025 + 4 + 2 + + + 2026 + 5 + 8 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 23 + 14 + + + aheadofprint + + 42215447 + 10.1038/s41467-026-73454-w + 10.1038/s41467-026-73454-w + + + + + + 42215401 + + 2026 + 05 + 29 + +
+ + 1879-2022 + + + 2026 + May + 13 + + + ISA transactions + ISA Trans + + Interpretable physical-informed machine learning for dynamic characteristics prediction of air spring with limited measurement information. + S0019-0578(26)00247-8 + 10.1016/j.isatra.2026.05.010 + + Pipe-type air spring with auxiliary airbag (ASAA) are important vibration isolation component due to its tunable volume and adjustable dynamic characteristics. However, its dynamic modeling is challenging because airbag expansion work, rubber bladder viscoelasticity, and turbulence-dominated throttle dynamics are strongly coupled under large-amplitude excitations. Traditional data-driven models often require large datasets and lack interpretability. To address this issue, this study proposes an interpretable physics-informed machine learning (PIML) framework for ASAA dynamic prediction. A reduced-order physical model is first developed to provide baseline predictions of stiffness and lag angle, and the discrepancies between physical predictions and experimental measurements are then used to construct residual datasets. An interpretable Kolmogorov-Arnold Network (KAN) is employed to learn these residuals through adaptive B-spline activation functions and derive explicit symbolic correction terms. Experimental validation shows that the proposed hybrid model improves prediction accuracy while retaining physical interpretability, outperforming pure data-driven, advanced physics-data and physical models. + Copyright © 2026 ISA. Published by Elsevier Ltd. All rights reserved. + + + + Qin + Wu + W + + School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China. Electronic address: qw@ecjtu.edu.cn. + + + + Liao + Xundong + X + + School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China. Electronic address: 2023038080200027@ecjtu.edu.cn. + + + + Qi + Junyu + J + + State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China; Reutlingen University, Alteburgstraße 150, Reutlingen 72762, Germany. Electronic address: junyu.qi@reutlingen-university.de. + + + + He + Yi + Y + + School of Mechanical Engineering, South China University of Technology, China. Electronic address: 202111080705@mail.scut.edu.cn. + + + + Liu + Feifei + F + + School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China. Electronic address: 2971@ecjtu.edu.cn. + + + + Chen + Zhuyun + Z + + State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China. Electronic address: mezychen@gdut.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + United States + ISA Trans + 0374750 + 0019-0578 + + IM + + Air spring + Data-driven + Dynamic characteristic predictions + Physics-informed Machine Learning + + Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2025 + 9 + 12 + + + 2026 + 5 + 10 + + + 2026 + 5 + 10 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 21 + 58 + + + aheadofprint + + 42215401 + 10.1016/j.isatra.2026.05.010 + S0019-0578(26)00247-8 + + + + + + 42215359 + + 2026 + 05 + 29 + +
+ + 1879-0828 + + + 2026 + May + 29 + + + European journal of internal medicine + Eur J Intern Med + + Artificial intelligence in cardiovascular care for internal medicine: from promising algorithms to useful clinical services. + + 106944 + 106944 + + 10.1016/j.ejim.2026.106944 + S0953-6205(26)00249-9 + + Artificial intelligence is expanding rapidly in cardiovascular medicine, but its value in internal medicine depends less on raw model performance than on whether it improves triage, risk targeting, and coordination of care within real-world workflows. Rather than cataloguing applications, this review focuses on three use cases with near-term relevance in internal medicine: AI-augmented electrocardiography, risk prediction for targeted prevention, and AI-enabled clinical decision support, including selected applications of large language models. Across these domains, evaluation should extend beyond discrimination to calibration, positive predictive value at action thresholds, net benefit, alert burden, override rates, and downstream testing. Cardiovascular AI should not be judged as an autonomous decision-maker, but as a supervised component of care delivery. Its usefulness depends on predefined confirmation pathways, integration into existing information systems, actionable outputs, user training, and post-deployment monitoring for drift, safety, and equity. We propose a practical implementation lens - Train, Explain, Integrate, Accompany (TEIA) - to structure deployment in routine care, and summarize governance issues relevant to European practice, including intended use, interoperability, traceability, cybersecurity, and lifecycle oversight. The central question is not whether an AI tool can classify or predict, but whether its use in routine care supports better decisions with acceptable workload, safer pathways, and measurable clinical value. + Copyright © 2026 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved. + + + + Desroche + Louis-Marie + LM + + Department of Cardiology, North Site, CHU de La Réunion, Allée des Topazes, Saint-Denis, La Réunion 97400, France; CIC-EC INSERM 1410, CHU de La Réunion, Saint-Denis, France. Electronic address: louis-marie.desroche@chu-reunion.fr. + + + + Trimaille + Antonin + A + + Department of Cardiology, Strasbourg University Hospital, Strasbourg, France; UR3074, Translational Cardiovascular Medicine, University of Strasbourg, Strasbourg, France; GERCA (Groupe pour l'Enseignement et la Recherche Cardiologique en Alsace), Strasbourg, France. + + + + Lequeux + Benoît + B + + Department of Cardiology, Poitiers University Hospital, Poitiers, France. + + + + Homehr + Nicolas + N + + Primary Care Practice, Lherm, France; CPTS du Sud Toulousain, Haute-Garonne, France. + + + + Girerd + Nicolas + N + + CHRU de Nancy, Centre d'Investigations Cliniques Plurithématique 1433, INSERM, Université de Lorraine, INSERM U1116 - DCAC, and F-CRIN INI-CRCT(Cardiovascular and Renal Clinical Trialists), Nancy, France. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 29 + +
+ + Netherlands + Eur J Intern Med + 9003220 + 0953-6205 + + IM + + Artificial intelligence + Cardiovascular disease + Clinical decision support + Electrocardiography + Governance + Implementation + Internal medicine + Machine learning + Risk prediction + + Declaration of competing interest AT has received a fellowship grant from Edwards Lifescience. NG declares honoraria from Alnylam, AstraZeneca, Bayer, Boehringer, Cardiostory, Lilly, Echosens, GSK, NP medical, Novartis, Novo Nordisk, Roche diagnostics. The other authors declare no conflict of interest. + + + + + 2026 + 4 + 29 + + + 2026 + 5 + 12 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 21 + 57 + + + aheadofprint + + 42215359 + 10.1016/j.ejim.2026.106944 + S0953-6205(26)00249-9 + + + + + + 42215334 + + 2026 + 05 + 29 + +
+ + 2588-9311 + + + 2026 + May + 29 + + + European urology oncology + Eur Urol Oncol + + AI-MIRACLE: Artificial Intelligence and MultIpaRAmetric MRI Predict CLinical OutcomEs to Neoadjuvant Immunotherapy in Patients with Muscle-invasive Bladder Cancer Undergoing Radical Cystectomy. + S2588-9311(26)00133-1 + 10.1016/j.euo.2026.05.006 + + Neoadjuvant immune-checkpoint inhibitors (ICIs) in muscle-invasive bladder cancer (MIBC) were tested in patient's ineligible for cisplatin-based chemotherapy. The PURE-01 trial (NCT02736266) evaluated three courses of pembrolizumab before radical cystectomy (RC). We developed AI-MIRACLE, an international study assessing artificial intelligence (AI) and multiparametric magnetic resonance imaging (mpMRI) for predicting treatment response. + This multi-institutional study included data acquisition in Italy, and centralized analysis in the United States. Among 112 PURE-01 patients, pre- and post-ICI MRIs were analyzed. T2-weighted signal intensities were standardized for radiomics (Image Biomarker Standardization Initiative-compatible Python-based Computational Environment for Radiological Research (pyCERR)) and deep feature extraction (AI-BLADE toolbox using VGG19). Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI data underwent model-based analysis. Supervised machine learning algorithms (elastic net, random forest) were trained and cross-validated to predict pathological major response (pMR:ypT<2N0 residual disease) and pathological complete response (pCR: ypT0) pathological response. + The predictive models using post-ICI mpMRI with either a combination of radiomics and DCE-derived features or radiomics alone achieved the same high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.96 for pMR. A shape-based radiomic model achieved an AUC of 0.86 for predicting pCR. These models outperformed benchmark models based on clinical predictors. + Shape-based radiomics, DCE-derived features, and deep features may serve as noninvasive imaging biomarkers for predicting response to neoadjuvant pembrolizumab in MIBC. This imaging-based approach provides a non-invasive assessment of treatment response following neoadjuvant immunotherapy, which may help inform bladder-preserving management decisions prior to definitive surgery. + Copyright © 2026 The Author(s). Published by Elsevier B.V. All rights reserved. + + + + Necchi + Andrea + A + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy; Department of Medical Oncology, Vita-Salute San Raffaele University, Milan, Italy. Electronic address: necchi.andrea@hsr.it. + + + + Brembilla + Giorgio + G + + Department of Medical Oncology, Vita-Salute San Raffaele University, Milan, Italy; Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Whiting + Karissa + K + + Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Arita + Yuki + Y + + Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Akin + Oguz + O + + Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Apte + Aditya + A + + Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Awais + Muhammad + M + + Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Lema-Dopico + Alfonso + A + + Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Paudyal + Ramesh + R + + Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Cosenza + Michele + M + + Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Maiorano + Brigida Anna + BA + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Tateo + Valentina + V + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Cigliola + Antonio + A + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Mercinelli + Chiara + C + + Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + De Cobelli + Francesco + F + + Department of Medical Oncology, Vita-Salute San Raffaele University, Milan, Italy; Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy. + + + + Capanu + Marinela + M + + Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + Shukla-Dave + Amita + A + + Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. Electronic address: davea@mskcc.org. + + + + Schwartz + Lawrence H + LH + + Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Netherlands + Eur Urol Oncol + 101724904 + 2588-9311 + + IM + + Bladder cancer + Deep feature + MRI + Radiomics + + + + + + 2025 + 12 + 18 + + + 2026 + 4 + 28 + + + 2026 + 5 + 6 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 21 + 56 + + + aheadofprint + + 42215334 + 10.1016/j.euo.2026.05.006 + S2588-9311(26)00133-1 + + + + + + 42215211 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1095-9998 + + 139 + + 2026 + Oct + + + Food microbiology + Food Microbiol + + Single-cell Raman spectroscopy-machine learning combination: Rapid and accurate strain-level identification of Bifidobacterium animalis subsp. lactis J12. + + 105131 + 105131 + + 10.1016/j.fm.2026.105131 + S0740-0020(26)00095-X + + Rapid and accurate strain-level identification of Bifidobacterium animalis is critical for probiotic quality control and intellectual property protection in the food industry. In this study, a novel identification method for Bifidobacterium animalis subsp. lactis J12 was established by integrating single-cell Raman spectroscopy (SCRS) with machine learning (ML) algorithms. A total of 500-1000 valid single-cell Raman spectra were collected for each of five Bifidobacterium animalis subsp. lactis strains and seven heterologous lactic acid bacterial strains. The SCRS data were processed and optimized using t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), and support vector machine (SVM) algorithms. Results showed that stationary-phase cells exhibited the most distinctive chemical fingerprints, enabling the SVM model to identify J12 among the five target strains with an accuracy of over 99%. The established method displayed high robustness: classification accuracy and recall for lyophilized bacterial powders both exceeded 99.8%; the predicted proportions of strains in artificial mixed samples were highly consistent with theoretical values, with a mean absolute error (MAE) of <0.5% and a correlation coefficient (R2) of >0.998; and the identification accuracy for J12 isolates from commercial yogurt remained above 99%. Comparative genomics analysis confirmed that the unique Raman phenotypic characteristics of J12 were correlated with its specific genomic features, verifying the method's reliability at the genetic level. This label-free and pure culture-free SCRS-ML approach provides a rapid and high-precision tool for strain-specific identification in the probiotic industry, and fills the technical gap in rapid detection of highly homologous B. animalis strains. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Jiang + Yixuan + Y + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: 15245133725@163.com. + + + + Deng + Yu + Y + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: 18184400300@163.com. + + + + Feng + Bing + B + + Qingdao Singlecell Biotechnology Co., Ltd., Qingdao, 266000, China. Electronic address: fengbing@singlecellbiotech.com. + + + + Zhang + Hongxing + H + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: hxzhang511@163.com. + + + + Xie + Yuanhong + Y + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: xieyuanh@163.com. + + + + Gao + Jingyu + J + + Science and Technology Research Center of China Customs, Beijing, 100026, China. Electronic address: gjyhgxy@sina.com. + + + + Song + Xiaodong + X + + Inner Mongolia Mengniu Dairy (Group) Co., Ltd., Key Laboratory of Dairy Quality Intelligence Monitoring Technology, State Administration for Market Regulation, Hohhot, 011517, China. Electronic address: songxiaodong@mengniu.cn. + + + + Jin + Junhua + J + + College of Food Science and Engineering, Beijing University of Agriculture, Beijing, 102206, China. Electronic address: jinjunhua002008@163.com. + + + + eng + + Journal Article + Evaluation Study + + + 2026 + 04 + 22 + +
+ + England + Food Microbiol + 8601127 + 0740-0020 + + IM + + + Bifidobacterium animalis + classification + isolation & purification + chemistry + genetics + + + Spectrum Analysis, Raman + methods + + + Machine Learning + + + Support Vector Machine + + + Single-Cell Analysis + methods + + + Probiotics + + + Yogurt + microbiology + + + Discriminant Analysis + + + + Bifidobacterium animalis + Machine learning + Probiotics + Quality control + SCRS + Strain identification + + Declaration of competing interest The authors declare no conflicts of interest. + + + + + 2026 + 2 + 18 + + + 2026 + 4 + 20 + + + 2026 + 4 + 21 + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 20 + 55 + + + ppublish + + 42215211 + 10.1016/j.fm.2026.105131 + S0740-0020(26)00095-X + + + + + + 42215204 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1095-9998 + + 139 + + 2026 + Oct + + + Food microbiology + Food Microbiol + + Climatic factors and initial microbiota-driven prediction of the accumulated microbiota at the end of stacking fermentation for soy sauce aroma type baijiu. + + 105124 + 105124 + + 10.1016/j.fm.2026.105124 + S0740-0020(26)00088-2 + + Most traditional fermented foods are produced through spontaneous fermentation, which contain complex microbiota. However, it is still unclear what the key driving factors of the microbiota are and how to predict the microbiota at the end of fermentation. This study employed a typical spontaneous fermentation process, stacking fermentation for soy sauce aroma type baijiu, as a case to predict the microbiota at the end of fermentation based on key driving factors. The microbiota at the start of fermentation was mainly provided from the starter (daqu), air, and the ground of the fermentation room, contributing 45.22%, 10.05%, and 7.48% of the bacteria, and 41.83%, 10.77% and 1.21% of the fungi, respectively. Average air temperature and average photosynthetic photon flux density were identified as key climatic factors driving microbial succession. Finally, a Transformer-based machine learning model was constructed to predict the microbiota at the end of fermentation based on key climatic factors and the initial microbiota. 29 out of 34 dominant genera (average relative abundance ≥1%) could be accurately predicted (NRMSE <30%). Aitchison distance between true and predicted value using Transformer model was significantly lower than that using Random Forest model (P < 0.001). This work revealed the roles of climatic factors and the microbiota at the start of fermentation in shaping the microbiota at the end of fermentation. The constructed predictive model could facilitate the regulation of the microbiota in food fermentation. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + He + Binghao + B + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. + + + + Zheng + Yifu + Y + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. + + + + Ban + Shibo + S + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. + + + + Madadi + Meysam + M + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. + + + + Khaneghah + Amin Mousavi + AM + + Faculty of Biotechnologies (BioTech), ITMO University, 9 Lomonosova Street, Saint Petersburg, 191002, Russia. Electronic address: mousavi.amin@gmail.com. + + + + Aadil + Rana Muhammad + RM + + National Institute of Food Science and Technology, University of Agriculture, Faisalabad, 38000, Pakistan. Electronic address: muhammad.aadil@uaf.edu.pk. + + + + Wu + Qun + Q + + Key Laboratory of Industrial Biotechnology of the Ministry of Education, State Key Laboratory of Food Science and Resources, School of Biotechnology, Jiangnan University, 214122, Wuxi, China. Electronic address: wuq@jiangnan.edu.cn. + + + + eng + + Journal Article + + + 2026 + 04 + 16 + +
+ + England + Food Microbiol + 8601127 + 0740-0020 + + IM + + + Fermentation + + + Soy Foods + microbiology + analysis + + + Microbiota + + + Bacteria + classification + genetics + isolation & purification + metabolism + + + Climate + + + Odorants + analysis + + + Fungi + classification + genetics + metabolism + isolation & purification + + + Fermented Foods + microbiology + analysis + + + Food Microbiology + + + + Climate + Fermentation + Machine learning + Microbiota + Soy sauce aroma type baijiu + Succession distance + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 3 + 2 + + + 2026 + 4 + 12 + + + 2026 + 4 + 16 + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 20 + 55 + + + ppublish + + 42215204 + 10.1016/j.fm.2026.105124 + S0740-0020(26)00088-2 + + + + + + 42215160 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1876-4452 + + 66 + 3 + + 2026 + May + + + Science & justice : journal of the Forensic Science Society + Sci Justice + + On-site strip-type drug test kit enhanced by AI-based image analysis. + + 101408 + 101408 + + 10.1016/j.scijus.2026.101408 + S1355-0306(26)00014-6 + + The demand for technology that can rapidly and precisely detect drugs in the field is increasing because of the growing number of criminal incidents involving the tampering of beverages with drugs. This paper presents the development and validation of a strip-type drug test kit that is user-friendly and portable, reduces the consumption of reagents that are hazardous to humans, and produces less waste. A drawback of strip-type drug test kits is that they depend on visual interpretation of the results, which can vary depending on user skill, lighting, and slight color changes. To address this limitation, this study implemented an analytical method that minimizes uncertainty and facilitates quantitative evaluation by utilizing images of color reaction outcomes from the newly developed strip-type drug test kit. Specifically, we developed an automatic discrimination algorithm using artificial intelligence (AI) image analysis techniques. Using a collection of 7,295 kit images, we evaluated the efficacy of several deep-learning models, including Convolutional Neural Network (CNN), Vision Transformer (ViT), and CNN-GRU (a hybrid of CNN and Gated Recurrent Unit). The findings indicated that the CNN-GRU model showed superior performance across all parameters, including accuracy, precision, recall, F1-score, and Cohen's kappa, achieving a perfect score of 1.0000. The model achieved perfect accuracy, surpassing the average performance of 32 police officers in a comparative experiment. This study demonstrated that AI-based precision reading algorithms can significantly improve the automation and reliability of strip-type drug test kits, highlighting their potential applicability and effectiveness in investigative and forensic settings. + Copyright © 2026. Published by Elsevier B.V. + + + + An + Sora + S + + Department of Forensic Science, Sungkyunkwan University, Suwon 16419, the Republic of Korea. + + + + Lee + Joong + J + + Institute of AI and Big Data in Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, the Republic of Korea. + + + + Lee + Sangki + S + + Department of Forensic Science, Sungkyunkwan University, Suwon 16419, the Republic of Korea. + + + + Chung + Heesun + H + + Department of Forensic Science, Sungkyunkwan University, Suwon 16419, the Republic of Korea. Electronic address: hschung1024@gmail.com. + + + + eng + + Journal Article + + + 2026 + 02 + 18 + +
+ + England + Sci Justice + 9508563 + 1355-0306 + + + + 0 + Reagent Strips + + + IM + + + Humans + + + Image Processing, Computer-Assisted + + + Artificial Intelligence + + + Substance Abuse Detection + instrumentation + methods + + + Algorithms + + + Convolutional Neural Networks + + + Intelligent Systems + + + Deep Learning + + + Reagent Strips + + + + AI-based precision + Artificial Intelligence (AI) + Deep-learning models + Image analysis + Strip-type drug test kit + + Declaration of competing interest the authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Heesun Chung reports financial support was provided by Commercializations Promotion Agency for R&D Outcomes. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2025 + 9 + 22 + + + 2025 + 12 + 30 + + + 2026 + 2 + 11 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 20 + 55 + + + ppublish + + 42215160 + 10.1016/j.scijus.2026.101408 + S1355-0306(26)00014-6 + + + + + + 42215144 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2162-9471 + + 133 + + 2026 + + + Advances in clinical chemistry + Adv Clin Chem + + Drug induced renal injury (DIRI) biomarkers. + + 1 + 44 + 1-44 + + 10.1016/bs.acc.2026.01.006 + S0065-2423(26)00020-X + + Drug-induced renal injury (DIRI) remains a substantial issue in clinical practice, contributing to high morbidity, mortality, socioeconomic and healthcare cost. Traditional markers, such as serum creatinine or blood urea nitrogen, have low specificity and sensitivity for early identification, underscoring the need for new reliable biomarkers. This chapter investigates biomarker improvements for DIRI, focusing on clinical uses, limitations, and future possibilities. The chapter examines biomarkers such as neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, cystatin C, β2-microglobulin and clusterin for diagnosing nephrotoxicity. In addition, we highlight the role of proteomics and metabolomics in facilitating the identification and development of novel biomarker candidates via high-throughput ultra-sensitive technology in investigating DIRI mechanisms. The chapter discusses validation frameworks, regulatory criteria for biomarker adoption in clinical settings, and how they are integrated into treatment monitoring and personalized medicine. Case examples demonstrate the real-world applicability in early diagnosis, risk classification, and therapy optimization. Standardization, cost, and translational gaps are discussed, as well as the potential for artificial intelligence and machine learning to improve biomarker processing and interpretation. Finally, this chapter emphasizes the transformative potential of DIRI biomarkers in providing a pathway to more accurate and timely identification of renal injury, improved patient outcomes, and reduced healthcare costs. Future research and global collaboration are required to fully realize the potential of these advances in clinical and regulatory settings. + Copyright © 2026. Published by Elsevier Inc. + + + + Sher + Emina Karahmet + EK + + School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom; International Society of Engineering Science and Technology, Nottingham, United Kingdom. Electronic address: emina.karahmet@gmail.com. + + + + Boras + Marijana Marković + MM + + Department of Laboratory Diagnostics, University Clinical Hospital Mostar, Mostar, Bosnia and Herzegovina; International Society of Engineering Science and Technology, Nottingham, United Kingdom. + + + + Fajkić + Almir + A + + Department of Pathophysiology, Faculty of Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina. + + + + Begović + Faris + F + + International Society of Engineering Science and Technology, Nottingham, United Kingdom. + + + + eng + + Journal Article + Review + + + 2026 + 02 + 23 + +
+ + United States + Adv Clin Chem + 2985173R + 0065-2423 + + + + 0 + Biomarkers + + + 0 + Hepatitis A Virus Cellular Receptor 1 + + + 0 + Cystatin C + + + IM + + + Humans + + + Biomarkers + analysis + blood + + + Acute Kidney Injury + diagnosis + chemically induced + + + Proteomics + + + Metabolomics + + + Hepatitis A Virus Cellular Receptor 1 + + + Cystatin C + + + + Clinical chemistry + Cystatin c + Drug-induced renal injury biomarkers + Early diagnosis + Kidney injury molecule-1 + Metabolomics + Nephrotoxicity + Neutrophil gelatinase-associated lipocalin + Personalized medicine + Proteomics + + + + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 20 + 55 + + + ppublish + + 42215144 + 10.1016/bs.acc.2026.01.006 + S0065-2423(26)00020-X + + + + + + 42215142 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + A hybrid chemometric and deep learning model for monitoring quality loss in thermally processed edible oils. + + 119497 + 119497 + + 10.1016/j.foodres.2026.119497 + S0963-9969(26)01180-4 + + Edible oils are routinely exposed to high temperatures during domestic and commercial cooking, leading to chemical transformations that degrade the quality of oils and pose potential health risks. This study investigated the thermal degradation of groundnut, sesame, and restaurant-collected oils, with particular emphasis on polymerization, oxidation, and hydrolysis processes. Fourier-transform infrared (FTIR) spectroscopy and dielectric measurements were employed to monitor the molecular changes induced by heating the oils, whereas quantitative assessments of oil deterioration were performed using key chemical parameters and dielectric indices. Advanced chemometric and deep learning models like CARS-PLS, RF-PLS, and 1D-CNN were used to find the most important spectral regions and key chemical and dielectric features linked to oil degradation. Among these, the 1D-CNN model exhibited the highest predictive accuracy for both acid value (AV) and dielectric constant (DC), achieving training R2values of 0.9984 and 0.9931, and an external test R2values of 0.9904 and 0.9898, respectively, with the lowest RMSE. Heating oil shows the results with significant alterations in the FTIR bands, an increased dielectric constant, and elevated AV, corresponding to the accumulation of oxidized triglycerides, free fatty acids, and high-molecular-weight polymeric residues. Furthermore, explainable artificial intelligence (XAI) using SHAP analysis revealed key spectral contributions corresponding to ester groups, carbonyl stretching of triglycerides, free fatty acids, and CH₂/CH₃ vibrations. This study demonstrates that integrating spectroscopic and dielectric analyses with predictive modeling provides a sensitive, non-destructive laboratory-based proof-of-concept approach for evaluating the oil degradation during thermal processing. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Varma + Shruti O + SO + + Department of Physics, The Institute of Science, Dr. Homi Bhabha State University, Mumbai 400032, Maharashtra, India. + + + + Vishwakarma + Ajay L + AL + + Department of Physics, The Institute of Science, Dr. Homi Bhabha State University, Mumbai 400032, Maharashtra, India. + + + + Sonawane + M R + MR + + Department of Physics, The Institute of Science, Dr. Homi Bhabha State University, Mumbai 400032, Maharashtra, India. Electronic address: smahadev123@gmail.com. + + + + Garad + Nitin P + NP + + School of Physical Sciences, Swami Ramanand Teerth Marathwada University, Nanded 431606, India. + + + + Kumbharkhane + A C + AC + + School of Physical Sciences, Swami Ramanand Teerth Marathwada University, Nanded 431606, India. + + + + eng + + Journal Article + + + 2026 + 05 + 20 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + + + 0 + Plant Oils + + + IM + + + Hot Temperature + + + Spectroscopy, Fourier Transform Infrared + + + Deep Learning + + + Cooking + methods + + + Plant Oils + chemistry + analysis + + + Food, Processed + + + Oxidation-Reduction + + + Hydrolysis + + + + Chemical properties + Chemometrics + Deep learning + Dielectric properties + Explainable artificial intelligence (XAI) + Thermal degradation + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 2 + 25 + + + 2026 + 5 + 17 + + + 2026 + 5 + 19 + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215142 + 10.1016/j.foodres.2026.119497 + S0963-9969(26)01180-4 + + + + + + 42215140 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + Multimodal analysis of spontaneous speech for predicting food liking: Integrating linguistic and prosodic features with machine learning. + + 119483 + 119483 + + 10.1016/j.foodres.2026.119483 + S0963-9969(26)01166-X + + Spontaneous speech offers a promising yet underexplored window into consumers' emotional experiences during food evaluation. Unlike traditional self-reported measures, vocal expression captures both explicit linguistic content and implicit affective cues, potentially providing a richer account of hedonic perception. This study investigates whether linguistic and prosodic cues embedded in natural spoken responses can predict product liking. Ninety participants evaluated three chocolates and three plant-based beverages, providing hedonic ratings and open-ended spoken descriptions. Speech was analysed using psycholinguistic, prosodic, and representation-based features. Multiple machine learning algorithms were applied for both classification (low/medium vs high liking) and regression (continuous liking prediction). Feature-level comparisons showed that chocolates elicited richer emotional language, higher vocal intensity, and greater acoustic variability than plant-based drinks (p < 0.005). Across the full dataset, linguistic markers were the strongest predictors of liking (F1-score = 0.695; mean absolute error (MAE) = 1.530), while prosodic features provided complementary information related to arousal and engagement. Speech embeddings showed limited generalisation (area under the curve (AUC) < 0.518; MAE > 1.602). Product-specific analyses revealed higher predictive accuracy for chocolates (F1-score = 0.825; MAE = 1.213) than for plant-based beverages (F1-score = 0.559; MAE = 1.619), indicating that the strength of affective cues in speech depends on the emotional resonance of the product being evaluated. These findings demonstrate that spontaneous speech contains measurable emotional signals related to food liking. Interpretable linguistic and prosodic features can partially predict hedonic responses, highlighting the potential of multimodal, speech-based emotion analysis as a complementary tool in sensory and consumer research. + Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved. + + + + Sousa + Pedro M + PM + + GreenUPorto/Inov4Agro, DGAOT, Faculty of Sciences, University of Porto, Portugal. + + + + Lima + Rui C + RC + + Sense Test, Lda, Vila Nova de Gaia, Portugal. + + + + García-Segovia + Purificación + P + + i-Food, IUIA-FoodUPV, Universitat Politècnica de València, Spain. + + + + Alcañiz + Mariano + M + + LabLENI, HumanTech/LabLENI, Universitat Politècnica de València, València, Spain. + + + + Cunha + Luís M + LM + + GreenUPorto/Inov4Agro, DGAOT, Faculty of Sciences, University of Porto, Portugal. Electronic address: lmcunha@fc.up.pt. + + + + eng + + Journal Article + + + 2026 + 05 + 18 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + IM + + + Humans + + + Machine Learning + + + Speech + + + Food Preferences + psychology + + + Female + + + Emotions + + + Male + + + Young Adult + + + Cues + + + Adult + + + Linguistics + + + Consumer Behavior + + + + Acoustic features + Affective computing + Sensory analysis + Speech emotion + Supervised learning + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 1 + 4 + + + 2026 + 4 + 28 + + + 2026 + 5 + 16 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215140 + 10.1016/j.foodres.2026.119483 + S0963-9969(26)01166-X + + + + + + 42215133 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + Translating Food Biosensing into Practice: Smart Packaging, On-Site Analytics, and AI Integration. + + 119473 + 119473 + + 10.1016/j.foodres.2026.119473 + S0963-9969(26)01156-7 + + The global food system encounters increasing challenges from contamination threats, necessitating innovative monitoring technologies to guarantee safety and quality. This review investigates novel precision monitoring methodologies, emphasizing sophisticated biosensing technologies, intelligent packaging systems, molecular docking, and AI-integrated platforms. Biosensors utilize electrochemical, optical, and thermal transducers for the swift, on-site identification of contaminants, toxins, and allergens, surpassing conventional methods in both speed and accessibility. The incorporation of intelligent packaging improves real-time quality assurance, while molecular docking determines contaminant interactions to improve sensor design. Advancements in AI and machine learning enhance predictive analytics and traceability, meeting intricate supply chain requirements. Nonetheless, obstacles such as matrix interference, sensor stability, and regulatory deficiencies remain. Future directions highlight nanotechnology, biodegradable materials, and defined standards to improve scalability and sustainability. These breakthroughs will continue to transform food safety, minimize waste, and enhance global health equity, thereby revolutionizing the food industry for a sustainable future. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Seyedhosseini-Ghaheh + Hooria + H + + Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. Electronic address: h.hosseini@nutr.mui.ac.ir. + + + + Rafieian + Fatemeh + F + + Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. + + + + Rezaei + Atefe + A + + School of Life Sciences, Plant Proteins and Nutrition, Technical University of Munich, Freising 85354, Germany. + + + + Askari + Gholamreza + G + + Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. + + + + Zarrabi + Ali + A + + Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul 34396, Türkiye; Graduate School of Biotechnology and Bioengineering, Yuan Ze University, Taoyuan 320315, Taiwan. Electronic address: ali.zarrabi@istinye.edu.tr. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 16 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + IM + + + Biosensing Techniques + methods + + + Artificial Intelligence + + + Food Packaging + methods + + + Food Safety + methods + + + Food Contamination + analysis + + + Molecular Docking Simulation + + + Intelligent Systems + + + Humans + + + + Artificial Intelligence + Food biosensors + Molecular Docking + Smart Packaging + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 3 + 23 + + + 2026 + 4 + 24 + + + 2026 + 5 + 15 + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215133 + 10.1016/j.foodres.2026.119473 + S0963-9969(26)01156-7 + + + + + + 42215117 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + Artificial intelligence integrated in non-destructive technologies for monitoring food freshness: A review of next generation approach. + + 119450 + 119450 + + 10.1016/j.foodres.2026.119450 + S0963-9969(26)01132-4 + + Food freshness is a significant quality and safety attribute, which contributes in consumers' health and their food choices. The adoption of rapid, accurate, and reliable food freshness detection novel approaches is of great significance. Non-destructive testing (NDT) offers precise, fast and efficient detection of food freshness, quality and safety but, still confronts multiple issues in data preprocessing, accuracy, reliability and adaptability. However, artificial intelligence (AI) integration in NDT upholds significant promise to overcome these issues. This review, spotlights the significance of integrating the AI in NDT (spectroscopy, imaging and other technologies) for boosting the performance of NDT. It further probes the merits, and demerits, and the principles of AI assisted NDT technologies. Moving forward, integrating AI in NDT technologies have significantly boosted the reliable and accurate freshness detection in complex food matrices, plant and animal derived foods. In spite of recent advancements in this field of research, there are still several existing challenges of compatibility, precision, and adaptability associated with the NDT approaches coupled with AI. Therefore, this review is comprehensively emphasized on the promising transformative prospects of AI integrated in NDT approaches for the non-destructive, rapid, reliable and accurate detection of food freshness based on its quality indicators accompanied with highlighting the future challenges, innovations and directions. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Waseem + Muhammad + M + + College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China; Department of Food Science and Technology, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, 63100, Pakistan. + + + + Javed + Muhammad Rizwan + MR + + Department of Food Science and Technology, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, 63100, Pakistan. + + + + Manzoor + Muhammad Faisal + MF + + Guangdong Provincial Key Laboratory of Intelligent Food Manufacturing, School of Food Science and Engineering, Foshan University, Foshan, China. + + + + Xia + Xiuqi + X + + College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China. + + + + Huang + Yilei + Y + + College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China. + + + + Chen + Xiaomei + X + + College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China. Electronic address: xmchen@jmu.edu.cn. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 14 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + IM + + + Artificial Intelligence + + + Food Analysis + methods + + + Humans + + + Food Quality + + + Animals + + + Intelligent Systems + + + + Artificial intelligence + Deep learning + Freshness + Machine learning + Rapid monitoring + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2025 + 12 + 22 + + + 2026 + 5 + 6 + + + 2026 + 5 + 11 + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215117 + 10.1016/j.foodres.2026.119450 + S0963-9969(26)01132-4 + + + + + + 42215102 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1873-7145 + + 238 + + 2026 + Aug + 31 + + + Food research international (Ottawa, Ont.) + Food Res Int + + Towards sustainable serum-free media development from alternative sources for cultivated meat. + + 119434 + 119434 + + 10.1016/j.foodres.2026.119434 + S0963-9969(26)01116-6 + + The economic feasibility and scalability of cultivated meat (CM) is critically hindered by the high manufacturing costs, particularly the cost associated with cell culture media. Therefore, numerous studies have proposed the use of sustainable alternatives to replace the expensive cell culture media components, especially animal serum. In this review, we comprehensively surveyed such developments and interpreted the opportunities and limitations of animal serum-reduction or replacement methodologies in designing culture media for CM production. Among the various alternative sources investigated, plant-derived sources and recombinant proteins have been the most extensively studied. We also highlight the critical gaps in existing studies such as the lack of demonstration of scalability, cost-effectiveness and long-term applicability of proposed alternative sources. Subsequently, we provide a roadmap for future studies on how to demonstrate the proposed alternative does not have adverse effects on cellular long-term stability, as well as comply with food safety and regulatory standards while being cost-effective which are critical for large scale CM manufacturing. We further propose a two-pronged framework based on high-throughput screening, systems biology, and Machine Learning (ML) tools to optimize serum-free CM media formulations using alternative sources in a rapid and scalable manner. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Sivakumar + Sanjay + S + + Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India. + + + + Manayankath + Aparna + A + + Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India. + + + + Hong + Yi Fan + YF + + Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Department of Biomedical Engineering, National University of Singapore, Singapore. + + + + Thrivikraman + Greeshma + G + + Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India. + + + + Tekkatte + Chandana + C + + The Good Food Institute India, Bengaluru, India. + + + + Choudhury + Deepak + D + + Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Department of Food Science and Technology, National University of Singapore, Singapore; Bezos Centre for Sustainable Protein at the National University of Singapore, Singapore. Electronic address: Deepak_Choudhury@a-star.edu.sg. + + + + Pang + Kuin Tian + KT + + Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Department of Biomedical Engineering, National University of Singapore, Singapore; Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore; School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore. Electronic address: zach_pang@a-star.edu.sg. + + + + Lakshmanan + Meiyappan + M + + Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India; Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; Wadhwani School of Data Science and AI (WSAI), Indian Institute of Technology Madras, Chennai, India; Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology Madras, Chennai, India. Electronic address: meiyappan@iitm.ac.in. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 13 + +
+ + Canada + Food Res Int + 9210143 + 0963-9969 + + + + 0 + Culture Media, Serum-Free + + + IM + + + In Vitro Meat + + + Animals + + + Culture Media, Serum-Free + chemistry + + + Meat Substitutes + + + + Alternative proteins + Animal serum alternatives + Cultivated meat + Serum-free media + Sustainability + Systems biology + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2025 + 12 + 11 + + + 2026 + 3 + 21 + + + 2026 + 5 + 9 + + + 2026 + 5 + 30 + 0 + 34 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 20 + 54 + + + ppublish + + 42215102 + 10.1016/j.foodres.2026.119434 + S0963-9969(26)01116-6 + + + + + + 42214983 + + 2026 + 05 + 29 + +
+ + 1879-3363 + + 231 + + 2026 + May + 29 + + + Marine pollution bulletin + Mar Pollut Bull + + Deep learning-based prediction of trace silver concentrations in marine sediments from mid-infrared spectroscopy. + + 119930 + 119930 + + 10.1016/j.marpolbul.2026.119930 + S0025-326X(26)00717-4 + + Silver (Ag) is a biologically toxic element, and the rapid detection of its concentrations in environmental samples is thus of great importance. This study investigates mid-infrared (MIR) spectra of low-Ag sediments from the South China Sea (SCS), and successfully predicts Ag concentrations by three machine learning models, including Kennard Stone-competitive adaptive reweighted sampling-partial least squares regression (KS-CARS-PLSR), Kennard Stone-random forest (KS-RF), and random selecting-squeeze excitation attention-residual network (RS-SE-ResNet-1D). Among the three techniques, the RS-SE-ResNet-1D model performs best in terms of Ag prediction, achieving an R2 of 0.82, an RMSE of 0.03, and an RPD of 2.38. A common feature wavenumber band in the range of 1408.3-1408.7 cm-1, corresponding to the v3 symmetric stretching absorption band of carbonate ions (CO3 + 2-), is identified from all three models. This suggests that marine productivity is possibly the dominant factor controlling Ag levels in marine sediments of the SCS. The machine learning models based on MIR spectral data developed in this study provide a new analytical approach for determining Ag concentrations in low-Ag marine sediments, and offer new insights into the study of biogeochemical cycling of Ag. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Yu + Boyuan + B + + School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, Anhui, China. + + + + Xu + Liqiang + L + + School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, Anhui, China. Electronic address: xlq@hfut.edu.cn. + + + + Ning + Yu + Y + + Instrumental Analysis Center, Hefei University of Technology, Hefei, Anhui, China. + + + + Xu + Fang + F + + School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui, China. + + + + Han + Qingli + Q + + School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Mar Pollut Bull + 0260231 + 0025-326X + + IM + + Machine learning + Marine sediments + Mid-infrared spectra + Productivity + Silver + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 1 + 28 + + + 2026 + 5 + 23 + + + 2026 + 5 + 25 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 19 + 52 + + + aheadofprint + + 42214983 + 10.1016/j.marpolbul.2026.119930 + S0025-326X(26)00717-4 + + + + + + 42214978 + + 2026 + 05 + 29 + +
+ + 1878-0539 + + 118 + + 2026 + May + 14 + + + Journal of plastic, reconstructive & aesthetic surgery : JPRAS + J Plast Reconstr Aesthet Surg + + Development and nested cross-validation of machine learning models for predicting transfusion requirements in pediatric craniosynostosis surgery. + + 209 + 220 + 209-220 + + 10.1016/j.bjps.2026.05.033 + S1748-6815(26)00288-3 + + Blood transfusion is common during pediatric craniosynostosis surgery; however, transfusion volumes and use of cell salvage systems can vary considerably. To support preoperative transfusion planning in patients undergoing craniectomy for craniosynostosis, we developed and internally validated machine learning (ML) models to predict perioperative transfusion risks and transfusion volumes in this patient population. Data from eligible patients ≤36 months of age from the NSQIP® Pediatric dataset were included for modeling. A classifier pipeline was developed to predict probabilities associated with transfusion risk, and a probabilistic/quantile regression pipeline was developed to predict the median transfusion volume (in mL/kg) with interquartile range. Performance of the modeling pipelines was evaluated using nested cross-validation with 5-fold outer loops. In total, 8045 patients were included with 4091 (50.9%) receiving perioperative transfusion. For the classification pipeline, the optimal model configuration was a LightGBM classifier. The modeling pipeline achieved a mean area under the receiver operating characteristic curve (AUC-ROC) of 0.77 (95% confidence interval [CI] 0.75-0.78) and mean Brier score of 0.20 (95% CI 0.19-0.20) across nested cross-validation. For the regression stage, the optimal model configuration was a probabilistic NGBoost regressor. The modeling pipeline achieved an average median absolute error of 6.86 mL/kg (95% CI 6.61-7.11), with a mean IQR coverage of 53% (95% CI 52-53%). These models may support individualized blood product preparation and selective use of cell salvage systems. External validation and further real-world testing are needed prior to the clinical implementation of this tool. + Copyright © 2026 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved. + + + + Deng + Jiawen + J + + Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada. + + + + Gharavi + Aidin + A + + Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, MN, USA. + + + + Heybati + Kiyan + K + + Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA. + + + + Haile + Dawit + D + + Department of Anesthesiology, Mayo Clinic, Rochester, MN, USA. + + + + Ahn + Edward S + ES + + Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA. + + + + Mardini + Samir + S + + Division of Plastic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN, USA. + + + + Gibreel + Waleed + W + + Division of Plastic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN, USA; Department of Pediatric and Adolescents Medicine, Mayo Clinic, Rochester, MN, USA. Electronic address: Gibreel.Waleed@mayo.edu. + + + + eng + + Journal Article + + + 2026 + 05 + 14 + +
+ + Netherlands + J Plast Reconstr Aesthet Surg + 101264239 + 1748-6815 + + IM + + Blood transfusion + Craniectomy + Craniosynostoses + Machine learning + Operative blood salvage + + Declaration of Competing Interest The authors declare no conflict of interest. The ACS-NSQIP® and participating hospitals are the source of this data; they have not verified nor are they responsible for the statistical validity of the data analysis or the conclusions derived by the authors. + + + + + 2025 + 12 + 9 + + + 2026 + 4 + 20 + + + 2026 + 5 + 12 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 19 + 52 + + + aheadofprint + + 42214978 + 10.1016/j.bjps.2026.05.033 + S1748-6815(26)00288-3 + + + + + + 42214919 + + 2026 + 05 + 29 + +
+ + 1090-2104 + + 827 + + 2026 + May + 25 + + + Biochemical and biophysical research communications + Biochem Biophys Res Commun + + Employing network toxicology, molecular docking, machine learning, and single-cell analysis to analyze BPA exposure-induced ccRCC. + + 154037 + 154037 + + 10.1016/j.bbrc.2026.154037 + S0006-291X(26)00801-6 + + This study investigates the molecular mechanisms underlying bisphenol A (BPA)-induced clear cell renal cell carcinoma (ccRCC). We integrated transcriptomic data from multiple GEO datasets and performed differential expression analysis and WGCNA to identify BPA-associated candidate genes. Enrichment analyses implicated pathways including cell adhesion, lipid metabolism, arachidonic acid signaling, and innate immune response. Using twelve machine learning algorithms, we identified four core genes (ITGB2, TBXAS1, LIPA, and CLEC7A), all upregulated in ccRCC. Molecular docking suggested stable BPA-protein interactions with favorable binding energies. Single-cell analysis showed predominant expression of these genes in monocytes and macrophages. LIPA was elevated in kidney cancer tissues and associated with clinical outcomes. In vitro experiments confirmed that BPA exposure promoted ccRCC cell progression by regulating core gene expression. This integrated approach offers new insights into the molecular mechanisms of BPA-induced ccRCC and identifies potential biomarkers for environmental risk assessment. + Copyright © 2026 Elsevier Inc. All rights reserved. + + + + Chen + Jianyu + J + + Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. + + + + Wu + Lianquan + L + + Department of Urology, Longgang People's Hospital, Wenzhou, 325000, China. + + + + Jin + Haoqi + H + + Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. + + + + Li + Yeping + Y + + Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China; Institute of Urology, Wenzhou Medical University, Wenzhou, 325000, China. Electronic address: liypwz@126.com. + + + + eng + + Journal Article + + + 2026 + 05 + 25 + +
+ + United States + Biochem Biophys Res Commun + 0372516 + 0006-291X + + IM + + Bisphenol A + Clear cell renal cell carcinoma + Machine learning + Molecular docking + Single-cell analysis + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 22 + + + 2026 + 5 + 25 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 19 + 51 + + + aheadofprint + + 42214919 + 10.1016/j.bbrc.2026.154037 + S0006-291X(26)00801-6 + + + + + + 42214908 + + 2026 + 05 + 29 + +
+ + 1876-035X + + 19 + 7 + + 2026 + May + 25 + + + Journal of infection and public health + J Infect Public Health + + The Italian hand hygiene project: A joint-commission Italian network analysis. + + 103268 + 103268 + + 10.1016/j.jiph.2026.103268 + S1876-0341(26)00140-1 + + Hand hygiene (HH) is a straightforward yet highly effective preventive measure against healthcare-associated infections; however, global compliance remains suboptimal. This study analyzed a substantial Italian dataset, focusing on adherence to HH guidelines among healthcare professionals, and identified key factors influencing compliance. + Direct multi- wave cross-sectional observational data on hand hygiene from over 749,000 opportunities were collected between 2017 and 2024 across 30 Italian hospitals participating in the Joint Commission International Hand Hygiene Project. Traditional statistical analyses and supervised machine learning (ML) models were employed to evaluate compliance rates and to examine their association with the year of observation, clinical area, professional role, and patient interaction moment (opportunity). Additionally, ML models were implemented to support proactive, risk- based surveillance within hospital settings by identifying high- risk contexts and facilitating more targeted monitoring strategies. + Overall hand hygiene compliance increased from 73.5% in 2017-82% in 2024, surpassing the World Health Organization's (WHO) target of 80% from 2020 onwards. Nursing staff demonstrated the highest adherence. High-risk opportunities, such as pre-aseptic procedures and post-exposure to bodily fluids, exhibited higher compliance than other moments. Emergency departments recorded the lowest compliance rates (76.3%). ML models confirmed that the year, patient interaction moment, and professional role are the most significant predictors of adherence. The most effective ML model achieved an accuracy of 64.6% in classifying hand hygiene actions. + This analysis underscores an improving trend in hand hygiene practices within Italian hospitals and concurrently identifies specific contexts-such as emergency departments-that require targeted interventions. The integration of observational monitoring with predictive artificial intelligence models could facilitate proactive, risk-based surveillance and focused improvement strategies. Accreditation-driven, multi-modal initiatives like this project highlight the vital role of measurement in fostering a culture of hand hygiene and advancing patient safety. + Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved. + + + + Buja + A + A + + Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua I-35131, Italy. Electronic address: alessandra.buja@unipd.it. + + + + Damiani + G + G + + Center of Research in Precision Medicine and Chronic Inflammation, Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan 20122, Italy; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan 20122, Italy. + + + + Tangherloni + A + A + + Center of Research in Precision Medicine and Chronic Inflammation, Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan 20122, Italy; Department of Computing Sciences, Bocconi University, Milan 20136, Italy. + + + + Manfredi + M + M + + Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua I-35131, Italy. + + + + Lanza + G + G + + Network Joint Commission Italia, Italy. + + + + Palmeri + C + C + + Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua I-35131, Italy. + + + + eng + + Journal Article + + + 2026 + 05 + 25 + +
+ + England + J Infect Public Health + 101487384 + 1876-0341 + + IM + + Hand hygiene + Health Care Associated Infection + Hospital infection + Nosocomial infection + Prevention and control + + Declaration of Competing Interest The authors declare that there are no conflicts of interest regarding the publication of this manuscript. + + + + + 2026 + 1 + 13 + + + 2026 + 5 + 11 + + + 2026 + 5 + 19 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 19 + 50 + + + aheadofprint + + 42214908 + 10.1016/j.jiph.2026.103268 + S1876-0341(26)00140-1 + + + + + + 42214885 + + 2026 + 05 + 29 + +
+ + 1879-0771 + + 132 + + 2026 + May + 24 + + + Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society + Comput Med Imaging Graph + + An interpretable multimodal model integrating clinical, spectral CT imaging, and deep learning analysis of intra- and peritumoral regions for preoperative prediction of perineural invasion in gastric cancer: A prospective, multicenter study. + + 102776 + 102776 + + 10.1016/j.compmedimag.2026.102776 + S0895-6111(26)00079-0 + + Perineural invasion (PNI) is crucial for risk stratification and treatment planning. This study aims to develop a novel multimodal model for preoperative prediction of PNI in gastric cancer. + A total of 250 patients with pathologically confirmed gastric cancer from multiple centers were enrolled and divided into a training cohort (n = 138), an internal validation cohort (n = 59), and an external validation cohort (n = 53). Spectral CT parameters from both intra- and peritumoral regions were acquired using the GSI viewer software, while deep learning features were extracted via a ResNet-50 architecture. Clinical features were screened through univariate and multivariate regression analyses. Based on machine learning, we developed a spectral parameter model, a deep learning feature model, a clinical prediction model, and a multimodal fusion model (MFM). The predictive performance of the models was evaluated using calibration curves, receiver operating characteristic curves, and decision curve analysis (DCA). Model interpretability was achieved by employing Shapley Additive exPlanations (SHAP). + Compared with individual models, MFM demonstrated the best predictive performance, with area under the curve (AUC) values of 0.926, 0.885, and 0.871 in the training, internal validation, and external validation cohorts, respectively. DCA confirmed that MFM provided a higher net benefit over a wide range of threshold probability. Calibration curve showed MFM had better predictive consistency, and the Hosmer-Lemeshow test indicated good fit across all cohorts (all p-values > 0.05). SHAP effectively interpreted the decision-making process of the model. + The novel multimodal fusion model, which integrates clinical, spectral CT parameters, and deep learning features from intra- and peritumoral regions, demonstrated outstanding performance in preoperatively predicting PNI in gastric cancer. + Copyright © 2026 Elsevier Ltd. All rights reserved. + + + + Zhao + Yuncong + Y + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China. + + + + Chen + Lihong + L + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China; Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou 350001, China. + + + + Wang + Lili + L + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China; Clinical Research Center for Radiology and Radiotherapy of Fujian Province (Digestive, Hematological and Breast Malignancies), Fuzhou 350001, China. + + + + Yue + Wenjing + W + + The Fourth Clinical College, Henan Medical University, Xinxiang, 453003, China. + + + + Zeng + Deye + D + + Department of Pathology, Fujian Medical University Union Hospital, Fuzhou 350001, China. + + + + You + Yaru + Y + + Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou 450052, China; Henan Key Laboratory of CT Imaging, Zhengzhou 450052, China. + + + + Weng + Caiming + C + + Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou 350001, China. + + + + Li + Weixing + W + + Department of Radiology, Xinxiang Central Hospital, The Fourth Clinical College, Henan Medical University, Xinxiang, 453003, China. + + + + Chen + Yilin + Y + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China. + + + + Lin + Lin + L + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China; Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou 350001, China. Electronic address: linlin@fjmu.edu.cn. + + + + Xu + Haiping + H + + School of Computer and Data Science, Minjiang University, Fuzhou 350108, China. Electronic address: haiping@mju.edu.cn. + + + + Xue + Yunjing + Y + + Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China; The School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China; Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou 350001, China. Electronic address: xueyunjing@fjmu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 24 + +
+ + United States + Comput Med Imaging Graph + 8806104 + 0895-6111 + + IM + + Deep learning + Gastric cancer + Peripheral nerve invasion + SHAP + Spectral CT + + Declaration of Competing Interest The authors have no relevant financial or non-financial interests to disclose. + + + + + 2025 + 10 + 15 + + + 2026 + 4 + 27 + + + 2026 + 5 + 13 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 19 + 50 + + + aheadofprint + + 42214885 + 10.1016/j.compmedimag.2026.102776 + S0895-6111(26)00079-0 + + + + + + 42214863 + + 2026 + 05 + 29 + +
+ + 1873-3336 + + 513 + + 2026 + May + 28 + + + Journal of hazardous materials + J Hazard Mater + + Advances in multispectral and hyperspectral inversion for soil heavy metal contamination: Mechanisms, machine learning algorithms, and future perspectives. + + 142542 + 142542 + + 10.1016/j.jhazmat.2026.142542 + S0304-3894(26)01520-7 + + Heavy metal (HM) contamination in soil exhibits insidious and cumulative effects, posing long-term risks to ecosystems and human health. Traditional field sampling and laboratory analysis are increasingly insufficient for large-scale continuous monitoring, driving the adoption of multispectral (MS) and hyperspectral (HS) remote sensing. Bibliometric analysis reveals clear research trends: target elements are primarily copper, lead, and zinc, while data acquisition has progressively shifted from laboratory spectroscopy to portable devices and satellite platforms, reflecting an expansion from local to regional scales. This has led to increasing data complexity and greater demands on model robustness and generalization. However, expanding the spatial scale and transitioning to satellite observations introduce fundamental challenges. Mixed pixels and moisture-induced spectral distortions reduce signal purity, while the indirect spectral response of HMs further complicates quantitative inversion. Sample scarcity and spatial heterogeneity also limit cross-regional generalization, constraining model robustness and stability. In response, models have evolved from traditional linear regression to ensemble learning methods such as Extreme Gradient Boosting (XGBoost), and further to deep learning frameworks, including Convolutional Neural Networks (CNN) and Transformers, enabling hierarchical feature extraction and task-oriented structural design. This paper reviews the key technical bottlenecks in soil HM spectral inversion, integrating bibliometric insights with methodological advances to provide a comprehensive framework for understanding current progress and guiding future developments in large-scale, high-precision inversion. + Copyright © 2026 Elsevier B.V. All rights reserved. + + + + Huang + Minghao + M + + School of Geographic Sciences, School of Resources and Environmental Science, Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China. + + + + Wu + Xiang + X + + School of Geographic Sciences, School of Resources and Environmental Science, Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China; Key Laboratory of Danjiangkou Reservoir Area's Aquatic Eco-Environment and Health, Shiyan City (Hanjiang Normal University), Shiyan 442000, China. + + + + Peng + Qian + Q + + School of Geographic Sciences, School of Resources and Environmental Science, Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China; Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources (Ministry of Education), Sichuan Normal University, Chengdu 610068, China. Electronic address: qianpeng@hubu.edu.cn. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 28 + +
+ + Netherlands + J Hazard Mater + 9422688 + 0304-3894 + + IM + + Hyperspectral + Indirect Inversion Mechanisms + Machine learning + Multispectral + Soil Heavy metals + Spectral Feature Selection + + Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 2 + 21 + + + 2026 + 5 + 25 + + + 2026 + 5 + 26 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 19 + 49 + + + aheadofprint + + 42214863 + 10.1016/j.jhazmat.2026.142542 + S0304-3894(26)01520-7 + + + + + + 42214786 + + 2026 + 05 + 29 + +
+ + 1876-4754 + + + 2026 + May + 28 + + + Brain stimulation + Brain Stimul + + EEG Biomarkers of Accelerated Prolonged Intermittent Theta-Burst Stimulation in Depression: Machine Learning with a Minimal-Electrode Montage. + + 103135 + 103135 + + 10.1016/j.brs.2026.103135 + S1935-861X(26)00112-9 + + + Chang + Chun-Hung + CH + + An Nan Hospital, China Medical University, Tainan, Taiwan; Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan; Shu-Zen Junior College of Medicine and Management, Tainan, Taiwan. + + + + Sack + Alexander T + AT + + Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Brain+Nerve Centre, Maastricht University Medical Centre (MUMC), The Netherlands. + + + + Chu + Che-Sheng + CS + + Department of Psychiatry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Non-Invasive Neuromodulation Consortium for Mental Disorders, Society of Psychophysiology, Taipei, Taiwan; Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan. Electronic address: youngtzuchi@hotmail.com. + + + + Chang + Hsin-An + HA + + Non-Invasive Neuromodulation Consortium for Mental Disorders, Society of Psychophysiology, Taipei, Taiwan; Department of Psychiatry, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan. Electronic address: chang.ha@mail.ndmctsgh.edu.tw. + + + + eng + + Letter + + + 2026 + 05 + 28 + +
+ + United States + Brain Stimul + 101465726 + 1876-4754 + + IM + + EEG biomarkers + accelerated piTBS + brain stimulation + machine learning + major depressive disorder + resting-state electroencephalography + + Declaration of Competing Interest ☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: ATS is director of the Academy of Brain Stimulation (www.brainstimulation-academy.com) and the International Clinical TMS Certification Course (www.tmscourse.eu), receiving equipment support from MagVenture, MagStim, Deymed, Yingchi, Brainsway. He also serves as scientific advisor for Platoscince Medical and Alpha Brain Technologies. Other authors declare that they have no competing financial interests or personal relationships that could be perceived to have influenced the work reported in this paper. + + + + + 2026 + 3 + 23 + + + 2026 + 5 + 25 + + + 2026 + 5 + 26 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 19 + 35 + + + aheadofprint + + 42214786 + 10.1016/j.brs.2026.103135 + S1935-861X(26)00112-9 + + + + + + 42214688 + + 2026 + 05 + 29 + +
+ + 1878-1632 + + + 2026 + May + 28 + + + The spine journal : official journal of the North American Spine Society + Spine J + + From Three to Eight Clusters: A Machine Learning-Based Classification of Operated Adult Spinal Deformity Patients. + S1529-9430(26)00157-9 + 10.1016/j.spinee.2026.05.003 + + Adult spinal deformity (ASD) is a heterogeneous condition encompassing diverse etiologies, clinical presentations, and surgical challenges. While previous unsupervised learning models have stratified ASD patients into three broad phenotypes, these often lack clinical granularity for surgical decision-making. + To refine the classification of operated ASD patients using unsupervised machine learning and assess whether expanding from 3 to 8 clusters improves clinical discrimination and predictive robustness. + Retrospective multicenter study using machine learning clustering. + 471 adult patients who underwent surgery for ASD across six specialized spine centers. + ODI, SRS-22, spinal alignment metrics, surgical strategy, and postoperative complications. + A k-means clustering algorithm was applied to a selected set of 12 demographic, radiographic, and functional variables (C12). Clustering solutions with 3 and 8 groups were compared. Each cluster was analyzed for age, etiology, disability scores (ODI, SRS-22), spinal alignment, surgical strategy, and complication rates. Predictive models using LDA and KNN assessed classification accuracy for new patient assignment. + The 8-cluster model identified clinically distinct phenotypes, including subgroups of young patients with idiopathic scoliosis, structural hyperkyphosis, and early sagittal decompensation. In elderly patients, clusters differentiated pain profiles, alignment, and frailty. The most severe clusters distinguished coronal-dominant versus sagittal collapse deformities, with differing risks and outcomes. LDA maintained robust accuracy (91.4%) in predicting cluster assignment across 50 testing splits, outperforming KNN. + To our knowledge, this is among the most granular unsupervised ML-based phenotypic classifications of operated ASD patients reported to date. This refined 8-cluster model enhances clinical phenotyping in ASD surgery, offering more precise subgroup stratification than traditional 3-cluster models. It supports the integration of unsupervised learning into personalized surgical planning, risk stratification, and multicenter outcome trials. + Copyright © 2026 Elsevier Inc. All rights reserved. + + + + Roscop + Cecile + C + + ELSAN, Polyclinique Jean Villar, Bruges Cedex, France. Electronic address: cecile.roscop@gmail.com. + + + + Bourghli + Anouar + A + + Spine Surgery Department, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia. + + + + Baroncini + Alice + A + + Casa di Cura Humanitas San Pio X, Milano, Italy. + + + + Alanay + Ahmet + A + + Spine Center, Acibadem University School of Medicine, Istanbul, Turkey. + + + + Pellise + Ferran + F + + Spine Surgery Unit, Vall D'Hebron Hospital, Barcelona, Spain. + + + + Kleinstueck + Frank + F + + Schulthess Klinik, Zürich, Switzerland. + + + + Pizones + Javier + J + + Spine Surgery Unit, Hospital Universitario La Paz, Madrid, Spain. + + + + Charles + Yann-Philippe + YP + + Hôpitaux Universitaires de Strasbourg, France. + + + + Larrieu + Daniel + D + + ELSAN, Polyclinique Jean Villar, Bruges Cedex, France. + + + + Boissière + Louis + L + + ELSAN, Polyclinique Jean Villar, Bruges Cedex, France. + + + + Obeid + Ibrahim + I + + ELSAN, Polyclinique Jean Villar, Bruges Cedex, France. + + + + European Spine Study Group (ESSG) + + No institution. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Spine J + 101130732 + 1529-9430 + + IM + + Adult spinal deformity + Clustering + K-means algorithm + Machine learning + Phenotyping + Predictive modeling + Surgical planning + + Declaration of competing interest The authors declare no competing interests. + + + + + 2025 + 12 + 11 + + + 2026 + 5 + 15 + + + 2026 + 5 + 25 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 19 + 34 + + + aheadofprint + + 42214688 + 10.1016/j.spinee.2026.05.003 + S1529-9430(26)00157-9 + + + + + + 42214660 + + 2026 + 05 + 29 + +
+ + 1878-3511 + + + 2026 + May + 28 + + + International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases + Int J Infect Dis + + Investigating Infection and Clinical Data to Predict Effective Antibiotic Therapy and Monitor Resistance Trends. + + 108842 + 108842 + + 10.1016/j.ijid.2026.108842 + S1201-9712(26)00477-7 + + Data-driven approaches to effectively select antibiotics are crucial to improving patient outcomes and reducing antibiotic resistance. This study aimed to determine whether routinely collected clinical and microbiological data can be used to train machine learning (ML) models to predict antibiotic resistance in patients with bacterial infections. + We conducted a retrospective observational study on clinical data from two separate Italian hospitals, analyzing 15,581 bacterial isolates collected from 9,966 patients between 2018 and 2024 at a multi-department hospital located in Rome and secondary clinic located in Capranica, about 70 Km away from Rome. Multiple ML models were trained, using both unbalanced and SMOTE-balanced datasets. Performance was assessed using cross-validation and independent test sets, comprising 2023-2024 isolates. Performance was measured using the Area Under the Receiver Operating Characteristic curve (AUROC), F1 score, accuracy, precision, and recall. + XGBoost consistently outperformed other trained models, achieving AUROCs of 0.882 and 0.878 for Gram-positive and Gram-negative datasets, respectively. Species-specific models further improved discrimination, reaching AUROC scores up to 0.946 for P. aeruginosa, 0.941 for K. pneumoniae, 0.938 for E. faecalis, 0.919 for E. coli, 0.894 for P. mirabilis, and 0.891 for S. aureus. + These results demonstrate the utility of ML models in accurately predicting antibiotic susceptibility from routine clinical data, thus facilitating rapid initiation of targeted therapy. Such an approach can potentially reduce treatment delays by up to 48 hours compared to traditional diagnostic methods, presenting a useful tool to manage patients in critical conditions and combat antibiotic resistance in clinical practice. + Copyright © 2026. Published by Elsevier Ltd. + + + + Benedetto + Marco + M + + Department of Management and Innovation Systems, University of Salerno, Fisciano (SA) 84084, Italy; Kelyon S.r.l., via Benedetto Brin, 59 C5/C6, 80100 Naples, Italy. + + + + Piccinni + Giuseppe + G + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + De Leo + Cristina + C + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + Cirielli + Corrado + C + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + Facchiano + Francesco + F + + Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy. + + + + Facchiano + Angelo + A + + Laboratory of Bioinformatics and Computational Biology, Institute of Food Science, CNR, via Roma 52 A/C, 83100 Avellino, Italy. + + + + Panebianco + Annarita + A + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + Tagliaferri + Stefano + S + + Kelyon S.r.l., via Benedetto Brin, 59 C5/C6, 80100 Naples, Italy. + + + + La Rocca + Michele + M + + Department of Economics and Statistics, University of Salerno, Fisciano (SA) 84084, Italy. + + + + Marchetti + Paolo + P + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy. + + + + Tagliaferri + Roberto + R + + Department of Management and Innovation Systems, University of Salerno, Fisciano (SA) 84084, Italy. + + + + Facchiano + Antonio + A + + Istituto Dermopatico dell'Immacolata, IDI-IRCCS, Rome, Italy; Department of Life Sciences, Health and Health Professions, Link University, 00165 Rome, Italy. Electronic address: a.facchiano@idi.it. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + Canada + Int J Infect Dis + 9610933 + 1201-9712 + + IM + + Antimicrobial resistance + antibiotic susceptibility prediction + antimicrobial stewardship + machine learning + precision medicine + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2025 + 11 + 28 + + + 2026 + 4 + 17 + + + 2026 + 5 + 26 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 19 + 33 + + + aheadofprint + + 42214660 + 10.1016/j.ijid.2026.108842 + S1201-9712(26)00477-7 + + + + + + 42214547 + + 2026 + 05 + 29 + +
+ + 1873-6424 + + + 2026 + May + 28 + + + Environmental pollution (Barking, Essex : 1987) + Environ Pollut + + Machine-learning-based estimation of ground-level NO2 concentrations across Southeastern Europe using multi-source satellite, reanalysis, and emission data. + + 128461 + 128461 + + 10.1016/j.envpol.2026.128461 + S0269-7491(26)00831-6 + + Reliable estimation of ground-level nitrogen dioxide (NO2) remains challenging due to limited monitoring coverage and complex interactions among emissions, meteorology, and land-use factors. This study develops a machine-learning framework to estimate surface NO2 concentrations across Southeastern Europe, covering thirteen countries with diverse emission sources and sparse monitoring networks. Daily NO2 observations from 317 monitoring stations were integrated with multiple satellite and ancillary datasets, including tropospheric NO2 from Sentinel-5P TROPOMI, vegetation indices from MODIS, and nighttime light data from VIIRS. Additional inputs included meteorological variables from ERA5, anthropogenic emissions from EDGAR v8.1, land-use data from Copernicus Corine Land Cover, elevation from the Copernicus Digital Elevation Model, and population density from the Global Human Settlement Layer. All datasets were processed and harmonized using Google Earth Engine. Three Gradient Boosting Decision Tree models (XGBoost, LGBM, and CatBoost) were trained and optimized with Optuna. Model performance was assessed using 10-fold cross-validation and independent test sets with R2, MAE, and RMSE metrics. All models performed consistently, with LGBM achieving the best results (R2 = 0.86; MAE = 5.34 μg/m3; RMSE = 8.32 μg/m3). SHapley Additive exPlanations (SHAP) identified key predictors and improved interpretability. Seasonal analysis showed higher accuracy in summer and autumn than in winter and spring. These results demonstrate that integrating multi-source data with advanced machine learning enables reliable surface NO2 estimation in regions with limited monitoring, supporting air quality assessment, exposure analysis, and evidence-based policymaking in Southeastern Europe. + Copyright © 2026. Published by Elsevier Ltd. + + + + Bilgiç + Efem + E + + Department of Environmental Engineering, Faculty of Engineering, Dokuz Eylul University, Izmir, Türkiye. Electronic address: efem.bilgic@deu.edu.tr. + + + + Elbir + Tolga + T + + Department of Environmental Engineering, Faculty of Engineering, Dokuz Eylul University, Izmir, Türkiye; Environmental Research and Application Center (CEVMER), Dokuz Eylul University, Izmir, Türkiye. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Environ Pollut + 8804476 + 0269-7491 + + IM + + Nitrogen dioxide + Southeastern Europe + air quality modeling + machine learning + satellite data integration + + Declaration of Competing Interest ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 1 + 26 + + + 2026 + 5 + 24 + + + 2026 + 5 + 28 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 19 + 32 + + + aheadofprint + + 42214547 + 10.1016/j.envpol.2026.128461 + S0269-7491(26)00831-6 + + + + + + 42214540 + + 2026 + 05 + 29 + +
+ + 1873-6424 + + + 2026 + May + 28 + + + Environmental pollution (Barking, Essex : 1987) + Environ Pollut + + Nonlinear meteorological controls on coastal air quality along the Indian East Coast: Insights from a multi-city interpretable machine learning framework. + + 128448 + 128448 + + 10.1016/j.envpol.2026.128448 + S0269-7491(26)00818-3 + + This study investigates the role of meteorological variability in shaping ambient air quality across five coastal cities along the Indian East Coast (Haldia, Balasore, Visakhapatnam, Chennai, and Puducherry). Despite their shared coastal setting, the cities exhibit pronounced meteorological heterogeneity, leading to a clear latitudinal gradient in air pollution, with northern cities (Haldia and Balasore) experiencing substantially higher particulate matter concentrations and more frequent exceedances than southern cities (Chennai and Puducherry). Across all cities, PM10 is the dominant contributor to variability in Air Quality Index (AQI) variability. A Random Forest model reveals that meteorological parameters explain a substantial fraction of particulate matter variability (R2 up to ∼0.6), whereas gaseous pollutants show lower predictability (R2 < 0.35). Using Shapley Additive exPlanations (SHAP) analysis, we identify critical nonlinear thresholds and binary regime switches governing pollutant accumulation. A consistent regional specific humidity (SH) threshold of ∼15 ± 2 g/kg is identified as a key regime-switching threshold, with values below this level consistently associated with PM10 accumulation across all cities. In southern cities, a Sea Breeze Component (SBC) > 3 m/s is the threshold for coastal ventilation; below this level, pollutants remain trapped. Furthermore, we uncover a monsoon break thermal trap, in which temperatures exceeding 32 °C flip the expected seasonal dispersion into an accumulation regime. These findings demonstrate that coastal air quality is governed by discrete meteorological regime transitions rather than linear responses. The identified thresholds provide a physically interpretable framework for improving localized air quality forecasting and mitigation strategies in complex coastal environments. + Copyright © 2026. Published by Elsevier Ltd. + + + + Varaprasad + V + V + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. + + + + Madhavan + B L + BL + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. + + + + Jain + Chaithanya D + CD + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. + + + + Krishnaveni + A Sai + AS + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. + + + + Ratnam + M Venkat + MV + + National Atmospheric Research Laboratory, Government of India, Tirupati, India. Electronic address: vratnam@narl.gov.in. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Environ Pollut + 8804476 + 0269-7491 + + IM + + Coastal air quality + RF model + SHAP + boundary layer dynamics + meteorological variability + nonlinear interactions + + Declaration of Competing Interest ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 8 + + + 2026 + 5 + 22 + + + 2026 + 5 + 27 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 19 + 32 + + + aheadofprint + + 42214540 + 10.1016/j.envpol.2026.128448 + S0269-7491(26)00818-3 + + + + + + 42214280 + + 2026 + 05 + 29 + +
+ + 1532-8708 + + 53 + 4 + + 2026 + Apr + 07 + + + Seminars in oncology + Semin Oncol + + Physics-informed machine learning for tumor microenvironment-responsive nanomedicine: Recent updates. + + 152498 + 152498 + + 10.1016/j.seminoncol.2026.152498 + S0093-7754(26)00045-X + + Physics-informed machine learning (PIML) is rapidly emerging as a transformative paradigm for designing tumor microenvironment (TME)-responsive nanomedicines. While existing reviews have surveyed PIML in broader biomedical contexts, this work provides a focused, critical synthesis specifically at the intersection of PIML, multiscale TME biophysics, and clinically actionable nanomedicine design. We argue that the unique value of PIML lies not merely in combining physics with data, but in its capacity to resolve the "personalization paradox" in oncology: the tension between the need for patient-specific models and the scarcity of patient-specific data. By embedding governing physical laws-such as Darcy's flow, reaction-diffusion kinetics, and Navier-Stokes equations-as soft constraints, PIML models can generate physically plausible, patient-tailored predictions even with sparse clinical inputs. This review uniquely articulates a translational roadmap that systematically links fundamental PIML methodologies to concrete nanomedicine optimization tasks: predicting nanoparticle transport in heterogeneous TMEs, designing stimulus-responsive nanocarriers, and integrating multi-omics/imaging for personalized therapy. We further introduce a novel comparative framework evaluating PIML against purely physics-based and purely data-driven approaches, highlighting its superior data efficiency and interpretability for TME applications. However, significant challenges remain, including data standardization, computational scalability, and regulatory adaptation. Looking forward, we identify under-explored yet high-impact frontiers, such as quantum-informed PIML for molecular-scale nanocarrier design, real-time adaptive nanomedicine guided by patient digital twins, and the ethical-regulatory frameworks needed for clinical deployment. By synthesizing cross-disciplinary insights and proposing a clear path from bench to bedside, this review aims to not only summarize the state-of-the-art but also to catalyze the next generation of intelligent, patient-centric cancer nanotherapeutics. + Copyright © 2026 Elsevier Inc. All rights reserved. + + + + Hasannia + Maliheh + M + + Cancer Research Center, Semnan University of Medical Sciences, Semnan, Iran. + + + + Abounoori + Ali + A + + Department of Microbial Biotechnology, Faculty of Biotechnology, Amol University of Special Modern Technologies, Amol, Iran. + + + + Abounoori + Mahdi + M + + Cancer Research Center, Semnan University of Medical Sciences, Semnan, Iran. + + + + Shirzad + Maryam + M + + Nanotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran. + + + + Rahdar + Abbas + A + + Department of Physics, University of Zabol, Zabol, Iran. Electronic address: a.rahdar@uoz.ac.ir. + + + + Fathi-Karkan + Sonia + S + + Natural Products and Medicinal Plants Research Center, North Khorasan University of Medical Sciences, Bojnurd, Iran; Department of Advanced Sciences and Technologies in Medicine, School of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran; Food and Drug Research Center, Food and Drug Administration, Ministry of Health and Medical Education, Tehran, Iran. Electronic address: Soniafathi92@gmail.com. + + + + Romanholo Ferreira + Luiz Fernando + LF + + Graduate Program in Genomic Sciences and Biotechnology, Catholic University of Brasília, Brasília, 71966-700, Brazil. Electronic address: luiz.romanholo@p.ucb.br. + + + + eng + + Journal Article + Review + + + 2026 + 04 + 07 + +
+ + United States + Semin Oncol + 0420432 + 0093-7754 + + IM + + Multi-omics integration + Nanomedicine + Nanoparticle transport + Physics-informed machine learning + Tumor microenvironment + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2025 + 11 + 14 + + + 2026 + 2 + 12 + + + 2026 + 4 + 5 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 30 + 0 + 32 + + + 2026 + 5 + 29 + 18 + 10 + + + aheadofprint + + 42214280 + 10.1016/j.seminoncol.2026.152498 + S0093-7754(26)00045-X + + + + + + 42214151 + + 2026 + 05 + 29 + +
+ + 1873-3573 + + 309 + + 2026 + May + 28 + + + Talanta + Talanta + + Machine learning assisted transparent bending-insensitive SERS substrate for reliable quantitative analysis of thiram residues on fruit surfaces. + + 130059 + 130059 + + 10.1016/j.talanta.2026.130059 + S0039-9140(26)00715-0 + + Transparent and flexible surface-enhanced Raman scattering (SERS) substrates are highly suitable for rapid pesticide detection on fruit surfaces, owing to their capability for in situ detection on curved surfaces. However, achieving reproducible and stable SERS signals from flexible SERS substrates under bending conditions remains a major challenge. Additionally, integrating machine learning into Raman spectral analysis can further enhance the practicality of SERS technology. Herein, we develop a novel sensing platform that combines a transparent, bending-insensitive SERS substrate with machine learning for the rapid quantitative determination of thiram residues on fruit surfaces. The flexible SERS substrate was constructed by depositing silver nanoparticles (AgNPs) onto a polydimethylsiloxane (PDMS) twisted nanowrinkle array. Endowed with high SERS activity, exceptional transparency, and bending insensitivity, this substrate enabled reliable and consistent detection of thiram residues on apple and cherry tomato peels, with a sensitivity as low as 10-7 M. Furthermore, a back propagation neural network (BPNN)-based machine learning model was developed to predict thiram concentrations, achieving remarkable accuracy with a coefficient of determination (R2) of 0.9954 and a root mean square error (RMSE) of 0.0985. Thus, the proposed sensing platform provides a rapid, accurate, and reliable approach for the identification and quantification of thiram in fruits. + Copyright © 2026 Elsevier B.V. All rights reserved. + + + + Wang + Yue + Y + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Huang + Lili + L + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Wang + Jie + J + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Li + Zetong + Z + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Bian + Ziyang + Z + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Yang + Zhenshan + Z + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. + + + + Li + Hefu + H + + School of Physical Science and Information Engineering, Liaocheng University, Liaocheng, 252000, China. Electronic address: lihefu@lcu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + Netherlands + Talanta + 2984816R + 0039-9140 + + IM + + BPNN + Bending insensitivity + Quantitative analysis + SERS + Thiram + + Declaration of competing interest The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this study. + + + + + 2026 + 3 + 9 + + + 2026 + 5 + 23 + + + 2026 + 5 + 27 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 18 + 7 + + + aheadofprint + + 42214151 + 10.1016/j.talanta.2026.130059 + S0039-9140(26)00715-0 + + + + + + 42214146 + + 2026 + 05 + 29 + +
+ + 1879-0534 + + 212 + + 2026 + May + 29 + + + Computers in biology and medicine + Comput Biol Med + + A recurrent interferon, stress, and survival axis identifies a rare malignant programme across glioblastoma single-cell, spatial, and longitudinal cohorts. + + 111780 + 111780 + + 10.1016/j.compbiomed.2026.111780 + S0010-4825(26)00344-6 + + Glioblastoma exhibits profound intratumoral heterogeneity and rapid adaptation under stress, complicating durable therapeutic control. Here, a biologically grounded transcriptional axis is defined by three complementary modules capturing type I interferon signalling, immediate-early stress response, and pro-survival signalling enriched in unfolded protein response components. Using this equal-weighted axis, a rare axis-high programme was identified in a glioblastoma training cohort (GSE173278; 75,075 cells after filtering; 5.95% prevalence) and mapped to external cohorts through marker-anchored cohort-specific clusters. To enable reproducible detection, an interpretable 40-gene regularised logistic regression classifier was trained using fold-internal panel selection and Platt calibration fitted strictly on out-of-fold predictions. The conservative out-of-fold training evaluation showed informative discrimination and good probability calibration (AUC 0.767, AP 0.116, Brier 0.055, ECE 0.011). When the final model was applied to marker-anchored operational labels, ranking performance was high in an independent glioblastoma cohort (GSE131928; AUC 0.950, AP 0.351) and in an exploratory pan-cancer transfer setting (GSE203612; AUC 0.944, AP 0.343), although calibration slopes indicated dataset-dependent probability-scale shift. Longitudinal CARE glioblastoma samples showed that recalibration on T1 samples improved probability-scale reliability on later T2/T3 samples without changing rank discrimination. Spatial stratification based on available sample annotations linked higher detector probabilities to necrotic, bulk, and tumour-region samples. Irradiation feature-count contrasts supported positive shifts of the 40-gene panel and axis-related modules, while cell-level treated/control summaries were interpreted as context-specific perturbational evidence. Together, these results define a rare axis-high glioblastoma programme and provide a recalibratable gene-panel detector for probability-guided experimental triage and mechanistic follow-up. + Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved. + + + + Pastore + Emmanuel Pio + EP + + Department of Biology, Ecology and Earth Science, University of Calabria, 87036 Rende, Italy. + + + + De Rango + Francesco + F + + Department of Biology, Ecology and Earth Science, University of Calabria, 87036 Rende, Italy. Electronic address: francesco.derango@unical.it. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + Comput Biol Med + 1250250 + 0010-4825 + + IM + + Glioblastoma + Interferon signaling + Machine learning + Protein response + Reproducible bioinformatics + Single-cell RNA-seq + Stress response + Unfolded + + Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 3 + 4 + + + 2026 + 5 + 11 + + + 2026 + 5 + 25 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 30 + 0 + 33 + + + 2026 + 5 + 29 + 18 + 7 + + + aheadofprint + + 42214146 + 10.1016/j.compbiomed.2026.111780 + S0010-4825(26)00344-6 + + + + + + 42214095 + + 2026 + 05 + 29 + +
+ + 1933-0693 + + + 2026 + May + 29 + + + Journal of neurosurgery + J Neurosurg + + Assessment system for short-term lower cranial nerve dysfunction following medulla oblongata glioma surgery: risk stratification and optimal surgical strategy. + + 1 + 12 + 1-12 + + 10.3171/2025.12.JNS251593 + + Medullary gliomas pose significant surgical risks, particularly the risk of postoperative lower cranial nerve (LCN) dysfunction, which profoundly affects quality of life. The lack of standardized risk assessment hinders optimal surgical planning. The aim of this study was to develop and validate an individualized predictive model for short-term postoperative LCN impairment integrating clinical and imaging data and to estimate individual risk across a range of resection extent to optimize surgical planning. + A retrospective cohort (n = 111, January 2020-February 2023) was used for model development, with prospective validation (n = 45, February 2023-December 2024). The primary outcome was postoperative LCN dysfunction (inability to be extubated within 14 days or requiring tracheotomy with persistent ventilation). Predictive modeling was performed using logistic regression, incorporating multistage feature selection, hyperparameter optimization, and bootstrapped validation. Model performance was evaluated using metrics such as area under the curve (AUC), Brier score, calibration, and decision curve analysis (DCA) in the prospective validation set. Shapley Additive Explanations (SHAP) analysis was used to interpret feature contributions, and a nomogram was constructed for clinical implementation. Optimal extent of resection (EOR) thresholds were explored to balance functional preservation and tumor clearance. + Four independent predictors of LCN dysfunction were identified: EOR (OR 1.84, 95% CI 1.07-3.16), infiltrative growth (OR 10.46 [95% CI 2.70-40.52]), preoperative LCN impairment (OR 6.79 [95% CI 2.54-18.16]), and cervical cord involvement (OR 4.64 [95% CI 1.55-13.91]). The model demonstrated strong discrimination (training AUC 0.85 [95% CI 0.76-0.92], testing AUC 0.89 [95% CI 0.79-0.97]) and good calibration (Brier score 0.12). High-risk patients, defined as those with a model predicted risk probability > 0.471 based on Youden's index, had significantly higher rates of pneumonia, tracheostomy, and prolonged mechanical ventilation. Stratified resection plans showed that low-risk patients benefited from gross-total resection (EOR 88.4%, [95% CI 75.7%-100.0%]), while high-risk patients achieved optimal functional outcomes with limited resection (EOR 40.8%, [95% CI 38.2%-50.5%]). SHAP and nomogram analyses provided transparent, patient-specific information for risk consultations. + This study presents the first predictive model tailored to short-term postoperative LCN outcomes following medullary glioma surgery, proposing a dynamic resection paradigm based on individualized risk stratification. By guiding surgical planning and intraoperative decision-making, this model facilitates a balance between maximal tumor control and functional preservation. + + + + Zhang + Mingxin + M + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + 2Chinese Institutes for Medical Research, Beijing. + + + + Xiao + Xiong + X + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Jia + Heyuan + H + + 5School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing. + + + + Liu + Yinan + Y + + 6Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing; and. + + + + Jiang + Wenju + W + + 7Beijing Arion Cancer Center, Beijing, China. + + + + Xie + Luyang + L + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + 2Chinese Institutes for Medical Research, Beijing. + + + + Pan + Changcun + C + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Wu + Zhen + Z + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Zhang + Junting + J + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Zhang + Liwei + L + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + 2Chinese Institutes for Medical Research, Beijing. + + + 3Beijing Neurosurgical Institute, Capital Medical University, Beijing. + + + 4China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + Zhang + Peng + P + + 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + J Neurosurg + 0253357 + 0022-3085 + + IM + + lower cranial nerve dysfunction + machine learning + medullary glioma + oncology + predictive model + risk stratification + skull base + surgical decision-making + tumor + + + + + + 2025 + 6 + 21 + + + 2025 + 12 + 19 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 17 + 52 + + + aheadofprint + + 42214095 + 10.3171/2025.12.JNS251593 + + + + + + 42214078 + + 2026 + 05 + 29 + +
+ + 1520-5215 + + + 2026 + May + 29 + + + The journal of physical chemistry. A + J Phys Chem A + + IPSBrain: A Unified Intelligent Data Analysis Platform for Multimodal Experimental Characterization at Advanced Photon Source. + 10.1021/acs.jpca.6c01154 + + Advanced photon sources deliver X-ray with unprecedented brilliance and coherence, facilitating sophisticated multimodal characterization of diverse materials while simultaneously generating data volumes that far exceed the capacity of traditional, expert-driven analytical workflows. This growing gap between data acquisition and interpretation has become a critical bottleneck for timely scientific discovery, which creates a critical need for high-throughput and automated data analysis tools. Recent advances in artificial intelligence (AI) offer a transformative potential to address this challenge, enabling the direct mapping of complex experimental observations into scientific insights. However, existing AI-driven tools are remain largely fragmented and task-specific, limiting their adoption of modern multimodal experiments at the facility scale. Here we present IPSBrain, a unified AI-powered platform that serves as Intelligent Photon Source Brain for automated analysis of multimodal synchrotron experimental data. The platform integrated a series of novel, self-developed machine-learning-based models that enable automated data analysis from multiple experimental techniques, including diffraction, scattering, X-ray absorption, and tomograph. The platform is accessible through an intuitive web interface, which significantly lowers the technical barrier to advanced scientific data interpretation. By enabling end-to-end intelligent workflows from data acquisition to quantitative physical insight, IPSBrain supports high-throughput, reproducible, and scalable analysis aligned with the operational demands of modern synchrotron radiation sources. IPSBrain not only demonstrates a practical and extensible framework for AI-driven synchrotron data analysis but also establishes a transferable paradigm for intelligent data analysis of other large-scale research infrastructures, such as neutron sources and free-electron lasers. + + + + Pang + Yihe + Y + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + Spallation Neutron Source Science Center, Dongguan 523800, China. + + + + Li + Lin + L + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China. + + + + Deng + Xiangwen + X + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + University of Chinese Academy of Sciences, Beijing 100049, China. + + + + Xi + Zuoxin + Z + 0000-0003-1237-7780 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Information Engineering, Minzu University of China, Beijing 100081, China. + + + + Li + Jing + J + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Information Engineering, Minzu University of China, Beijing 100081, China. + + + + Han + Mengdan + M + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Information Engineering, Minzu University of China, Beijing 100081, China. + + + + Sun + Yaping + Y + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + School of Science, China University of Geosciences Beijing, Beijing 100083, China. + + + + Yao + Haodong + H + 0000-0001-5959-8594 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + University of Chinese Academy of Sciences, Beijing 100049, China. + + + + Wu + Liyuan + L + 0000-0002-4890-2770 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + + Yuan + Qin + Q + 0009-0001-7123-6155 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + + Zhao + Lina + L + 0000-0002-9796-0221 + + Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. + + + University of Chinese Academy of Sciences, Beijing 100049, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + J Phys Chem A + 9890903 + 1089-5639 + + IM + + + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 17 + 12 + + + aheadofprint + + 42214078 + 10.1021/acs.jpca.6c01154 + + + + + + 42214077 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2561-3278 + + 11 + + 2026 + May + 29 + + + JMIR biomedical engineering + JMIR Biomed Eng + + Psychosocial Stress in the Chinese Community: Speech Analytics Through Linguistic and Acoustic Fusion Using Machine Learning. + + e91138 + e91138 + + 10.2196/91138 + + Family caregivers experience significant stress due to intensive caregiving activities, making them highly susceptible to adverse psychosocial health conditions. Early detection of this stress is crucial for timely interventions to prevent disease progression and long-term disability. + This study aimed to develop and validate the Linguistic and Acoustic Speech Analytics Program, a novel machine learning approach capable of providing a fusion analysis of linguistic and acoustic speech features to enhance the effectiveness of psychosocial stress assessment. + This quantitative study analyzed speech data collected from 100 Chinese family caregivers. Participants responded to 12 open-ended questions, and their voices were recorded for linguistic and acoustic feature extraction. Various machine learning classifiers, including support vector machine, were developed to process speech data. A key methodological step was the application of an orthogonalization procedure to decorrelate acoustic features from linguistic features before fusion analysis. The classifiers were then trained to evaluate psychosocial stress levels based on the processed and fused linguistic and acoustic speech features. Model performance was measured using receiver operating characteristic-area under the curve, F1-score, and accuracy. + The linear support vector machine model emerged as the top performer, achieving a receiver operating characteristic-area under the curve of 78.28%, an F1-score of 75.27%, and an accuracy of 73%. These results demonstrate the model's strong capability in identifying stressed participants based on their speech. Critically, the fusion of linguistic and acoustic features significantly outperformed models using either feature type alone. Furthermore, the orthogonalization procedure proved essential, as decorrelating features before fusion markedly enhanced classification accuracy compared to using non-orthogonalized features. + This study demonstrates that fusion analysis of linguistic and acoustic features effectively identifies psychosocial stress among family caregivers. It also emphasizes the importance of proper feature processing when combining multiple features extracted from the same audio sample. These findings provide valuable insights for developing machine learning models for psychosocial stress assessment and addressing various psychosocial conditions in different contexts, supporting population mental health management. + © Amanda M Y Chu, Benson S Y Lam, Jenny T Y Tsang, Agnes Tiwari, Jacky N L Chan, Mike K P So. Originally published in JMIR Biomedical Engineering (http://biomsedeng.jmir.org). + + + + Chu + Amanda M Y + AMY + 0000-0002-9543-747X + + Department of Social Sciences and Policy Studies, The Education University of Hong Kong, Tai Po, Hong Kong, China. + + + + Lam + Benson S Y + BSY + 0000-0002-0836-4162 + + Department of Mathematics, Statistics and Insurance, The Hang Seng University of Hong Kong, Shatin, Hong Kong, China. + + + + Tsang + Jenny T Y + JTY + 0000-0003-4558-9384 + + School of Nursing, Tung Wah College, Homantin, Hong Kong, China. + + + + Tiwari + Agnes + A + 0000-0002-3993-8552 + + School of Nursing, Hong Kong Sanatorium and Hospital, Hong Kong, China. + + + + Chan + Jacky N L + JNL + 0000-0003-1704-4221 + + Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China, 852 2358 7726. + + + + So + Mike K P + MKP + 0000-0003-0781-8166 + + Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China, 852 2358 7726. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Canada + JMIR Biomed Eng + 101771351 + 2561-3278 + + + caregivers + digital health + psychosocial health + speech analytics + text analytics + + + + + + 2026 + 1 + 11 + + + 2026 + 3 + 23 + + + 2026 + 3 + 24 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 17 + 12 + + + epublish + + 42214077 + 10.2196/91138 + v11i1e91138 + + + + + + 42214073 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1929-0748 + + 15 + + 2026 + May + 29 + + + JMIR research protocols + JMIR Res Protoc + + Creation of an mHealth Infrastructure to Support the Development and Delivery of mHealth Interventions: Protocol for Demonstration Projects Addressing Smoking Cessation in Cancer Care. + + e92288 + e92288 + + 10.2196/92288 + + Cancer remains a leading cause of morbidity worldwide. To reduce this burden, scalable, effective approaches are needed to address modifiable risk factors for cancer and support behavioral self-management. With smartphone ownership now nearly ubiquitous, mobile health (mHealth) interventions offer a powerful means to extend the reach, accessibility, and sustainability of evidence-based treatments for a variety of modifiable risk factors (eg, excessive alcohol use, physical inactivity, poor diet, and smoking). Moreover, the flexibility of mHealth platforms enables efficient delivery of novel interventions, supports innovative study designs, and facilitates real-time data collection to advance public health research. + Despite the great potential of mHealth interventions, developing high-quality mHealth tools is complex, time-consuming, and resource-intensive. To address these challenges, we are developing a coordinated, accessible, research-grade infrastructure for mHealth app development, testing, and dissemination. + The mHealth Florida infrastructure (mFLi) will provide a comprehensive, low-code software platform that enables researchers to build apps compatible with major mobile operating systems, namely, Apple iOS and Google Android. Through a modular interface, users will select from a menu of prebuilt features to tailor functionality to specific study needs. The platform will include 3 integrated environments (development, testing, and production), allowing researchers to prototype, evaluate, and deploy mHealth interventions. This infrastructure will be developed and maintained by a multidisciplinary team, ensuring that the platform is technically robust and usable and adheres to institutional and regulatory standards. To demonstrate the platform's functionality, utility, and adaptability, a multisite study comprising three initial projects focused on smoking cessation among patients with cancer is being conducted: (1) participant screening and enrollment, (2) randomization and treatment delivery, and (3) data processing using machine learning methods with on-device and cloud-based approaches. + This study was funded in May 2023, and ethics approval was obtained from all involved sites' institutional review boards between February 2024 and October 2025. Recruitment began in March 2025 and enrollment is ongoing. As of January 2026, 41% (37/90) of the target sample have been enrolled and 21% (19/90) have completed their 6-month assessment. Data collection will be completed once the final participant completes their 6-month assessment (expected May 2027), with analyses commencing thereafter. Study findings are anticipated to be published in a peer-reviewed journal in 2027. + Collectively, these projects will illustrate how mFLi can streamline app development, facilitate rapid translation of research into practice, and reduce barriers for researchers and developers. Ultimately, mFLi is designed to accelerate innovation in mHealth research, enhance access to behavioral interventions, and improve health outcomes among diverse populations. + ClinicalTrials.gov NCT06909357; https://clinicaltrials.gov/study/NCT06909357. + PRR1-10.2196/92288. + ©Lindsey S Sparrock, Jennifer I Vidrine, Christine E Vinci, Issam M El Naqa, Steven K Sutton, Ramzi G Salloum, Jesse Dallery, Tracy E Crane, Frank J Penedo, Samuel J Brockway, Sarah R Jones, Charles E Hoogland, Richard R Reich, Guillermo Gonzalez-Calderon, Vani N Simmons, Damon J Vidrine. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 29.05.2026. + + + + Sparrock + Lindsey S + LS + 0000-0003-3230-7886 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + + Vidrine + Jennifer I + JI + 0000-0001-7159-7252 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Psychology, College of Arts and Sciences, University of South Florida, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + + Vinci + Christine E + CE + 0000-0001-8883-0989 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Psychology, College of Arts and Sciences, University of South Florida, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + + El Naqa + Issam M + IM + 0000-0001-6023-1132 + + Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Gastrointestinal Oncology, Moffitt Cancer Center, Tampa, FL, United States. + + + + Sutton + Steven K + SK + 0000-0002-2504-7601 + + Department of Psychology, College of Arts and Sciences, University of South Florida, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, United States. + + + + Salloum + Ramzi G + RG + 0000-0002-8139-2418 + + Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States. + + + Health Cancer Center, University of Florida, Gainesville, FL, United States. + + + + Dallery + Jesse + J + 0000-0002-2882-1105 + + Department of Psychology, University of Florida, Gainesville, FL, United States. + + + + Crane + Tracy E + TE + 0000-0002-4288-134X + + Department of Medicine, Miller School of Medicine, University of Miami, Miami, FL, United States. + + + + Penedo + Frank J + FJ + 0000-0002-2780-0417 + + Department of Medicine, Miller School of Medicine, University of Miami, Miami, FL, United States. + + + Department of Psychology, University of Miami, Coral Gables, FL, United States. + + + + Brockway + Samuel J + SJ + 0009-0000-4761-768X + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + + Jones + Sarah R + SR + 0000-0001-5513-2527 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + + Hoogland + Charles E + CE + 0000-0001-7735-1931 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + + Reich + Richard R + RR + 0009-0009-9559-6858 + + Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, United States. + + + + Gonzalez-Calderon + Guillermo + G + 0000-0002-4075-8946 + + Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, United States. + + + + Simmons + Vani N + VN + 0000-0002-4381-5336 + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Psychology, College of Arts and Sciences, University of South Florida, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + + Vidrine + Damon J + DJ + 0000-0002-8711-796X + + Department of Health Outcomes & Behavior, Moffitt Cancer Center, Tampa, FL, United States. + + + Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States. + + + + eng + + + ClinicalTrials.gov + + NCT06909357 + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Canada + JMIR Res Protoc + 101599504 + 1929-0748 + + IM + + + Humans + + + Smoking Cessation + methods + + + Telemedicine + + + Neoplasms + therapy + + + Mobile Applications + + + Digital Health + + + + cancer + feasibility + mHealth + randomized controlled trial + smoking + tobacco cessation + + + + + + 2026 + 2 + 9 + + + 2026 + 3 + 18 + + + 2026 + 3 + 17 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 16 + 53 + + + epublish + + 42214073 + 10.2196/92288 + v15i1e92288 + + + + + + 42214056 + + 2026 + 05 + 29 + +
+ + 2688-1535 + + + 2026 + May + 29 + + + JCO oncology practice + JCO Oncol Pract + + Development and External Validation of a Machine Learning-Based Risk Classification Score for Prostate Cancer-Specific Cardiovascular Risk. + + OP2600131 + OP2600131 + + 10.1200/OP-26-00131 + + Patients with prostate cancer (PC) face elevated cardiovascular (CV) risk. Conventional CV risk scores are often poorly calibrated for PC and require laboratory tests that are not routinely available in PC clinics, limiting oncology use. + The development cohort (DC) included men with PC (age ≥18 years) diagnosed in 2010-2014. Validation cohort 1 (V1) included men age 45 years and older within 1 year of PC diagnosis or near initiation of androgen deprivation therapy. Validation cohort 2 (V2) included men with PC diagnosed in 2006-2019. Outcomes were cardiovascular disease (CVD), atherosclerotic CVD (ASCVD), and heart failure (HF). Predictors were selected with XGBoost and LASSO, modeled with Fine-Gray and penalized Cox regression, and scaled to additive point scores (low/intermediate/high risk). Performance was assessed using time-dependent AUC (TDAUC), which quantifies the model's ability to correctly rank patients by risk at each time point, and compared with conventional scores. + The DC included 1,815 patients, and V1 and V2 included 4,022 and 1,729 patients, respectively (median follow-up 10.3 years). At 10 years, cumulative incidence was 15.0% for composite CVD, 9.4% for ASCVD, and 10.1% for HF. The composite CVD score (age, race, smoking history, high-risk Gleason score, and household members) achieved a 10-year TDAUC of 0.71 (95% CI, 0.64 to 0.78) in the DC and 0.59 (95% CI, 0.52 to 0.65) in V2, with a 2-year TDAUC of 0.66 (95% CI, 0.56 to 0.75) in V1. In the DC, ASCVD and HF scores achieved 10-year TDAUCs of 0.66 (95% CI, 0.58 to 0.75) and 0.70 (95% CI, 0.62 to 0.79), respectively, and retained discrimination in validation. + To our knowledge, these are the first PC-specific CV risk scores derived from oncology-available variables, supporting pragmatic CV risk stratification in PC clinics and motivating further validation and implementation studies. + + + + Stabellini + Nickolas + N + 0000-0002-3135-7980 + + Case Western Reserve University, Cleveland, OH. + + + Department of Hematology-Oncology, University Hospitals Seidman Cancer Center, Cleveland, OH. + + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Nahle + Tarek + T + 0009-0004-4584-3308 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Shah + Viraj + V + 0009-0003-5850-9862 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Kollapaneni + Sai S + SS + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Makram + Omar M + OM + 0000-0001-8361-9068 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Cullen + Jennifer + J + + Cancer Prevention and Control Program, Houston Methodist Neal Cancer Center, Houston, TX. + + + + Weintraub + Neal L + NL + 0000-0002-5138-3705 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + Vascular Biology Center, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Kunhiraman + Harikrishnan H + HH + 0000-0002-6395-7956 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + + Swami + Umang + U + 0000-0003-3518-0411 + + Division of Oncology, Department of Internal Medicine, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT. + + + + Mahmoudi + Morteza + M + 0000-0002-2575-9684 + + Department of Radiology and Precision Health Program, Michigan State University, East Lansing, MI. + + + + Fillmore + Nathanael + N + 0000-0002-8058-3423 + + Department of Medicine, Harvard Medical School, Boston, MA. + + + + Garraway + Isla P + IP + + Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, CA. + + + Department of Surgical and Perioperative Care, VA Greater Los Angeles, Los Angeles, CA. + + + + Leong + Darryl + D + 0000-0002-1968-169X + + Population Health Research Institute and Department of Medicine, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada. + + + + Pinthus + Jehonathan + J + + Division of Urology, Department of Surgery, McMaster University, Hamilton, ON, Canada. + + + + Shanahan + John + J + + Cancer Informatics, Seidman Cancer Center at University Hospitals of Cleveland, Cleveland, OH. + + + + Montero + Alberto J + AJ + 0000-0002-8221-8120 + + Case Western Reserve University, Cleveland, OH. + + + Department of Hematology-Oncology, University Hospitals Seidman Cancer Center, Cleveland, OH. + + + + Agarwal + Neeraj + N + 0000-0003-1076-0428 + + Huntsman Cancer Institute, University of Utah, Salt Lake City, UT. + + + + Guha + Avirup + A + 0000-0003-0253-1174 + + Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA. + + + Cardio-Oncology Program, Medical College of Georgia at Augusta University, Augusta, GA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + JCO Oncol Pract + 101758685 + 2688-1527 + + IM + + + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 16 + 12 + + + aheadofprint + + 42214056 + 10.1200/OP-26-00131 + + + + + + 42213920 + + 2026 + 05 + 29 + +
+ + 1079-7114 + + 136 + 19 + + 2026 + May + 15 + + + Physical review letters + Phys Rev Lett + + Hierarchical and Ultrametric Barriers in the Energy Landscape of Jammed Granular Matter. + + 198202 + 198202 + + 10.1103/mtn5-s26m + + According to the mean-field glass theory, the (free) energy landscape of disordered systems is hierarchical and ultrametric if they belong to the full-replica-symmetry-breaking universality class. However, examining this theoretical picture in three-dimensional systems remains challenging, where the energy barriers become finite. Here, we numerically explore the energy landscape of granular models near the jamming transition using a saddle dynamics algorithm to locate both local energy minima and saddles. The multiscale distances and energy barriers between minima are characterized by two metrics, both of which exhibit signatures of an ultrametric space. The scale-free distributions of energy barriers confirm the landscape's hierarchical organization and align with the distribution of elastic avalanches under athermal quasistatic shear. + + + + Wu + Shuonan + S + + Peking University, School of Mathematical Sciences, Beijing 100871, China. + https://ror.org/02v51f717 + + + + Xie + Yuchen + Y + + Peking University, School of Mathematical Sciences, Beijing 100871, China. + https://ror.org/02v51f717 + + + + Pan + Deng + D + + Chinese Academy of Sciences, Institute of Theoretical Physics, Beijing 100190, China. + https://ror.org/034t30j35 + + + + Zhang + Lei + L + + Peking University, School of Mathematical Sciences, Beijing 100871, China. + https://ror.org/02v51f717 + + + Peking University, Beijing International Center for Mathematical Research, Beijing, 100871, China. + https://ror.org/02v51f717 + + + Peking University, Center for Quantitative Biology, Beijing, 100871, China. + https://ror.org/02v51f717 + + + Peking University, Center for Machine Learning Research, Beijing, 100871, China. + https://ror.org/02v51f717 + + + + Jin + Yuliang + Y + + Chinese Academy of Sciences, Institute of Theoretical Physics, Beijing 100190, China. + https://ror.org/034t30j35 + + + University of Chinese Academy of Sciences, School of Physical Sciences, Beijing 100049, China. + https://ror.org/05qbk4x57 + + + University of Chinese Academy of Sciences, Center for Theoretical Interdisciplinary Sciences, Wenzhou Institute, Wenzhou, Zhejiang 325001, China. + https://ror.org/05qbk4x57 + + + + eng + + Journal Article + +
+ + United States + Phys Rev Lett + 0401141 + 0031-9007 + + IM + + + + + 2025 + 12 + 22 + + + 2026 + 2 + 10 + + + 2026 + 4 + 21 + + + 2026 + 5 + 29 + 18 + 35 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 15 + 12 + + + ppublish + + 42213920 + 10.1103/mtn5-s26m + + + + + + 42213819 + + 2026 + 05 + 29 + +
+ + 1535-1386 + + + 2026 + May + 29 + + + The Journal of bone and joint surgery. American volume + J Bone Joint Surg Am + + Compliance with the CONSORT AI Extension in Orthopaedic Randomized Controlled Trials. + 10.2106/JBJS.25.01439 + + The utilization of artificial intelligence (AI), such as machine learning, in health care has risen exponentially, underscoring the necessity of transparency and traceability in the reporting of AI in randomized controlled trials (RCTs) that involve AI. In 2020, a CONSORT (Consolidated Standards of Reporting Trials) AI extension was published that included 14 additional items that should be reported when AI is used in RCTs. This study aimed to evaluate compliance with these additional guidelines. + A systematic approach was used to identify all RCTs in orthopaedics that involved AI since the extension was published. Three databases were searched (PubMed, Embase, Cochrane) using the search terms ([orthopaedic] OR [orthopedic]) AND ([artificial intelligence] OR [AI] OR [machine learning] OR [deep learning]). + One hundred and seventy-four articles were identified, and 11 articles satisfied the inclusion and exclusion criteria and were assessed for compliance. The average number of CONSORT AI-extension items met per study was 11.4 ± 1.6 (81.2% ± 12.1%). Eight items (1a [i], 1b [ii], 2a [i], 4a [i], 5 [ii], 5 [v], 5 [vi], 19) that were related to standard reporting of AI utilization were reported in all articles. The items with the lowest reporting rates were related to data transparency and included 4a (ii), 5 (iii), and 25, with only 9.1%, 36.4%, and 45.5% of all studies complying, respectively. + Orthopaedic RCTs involving AI showed high compliance with respect to standard trial reporting and broad AI descriptors but low compliance with respect to detailed data transparency, data-quality handling, and code accessibility. Addressing these weaker areas is essential to improve reproducibility, reduce bias, and strengthen trust in AI-driven clinical research. + Improved compliance with CONSORT-AI reporting standards may help orthopaedic clinicians to critically evaluate AI-based randomized trials, reproduce findings, and determine whether AI interventions are reliable and safe for clinical use. + Copyright © 2026 by The Journal of Bone and Joint Surgery, Incorporated. + + + + Strasser + Thomas + T + 0000-0003-4456-8178 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + + Baker + Christopher + C + 0009-0009-9822-6633 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + + Cheminant + Jenna + J + 0009-0001-2747-5263 + + College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, California. + + + + Han + Blake + B + 0009-0002-9570-1944 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, California. + + + + Canumay + Sallie + S + 0009-0009-1627-8828 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + + Schlechter + John + J + 0000-0001-8389-8407 + + Department of Orthopedic Surgery, Riverside University Health System, Moreno Valley, California. + + + Rady Children's Health Orange County, Orange, California, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + J Bone Joint Surg Am + 0014030 + 0021-9355 + + IM + Disclosure: No external funding was received for this work. The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJS/J301). + + + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 18 + 34 + + + 2026 + 5 + 29 + 14 + 2 + + + aheadofprint + + 42213819 + 10.2106/JBJS.25.01439 + 00004623-990000000-01850 + + + + Schulz KF, Altman DG, Moher D; CONSORT Group. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010 Mar 23;340:c332. + + + Moher D, Hopewell S, Schulz KF, Montori V, Gøtzsche PC, Devereaux PJ, Elbourne D, Egger M, Altman DG. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ. 2010 Mar 23;340:c869. + + + Ioannidis JP. How to make more published research true. PLoS Med. 2014 Oct 21;11(10):e1001747. + + + Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020 Sep;26(9):1364-74. + + + Fan X, Qiao X, Wang Z, Jiang L, Liu Y, Sun Q. Artificial Intelligence-Based CT Imaging on Diagnosis of Patients with Lumbar Disc Herniation by Scalpel Treatment. Comput Intell Neurosci. 2022 May 27;2022:3688630. + + + Anderson PG, Baum GL, Keathley N, Sicular S, Venkatesh S, Sharma A, Daluiski A, Potter H, Hotchkiss R, Lindsey RV, Jones RM. Deep learning assistance closes the accuracy gap in fracture detection across clinician types. Clin Orthop Relat Res. 2023 Mar 1;481(3):580-8. + + + Gauci MO, Olmos M, Cointat C, Chammas PE, Urvoy M, Murienne A, Bronsard N, Gonzalez JF. Validation of the shoulder range of motion software for measurement of shoulder ranges of motion in consultation: coupling a red/green/blue-depth video camera to artificial intelligence. Int Orthop. 2023 Feb;47(2):299-307. + + + Zhu Y, Wu T, Wang W, Cai C, Zhu B, Lin W, Xu H, Liang Q, Wang Y. Artificial intelligence algorithm–based lumbar and spinal MRI for evaluation of efficacy of Chinkuei Shin Chewan decoction on lumbar spinal stenosis. Contrast Media Mol Imaging. 2021 Dec 29;2021:2700452. + + + Zhang H, He Q, Liu Q, He Y, Wang T, Feng Z, Zhang Q, He S. Comparative analysis of artificial intelligence and two-dimensional digital template preoperative planning–assisted total hip arthroplasty to predict prosthesis size. Chin J Tissue Eng Res. 2023;27(29):4620-7. + + + Zheng H, Feng E, Xiao Y, Liu X, Lai T, Xu Z, Chen J, Xie S, Lin F, Zhang Y. Is AI 3D-printed PSI an accurate option for patients with developmental dysplasia of the hip undergoing THA? BMC Musculoskelet Disord. 2024 Apr 22;25(1):308. + + + Gao L, Xing B. Bone cement reinforcement improves the therapeutic effects of screws in elderly patients with pelvic fragility factures. J Orthop Surg Res. 2024 Mar 18;19(1):191. + + + Baker HP, Dwyer E, Kalidoss S, Hynes K, Wolf J, Strelzow JA. ChatGPT’s Ability to Assist with Clinical Documentation: A Randomized Controlled Trial. J Am Acad Orthop Surg. 2024 Feb 1;32(3):123-9. + + + Bates NA, Huffman A, Goodyear E, Nagai T, Rigamonti L, Breuer L, Holmes BD, Schilaty ND. Physical clinical care and artificial-intelligence-guided core resistance training improve endurance and patient-reported outcomes in subjects with lower back pain. Clin Biomech (Bristol). 2023 Mar;103:105902. + + + Zhang Y, Hui Z, Qi W, Zhang J, Wang M, Zhu D. Clinical study on the safety and feasibility of AiWalker-K for lower limbs exercise rehabilitation in children with cerebral palsy. PLoS One. 2024 May 22;19(5):e0303517. + + + Thiengwittayaporn S, Wattanapreechanon P, Sakon P, Peethong A, Ratisoontorn N, Charoenphandhu N, Charoensiriwath S. Development of a mobile application to improve exercise accuracy and quality of life in knee osteoarthritis patients: a randomized controlled trial. Arch Orthop Trauma Surg. 2023 Feb;143(2):729-38. + + + Jayakumar P, Moore MG, Furlough KA, Uhler LM, Andrawis JP, Koenig KM, Aksan N, Rathouz PJ, Bozic KJ. Comparison of an artificial intelligence–enabled patient decision aid vs educational material on decision quality, shared decision-making, patient experience, and functional outcomes in adults with knee osteoarthritis: a randomized clinical trial. JAMA Netw Open. 2021 Feb 1;4(2):e2037107. + + + Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 Mar 29;372:n71. + + + Sataloff RT, Bush ML, Chandra R, Chepeha D, Rotenberg B, Fisher EW, Goldenberg D, Hanna EY, Kerschner JE, Kraus DH, Krouse JH, Li D, Link M, Lustig LR, Selesnick SH, Sindwani R, Smith RJ, Tysome J, Weber PC, Welling DB. Systematic and other reviews: criteria and complexities. Ear Nose Throat J. 2021 Jul;100(6):403-6. + + + Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ. 2020 Sep 9;370:m3164. + + + Anderson JM, Howard C, Staggs J, Steele R, Strasser T, Small T, Vassar M, Checketts JX. Inadequate reporting of complications in randomized controlled trials cited as supporting evidence underpinning AAOS CPG recommendations for hip and knee osteoarthritis: application of the CONSORT Harms checklist. J Arthroplasty. 2022 Dec;37(12):2466-2472.e2. + + + Marcus E, Teuwen J. Artificial intelligence and explanation: How, why, and when to explain black boxes. Eur J Radiol. 2024 Apr;173:111393. + + + Wyles CC, Saniei S, Mulford KL, Girod MM, Taunton MJ. Reporting Guidelines for Artificial Intelligence Use in Orthopaedic Surgery Research. J Arthroplasty. 2025 Oct;40(10):2737-2743.e1. + + + Martindale APL, Llewellyn CD, de Visser RO, Ng B, Ngai V, Kale AU, Ferrante di Ruffano L, Golub RM, Collins GS, Moher D, McCradden MD, Oakden-Rayner L, Cruz Rivera S, Calvert M, Kelly CJ, Lee CS, Yau C, Chan AW, Keane PA, Beam AL, Denniston AK, Liu X. Concordance of randomized controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines. Nature Communications. 2024;15(1):1619. + + + Shahzad R, Ayub B, Siddiqui MAR. Quality of reporting of randomised controlled trials of artificial intelligence in healthcare: a systematic review. BMJ Open. 2022 Sep 5;12(9):e061519. + + + Misir A, Yuce A. AI in Orthopedic Research: A Comprehensive Review. J Orthop Res. 2025 Aug;43(8):1508-27. + + + + + + + 42213782 + + 2026 + 05 + 29 + +
+ + 2211-1247 + + 45 + 6 + + 2026 + May + 28 + + + Cell reports + Cell Rep + + Non-centromeric CENP-A regulates epithelial-mesenchymal plasticity and heterogeneity in human cells. + + 117405 + 117405 + + 10.1016/j.celrep.2026.117405 + S2211-1247(26)00483-3 + + Centromere protein A (CENP-A), a centromeric histone H3 variant highly expressed in aggressive cancers, promotes epithelial-mesenchymal transition (EMT), yet its underlying mechanisms remain unresolved. Here, we used a reversible high-CENP-A expression system in human cells to follow EMT-state trajectories and CENP-A localization over time. Sustained CENP-A elevation shifted hybrid populations toward mesenchymal states and increased both centromeric loading and ectopic chromatin incorporation. Chromatin immunoprecipitation revealed ectopic CENP-A enrichment at EMT-associated loci. Single-nucleus multi-omics further resolved two EMT programs engaged at distinct cell cycle stages: CENP-A strengthened a pre-existing inflammatory program and triggered a developmental program. Importantly, restoring basal CENP-A levels erased these transcriptional programs and eliminated ectopic incorporation, consistent with a reversible, non-genetic mechanism. Together, our findings uncover a non-centromeric function for CENP-A in shaping epithelial-mesenchymal plasticity and cellular heterogeneity. + Copyright © 2026 The Author(s). Published by Elsevier Inc. All rights reserved. + + + + Renaud-Pageot + Charlène + C + + Institut Curie, Université PSL, Sorbonne Université, CNRS, UMR 3664 Nuclear Dynamics, Chromatin Dynamics Lab, Equipe Labellisée Ligue Contre le Cancer, 75005 Paris, France. + + + + Capocefalo + Daniele + D + + Institut Pasteur, Université Paris Cité, CNRS, UMR 3738, Machine Learning for Integrative Genomics Lab, 75015 Paris, France. + + + + Lemaire + Sébastien + S + + Institut Curie, Université PSL, Sorbonne Université, CNRS, UMR 3664 Nuclear Dynamics, Chromatin Dynamics Lab, Equipe Labellisée Ligue Contre le Cancer, 75005 Paris, France. + + + + Forest + Audrey + A + + Institut Curie, Université PSL, Sorbonne Université, CNRS, UMR 3664 Nuclear Dynamics, Chromatin Dynamics Lab, Equipe Labellisée Ligue Contre le Cancer, 75005 Paris, France. + + + + Cantini + Laura + L + + Institut Pasteur, Université Paris Cité, CNRS, UMR 3738, Machine Learning for Integrative Genomics Lab, 75015 Paris, France. + + + + Almouzni + Geneviève + G + + Institut Curie, Université PSL, Sorbonne Université, CNRS, UMR 3664 Nuclear Dynamics, Chromatin Dynamics Lab, Equipe Labellisée Ligue Contre le Cancer, 75005 Paris, France. Electronic address: genevieve.almouzni@curie.fr. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Cell Rep + 101573691 + + IM + + CENP-A + CP: developmental biology + CP: molecular biology + cell cycle + centromere + chromatin + epigenetics + epithelial-mesenchymal transition + single-nucleus multi-omics + + Declaration of interests The authors declare no competing interests. + + + + + 2025 + 10 + 1 + + + 2026 + 2 + 10 + + + 2026 + 4 + 27 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 18 + 33 + + + 2026 + 5 + 29 + 13 + 52 + + + aheadofprint + + 42213782 + 10.1016/j.celrep.2026.117405 + S2211-1247(26)00483-3 + + + + \ No newline at end of file diff --git a/sources/samples/standardized_scopus_test.xlsx b/sources/samples/standardized_scopus_test.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..11b463506060782e824bb2012c4809e8e88c5521 GIT binary patch literal 127113 zcmY&;Q?M{htmLt6+qP}nwr$(C?eiVmwr$(C_rJR@dmoaSR3%l@{n9g&C`bc?pa1{> zKmc6o_Ued@$x5XD7tQ=<2>+Rhy|IFmy@NBop@RdRhpmn5oGkPp0}R-HpORZ!W)xlt 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z%GRNOR4QRgFN;mEC*Nn;#M7&~GcKQ;SHgiG&XDY&L$)(YW^1M4Q=_EVaOi={doNKn zxeH&liR9o8eEZws@ERbD+CW|d{x_$Mf*BoJFOaTF% zzZMEKoJl#lp>9ekZCX_APF{C=`YfWT8`u>$x%-M_#k=QktaUK`;5CH%PB?Xm-gnk5 zT-&jvNB}yB*cLVbeJ}|Qfd%%@RQmfPl}ZjPNGE-8IQem)eH{P#_bv z;LK{|;P4l%e@Ax>ZVR7(T*vs~W%PeRKUzQV{{KXq*gKj1FL8S!2f%3@t*|z1^xT1{cmW`G5CMZ;eQAIeHQ#Tu=Mvo?)jgF z{X6*YF8?{4wU-XMf4${{RA% BY~cU^ literal 0 HcmV?d00001 diff --git a/www/services/file_extractor.py b/www/services/file_extractor.py index bbc6193..13fdc8f 100644 --- a/www/services/file_extractor.py +++ b/www/services/file_extractor.py @@ -1,102 +1,118 @@ -import pandas as pd +"""Extract raw bibliographic records from files before standardization.""" + +from __future__ import annotations + import os +import tempfile +import zipfile + +import pandas as pd +from bibtexparser.bparser import BibTexParser + +from .parsers import parse_cochrane_data, parse_pubmed_medline_text, parse_wos_data + + +SUPPORTED_EXTENSIONS = {".csv", ".xlsx", ".xls", ".txt", ".ciw", ".bib"} + + +def _annotate_records(records: list[dict], file_extension: str, source: str) -> list[dict]: + """Attach source metadata used by the transformation dispatcher.""" + for record in records: + record["_bibliometrix_file_type"] = file_extension + record["_bibliometrix_source"] = source + return records + + +def _read_tabular_file(file_path: str, file_extension: str, source_upper: str) -> list[dict]: + skiprows = 1 if source_upper == "DIMENSIONS" else 0 + + if file_extension == ".csv": + df = pd.read_csv( + file_path, + dtype=str, + skiprows=skiprows, + on_bad_lines="skip", + encoding="utf-8", + ) + else: + df = pd.read_excel(file_path, dtype=str, skiprows=skiprows) + + return df.fillna("").to_dict(orient="records") + + +def _read_bibtex_file(file_path: str) -> list[dict]: + with open(file_path, "r", encoding="utf-8") as file: + parser = BibTexParser() + return parser.parse_file(file).entries + + +def _extract_zip_file(file_path: str, source_upper: str) -> list[dict]: + all_records: list[dict] = [] + with zipfile.ZipFile(file_path, "r") as archive: + with tempfile.TemporaryDirectory() as tmp_dir: + archive.extractall(tmp_dir) + for root, _, files in os.walk(tmp_dir): + for filename in files: + nested_path = os.path.join(root, filename) + nested_ext = os.path.splitext(filename)[1].lower() + if nested_ext in SUPPORTED_EXTENSIONS: + all_records.extend(extract_from_file(nested_path, source_upper)) + return all_records -from .parsers import parse_pubmed_medline_text, parse_wos_data, parse_cochrane_data def extract_from_file(file_path: str, source: str) -> list[dict]: + """Read one raw bibliographic export and return a list of record dicts. + + The function is intentionally limited to the extraction phase: it never + renames columns or enforces the WoS schema. That work is delegated to + ``standardizer.convert2df``. """ - Legge un file grezzo esportato manualmente dalle principali piattaforme - bibliometriche e lo converte in una struttura dati Python standardizzata. - - Args: - file_path (str): Il percorso assoluto o relativo del file da leggere. - source (str): Il nome del database di origine (es. "Scopus", "PubMed"). - - Returns: - list[dict]: Una lista dove ogni dizionario rappresenta un singolo articolo. - - Raises: - FileNotFoundError: Se il percorso del file indicato non esiste. - ValueError: Se l'estensione del file o la fonte testuale non sono supportate. - """ - - # Controlliamo subito se il file esiste sul disco. if not os.path.exists(file_path): - raise FileNotFoundError(f"Errore: Il file '{file_path}' non esiste.") - - # Pulizia dell'input: trasformiamo la fonte in maiuscolo e togliamo gli spazi accidentali + raise FileNotFoundError(f"Il file '{file_path}' non esiste.") + source_upper = source.upper().strip() - - # Estraiamo l'estensione del file e la mettiamo in minuscolo file_extension = os.path.splitext(file_path)[1].lower() - - # FILE DI TESTO PROPRIETARI (TXT/CIW) - # Wos, Cochrane e PubMed - if file_extension in ['.txt', '.ciw']: - print(f"[{source_upper}] Lettura file testuale proprietario: {file_path}") - - # Gestione specifica per PubMed: + + if file_extension == ".zip": + return _extract_zip_file(file_path, source_upper) + + if file_extension == ".bib": + print(f"[{source_upper}] Lettura file BibTeX: {file_path}") + records = _read_bibtex_file(file_path) + return _annotate_records(records, file_extension, source_upper) + + if file_extension in {".txt", ".ciw"}: + print(f"[{source_upper}] Lettura file testuale: {file_path}") + if source_upper == "PUBMED": - # PubMed richiede che il testo venga letto qui e passato al parser come stringa. - with open(file_path, 'r', encoding='utf-8') as f: - text_content = f.read() - return parse_pubmed_medline_text(text_content) - - # Gestione per Web of Science: + with open(file_path, "r", encoding="utf-8") as file: + records = parse_pubmed_medline_text(file.read()) elif source_upper == "WEB_OF_SCIENCE": - return parse_wos_data(file_path) - - # Gestione per Cochrane: + records = parse_wos_data(file_path) elif source_upper == "COCHRANE": - return parse_cochrane_data(file_path) - + records = parse_cochrane_data(file_path) else: raise ValueError( - f"I file di testo (.txt/.ciw) sono supportati solo per PUBMED, WEB_OF_SCIENCE e COCHRANE. " - f"Ricevuto: {source_upper}" + "I file .txt/.ciw sono supportati solo per PUBMED, " + f"WEB_OF_SCIENCE e COCHRANE. Ricevuto: {source_upper}" ) - # FILE TABELLARI (CSV/XLSX/XLS) - # Scopus, Dimensions, Lens, OpenAlex e WoS tabellare + return _annotate_records(records, file_extension, source_upper) - elif file_extension in ['.csv', '.xlsx', '.xls']: + if file_extension in {".csv", ".xlsx", ".xls"}: print(f"[{source_upper}] Lettura file tabellare {file_extension}: {file_path}") - try: - # Calcola quante righe saltare: 1 per Dimensions (disclaimer), 0 per gli altri - righe_da_saltare = 1 if source_upper == "DIMENSIONS" else 0 - - if file_extension == '.csv': - df = pd.read_csv( - file_path, - dtype=str, # Legge tutto come stringa. - skiprows=righe_da_saltare, # Salta la prima riga se la fonte è Dimensions - on_bad_lines='skip', # Se una riga del CSV è corrotta la salta. - encoding='utf-8' - ) - else: - df = pd.read_excel( - file_path, - dtype=str, - skiprows=righe_da_saltare # Salta la prima riga se la fonte è Dimensions - ) - - # Sostituiamo i NaN con stringhe vuote. - df = df.fillna("") - - # Converte il DataFrame in una una lista di dizionari, dove ogni dizionario è una riga della tabella. - return df.to_dict(orient="records") - + records = _read_tabular_file(file_path, file_extension, source_upper) except pd.errors.EmptyDataError: - print(f"[ERRORE] Il file '{file_path}' è vuoto.") - return [] - - except Exception as e: - print(f"[ERRORE] Impossibile leggere il file tabellare: {e}") - return [] - - else: - raise ValueError( - f"Formato file non supportato: {file_extension}. " - f"I formati accettati sono: .csv, .xlsx, .txt, .ciw" - ) \ No newline at end of file + print(f"[ERRORE] Il file '{file_path}' e' vuoto.") + return [] + except Exception as exc: + print(f"[ERRORE] Impossibile leggere il file tabellare: {exc}") + return [] + + return _annotate_records(records, file_extension, source_upper) + + raise ValueError( + f"Formato file non supportato: {file_extension}. " + "Formati accettati: .csv, .xlsx, .xls, .txt, .ciw, .bib, .zip" + ) diff --git a/www/services/format_functions.py b/www/services/format_functions.py index 1a8ee7a..c5de6b2 100644 --- a/www/services/format_functions.py +++ b/www/services/format_functions.py @@ -1176,6 +1176,7 @@ def format_sr_column(entry, source, file_type): # Function for SR Column (forma initials = ''.join([name[0] + '.' for name in names.split() if name]) author_dict = {'Surname': surname, 'Name Initials': initials} + break publication_year = entry.get('year', '') journal = entry.get('journal', '').replace('\n', ' ') diff --git a/www/services/standardizer.py b/www/services/standardizer.py index 57cc8fc..bd5cde0 100644 --- a/www/services/standardizer.py +++ b/www/services/standardizer.py @@ -1,256 +1,420 @@ -# ============================================================================= -# standardizer.py — Phase 2: TRANSFORM – RENAME (Integration with format_functions) -# ============================================================================= +"""Central transformation step for the Bibliometrix ETL pipeline. + +The goal of this module is intentionally simple: +1. receive raw records from ``file_extractor`` or ``api_retriever``; +2. map them to the internal WoS-like schema used by the app; +3. enforce predictable types and null handling; +4. return a DataFrame ready for the dashboard. +""" from __future__ import annotations -import pandas as pd + +import ast +import re from typing import Any -# Importiamo le funzioni di formattazione dal file fornito +import pandas as pd + from . import format_functions as ff +from .validation import validate_dataframe_contract, validate_record_contract + # ----------------------------------------------------------------------------- -# 1. CONTRATTI DI TIPO E DEFAULT +# 1. Target schema and type contracts # ----------------------------------------------------------------------------- + COLUMN_TYPE_CONTRACTS: dict[str, type] = { - "DB": str, "UT": str, "DI": str, "PMID": str, - "TI": str, "SO": str, "JI": str, "PY": str, - "DT": str, "LA": str, "RP": str, "AB": str, - "VL": str, "IS": str, "BP": str, "EP": str, - "SR": str, "TC": int, - "AU": list, "AF": list, "C1": list, - "CR": list, "DE": list, "ID": list, + "DB": str, + "UT": str, + "DI": str, + "PMID": str, + "TI": str, + "SO": str, + "JI": str, + "PY": str, + "DT": str, + "LA": str, + "RP": str, + "AB": str, + "VL": str, + "IS": str, + "BP": str, + "EP": str, + "SR": str, + "TC": int, + "AU": list, + "AF": list, + "C1": list, + "CR": list, + "DE": list, + "ID": list, } -_TYPE_DEFAULTS: dict[type, Any] = {str: "", int: 0, list: []} -CSV_DELIMITER: str = ";" +LIST_COLUMNS = {"AU", "AF", "C1", "CR", "DE", "ID"} +CSV_DELIMITER = ";" +NULL_STRINGS = {"", "nan", "none", "null", "na", "n/a"} +INTERNAL_KEYS = {"_bibliometrix_file_type", "_bibliometrix_source"} + -def _get_default_value(expected_type: type) -> Any: +def _default(expected_type: type) -> Any: + """Return a safe default for the requested contract type.""" if expected_type is list: return [] - return _TYPE_DEFAULTS.get(expected_type, "") + if expected_type is int: + return 0 + return "" + + +def _is_null(value: Any) -> bool: + """Detect null-like values without breaking on lists or arrays.""" + if value is None: + return True + if isinstance(value, str): + return value.strip().lower() in NULL_STRINGS + try: + return bool(pd.isna(value)) + except (TypeError, ValueError): + return False + + +def _clean_string(value: Any) -> str: + if _is_null(value): + return "" + return str(value).replace("\r", " ").replace("\n", " ").strip() + + +def _parse_literal_list(value: str) -> list[Any] | None: + """Parse strings saved by Excel/CSV as "['a', 'b']" when present.""" + stripped = value.strip() + if not (stripped.startswith("[") and stripped.endswith("]")): + return None + try: + parsed = ast.literal_eval(stripped) + except (SyntaxError, ValueError): + return None + return parsed if isinstance(parsed, list) else None + + +def _as_list(value: Any) -> list[str]: + """Convert multi-value fields to list[str], splitting serialized strings.""" + if _is_null(value): + return [] + + if isinstance(value, str): + parsed = _parse_literal_list(value) + if parsed is not None: + return _as_list(parsed) + + text = _clean_string(value) + if not text: + return [] -def _cast_scalar(value: Any, expected_type: type) -> Any: - if value is None or (isinstance(value, float) and pd.isna(value)): - return _get_default_value(expected_type) + separator = CSV_DELIMITER if CSV_DELIMITER in text else None + parts = text.split(separator) if separator else [text] + return [part.strip() for part in parts if part.strip().lower() not in NULL_STRINGS] + if isinstance(value, (list, tuple, set)): + values: list[str] = [] + for item in value: + values.extend(_as_list(item)) + return values + + text = _clean_string(value) + return [text] if text else [] + + +def _as_int(value: Any) -> int: + if isinstance(value, (list, tuple, set)): + value = next((item for item in value if not _is_null(item)), 0) + if _is_null(value): + return 0 + try: + return int(float(str(value).strip())) + except (TypeError, ValueError): + return 0 + + +def _as_year(value: Any) -> str: + match = re.search(r"\d{4}", _clean_string(value)) + return match.group(0) if match else "" + + +def _cast(tag: str, value: Any) -> Any: + """Cast one value according to the target schema.""" + expected_type = COLUMN_TYPE_CONTRACTS[tag] + if expected_type is list: + return _as_list(value) if expected_type is int: - try: - return int(float(value)) - except (ValueError, TypeError): - return 0 - elif expected_type is list: - return value if isinstance(value, list) else [str(value)] + return _as_int(value) + if tag == "PY": + return _as_year(value) + return _clean_string(value) + + +def _empty_record(db: str) -> dict[str, Any]: + record = {tag: _default(contract) for tag, contract in COLUMN_TYPE_CONTRACTS.items()} + record["DB"] = db + return record - return str(value).strip() # ----------------------------------------------------------------------------- -# 2. INTEGRAZIONE CON FORMAT_FUNCTIONS (Il Bridge) +# 2. Bridge to the existing format_functions.py # ----------------------------------------------------------------------------- -# Mappa dei tag WoS alle specifiche funzioni di format_functions.py + FORMAT_FUNCTIONS_MAP = { - 'AB': ff.format_ab_column, - 'AF': ff.format_af_column, - 'AU': ff.format_au_column, - 'BP': ff.format_bp_column, - 'C1': ff.format_c1_column, - 'CR': ff.format_cr_column, - 'DE': ff.format_de_column, - 'DI': ff.format_di_column, - 'DT': ff.format_dt_column, - 'EP': ff.format_ep_column, - 'IS': ff.format_is_column, - 'JI': ff.format_ji_column, - 'ID': ff.format_id_column, - 'LA': ff.format_la_column, - 'PMID': ff.format_pmid_column, - 'PY': ff.format_py_column, - 'RP': ff.format_rp_column, - 'SO': ff.format_so_column, - 'TC': ff.format_tc_column, - 'TI': ff.format_ti_column, - 'UT': ff.format_ut_column, - 'VL': ff.format_vl_column, - 'SR': ff.format_sr_column + "AB": ff.format_ab_column, + "AF": ff.format_af_column, + "AU": ff.format_au_column, + "BP": ff.format_bp_column, + "C1": ff.format_c1_column, + "CR": ff.format_cr_column, + "DE": ff.format_de_column, + "DI": ff.format_di_column, + "DT": ff.format_dt_column, + "EP": ff.format_ep_column, + "IS": ff.format_is_column, + "JI": ff.format_ji_column, + "ID": ff.format_id_column, + "LA": ff.format_la_column, + "PMID": ff.format_pmid_column, + "PY": ff.format_py_column, + "RP": ff.format_rp_column, + "SO": ff.format_so_column, + "TC": ff.format_tc_column, + "TI": ff.format_ti_column, + "UT": ff.format_ut_column, + "VL": ff.format_vl_column, + "SR": ff.format_sr_column, } -# Mappatura dei nomi dei DB da uppercase a quelli attesi da format_functions SOURCE_NAME_MAP = { "WEB_OF_SCIENCE": "Web_of_Science", "SCOPUS": "Scopus", "PUBMED": "PubMed", "DIMENSIONS": "Dimensions", "LENS": "The_Lens", - "COCHRANE": "Cochrane" + "THE_LENS": "The_Lens", + "COCHRANE": "Cochrane", } -def transform_via_format_functions(raw_record: dict, source_upper: str, file_type: str) -> dict: - """ - Funzione universale che delega l'estrazione dei campi a format_functions.py - e assicura il rispetto dei Type Contracts di standardizer. - """ - standardized: dict = { - tag: _get_default_value(contract) - for tag, contract in COLUMN_TYPE_CONTRACTS.items() - } - - standardized["DB"] = source_upper - ff_source_name = SOURCE_NAME_MAP.get(source_upper, source_upper) - for tag, extract_func in FORMAT_FUNCTIONS_MAP.items(): +def _effective_file_type(raw_record: dict[str, Any], source: str, file_type: str) -> str: + """Prefer file metadata added by file_extractor, then normalize extensions.""" + effective = str(raw_record.get("_bibliometrix_file_type") or file_type or "").lower().strip() + if effective and effective != "api" and not effective.startswith("."): + effective = f".{effective}" + + # PubMed API records are parsed into Medline-like keys, so the text formatter works. + if source == "PUBMED" and effective in {"api", ".xml", "xml", ""}: + return ".txt" + return effective + + +def _prepare_for_formatters(raw_record: dict[str, Any], source: str) -> dict[str, Any]: + """Small adapter for the older format_functions expectations.""" + prepared = {k: v for k, v in raw_record.items() if k not in INTERNAL_KEYS} + + if source == "PUBMED": + for key, value in list(prepared.items()): + if isinstance(value, (list, tuple, set)): + prepared[key] = CSV_DELIMITER.join(_as_list(value)) + if "MH" not in prepared and "OT" in prepared: + prepared["MH"] = prepared["OT"] + + return prepared + + +def _build_sr(record: dict[str, Any]) -> str: + """Fallback short reference: FirstAuthor, Year, Source.""" + first_author = record["AU"][0] if record["AU"] else "" + return ", ".join(part for part in [first_author, record["PY"], record["SO"]] if part) + + +def _finalize_record(record: dict[str, Any]) -> dict[str, Any]: + """Apply common derived/cleanup fields after source-specific mapping.""" + if record["SO"]: + record["SO"] = record["SO"].upper() + if not record["JI"] and record["SO"]: + record["JI"] = record["SO"] + if not record["SR"]: + record["SR"] = _build_sr(record) + return record + + +def transform_with_format_functions( + raw_record: dict[str, Any], source: str, file_type: str +) -> dict[str, Any]: + """Map records supported by the existing format_functions module.""" + record = _empty_record(source) + formatter_source = SOURCE_NAME_MAP.get(source, source) + prepared = _prepare_for_formatters(raw_record, source) + + for tag, formatter in FORMAT_FUNCTIONS_MAP.items(): try: - # Invoca la funzione specifica del campo passando i tre parametri richiesti - raw_value = extract_func(raw_record, ff_source_name, file_type) - except Exception as e: - raw_value = None # Fallback sicuro in caso di KeyError interni - - # Cast al tipo atteso (int, str, list) per evitare disallineamenti - standardized[tag] = _cast_scalar(raw_value, COLUMN_TYPE_CONTRACTS[tag]) - - # Pulizia standard su Nome Rivista (SO) - if standardized.get("SO"): - standardized["SO"] = standardized["SO"].upper() - - return standardized + value = formatter(prepared, formatter_source, file_type) + except Exception: + value = None + record[tag] = _cast(tag, value) + + return _finalize_record(record) + # ----------------------------------------------------------------------------- -# 3. LOGICHE RESIDUE (Solo per OpenAlex, non presente in format_functions) +# 3. OpenAlex mapping, because it is not covered by format_functions.py # ----------------------------------------------------------------------------- -def clean_scalar_fields_oa(raw_record: dict, mapping_dict: dict) -> dict: - res = {} - for oa_key, wos_tag in mapping_dict.items(): - val = raw_record.get(oa_key) - res[wos_tag] = _cast_scalar(val, COLUMN_TYPE_CONTRACTS[wos_tag]) - return res - -def transform_openalex_record(raw_record: dict, file_type: str = "") -> dict: - # (Mantieni qui la logica di base per OpenAlex JSON originale) - standardized = {t: _get_default_value(c) for t, c in COLUMN_TYPE_CONTRACTS.items()} - standardized["DB"] = "OPENALEX" - - mapping = {"id": "UT", "doi": "DI", "title": "TI", "publication_year": "PY", "type": "DT", "cited_by_count": "TC"} - standardized.update(clean_scalar_fields_oa(raw_record, mapping)) - - # Autori (estrazione semplificata) - authorships = raw_record.get("authorships", []) - if authorships: - standardized["AU"] = [str(a.get("author", {}).get("display_name", "")) for a in authorships] - standardized["AF"] = standardized["AU"] - standardized["C1"] = [inst.get("display_name", "") for auth in authorships for inst in auth.get("institutions", [])] - return standardized - -def transform_openalex_csv_record(raw_record: dict, file_type: str = "") -> dict: - # (Mantieni qui la logica di base per OpenAlex CSV originale) - standardized = {t: _get_default_value(c) for t, c in COLUMN_TYPE_CONTRACTS.items()} - standardized["DB"] = "OPENALEX" - - mapping = {"id": "UT", "doi": "DI", "title": "TI", "publication_year": "PY", "type": "DT", "cited_by_count": "TC", "host_venue": "SO"} - standardized.update(clean_scalar_fields_oa(raw_record, mapping)) - - authors_str = str(raw_record.get("authors", raw_record.get("author_display_names", ""))) - if authors_str: - sep = ";" if ";" in authors_str else "," - standardized["AU"] = [a.strip() for a in authors_str.split(sep) if a.strip()] - standardized["AF"] = standardized["AU"] - return standardized + +def _openalex_source(raw_record: dict[str, Any]) -> str: + location = raw_record.get("primary_location") or {} + source = location.get("source") or raw_record.get("host_venue") or {} + if isinstance(source, dict): + return _clean_string(source.get("display_name")) + return _clean_string(source) + + +def _openalex_authors_and_affiliations(raw_record: dict[str, Any]) -> tuple[list[str], list[str]]: + authors: list[str] = [] + affiliations: list[str] = [] + + for authorship in raw_record.get("authorships", []) or []: + author_name = _clean_string((authorship.get("author") or {}).get("display_name")) + if author_name: + authors.append(author_name) + + for institution in authorship.get("institutions", []) or []: + name = _clean_string(institution.get("display_name")) + if name and name not in affiliations: + affiliations.append(name) + + return authors, affiliations + + +def _openalex_abstract(raw_record: dict[str, Any]) -> str: + inverted_index = raw_record.get("abstract_inverted_index") + if not isinstance(inverted_index, dict): + return "" + + words: list[tuple[int, str]] = [] + for word, positions in inverted_index.items(): + for position in positions or []: + words.append((int(position), str(word))) + return " ".join(word for _, word in sorted(words)) + + +def transform_openalex_record(raw_record: dict[str, Any]) -> dict[str, Any]: + """Map one OpenAlex API/JSON record to the target schema.""" + record = _empty_record("OPENALEX") + biblio = raw_record.get("biblio") or {} + ids = raw_record.get("ids") or {} + authors, affiliations = _openalex_authors_and_affiliations(raw_record) + source_name = _openalex_source(raw_record) + concepts = raw_record.get("concepts") or raw_record.get("keywords") or [] + keywords = [ + _clean_string(item.get("display_name") or item.get("keyword")) + for item in concepts + if isinstance(item, dict) + ] + + values = { + "UT": raw_record.get("id"), + "DI": raw_record.get("doi"), + "PMID": ids.get("pmid"), + "TI": raw_record.get("display_name") or raw_record.get("title"), + "SO": source_name, + "JI": source_name, + "PY": raw_record.get("publication_year"), + "DT": raw_record.get("type"), + "LA": raw_record.get("language"), + "TC": raw_record.get("cited_by_count"), + "AB": _openalex_abstract(raw_record), + "VL": biblio.get("volume"), + "IS": biblio.get("issue"), + "BP": biblio.get("first_page"), + "EP": biblio.get("last_page"), + "AU": authors, + "AF": authors, + "C1": affiliations, + "CR": raw_record.get("referenced_works"), + "DE": keywords, + "ID": keywords, + } + + for tag, value in values.items(): + record[tag] = _cast(tag, value) + + return _finalize_record(record) + # ----------------------------------------------------------------------------- -# 4. VALIDAZIONE E SERIALIZZAZIONE +# 4. Already standardized XLSX/CSV rows # ----------------------------------------------------------------------------- -class ValidationError(Exception): pass -def validate_record(record: dict) -> None: - errors = [] - for tag, expected_type in COLUMN_TYPE_CONTRACTS.items(): - if tag not in record: - errors.append(f"[MISSING_COLUMN] Tag '{tag}' assente.") - continue - val = record[tag] - if not isinstance(val, expected_type): - errors.append(f"[TYPE_ERROR] Tag '{tag}': atteso {expected_type.__name__}, trovato {type(val).__name__}.") - if errors: - raise ValidationError("\n".join(errors)) +def _looks_standardized(raw_record: dict[str, Any]) -> bool: + target_columns = set(COLUMN_TYPE_CONTRACTS) + return "SR" in raw_record and len(target_columns.intersection(raw_record)) >= 10 + + +def transform_standardized_record(raw_record: dict[str, Any], default_db: str) -> dict[str, Any]: + """Re-load rows previously exported from this standardized schema.""" + record = _empty_record(default_db) + for tag in COLUMN_TYPE_CONTRACTS: + record[tag] = _cast(tag, raw_record.get(tag, record[tag])) + return _finalize_record(record) + + +def serialize_for_csv(record: dict[str, Any]) -> dict[str, Any]: + """Flatten list columns using the internal semicolon delimiter.""" + return { + tag: CSV_DELIMITER.join(value) if isinstance(value, list) else value + for tag, value in record.items() + } -def serialize_for_csv(record: dict) -> dict: - return {tag: (CSV_DELIMITER.join(str(i) for i in val) if isinstance(val, list) else val) for tag, val in record.items()} # ----------------------------------------------------------------------------- -# 5. ENTRY POINT PRINCIPALE +# 5. Public entry point # ----------------------------------------------------------------------------- + def convert2df( - raw_records: list[dict], + raw_records: list[dict[str, Any]], source: str = "OPENALEX", - file_type: str = ".csv", # Aggiunto parametro per format_functions + file_type: str = ".csv", validate: bool = True, for_csv_export: bool = False, ) -> pd.DataFrame: - """ - Trasforma una lista di record grezzi in un DataFrame standardizzato. - """ - # --- SONDE DI DEBUG (da rimuovere una volta risolto) --- - print("\n" + "="*40) - print("🎯 DEBUG CONVERT2DF CHIAMATO!") - print(f"🔹 Source ricevuta: '{source}'") - print(f"🔹 File type ricevuto: '{file_type}'") - print(f"🔹 Tipo di raw_records: {type(raw_records)}") - - if isinstance(raw_records, list): - print(f"🔹 Numero di record: {len(raw_records)}") - if len(raw_records) > 0: - print(f"🔹 Chiavi del primo record: {list(raw_records[0].keys())[:5]}...") - else: - print("⚠️ ERRORE: raw_records NON è una lista!") - print("="*40 + "\n") - # ------------------------------------------------------- - - source_upper = source.upper() - - # Routing dinamico per OpenAlex vs Format Functions - if source_upper == "OPENALEX" and raw_records: - first_record = raw_records[0] - if "authorships" not in first_record and ("author_display_names" in first_record or "publication_year" in first_record): - source_upper = "OPENALEX_CSV" - - standardized_records: list[dict] = [] - - for idx, raw in enumerate(raw_records): - try: - # Logica di Routing - if source_upper == "OPENALEX": - record = transform_openalex_record(raw, file_type) - elif source_upper == "OPENALEX_CSV": - record = transform_openalex_csv_record(raw, file_type) - else: - # Per Scopus, WoS, PubMed, Dimensions, Cochrane e Lens usa il Bridge - record = transform_via_format_functions(raw, source_upper, file_type) - except Exception as exc: - print(f"[ERROR] Trasformazione fallita per il record {idx}: {exc}") + """Convert raw records into the standardized Bibliometrix DataFrame.""" + source = source.upper().strip() + records: list[dict[str, Any]] = [] + + for raw_record in raw_records or []: + if not isinstance(raw_record, dict): continue - if validate: - try: - validate_record(record) - except ValidationError as ve: - print(f"[WARN] Validazione fallita per il record {idx}:\n{ve}") + record_source = str(raw_record.get("_bibliometrix_source", source)).upper().strip() + effective_file_type = _effective_file_type(raw_record, record_source, file_type) + + if _looks_standardized(raw_record): + record = transform_standardized_record(raw_record, default_db=record_source) + elif record_source == "OPENALEX": + record = transform_openalex_record(raw_record) + else: + record = transform_with_format_functions(raw_record, record_source, effective_file_type) - if for_csv_export: - record = serialize_for_csv(record) + if validate: + validate_record_contract(record, COLUMN_TYPE_CONTRACTS) - standardized_records.append(record) + records.append(serialize_for_csv(record) if for_csv_export else record) - column_order = list(COLUMN_TYPE_CONTRACTS.keys()) - if not standardized_records: + column_order = list(COLUMN_TYPE_CONTRACTS) + if not records: return pd.DataFrame(columns=column_order) - df = pd.DataFrame(standardized_records) + df = pd.DataFrame(records) + for tag, expected_type in COLUMN_TYPE_CONTRACTS.items(): + if tag not in df.columns: + default_value = _default(expected_type) + df[tag] = [[] for _ in range(len(df))] if expected_type is list else default_value - # Assicuriamo che tutte le colonne siano presenti - for col in column_order: - if col not in df.columns: - if for_csv_export: - df[col] = "" - else: - default_val = _TYPE_DEFAULTS[COLUMN_TYPE_CONTRACTS[col]] - df[col] = [[] for _ in range(len(df))] if isinstance(default_val, list) else default_val + df = df[column_order] + if validate and not for_csv_export: + validate_dataframe_contract(df, COLUMN_TYPE_CONTRACTS) - return df[column_order] \ No newline at end of file + return df diff --git a/www/services/validation.py b/www/services/validation.py index e69de29..55bbaca 100644 --- a/www/services/validation.py +++ b/www/services/validation.py @@ -0,0 +1,78 @@ +"""Validation helpers for the Bibliometrix ETL pipeline. + +The ETL target schema mirrors the Web of Science tags used by the analytical +functions. These helpers keep validation separate from extraction and +transformation, as required by the exam trace. +""" + +from __future__ import annotations + +from typing import Any + +import pandas as pd + + +class ValidationError(Exception): + """Raised when a standardized record or DataFrame violates the ETL contract.""" + + +MULTI_VALUE_COLUMNS: set[str] = {"AU", "AF", "C1", "CR", "DE", "ID"} + + +def _is_missing(value: Any) -> bool: + """Return True for values that must not survive in standardized output.""" + if value is None: + return True + if isinstance(value, float) and pd.isna(value): + return True + if isinstance(value, str) and value.strip().lower() in {"nan", "none", "null"}: + return True + return False + + +def validate_record_contract(record: dict[str, Any], contracts: dict[str, type]) -> None: + """Validate one standardized bibliographic record against type contracts.""" + errors: list[str] = [] + + for tag, expected_type in contracts.items(): + if tag not in record: + errors.append(f"[MISSING_COLUMN] Tag '{tag}' assente.") + continue + + value = record[tag] + if _is_missing(value): + errors.append(f"[NULL_VALUE] Tag '{tag}' contiene un valore nullo.") + continue + + if not isinstance(value, expected_type): + errors.append( + f"[TYPE_ERROR] Tag '{tag}': atteso {expected_type.__name__}, " + f"trovato {type(value).__name__}." + ) + + if errors: + raise ValidationError("\n".join(errors)) + + +def validate_dataframe_contract(df: pd.DataFrame, contracts: dict[str, type]) -> None: + """Validate the finalized standardized DataFrame.""" + errors: list[str] = [] + + missing_columns = [col for col in contracts if col not in df.columns] + if missing_columns: + errors.append(f"[MISSING_COLUMNS] Colonne assenti: {', '.join(missing_columns)}") + + for col in [c for c in contracts if c in df.columns]: + expected_type = contracts[col] + for idx, value in df[col].items(): + if _is_missing(value): + errors.append(f"[NULL_VALUE] Riga {idx}, colonna '{col}' contiene un nullo.") + continue + if not isinstance(value, expected_type): + errors.append( + f"[TYPE_ERROR] Riga {idx}, colonna '{col}': atteso " + f"{expected_type.__name__}, trovato {type(value).__name__}." + ) + + if errors: + raise ValidationError("\n".join(errors[:25]))