From 441073135de8e44d11fb17a629163004b553d83e Mon Sep 17 00:00:00 2001 From: MARCOP001 Date: Fri, 29 May 2026 18:49:09 +0200 Subject: [PATCH] modifica risultati di ricerca api --- app.py | 40 +- data/raw/openalex_20260529_174737.json | 111089 ++++++++++++++++++++++ data/raw/openalex_20260529_175440.json | 111089 ++++++++++++++++++++++ data/raw/openalex_20260529_183916.json | 111089 ++++++++++++++++++++++ data/raw/openalex_20260529_184136.json | 111089 ++++++++++++++++++++++ data/raw/pubmed_20260529_184416.xml | 36619 +++++++ 6 files changed, 480997 insertions(+), 18 deletions(-) create mode 100644 data/raw/openalex_20260529_174737.json create mode 100644 data/raw/openalex_20260529_175440.json create mode 100644 data/raw/openalex_20260529_183916.json create mode 100644 data/raw/openalex_20260529_184136.json create mode 100644 data/raw/pubmed_20260529_184416.xml diff --git a/app.py b/app.py index c4bb2a2..6f2696e 100644 --- a/app.py +++ b/app.py @@ -801,15 +801,6 @@ def mostra(): except Exception as e: ui.notification_show(f"❌ Errore durante la standardizzazione: {e}", type="error", duration=10) - # Renderizza la preview dei dati (sostituisce il vecchio show_data()) - @render.ui - def show_table(): - current_data = df.get() - if current_data is not None and not current_data.empty: - html_table = current_data.head(100).to_html(classes="table table-striped", index=False) - return ui.HTML(f"
{html_table}
") - return ui.p("Nessun record caricato o elaborato.", style="color: gray;") - # -------- ADVICE BUTTON -------- @render.ui @reactive.event(input.advice_modal_completeness) @@ -883,6 +874,16 @@ def indicator_types_ui_all(): spinners=input.start_button() > 0 ) + # Visualizzazione reattiva della tabella + @render.ui + def show_data_table(): + current_data = df.get() + if current_data is not None and not current_data.empty: + html_table = current_data.head(100).to_html(classes="table table-striped", index=False) + return ui.HTML(f"
{html_table}
") + return ui.p("Nessun record caricato. Usa 'Import raw data file(s)' o la sezione API.", style="color: gray;") + + ui.h4("Description", style="color: #5567BB;") ui.p("This section allows you to import, load, or export your dataset. You can choose to import raw data files from various databases, load previously saved Bibliometrix files, or use a sample dataset for testing purposes. Once the data is loaded, you can view it in a table format and export it as an Excel file or R Data Format for further analysis. " \ "Biblioshiny supports various bibliographic databases, allowing users to import and analyze collections exported from these sources. Click on a database below to visit its official website and download your data for analysis."), @@ -939,24 +940,25 @@ def indicator_types_ui_all(): ui.input_action_button("btn_run_api", "Esegui Live API", icon=ICONS["play"], class_="btn-primary") with ui.card(full_screen=True): - @render.ui + @reactive.effect # <-- MODIFICATO: Da @render.ui a @reactive.effect @reactive.event(input.btn_run_api) def esegui_pipeline_api(): query = input.api_query() source = input.api_source() if not query: - return ui.notification_show("Inserisci una query valida prima di eseguire.", type="warning") + ui.notification_show("Inserisci una query valida prima di eseguire.", type="warning") + return # <-- MODIFICATO: Usa solo return vuoto per uscire, non 'return ui.notification_show' ui.notification_show("⏳ Interrogazione API in corso...", duration=10) try: # --- FASE 1: EXTRACT (via API) --- - # Seleziona la funzione corretta richiamando api_retriever.py raw_records = extract_data(query=query, source=source) if not raw_records: - return ui.notification_show("Nessun risultato compatibile trovato con la query.", type="warning") + ui.notification_show("Nessun risultato compatibile trovato con la query.", type="warning") + return # --- FASE 2, 3 e 4: TRANSFORM E LOAD --- source_mapped = "OPENALEX" if source == "openalex" else "PUBMED" @@ -971,13 +973,15 @@ def esegui_pipeline_api(): df.set(standardized_df) reset_all_analyses() - # Se il fetch va a buon fine reindirizza alla Overview - ui.update_navs("hidden_tabs", selected="overview") - return ui.notification_show(f"✅ Download API completato! Creati e testati {len(standardized_df)} record uniformati.", duration=5) + + ui.update_navs("hidden_tabs", selected="import") + + # <-- MODIFICATO: rimosso il 'return' + ui.notification_show(f"✅ Download API completato! Creati e testati {len(standardized_df)} record uniformati.", duration=5) except Exception as e: - return ui.notification_show(f"❌ Fallimento del processo API: {e}", type="error", duration=15) - + # <-- MODIFICATO: rimosso il 'return' + ui.notification_show(f"❌ Fallimento del processo API: {e}", type="error", duration=15) with ui.nav_panel("None", value="collections"): ui.h3("🚧 Warning: Merge Collection is under construction 🚧") diff --git a/data/raw/openalex_20260529_174737.json b/data/raw/openalex_20260529_174737.json new file mode 100644 index 0000000..e88f003 --- /dev/null +++ b/data/raw/openalex_20260529_174737.json @@ -0,0 +1,111089 @@ +[ + { + "id": "https://openalex.org/W2101234009", + "doi": "https://doi.org/10.48550/arxiv.1201.0490", + "title": "Scikit-learn: Machine Learning in Python", + "display_name": "Scikit-learn: Machine Learning in Python", + "relevance_score": 17405.58, + "publication_year": 2012, + "publication_date": "2012-01-02", + "ids": { + "openalex": "https://openalex.org/W2101234009", + "doi": "https://doi.org/10.48550/arxiv.1201.0490", + "mag": "2101234009" + }, + "language": "en", + "primary_location": { + "id": "pmh:oai:arXiv.org:1201.0490", + "is_oa": true, + "landing_page_url": "http://arxiv.org/abs/1201.0490", + "pdf_url": "https://arxiv.org/pdf/1201.0490", + "source": { + "id": "https://openalex.org/S4306400194", + "display_name": "arXiv (Cornell University)", + "issn_l": null, + "issn": null, + "is_oa": true, + "is_in_doaj": false, + "is_core": false, + "host_organization": "https://openalex.org/I205783295", + "host_organization_name": "Cornell University", + "host_organization_lineage": [ + "https://openalex.org/I205783295" + ], + "host_organization_lineage_names": [], + "type": "repository" + }, + "license": null, + "license_id": null, + "version": "submittedVersion", + "is_accepted": false, + "is_published": false, + "raw_source_name": "", + "raw_type": "text" + }, + "type": "preprint", + "indexed_in": [ + "arxiv", + "datacite" + ], + "open_access": { + "is_oa": true, + "oa_status": "green", + "oa_url": "https://arxiv.org/pdf/1201.0490", + "any_repository_has_fulltext": true + }, + "authorships": [ + { + "author_position": "first", + "author": { + "id": "https://openalex.org/A5105141183", + "display_name": "Fabián Pedregosa", + "orcid": "https://orcid.org/0000-0003-4025-3953" + }, + "institutions": [ + { + "id": "https://openalex.org/I2738703131", + "display_name": "Commissariat à l'Énergie Atomique et aux Énergies Alternatives", + "ror": "https://ror.org/00jjx8s55", + "country_code": "FR", + "type": "government", + "lineage": [ + "https://openalex.org/I2738703131" + ] + }, + { + "id": "https://openalex.org/I4210128565", + "display_name": "CEA Paris-Saclay", + "ror": "https://ror.org/03n15ch10", + "country_code": "FR", + "type": "government", + "lineage": [ + "https://openalex.org/I2738703131", + "https://openalex.org/I277688954", + "https://openalex.org/I4210128565" + ] + } + ], + "countries": [ + "FR" + ], + "is_corresponding": true, + "raw_author_name": "Pedregosa, Fabian", + "raw_affiliation_strings": [ + "LNAO - 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+ + 1463-9084 + + + 2026 + May + 28 + + + Physical chemistry chemical physics : PCCP + Phys Chem Chem Phys + + Two-dimensional materials as emerging electrocatalysts for the HER, ORR, and OER: design strategies, challenges, and prospects in sustainable energy conversion. + 10.1039/d6cp00222f + + Two-dimensional (2D) materials have emerged as a versatile platform for high-performance electrocatalysts in sustainable energy conversion and storage technologies, including fuel cells, water splitting, and metal-air batteries (MABs). The central part of the electrochemical reactions, such as the H2 evolution reaction (HER), O2 reduction reaction (ORR), and O2 evolution reaction (OER), determines the efficiency, performance, and stability of these 2D materials. While noble metals like Pt, Ir, and Ru exhibit superior activity, their high cost and limited durability hinder large-scale applications. 2D materials, including transition metal dichalcogenides, MXenes, doped graphene, and single-atom 2D catalysts, offer tunable electronic structures, high surface area, abundant active sites, and defect-rich architectures, enabling efficient and durable catalysis. Combined with advanced computational approaches, such as density functional theory (DFT) calculations and machine learning (ML), these materials provide a pathway for rational design and high-throughput screening of next-generation electrocatalysts. This review critically summarizes recent progress in 2D material-based electrocatalysts for the HER, ORR, and OER, highlighting design strategies, synthesis techniques, stability challenges, and emerging trends toward scalable and practical energy conversion technologies. + + + + Upadhyay + Shrish Nath + SN + 0000-0003-0029-4160 + + Theoretical Condensed Matter Physics & Advanced Computational Materials Science Laboratory, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, Madhya Pradesh, India. spakhira@iiti.ac.in. + + + + Joshi + Himani + H + 0000-0003-1493-5934 + + Theoretical Condensed Matter Physics & Advanced Computational Materials Science Laboratory, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, Madhya Pradesh, India. spakhira@iiti.ac.in. + + + + Sharma + Naveen + N + 0009-0008-9303-4950 + + Theoretical Condensed Matter Physics & Advanced Computational Materials Science Laboratory, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, Madhya Pradesh, India. spakhira@iiti.ac.in. + + + + Pakhira + Srimanta + S + 0000-0002-2488-300X + + Theoretical Condensed Matter Physics & Advanced Computational Materials Science Laboratory, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, Madhya Pradesh, India. spakhira@iiti.ac.in. + + + Theoretical Condensed Matter Physics & Advanced Computational Materials Science Laboratory, Centre for Advanced Electronics (CAE), Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, Madhya Pradesh, India. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 28 + +
+ + England + Phys Chem Chem Phys + 100888160 + 1463-9076 + + IM + + + + + 2026 + 5 + 28 + 12 + 35 + + + 2026 + 5 + 28 + 12 + 35 + + + 2026 + 5 + 28 + 11 + 53 + + + aheadofprint + + 42207562 + 10.1039/d6cp00222f + + + + + + 42207555 + + 2026 + 05 + 28 + +
+ + 1520-5851 + + + 2026 + May + 28 + + + Environmental science & technology + Environ Sci Technol + + Unveiling Hydrogen Fluoride Emission Mechanisms in Municipal Solid Waste Incineration Using a Machine Learning Approach. + 10.1021/acs.est.6c00686 + + Hydrogen fluoride (HF) emissions from municipal solid waste incineration (MSWI) pose significant environmental and health risks. However, their complex formation mechanisms remain poorly understood. This study presents an integrated machine learning framework combining XGBoost for HF prediction, SHAP for feature interpretation, structural equation modeling (SEM) for mechanistic analysis, generalized additive models (GAMs) for threshold identification, and self-adaptive nondominated sorting genetic algorithm II (SA-NSGA-II) for multiparameter optimization. Using over 150,000 high-frequency (5 s interval) sensor records from a waste-to-energy plant in Hainan Province, China (June 1-10, 2024), the XGBoost model showed the best performance among the evaluated models (R + 2 = 0.755, RMSE = 0.041 mg/m3, MAE = 0.031 mg/m3) via 5-fold cross-validation. SHAP analysis identified flue gas temperatures─especially the second flue right side (10.97%) and first flue top (10.19%)─as dominant factors. SEM confirmed the grate incineration zone as the primary HF source (path coefficient = 1.058, p < 0.001). GAM identified location-specific critical temperature thresholds for HF emission control, specifically 767 °C at the upper second flue gas pass, 875 °C at the first flue top, and 212 °C at the low-temperature economizer inlet. SA-NSGA-II optimization, validated with June 11 data, reduced HF emissions in 89.74% of cases, achieving a 17.61% average reduction (0.1176 mg/m3). This framework advances mechanistic understanding and provides data-driven strategies for sustainable MSWI operation and pollution mitigation. + + + + Feng + Xingyu + X + + Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province/Hainan Provincial Academician Team Innovation Center/International Joint Research Center for the Control and Prevention of Environmental Pollution on Tropical Islands of Hainan Province/School of Environment Science and Engineering/School of Computer Science and Technology, Hainan University, Haikou 570228, China. + + + + Liu + Longshun + L + + Everbright Environmental Energy (Danzhou) Co., Ltd, No.1 Guangda Environmental Protection Avenue, Nada Town, Danzhou 571754, China. + + + + Li + Jinshan + J + + Everbright Environmental Energy (Danzhou) Co., Ltd, No.1 Guangda Environmental Protection Avenue, Nada Town, Danzhou 571754, China. + + + + Ye + Mingshun + M + + School of Information Engineering, Shanghai Maritime University, Shanghai 201306, China. + + + + Mašek + Ondřej + O + 0000-0003-0713-766X + + UK Biochar Research Centre, School of GeoSciences, University of Edinburgh, Alexander Crum Brown Road, Edinburgh EH9 3FF, U.K. + + + + Gouda + Shaban + S + 0000-0002-4170-4026 + + Agricultural and Biosystems Engineering Department, Faculty of Agriculture, Benha University, Benha 13736, Egypt. + + + + Mohamed ElSayed Ali + Ibrahim + I + + Agricultural and Biosystems Engineering Department, Faculty of Agriculture, Benha University, Benha 13736, Egypt. + + + + Chang + Kenlin + K + 0000-0002-6390-9540 + + Institute of Environmental Engineering, National Sun Yat-Sen University, Taiwan 80424, China. + + + + Wang + Xu + X + + Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province/Hainan Provincial Academician Team Innovation Center/International Joint Research Center for the Control and Prevention of Environmental Pollution on Tropical Islands of Hainan Province/School of Environment Science and Engineering/School of Computer Science and Technology, Hainan University, Haikou 570228, China. + + + + Huang + Qing + Q + 0000-0001-9445-4422 + + Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province/Hainan Provincial Academician Team Innovation Center/International Joint Research Center for the Control and Prevention of Environmental Pollution on Tropical Islands of Hainan Province/School of Environment Science and Engineering/School of Computer Science and Technology, Hainan University, Haikou 570228, China. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Environ Sci Technol + 0213155 + 0013-936X + + IM + + Emission Control Strategies + Hydrogen fluoride (HF) + Machine learning + Municipal solid waste incineration (MSWI) + Structural Equation Modeling (SEM) + + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 11 + 44 + + + aheadofprint + + 42207555 + 10.1021/acs.est.6c00686 + + + + + + 42207507 + + 2026 + 05 + 28 + +
+ + 1538-0254 + + + 2026 + May + 28 + + + Journal of biomolecular structure & dynamics + J Biomol Struct Dyn + + mePTL: miRNA-encoded peptides identification using multi-source feature transformation and ensemble learning. + + 1 + 16 + 1-16 + + 10.1080/07391102.2026.2677004 + + MicroRNA (miRNA) is a type of non-coding RNA and participates in the post-transcriptional control of genes to regulate the expression of target genes. Inspired by the discovery of small peptides translated from other ncRNAs, small open reading frames (sORFs) of plant primary miRNA (pri-miRNA) have been demonstrated to encode small peptides, known as miPEPs. So far, the number of identified functional miPEPs has gradually increased but still remains concentrated in plant. Although miPEPs are involved in a range of life activities in organisms, few methods have been developed to identify plant miPEPs. In this article, we present a novel computational method (mePTL) that uses multi-source feature transformation and ensemble learning to identify miPEPs. Multi-source features, rich in representation information and extracted from class-imbalanced miPEPs and sORFs data, are transformed by a feature representation learning framework. Ensemble learning is applied to fuse the outputs of different machine learning models trained on the transformation features to enhance generalization ability. Experimental results show that mePTL achieves better performance compared with the existing methods on multiple independent-test sets. mePTL shows good accuracy as well as generalization ability in identifying miPEPs, and we hope the method can provide some reference for research in related fields. + + + + Zhao + Siyuan + S + + School of Information Engineering, Tianjin University of Commerce, Tianjin, China. + + + + Kang + Qiang + Q + + BGI Research, Shenzhen, China. + + + + Liu + Lingling + L + + School of Information Engineering, Tianjin University of Commerce, Tianjin, China. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + J Biomol Struct Dyn + 8404176 + 0739-1102 + + IM + + Class-imbalanced data + ensemble learning + feature transformation + miPEPs + multi-source features + sORFs + + + + + + 2026 + 5 + 28 + 12 + 35 + + + 2026 + 5 + 28 + 12 + 35 + + + 2026 + 5 + 28 + 11 + 33 + + + aheadofprint + + 42207507 + 10.1080/07391102.2026.2677004 + + + + + + 42207497 + + 2026 + 05 + 28 + +
+ + 1538-0254 + + + 2026 + May + 28 + + + Journal of biomolecular structure & dynamics + J Biomol Struct Dyn + + Machine learning-driven drug repurposing for GPR17: activity prediction via graph neural networks and multistage computational validation. + + 1 + 24 + 1-24 + + 10.1080/07391102.2026.2678411 + + To identify novel GPR17-targeting ligands with potential relevance to multiple sclerosis (MS) therapy, we developed an integrated computational workflow combining graph neural network (GNN)-based prediction with multistage structure-based validation. GPR17 is a class A G protein-coupled receptor implicated in oligodendrocyte differentiation, neuroinflammation, and remyelination, making it a remarkable target for myelin repair. A curated dataset of 323 chemically diverse ligands with experimentally reported pIC50 values was assembled from the literature and used to train an NNConv-based GNN model. The model demonstrated robust predictive performance across training, validation, and independent test sets (R2 = 0.868, 0.727, and 0.790, respectively). To further assess model generalizability, scaffold-based validation was additionally performed to evaluate prediction across structurally distinct chemotypes. Analysis of the learned chemical space revealed enrichment of antihistamine- and leukotriene-related scaffolds among highly ranked compounds, motivating a drug-repurposing strategy. Candidate molecules, including barmastine, astemizole, sulukast, and iralukast, were evaluated alongside reference ligands pranlukast and cangrelor. Predicted hits were subsequently refined through pharmacophore-guided virtual screening, molecular docking, ADMET profiling, molecular dynamics simulations, and MM-PBSA/MM-GBSA free-energy calculations. Several ligands exhibited stable binding modes supported by persistent hydrophobic interactions and hydrogen-bond occupancies reaching 99.8%. Importantly, this study proposes computationally prioritized candidates rather than experimentally validated inhibitors. Overall, the proposed hybrid workflow provides a practical strategy for early-stage GPR17 ligand discovery and future CNS-oriented drug development. + + + + Agha Babaie + Noushin + N + + Department of Chemistry, Kish International Campus, University of Tehran, Tehran, Iran. + + + + Farnia + Morteza + M + + School of Chemistry, College of Science, University of Tehran, Tehran, Iran. + + + + Ghasemi + Jahan B + JB + 0000-0002-0380-8000 + + School of Chemistry, College of Science, University of Tehran, Tehran, Iran. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + J Biomol Struct Dyn + 8404176 + 0739-1102 + + IM + + GPR17 + MM-GBSA + drug repurposing + graph neural network (GNN) + molecular dynamics + + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 11 + 32 + + + aheadofprint + + 42207497 + 10.1080/07391102.2026.2678411 + + + + + + 42207494 + + 2026 + 05 + 28 + +
+ + 1549-9626 + + + 2026 + May + 28 + + + Journal of chemical theory and computation + J Chem Theory Comput + + A Flexible and Generalized Constant-Potential Framework in i-PI. + 10.1021/acs.jctc.6c00504 + + Constant-potential molecular dynamics is essential for realistic simulations of electrochemical interfaces under Operando conditions. Although various constant-potential frameworks exist, most are tightly coupled to specific electronic-structure codes or numerical architectures, limiting portability and extensibility─especially for codes constrained to integer electron numbers. Here, we present a flexible constant-potential framework implemented in the i-PI driver, interfacing with multiple density functional theory (DFT) engines and, in principle, extensible to constant-potential machine-learning potentials. The method regulates and samples the electronic chemical potential by introducing an explicit electronic degree of freedom and a dedicated potentiostat module in i-PI. To bypass the integer-electron constraint without modifying the underlying DFT code, we employ a mixed-Hamiltonian interpolation scheme: two adjacent integer-charge clients are run in parallel, and their energies, forces, and electronic chemical potentials (Fermi level/work function) are linearly interpolated to obtain an effective fractional-charge description. We validate the method on a one-dimensional asymmetric double-well model and an Al(111) surface, demonstrating stable potential control and well-behaved charge fluctuations. Finally, we couple constant-potential ab initio molecular dynamics (AIMD) with enhanced sampling to study CO2 reduction on NiN4-doped graphene, enabling efficient characterization of potential-dependent reactivity and free-energy landscapes. Overall, this framework provides a portable and scalable platform for conducting rigorous constant-potential simulations across diverse electronic-structure clients and, in principle, machine-learning potentials. + + + + Zhang + Chenyu + C + 0009-0008-8849-2797 + + Hefei National Research Center for Physical Sciences at Microscale, Department of Chemical Physics, University of Science and Technology of China, Hefei 230026, P. R. China. + + + + Zhao + Ruoting + R + + Faculty of Synthetic Biology, Shenzhen University of Advanced Technology, Shenzhen 518107, P. R. China. + + + + He + Zhengda + Z + + Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, P. R. China. + + + + Iannuzzi + Marcella + M + 0000-0001-9717-2527 + + Department of Chemistry, University of Zurich, CH-8057 Zürich, Switzerland. + + + + Chen + Yanxia + Y + 0000-0002-1370-7422 + + Hefei National Research Center for Physical Sciences at Microscale, Department of Chemical Physics, University of Science and Technology of China, Hefei 230026, P. R. China. + + + + Lan + Jinggang + J + 0000-0001-6353-2539 + + Faculty of Synthetic Biology, Shenzhen University of Advanced Technology, Shenzhen 518107, P. R. China. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + J Chem Theory Comput + 101232704 + 1549-9618 + + IM + + + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 5 + 28 + 11 + 23 + + + aheadofprint + + 42207494 + 10.1021/acs.jctc.6c00504 + + + + + + 42207458 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2509-9280 + + 10 + 1 + + 2026 + May + 28 + + + European radiology experimental + Eur Radiol Exp + + Enhancing resolution and image quality in musculoskeletal MRI using deep learning reconstruction. + 78 + 10.1186/s41747-026-00743-w + + Deep learning-based noise reduction enhances image quality, overcoming the tradeoff among acquisition time, spatial resolution, and signal-to-noise ratio (SNR). We implemented deep learning reconstruction (DLR) into a 1.5-T musculoskeletal (MSK) magnetic resonance imaging (MRI) protocol to improve image quality without compromising SNR. + We retrospectively analyzed 39 MRI examinations performed on a 1.5-T scanner using standard-resolution (SR) sequences and sequences with higher resolution reconstructed with DLR (HR-DLR). Exams of the knees, shoulders, ankles, and hips were evaluated. The included sequences were: three-dimensional T2-weighted fast advanced spin-echo; T1-weighted and proton density-weighted fast spin-echo. One expert reader and two junior readers in agreement evaluated the visibility of various structures using a 5-point Likert scale in a blind manner. A fourth reader estimated the SNR and contrast-to-noise ratio (CNR) in bone and muscle. A mixed model was used to compare HR-DLR versus SR measures. The agreement between radiologists was assessed with the Kendall τ coefficient. + The HR-DLR sequences globally had a smaller pixel size and shorter acquisition time. A good inter-reader agreement was obtained for SR sequences (0.613 ≤ τ ≤ 0.788) and even higher levels of agreement for HR-DLR sequences (0.682 ≤ τ ≤ 0.961). All the structures had higher or similar Likert scores in HR-DLR sequences (p < 0.001), regardless of joint and sequence contrast. Apparent SNR and CNR of HR-DLR and SR were similar. + Incorporating DLR into 1.5-T MSK MRI protocols enhances resolution and maintains SNR and CNR, improving MSK structure visualization. + This study demonstrated the effectiveness of deep learning reconstruction in improving the efficiency of 1.5-T musculoskeletal MRI exams. Despite shorter acquisition times, the visibility of key MSK structures was consistently rated as superior or similar in higher-resolution images with DLR. + MRI is one of the primary diagnostic tools for evaluating MSK injuries and disorders. Deep learning reconstruction (DLR) implemented in a 1.5-T MSK protocol improved resolution, still preserving SNR and CNR. Higher scores were assigned by different raters to the DLR images, showing image quality improvement. + © 2026. The Author(s). + + + + Porta + Marco + M + + Department of Radiology, Istituti Clinici Zucchi, Monza (MB), Italy. + + + + Agresti + Giuseppe + G + + Department of Radiology, Istituti Clinici Zucchi, Monza (MB), Italy. + + + + Laganà + Maria Marcella + MM + 0000-0001-7848-1711 + + Canon Medical Systems, Rome, Italy. marcella.lagana@eu.medical.canon. + + + + Orofino + Stefano + S + + Canon Medical Systems, Rome, Italy. + + + + Pangaro + Sara + S + + Canon Medical Systems, Rome, Italy. + + + + Carapella + Nicola + N + + Department of Radiology, University of Brescia, Brescia, Italy. + + + + Bonaffini + Pietro Andrea + PA + + Department of Radiology, ASST Papa Giovanni XXIII Hospital, Bergamo, Italy. + + + School of Medicine, University Milano Bicocca, Milano, Italy. + + + + Bernasconi + Paolo + P + + Studio Radiologico Bernasconi, Seregno (MB), Italy. + + + + Genovese + Eugenio Annibale + EA + + Medicine and Surgery Department, Insubria University, Varese, Italy. + + + Medical Clinical Institute Intermedica-Columbus, Milano, Italy. + + + + Sironi + Sandro + S + + Department of Radiology, ASST Papa Giovanni XXIII Hospital, Bergamo, Italy. + + + School of Medicine, University Milano Bicocca, Milano, Italy. + + + + Aliprandi + Alberto + A + + Department of Radiology, Istituti Clinici Zucchi, Monza (MB), Italy. + + + School of Medicine, University Milano Bicocca, Milano, Italy. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Eur Radiol Exp + 101721752 + 2509-9280 + + IM + + + Humans + + + Deep Learning + + + Magnetic Resonance Imaging + methods + + + Retrospective Studies + + + Signal-To-Noise Ratio + + + Female + + + Image Processing, Computer-Assisted + methods + + + Male + + + Adult + + + Musculoskeletal System + diagnostic imaging + + + + Deep learning + Image processing (computer-assisted) + Magnetic resonance imaging + Musculoskeletal system + Signal-to-noise ratio + + Declarations. Ethics approval and consent to participate: Ethical Committee approval was obtained (Research Ethics Committee of the University of Insubria, approval number: 0047899; Approval date: May 6, 2025). Consent for publication: The images were irreversibly anonymized and retrospectively analyzed, so written informed consent was waived. Competing interests: MML, SO, and SP are employees of Canon Medical Systems, Italy. MML is also a member of the Scientific Editorial Board of European Radiology Experimental (section: Neuro and head/neck) and, as such, did not participate in the selection or review processes for this article. + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 33 + + + 2025 + 11 + 28 + + + 2026 + 5 + 5 + + + 2026 + 4 + 1 + + + 2026 + 5 + 28 + 11 + 21 + + + epublish + + 42207458 + 10.1186/s41747-026-00743-w + 10.1186/s41747-026-00743-w + + + + Sneag DB, Abel F, Potter HG et al (2023) MRI advancements in musculoskeletal clinical and research practice. Radiology 308:e230531 + + 10.1148/radiol.230531 + 37581501 + 10477516 + + + + Recht MP, White LM, Fritz J, Resnick DL (2023) Advances in musculoskeletal imaging: recent developments and predictions for the future. Radiology 308:e230615 + + 10.1148/radiol.230615 + 37642575 + + + + Kijowski R, Fritz J (2023) Emerging technology in musculoskeletal MRI and CT. 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Eur J Radiol 175:111471 + + 10.1016/j.ejrad.2024.111471 + 38636411 + + + + Estler A, Hauser T-K, Brunnée M et al (2024) Deep learning-accelerated image reconstruction in back pain-MRI imaging: reduction of acquisition time and improvement of image quality. Radiol Med 129:478–487 + + 10.1007/s11547-024-01787-x + 38349416 + 10943137 + + + + Hokamura M, Nakaura T, Yoshida N et al (2024) Super-resolution deep learning reconstruction approach for enhanced visualization in lumbar spine MR bone imaging. Eur J Radiol 178:111587 + + 10.1016/j.ejrad.2024.111587 + 39002269 + + + + Akai H, Yasaka K, Sugawara H et al (2022) Commercially available deep-learning-reconstruction of MR imaging of the knee at 1.5T has higher image quality than conventionally-reconstructed Imaging at 3T: a normal volunteer study. Magn Reson Med Sci 22:353–360 + + 10.2463/mrms.mp.2022-0020 + 35811127 + 10449552 + + + + Kiryu S, Akai H, Yasaka K et al (2023) Clinical impact of deep learning reconstruction in MRI. 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Springer, Cham, pp 129–136 + + + Xie Y, Tao H, Xiangwen L et al (2024) Prospective comparison of standard and deep learning–reconstructed turbo spin-echo MRI of the shoulder. Radiology 310:e231405 + + + + + + + 42207450 + + 2026 + 05 + 28 + +
+ + 1861-6429 + + + 2026 + May + 28 + + + International journal of computer assisted radiology and surgery + Int J Comput Assist Radiol Surg + + ProtoFlow: interpretable and robust surgical workflow modeling with learned dynamic scene graph prototypes. + 10.1007/s11548-026-03697-x + + Detailed surgical recognition is critical for advancing AI-assisted surgery, yet progress is hampered by high annotation costs, data scarcity, and a lack of interpretable models. While scene graphs offer a structured abstraction of surgical events, their full potential remains untapped. In this work, we introduce ProtoFlow, a novel framework that learns dynamic scene graph prototypes to model complex surgical workflows in an interpretable and robust manner. + ProtoFlow leverages a graph neural network (GNN) encoder-decoder architecture that combines self-supervised pretraining for rich representation learning with a prototype-based fine-tuning stage. This process discovers and refines core prototypes that encapsulate recurring, clinically meaningful patterns of surgical interaction, forming an explainable foundation for workflow analysis. + We evaluate our approach on the fine-grained CAT-SG dataset. ProtoFlow not only outperforms standard GNN baselines in overall accuracy but also demonstrates exceptional robustness in limited-data, few-shot scenarios, maintaining strong performance when trained on as few as one surgical video. Our qualitative analyses further show that the learned prototypes successfully identify distinct surgical sub-techniques and provide clear, interpretable insights into workflow deviations and rare complications. + By uniting robust representation learning with inherent explainability, ProtoFlow represents a significant step toward developing more transparent, reliable, and data-efficient AI systems, accelerating their potential for clinical adoption in surgical training, real-time decision support, and workflow optimization. + © 2026. The Author(s). + + + + Holm + Felix + F + + Chair for Computer-Aided Medical Procedures, Technical University Munich, Munich, Germany. felix.holm@tum.de. + + + Corporate Research and Technology, Carl Zeiss AG, Munich, Germany. felix.holm@tum.de. + + + Munich Center for Machine Learning (MCML), Munich, Germany. felix.holm@tum.de. + + + + Ghazaei + Ghazal + G + + Corporate Research and Technology, Carl Zeiss AG, Munich, Germany. + + + + Navab + Nassir + N + + Chair for Computer-Aided Medical Procedures, Technical University Munich, Munich, Germany. + + + Munich Center for Machine Learning (MCML), Munich, Germany. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + Germany + Int J Comput Assist Radiol Surg + 101499225 + 1861-6410 + + IM + + Graph neural networks + Prototype learning + Scene graphs + Surgical data science + + Declarations. Conflict of interest: The authors have no conflict of interest to declare that is relevant to the content of this article. + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 1 + 15 + + + 2026 + 4 + 27 + + + 2026 + 5 + 28 + 11 + 20 + + + aheadofprint + + 42207450 + 10.1007/s11548-026-03697-x + 10.1007/s11548-026-03697-x + + + + Twinanda AP, Shehata S, Mutter D, Marescaux J, De Mathelin M, Padoy N (2016) Endonet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans Med Imaging 36(1):86–97 + + 10.1109/TMI.2016.2593957 + 27455522 + + + + Jin A, Yeung S, Jopling JK, Krause J, Azagury DE, Milstein A, Fei-Fei L (2018) Tool detection and operative skill assessment in surgical videos using region-based convolutional neural networks. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 691–699 + + + Gastager D, Ghazaei G, Patsch C (2025) Watch and learn: leveraging expert knowledge and language for surgical video understanding. International Journal of Computer Assisted Radiology and Surgery, 1–10 + + + Sahu M, Mukhopadhyay A, Szengel A, Zachow S (2017) Addressing multi-label imbalance problem of surgical tool detection using CNN. Int J Comput Assist Radiol Surg 12(6):1013–1020. https://doi.org/10.1007/s11548-017-1565-x + + 10.1007/s11548-017-1565-x + 28357628 + + + + Funke I, Mees ST, Weitz J, Speidel S (2019) Video-based surgical skill assessment using 3d convolutional neural networks. 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In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp. 3668–3678. https://doi.org/10.1109/CVPR.2015.7298990 + + + Holm F, Ghazaei G, Czempiel T, Özsoy E, Saur S, Navab N (2023) Dynamic scene graph representation for surgical video. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 81–87 + + + Murali A, Alapatt D, Mascagni P, Vardazaryan A, Garcia A, Okamoto N, Mutter D, Padoy N (2023) Encoding surgical videos as latent spatiotemporal graphs for object and anatomy-driven reasoning. In: international conference on medical image computing and computer-assisted intervention, pp. 647–657. Springer + + + Özsoy E, Örnek EP, Eck U, Czempiel T, Tombari F, Navab N (2022) 4d-or: Semantic scene graphs for or domain modeling. In: International conference on medical image computing and computer-assisted intervention. Springer + + + Köksal Ç, Ghazaei G, Holm F, Farshad A, Navab N (2025) Sangria: Surgical video scene graph optimization for surgical workflow prediction. In: Ahmadi S-A, Kazi A (eds) Graphs in Biomedical Image Analysis. Springer, Cham, pp 106–117 + + 10.1007/978-3-031-83243-7_10 + + + + Dai E, Wang S (2025) Towards prototype-based self-explainable graph neural network. ACM Trans Knowl Discov Data. https://doi.org/10.1145/3689647 + + 10.1145/3689647 + + + + Holm F, Ünver G, Ghazaei G, Navab N (2025) Cat-sg: A large dynamic scene graph dataset for fine-grained understanding of cataract surgery. In: International conference on medical image computing and computer-assisted intervention, pp. 96–106. Springer + + + Huaulmé A, Voros S, Reche F, Faucheron JL, Moreau-Gaudry A, Jannin P (2019) Offline identification of surgical deviations in laparoscopic rectopexy. Artif Intell Med 104:101837 + + 10.1016/j.artmed.2020.101837 + + + + Al Hajj H, Lamard M, Conze P-H, Roychowdhury S, Hu X, Maršalkaitė G, Zisimopoulos O, Dedmari MA, Zhao F, Prellberg J, Sahu M, Galdran A, Araújo T, Vo DM, Panda C, Dahiya N, Kondo S, Bian Z, Vahdat A, Bialopetravičius J, Flouty E, Qiu C, Dill S, Mukhopadhyay A, Costa P, Aresta G, Ramamurthy S, Lee S-W, Campilho A, Zachow S, Xia S, Conjeti S, Stoyanov D, Armaitis J, Heng P-A, Macready WG, Cochener B, Quellec G (2019) Cataracts: Challenge on automatic tool annotation for cataract surgery. Med Image Anal 52:24–41. https://doi.org/10.1016/j.media.2018.11.008 + + 10.1016/j.media.2018.11.008 + 30468970 + + + + Brody S, Alon U, Yahav E (2022) How attentive are graph attention networks? In: International Conference on Learning Representations. https://openreview.net/forum?id=F72ximsx7C1 + + + Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Köpf A, Yang EZ, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2022) Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, pp. 8024–8035x. Curran Associates, Inc + + + Fey, M., Lenssen, J.E.: PyTorch Geometric [Computer software]. (2019) https://github.com/pygteam/pytorch_geometric + + + + + + + 42207441 + + 2026 + 05 + 28 + +
+ + 2730-6011 + + + 2026 + May + 28 + + + Discover oncology + Discov Oncol + + RAS pathway activity subtypes identified by machine learning define prognostic and immune microenvironment characteristics in lung adenocarcinoma. + 10.1007/s12672-026-05282-9 + + Lung adenocarcinoma (LUAD), the predominant histological subtype of non-small cell lung cancer, remains a leading cause of cancer-related mortality worldwide. The RAS signaling pathway plays a critical role in LUAD pathogenesis; however, the heterogeneity of RAS pathway activity and its clinical implications remain poorly understood. This study aimed to characterize RAS pathway activity subtypes and develop a robust prognostic model for LUAD patients. + Transcriptomic data from 624 LUAD patients (GEO datasets: GSE31210 and GSE72094) were analyzed as the training cohort, with TCGA-LUAD as validation. Consensus clustering stratified patients based on 238 RAS pathway-related genes. Candidate genes were identified through differential expression analysis and WGCNA. Machine learning algorithms (LASSO, Random Forest, SHAP) were applied to construct a prognostic risk model. Comprehensive analyses including GSEA, CIBERSORTx-based immune infiltration, ESTIMATE scoring, and drug sensitivity prediction were performed. + The study identified two distinct RAS pathway activity subtypes among LUAD patients. A three-gene prognostic signature (MAPK10, PLA2G12B, SHC3) was established, with the risk score serving as an independent prognostic indicator. Risk score was an independent prognostic factor. Immune landscape analysis demonstrated that high- and low-risk patients showed different expression levels of immune cells. All signature genes correlated positively with resting mast cells, while MAPK10 and PLA2G12B negatively correlated with activated CD4 + memory T cells. GSEA revealed high-risk tumors enriched in DNA replication, cell cycle, and base excision repair, whereas low-risk tumors favored drug and retinol metabolism pathways. Low-risk patients exhibited lower TIDE and exclusion scores, indicating better immunotherapy response potential. Eight therapeutic compounds (genistein, metformin, quercetin, JNK-9 L) demonstrated favorable binding to signature genes. + This study established a comprehensive landscape of RAS pathway activity subtypes in LUAD, identifying MAPK10, PLA2G12B, and SHC3 as novel prognostic biomarkers with significant associations with immune microenvironment remodeling, providing new insights for personalized treatment strategies and therapeutic target development. + © 2026. The Author(s). + + + + Shi + Yao + Y + + Department of Respiratory and Critical Care Medicine, The Third People's Hospital of Chengdu, Chengdu, 610014, China. 13730899577@163.com. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Discov Oncol + 101775142 + 2730-6011 + + + Immune microenvironment + Lung adenocarcinoma + Machine learning + Prognostic model + RAS pathway + + Declarations. Ethics approval and consent to participate: This study utilized publicly available datasets and did not involve direct human subjects research. Therefore, ethical approval was not required. Not applicable, as this study is based entirely on publicly available transcriptomic datasets. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 3 + 7 + + + 2026 + 5 + 20 + + + 2026 + 5 + 28 + 11 + 20 + + + aheadofprint + + 42207441 + 10.1007/s12672-026-05282-9 + 10.1007/s12672-026-05282-9 + + + + + + 42207432 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 1573-7438 + + 58 + 5 + + 2026 + May + 28 + + + Tropical animal health and production + Trop Anim Health Prod + + Animal production under climate change: a global scientometric analysis of research structure, thematic evolution, and knowledge gaps. + 282 + 10.1007/s11250-026-05071-0 + + Climate change is a major driver of transformation in livestock systems; however, existing reviews remain fragmented, often addressing environmental impacts or adaptation strategies in isolation, without systematically integrating the structure, evolution, and knowledge gaps of the field. This study addresses this limitation through a comprehensive bibliometric-scientometric analysis of global research on climate change and animal production. A total of 1,694 peer-reviewed articles and reviews indexed in Scopus (1974-2025) were retrieved using a structured search applied to titles, abstracts, and keywords. Data were processed through duplicate removal and keyword harmonization, and analyzed using Bibliometrix (R) and VOSviewer to perform co-occurrence network analysis, thematic clustering, and temporal trend evaluation. Results indicate a sustained annual growth rate of 9.47% and increasing international collaboration (35.71%), reflecting the rapid expansion of the field. The co-occurrence network reveals a highly interconnected structure, with "climate change" acting as the central organizing concept linking environmental, physiological, genetic, and production-related domains. Thematic analysis shows that research on greenhouse gas emissions and environmental impacts is well established, whereas emerging areas-such as climate-smart agriculture, One Health, and integrated sustainability frameworks-remain less connected to applied and policy-oriented research. Temporal trends highlight a shift, particularly after 2015, from impact-oriented studies toward more integrated approaches incorporating sustainability, animal welfare, resilience, and adaptive management, alongside increasing use of digital tools such as modeling and machine learning. In addition, life cycle modeling further indicates that the field remains in an early expansion stage, having reached approximately 11.6% of its estimated saturation level, with continued growth expected over the coming decades. Despite this progress, important gaps persist, particularly regarding the translation of scientific knowledge into practice and the uneven geographic distribution of research efforts. Strengthening region-specific and socially inclusive research, enhancing the integration between technological innovation and field-level application, and advancing interdisciplinary frameworks are key priorities to improve the adaptive capacity of livestock systems. By mapping the structure, evolution, and gaps of the field, this study provides a robust basis to inform future research agendas and support the transition toward more resilient and sustainable livestock systems under climate change. + © 2026. The Author(s). + + + + Silveira + Robson Mateus Freitas + RMF + 0000-0003-2285-9695 + + Environment Livestock Research Group (NUPEA), Department of Biosystems Engineering, "Luiz de Queiroz" Agriculture College (ESALQ), University of São Paulo (USP), Piracicaba, SP, 13418-900, Brazil. robsonsilveira@usp.br. + + + + McManus + Concepta + C + 0000-0002-1106-8962 + + Center for Nuclear Energy in Agriculture (CENA), University of São Paulo (USP), Av. Centenário, 303 - São Dimas, Piracicaba, SP, 13416-000, Brazil. + + + + da Silva + Iran José Oliveira + IJO + 0000-0002-4416-8433 + + Environment Livestock Research Group (NUPEA), Department of Biosystems Engineering, "Luiz de Queiroz" Agriculture College (ESALQ), University of São Paulo (USP), Piracicaba, SP, 13418-900, Brazil. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 28 + +
+ + United States + Trop Anim Health Prod + 1277355 + 0049-4747 + + IM + + + Animals + + + Climate Change + + + Animal Husbandry + + + Bibliometrics + + + Livestock + + + Research + + + Evidence Gaps + + + + Adaptation + Animal welfare + Food security + Greenhouse gases + Heat stress + Sustainability + + Declarations. Consent to participate: Not applicable. Consent for publication: Not applicable. Generative AI and AI-assisted technologies: During the preparation of this manuscript, the authors used ChatGPT to assist with readability improvement, language refinement, and manuscript organization. The manuscript was critically reviewed, revised, and validated by the authors, who assume full responsibility for the accuracy, originality, and integrity of the content. 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+ + 1559-0291 + + + 2026 + May + 28 + + + Applied biochemistry and biotechnology + Appl Biochem Biotechnol + + Engineering Bacillus Subtilis for Efficient Biosynthesis of Riboflavin: Current Knowledge and Future Perspectives. + 10.1007/s12010-026-05755-1 + + Riboflavin is an essential water-soluble vitamin that serves as a precursor for the biosynthesis of the flavin cofactors FMN and FAD, which play pivotal roles in numerous redox and energy metabolism reactions. With the growing global demand for sustainable vitamin production, microbial fermentation has become an attractive alternative to chemical synthesis due to its environmental and economic advantages. Among microbial hosts, Bacillus subtilis has emerged as a leading cell factory for riboflavin production owing to its GRAS status, well-characterized genetics, and efficient protein secretion system. This review provides a comprehensive overview of recent advances in metabolic engineering strategies to enhance riboflavin biosynthesis in B. subtilis. Key topics include strengthening biosynthetic and precursor pathways, relieving feedback inhibition, balancing metabolic flux and cell growth, employing adaptive laboratory evolution, and utilizing omics-guided optimization and 13C metabolic flux analysis. Moreover, the integration of synthetic biology tools such as riboswitch engineering, regulatory element design, and high-throughput screening has significantly accelerated strain improvement. Despite remarkable progress, challenges remain in achieving precise regulatory control, optimizing multi-gene expression, and enhancing genome integration efficiency. Future research combining multi-omics data, synthetic regulatory design, and machine learning-driven predictive modeling is expected to further advance the development of intelligent B. subtilis cell factories. However, the practical implementation of these systems remains constrained by the metabolic burden of overproduction and the lack of universal regulatory models that can predict strain performance across varying industrial scales. + © 2026. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. + + + + Yu + Xiao-Zheng + XZ + + College of Biological and Environmental Engineering, Zhejiang international Joint Laboratory on Low-Carbon Pollution Control and Resource utilization, Zhejiang Shuren University, Hangzhou, 310015, China. + + + + Liu + Zi-Yan + ZY + 0000-0002-0282-7314 + + State Key Laboratory of Biocontrol, Guangdong Province Key Laboratory for Aquatic Economic Animals, Guangdong Provincial Engineering Technology Research Center for Healthy Breeding of Important Economic Fish, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, China. liuziyan95@163.com. + + + School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, China. liuziyan95@163.com. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 28 + +
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+ + 1573-7284 + + + 2026 + May + 28 + + + European journal of epidemiology + Eur J Epidemiol + + Statistical methods for retrospective harmonization of longitudinal epidemiological data: a scoping review. + 10.1007/s10654-026-01404-3 + + Data harmonization is a prerequisite for joint cohort analyses. In this review, we aim to identify and contrast statistical methods for retrospective harmonization of longitudinal data. We performed a scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. Studies were included if they described statistical methods for retrospectively harmonizing longitudinal data at the participant level. From 35 included papers out of 1,234 hits, we identified three types of statistical methods applicable to tabular data commonly collected in longitudinal epidemiological studies (e.g., questionnaires): (1) distribution-based methods, (2) the proportion score model, and (3) latent variable models. Our results suggest that the suitability of a statistical harmonization method mainly depends on the measurement scales of the original variables as well as on the type of target variable (directly measurable vs. latent). The chosen harmonization method influences how missing subsets of variables are addressed. None of the included studies applied more automated approaches such as machine learning-based procedures for deriving a harmonized dataset. Based on our findings, we present a roadmap that can guide researchers in selecting the most appropriate statistical method for a specific harmonization task and in handling variables collected only in a subset of studies. Data harmonization is still a demanding task that requires the development and application of novel tools for automating the procedures. + © 2026. The Author(s). + + + + Zhang + Jiumeng + J + + Department of Statistical Methods in Epidemiology, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Achterstraße 30, 28359, Bremen, Germany. + + + + Behrendt + Jordan + J + + Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany. + + + + Schultz + Tanja + T + + Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany. + + + + Aleksandrova + Krasimira + K + + Department Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany. + + + Faculty of Human and Health Sciences, University of Bremen, Bremen, Germany. + + + + Iqbal + Khalid + K + + Department Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany. + + + + Pigeot + Iris + I + + Department of Statistical Methods in Epidemiology, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Achterstraße 30, 28359, Bremen, Germany. + + + Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany. + + + + Börnhorst + Claudia + C + 0000-0001-7587-603X + + Department of Statistical Methods in Epidemiology, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Achterstraße 30, 28359, Bremen, Germany. boern@leibniz-bips.de. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 28 + +
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Amsterdam: IOS; 2023. https://doi.org/10.3233/SHTI230074 . + + 10.3233/SHTI230074 + + + + McElroy E, Wood T, Bond R, Mulvenna M, Shevlin M, Ploubidis GB, et al. Using natural language processing to facilitate the harmonisation of mental health questionnaires: a validation study using real-world data. BMC Psychiatry. 2024;24:530. https://doi.org/10.1186/s12888-024-05954-2 . + + 10.1186/s12888-024-05954-2 + 39049010 + 11267737 + + + + + + + + 42207383 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 1573-7438 + + 58 + 5 + + 2026 + May + 28 + + + Tropical animal health and production + Trop Anim Health Prod + + Morphometric characterization and decision tree-based prediction of phenotypic traits in Pantaneiro sheep. + 297 + 10.1007/s11250-026-05088-5 + + The Pantaneiro sheep is a locally adapted genetic resource shaped by the seasonal flooding, thermal stress, and heterogeneous forage availability of the Brazilian Pantanal. Characterizing its morphological structure using objective and field-applicable traits is essential for standardized phenotyping and conservation-oriented breeding strategies. This study evaluated 211 Pantaneiro ewes raised under semi-extensive management to identify morphometric patterns and identify simple predictors of qualitative phenotypic traits. Twenty-three quantitative and ten qualitative traits were measured and analyzed using multivariate techniques, including factor analysis and hierarchical clustering, to describe morphological structure. Decision tree models were then developed to predict key phenotypic traits from morphometric variables. Four main morphological dimensions explained 64.6% of total variance, reflecting body capacity, limb robustness, and cranial morphology. Among the ten decision tree models generated, three achieved predictive accuracies above 80% head profile (86.3%), muzzle type (84.8%), and wool color (82.5%), indicating that metatarsus and tarsus circumferences are reliable non-invasive predictors of cranial and tegumentary traits. The integration of multivariate morphometry and machine learning proved effective for identifying simple predictors applicable to field management. Overall, these findings reinforce the adaptive potential of Pantaneiro ewe and their importance for sustainable and resilient livestock production in tropical floodplain ecosystems. + © 2026. The Author(s). + + + + Valério + Agda Costa + AC + 0000-0001-5051-7196 + + Faculty of Agrarian Sciences, Federal University of Grande Dourados, Dourados, MS, Brazil. + + + + Fernandes + Tatiane + T + 0000-0002-9825-5735 + + Virginia Tech, School of Animal Sciences, Blacksburg, USA. + + + + da Silva + Adrielly Lais Alves + ALA + 0000-0002-0207-0198 + + Faculty of Agrarian Sciences, Federal University of Grande Dourados, Dourados, MS, Brazil. + + + + Chagas + Renata Alves + RA + 0000-0002-6205-3971 + + Faculty of Agrarian Sciences, Federal University of Grande Dourados, Dourados, MS, Brazil. + + + + Leonardo + Ariadne Patrícia + AP + 0000-0002-2847-6145 + + Faculty of Agrarian Sciences, Federal University of Grande Dourados, Dourados, MS, Brazil. + + + + de Souza + Marcio Rodrigues + MR + 0000-0003-0314-845X + + Federal Institute of Mato Grosso do Sul, Campus Dourados, Dourados, MS, Brazil. + + + + da Silva + Núbia Michelle Vieira + NMV + 0000-0002-9800-9585 + + Faculty of Agrarian Sciences, Federal University of Grande Dourados, Dourados, MS, Brazil. + + + + de Vargas Junior + Fernando Miranda + FM + 0000-0002-3050-7107 + + Faculty of Agrarian Sciences, Federal University of Grande Dourados, Dourados, MS, Brazil. fernandojunior@ufgd.edu.br. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Trop Anim Health Prod + 1277355 + 0049-4747 + + IM + + + Animals + + + Phenotype + + + Female + + + Brazil + + + Decision Trees + + + Sheep, Domestic + anatomy & histology + genetics + + + Sheep + anatomy & histology + + + Breeding + + + + Biometric traits + Body condition score + Conservation + Decision tree + Multivariate analysis + + Declarations. Institutional review board statement: All procedures were approved and supervised by the Ethical Committee of the Federal University of Grande Dourados (Protocol: 001/2022). Conflict of interest: The authors declare no conflicts of interest. + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 33 + + + 2025 + 11 + 5 + + + 2026 + 5 + 7 + + + 2026 + 5 + 28 + 11 + 18 + + + epublish + + 42207383 + 10.1007/s11250-026-05088-5 + 10.1007/s11250-026-05088-5 + + + + Aranda AN, Silva MC da, Crispim BA do, Ledesma LLM, Lenis PR, Silva ALA da, Leonardo AP, Vargas Junior FM de, Barufatti A (2021) Qualitative characters of indigenous sheep in central brazil: putting phenotype into perspective. Diversity 13(11):512. https://doi.org/10.3390/d13110512 + + + Bakhshalizadeh S, Hashemi A, Gaffari M, Jafari S, Farhadian M (2016) Estimation of genetic parameters and genetic trends for biometric traits in Moghani sheep breed. 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Animals 11(8):2329. https://doi.org/10.3390/ani11082329 + + 10.3390/ani11082329 + 34438786 + 8388382 + + + + Güzel BC, Szara T, Ünal B, Duro S, İşbilir F, Yiğit F, Spataru M-C, Goździewska-Harłajczuk K, Gündemir O (2025) 3D geometric morphometric analysis of calcaneal morphology in domestic caprinae: Sheep (Ovis aries) and Goat (Capra hircus) Animals 15:556. https://doi.org/10.3390/ani15040556 + + + Hou S, Wang T, Qiao D, Xu DJ, Wang Y, Feng X, Khan WA, Ruan J (2025) Temporal-Spatial Fuzzy Deep Neural Network for the Grazing Behavior Recognition of Herded Sheep in Triaxial Accelerometer Cyber-Physical Systems. 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+ + 1573-9686 + + + 2026 + May + 28 + + + Annals of biomedical engineering + Ann Biomed Eng + + Quantum Machine Learning for Biomedical Classification Problems: A Feasibility Study on Real Quantum Hardware. + 10.1007/s10439-026-04204-8 + + Recent advances in quantum computing offer opportunities to explore alternative methods for the solution of classification problems commonly found in biomedical research. This study investigates the feasibility of using quantum kernel-based Support Vector Machines (QSVMs) to classify Autism Spectrum Disorder (ASD) using metabolomic measurements on real quantum hardware. This work evaluates the capabilities of current quantum computers for biomedical classification and establishes practical baselines for future studies. + A quantum classification pipeline was developed using a variety of angle encoding schemes. An exhaustive search was performed to identify an optimal subset of four metabolomic features via simulation. These features were used to benchmark multiple encoding strategies via simulation, followed by validation on IBM Quantum hardware. A baseline using Support Vector Machine (SVM) with the same features was established for comparison. + The best-performing QSVM achieved an average classification accuracy of 0.9434 on real quantum hardware, which is comparable to the accuracy using classical SVM of 0.9371 on the same feature set. These results highlight the potential of quantum kernels to capture meaningful feature interactions in biomedical data, despite the levels of noise and overhead of quantum computing. + This study demonstrates that quantum kernel SVMs can achieve classification performance comparable to classical methods on biomedical data. However, current limitations in quantum hardware, such as qubit communication overhead and noise, pose challenges for practical deployment. Continued improvements in hardware acceleration and error correction are needed to realize the potential of quantum machine learning for biomedical classification tasks. + © 2026. The Author(s) under exclusive licence to Biomedical Engineering Society. + + + + Liu + Hongbin + H + + Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, 12180, NY, USA. + + + Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, 110 8th Street, Troy, 12180, NY, USA. + + + + Zhang + Zhemin + Z + + Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, 12180, NY, USA. + + + Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, Troy, 12180, NY, USA. + + + + Zheng + Kangyu + K + + Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, Troy, 12180, NY, USA. + + + + Liang + Zhiding + Z + + The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. + + + + Chen + Chi-Sheng + CS + + Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, 02215, MA, USA. + + + + Carothers + Christopher + C + + Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, Troy, 12180, NY, USA. + + + + Hahn + Juergen + J + 0000-0002-1078-4203 + + Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, 12180, NY, USA. hahnj@rpi.edu. + + + Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, 110 8th Street, Troy, 12180, NY, USA. hahnj@rpi.edu. + + + Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, 12180, NY, USA. hahnj@rpi.edu. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Ann Biomed Eng + 0361512 + 0090-6964 + + IM + + Autism spectrum disorder + Classification + Metabolomics + Quantum machine learning + + Declarations. Conflict of interest: The authors have no conflict of interest to declare that are relevant to the content of this article. Consent for publication: All authors have read and approved the final manuscript and consent to its submission for publication. + + + + + 2026 + 5 + 28 + 12 + 35 + + + 2026 + 5 + 28 + 12 + 35 + + + 2025 + 11 + 19 + + + 2026 + 5 + 20 + + + 2026 + 5 + 28 + 11 + 18 + + + aheadofprint + + 42207361 + 10.1007/s10439-026-04204-8 + 10.1007/s10439-026-04204-8 + + + + Biamonte, J., P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd. Quantum machine learning. Nature. 549:195–202, 2017. https://doi.org/10.1038/nature23474 . + + 10.1038/nature23474 + 28905917 + + + + Zeguendry, A., Z. Jarir, and M. Quafafou. Quantum machine learning: A review and case studies. 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+ + 1573-7373 + + 178 + 1 + + 2026 + May + 28 + + + Journal of neuro-oncology + J Neurooncol + + Predicting late radiation-associated neurocognitive and endocrine toxicity in patients with brain tumors. + 26 + 10.1007/s11060-026-05646-9 + + Neurocognitive and endocrine dysfunction are potential complications of cranial irradiation. However, risk factors are poorly understood, impeding accurate prognostication and exploration of potential preventive interventions. The objective of this study was to evaluate the prognostic value of various vascular and genotypic risk factors for the development of radiation-related toxicities. + This single-institution retrospective cohort study included patients with metastatic and malignant primary brain tumors who received cranial irradiation as part of their initial tumor-directed therapy. Demographic and treatment characteristics were collected, as well as putative vascular and genotypic risk factors. Univariate, multivariate, and machine-learning analyses were performed using five pre-specified measures of radiation-related toxicity. The primary outcome was change in mini-mental status exam (MMSE). + Eighty patients (53% male, mean age 55.7, 53% primary and 44% metastatic brain tumors) were included. Elevated homocysteine and ApOE4 genotype were the strongest predictors of MMSE decline in the multivariate model (OR 3.96 [6.5-200, p < 0.001 and 2.85 [1.92-27.6], p = 0.004). Elevated homocysteine was associated white matter change on MRI and both physician and patient assessment. ApoE4 allele was associated with new endocrine deficiency, and physician assessment. An online nomogram provides risk predictions for each of the five late toxicity outcomes: ( https://afranklin22.shinyapps.io/PRMMSERadiationRiskFactors/ ) CONCLUSION: Two pre-treatment laboratory values (elevated homocysteine and ApOE genotype) were strongly associated with post-radiation neurocognitive and endocrine dysfunction using a variety of domains. Our predictive algorithm can aid clinicians in stratifying baseline risk and should be validated in prospective trials and with additional metrics of ND. + © 2026. The Author(s). + + + + Tuohy + Kyle + K + + Department of Neurosurgery, Penn State Hershey Medical Center, Hershey, PA, USA. + + + + Badani + Aarav + A + + University of California Berkeley, Berkeley, CA, USA. + + + + Franklin + Ava + A + + University of Virginia, Charlottesville, VA, USA. + + + + Zacharia + Brad + B + + Department of Neurosurgery, Penn State Hershey Medical Center, Hershey, PA, USA. + + + Department of Oncology, Penn State Cancer Institute, Hershey, PA, USA. + + + + Aregawi + Dawit + D + + Department of Neurosurgery, Penn State Hershey Medical Center, Hershey, PA, USA. + + + Department of Oncology, Penn State Cancer Institute, Hershey, PA, USA. + + + + Mansouri + Alireza + A + + Department of Neurosurgery, Penn State Hershey Medical Center, Hershey, PA, USA. + + + Department of Oncology, Penn State Cancer Institute, Hershey, PA, USA. + + + + Brown + Paul + P + + Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA. + + + + Glantz + Michael + M + + Department of Neurosurgery, Penn State Hershey Medical Center, Hershey, PA, USA. mglantz@pennstatehealth.psu.edu. + + + Department of Oncology, Penn State Cancer Institute, Hershey, PA, USA. mglantz@pennstatehealth.psu.edu. + + + Department of Neurosurgery - EC110, Penn State College of Medicine, Hershey Medical Center, 30 Hope Drive, Hershey, PA, 17033, USA. mglantz@pennstatehealth.psu.edu. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + J Neurooncol + 8309335 + 0167-594X + + IM + + + Humans + + + Female + + + Brain Neoplasms + radiotherapy + pathology + + + Male + + + Retrospective Studies + + + Middle Aged + + + Cranial Irradiation + adverse effects + + + Radiation Injuries + etiology + diagnosis + + + Prognosis + + + Aged + + + Risk Factors + + + Endocrine System Diseases + etiology + diagnosis + + + Adult + + + Follow-Up Studies + + + Machine Learning + + + + Brain tumors + Cognitive/endocrine dysfunction + Cranial irradiation + Machine learning + Neurotoxicity + + Declarations. Ethical approval: This study was approved by the Institutional Review Board at the Penn State College of Medicine (STUDY0027827) and in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study, informed consent was waived. Previous presentation: Portions of this study were presented at the 2022 American Society of Clinical Oncology Annual Meeting, Chicago, IL. 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+ + 1559-1174 + + 28 + 1 + + 2026 + May + 28 + + + Neuromolecular medicine + Neuromolecular Med + + Neuropilins in Multiple Sclerosis: Dual Roles of NRP-1 in Neuroinflammation and Neuroprotection. + 30 + 10.1007/s12017-026-08918-9 + + Neuropilins (NRPs), particularly NRP-1, are multifunctional co-receptors involved in neuroinflammatory and neuroprotective processes. Altered NRP expression has been observed in multiple sclerosis (MS) lesions and peripheral circulation, suggesting early involvement in disease progression. This review addresses the dual role of NRPs in MS and experimental autoimmune encephalomyelitis (EAE), emphasizing expression patterns, signaling pathways, and therapeutic interventions. NRP-1 is expressed by endothelial cells, microglia, and macrophages, while Sema3A, a key ligand, is produced by reactive astrocytes and contributes to a non-regenerative microenvironment. NRP-1 is involved in regulating blood-brain barrier (BBB) integrity, contributes to leukocyte trafficking, and modulates inflammatory signaling via the IFN-γ-STAT1-CXCL10 axis. In EAE, endothelial-specific NRP-1 deletion reduces disease severity, demyelination, and immune infiltration. Immunologically, NRP-1 governs interactions among T cells, dendritic cells, and macrophages, facilitating regulatory T cell (Treg) function and peripheral tolerance. Trogocytosis-mediated NRP-1 transfer from dendritic cells to T cells and polysialylated NRP-2 on dendritic cells further influence immune modulation. Tuftsin, a tetrapeptide targeting NRP-1, promotes anti-inflammatory microglial polarization and Treg activation, improving EAE outcomes. Therapeutic interventions, such as Bu-Shen-Yi-Sui Capsule (BSYSC), FTX-101 (a Sema3A-NRP-1 inhibitor), and tuftsin restore BBB function, reduce inflammation, enhance remyelination, and improve clinical scores. NRP-1 signaling thus exhibits context-dependent dual roles: promoting inflammatory cascades while enabling neuroprotection through regulatory immune networks and oligodendrocyte precursor cell support, highlighting NRP-1 as a therapeutic target in MS. + © 2026. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. + + + + Goleij + Pouya + P + + USERN Office, Kermanshah University of Medical Sciences, Kermanshah, 6715847141, Iran. medgenetic.1991@gmail.com. + + + + Babamohamadi + Mehregan + M + + Department of Biology, School of Natural Sciences, University of Tabriz, Tabriz, Iran. + + + + Heidari + Mohammad Mahdi + MM + + Department of Pediatrics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. + + + Aliasghar Clinical Research Development Center, Department of Pediatrics, School of Medicine, University of Medical Sciences, Tehran, Iran. + + + + Khazeei Tabari + Mohammad Amin + MA + + Student Research Committee, School of Medicine, Mazandaran University of Medical Sciences, Mazandaran, 4815733971, Iran. + + + + Abolfazli + Sajad + S + + Student Research Committee, School of Pharmacy, Mazandaran University of Medical Sciences, Mazandaran, 4815733971, Iran. + + + + Mohammadi + Soroush + S + + Department of Pharmacodynamics and Toxicology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, 9177948564, Iran. + + + + Majma Sanaye + Pantea + P + + School of Pharmacy, Zanjan University of Medical Sciences, Zanjan, 4513956184, Iran. + + + + Arefnezhad + Reza + R + + Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran. Reza.aref1374@gmail.com. + + + + Aschner + Michael + M + + Department of Molecular Pharmacology, Albert Einstein College of Medicine, Forchheimer 209, 1300 Morris Park Avenue, Bronx, NY, 10461, USA. + + + + Khan + Haroon + H + + Department of Pharmacy, Faculty of Chemical and Life Sciences, Abdul Wali Khan University, Mardan, Mardan, 23200, Pakistan. haroonkhan@awkum.edu.pk. + + + Department of Pharmacy, Korea University, Sejong, 20019, South Korea. haroonkhan@awkum.edu.pk. + + + + Rezaei Tavirani + Mostafa + M + + Proteomics Research Center, System Biology Institute, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. + + + + Movafagh + Abolfazl + A + + Proteomics Research Center, Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. movafagh.a@sbmu.ac.ir. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 28 + +
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+ + 1432-1904 + + 113 + 3 + + 2026 + May + 28 + + + Die Naturwissenschaften + Naturwissenschaften + + Multi-scale modeling of habitat suitability and human-induced risk for the critically endangered white-rumped vulture (Gyps bengalensis) in South and Southeast Asia. + 67 + 10.1007/s00114-026-02122-2 + + The White-rumped Vulture is a critically endangered species, facing rapid population declines across South and Southeast Asia due to habitat loss, anthropogenic disturbances, and toxicological pressures. This study uses species distribution modeling (SDM) to assess the current and future habitat suitability for the White-rumped Vulture across its geographic range in the region. We employed MaxEnt, a machine learning algorithm, to model habitat suitability based on 1,248 occurrence records from 2018 to 2024 and 19 bioclimatic, geomorphometric, and anthropogenic variables. The model achieved an excellent Area Under the Curve (AUC) value of 0.937, indicating strong predictive performance. Our results indicate that approximately 20.74% of the study area is currently classified as highly suitable habitat, with India, Pakistan, and Nepal hosting the most significant extents. However, projections for 2040, 2070, and 2100 under two Shared Socioeconomic Pathways (SSPs) SSP 126 (low emission) and SSP 370 (high emission) predict significant habitat loss, particularly under SSP 370, where highly suitable habitat may decrease by up to 60% by 2100. The study also identifies substantial gaps in conservation infrastructure, with over 88% of the highly suitable habitat unprotected. This study highlights the urgent need for region-specific conservation strategies that integrate habitat protection, restoration, and the mitigation of toxicological risks to ensure the long-term survival of the White-rumped Vulture. + © 2026. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. + + + + William + Gulzaman + G + + Department of Environmental Sciences, University of Gujrat, Hafiz Hayat Campus, 50700, Gujrat, Pakistan. gulzamanwilliam@gmail.com. + + + + Saqib + Zafeer + Z + + GIS and Eco-Informatics Laboratory, Department of Environmental Science, International Islamic University, Islamabad, Pakistan. + + + + Qadir + Abdul + A + + College of Earth and Environmental Sciences, University of the Punjab, Lahore, Pakistan. + + + + Siddiqua + Ayesha + A + + Department of Environmental Sciences, University of Gujrat, Hafiz Hayat Campus, 50700, Gujrat, Pakistan. + + + + Asam + Zaki Ul Zaman + ZUZ + + Department of Environmental Sciences, University of Gujrat, Hafiz Hayat Campus, 50700, Gujrat, Pakistan. + + + + Farid + Mujahid + M + + Department of Environmental Sciences, University of Gujrat, Hafiz Hayat Campus, 50700, Gujrat, Pakistan. + + + + Chaudhry + Muhammad Jamshed Iqbal + MJI + + WWF-Pakistan, Ferozpur Road, PO Box 5180, Lahore, 54600, Pakistan. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
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+ + 2366-0058 + + + 2026 + May + 28 + + + Abdominal radiology (New York) + Abdom Radiol (NY) + + Interpretable machine learning model for predicting hemorrhage following ultrasound-guided percutaneous renal biopsy: a multicenter study. + 10.1007/s00261-026-05582-2 + + This study aims to develop and validate an interpretable machine learning (ML) model for predicting post-procedural hemorrhage (PH) after ultrasound-guided percutaneous renal biopsy (PRB), aiding perioperative management. + A retrospective analysis was conducted using data from 664 patients to develop and internally validate the predictive model. Key ultrasound parameters and clinical data were collected. The Boruta algorithm selected predictive factors. Eight ML models underwent hyperparameter tuning with 5-fold cross-validation, using the area under the curve (AUC) to determine the best model, which was further validated externally (n = 137). Model interpretation was enhanced using SHAP values. + Important predictive factors identified included renal parenchymal thickness (RPT), distance from the puncture point to the lower edge of the kidney (D1), and the ratio of D1 to the Renal length (R1), disease course, eGFR and prothrombin time (PT). The random forest model outperformed others with AUCs of 0.831, 0.784, and 0.776 for the training, internal validation, and external validation sets, respectively, displaying an accuracy of 0.742, sensitivity of 0.675, specificity of 0.805, and F1 score of 0.691. SHAP analysis highlighted prolonged PT, thinner RPT, smaller D1, lower eGFR, and extended disease course as increasing PH risk. + The random forest model effectively predicts PH post-PRB, supporting early diagnosis and decision-making. + © 2026. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. + + + + Su + Liyang + L + + Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China. + + + + Nie + Jinliang + J + + The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China. + + + + Xie + Qiaojie + Q + + Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China. + + + + Li + Shilin + S + + The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China. lslqz@fjmu.edu.cn. + + + + Zhang + Qingquan + Q + + Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China. zhangqq200900353@163.com. + + + + eng + + + 2023Y9231 + the Joint funds for the innovation of science and technology, Fujian province + + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Abdom Radiol (NY) + 101674571 + + IM + + Interventional ultrasonography + Machine learning + Postoperative hemorrhage + Predictive models + Random forest + Renal insufficiency + + Declarations. Competing interests: The authors declare no competing interests. Ethical approval: This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of XX (Approval No. [2025]K235), which granted a waiver for informed consent due to the retrospective nature of the study. The study was registered on the Clinical Trials website (Registration No. MR-35-25-067753). + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 3 + 2 + + + 2026 + 5 + 16 + + + 2026 + 5 + 28 + 11 + 15 + + + aheadofprint + + 42207295 + 10.1007/s00261-026-05582-2 + 10.1007/s00261-026-05582-2 + + + + Murray S, Dumaine C, Wall C, Banerjee T, Barton J, Moser M. Technical and Institutional Factors Affecting Specimen Adequacy and Complications in Ultrasound-guided Kidney Biopsy: A Retrospective Cohort Study. Can J Kidney Health Dis. 2025;12:20543581251336551. + + 10.1177/20543581251336551 + 40336686 + 12056320 + + + + Ravizzini PIC, Lino H, Fleury EFC, Rangel DA, de Toledo LGM. Comparative analysis of ultrasound-guided percutaneous biopsy of native kidneys in children and adults using the free-flank supine antero-lateral decubitus positioning. Abdom Radiol (NY). 2024;49(5):1638–1645. + + 10.1007/s00261-024-04316-6 + 38658444 + + + + Jose L, Kaul A, Bhadauria D, et al. Desmopressin Acetate Before Percutaneous Ultrasound-Guided Kidney Biopsy in Patients with Renal Failure - Is it Really Beneficial? Indian J Nephrol. 2022;32(5):430–434. + + 10.4103/ijn.IJN_553_20 + 36568600 + 9775618 + + + + Li FF, Guan YX, Li TX, et al. Analysis of hemorrhage upon ultrasound-guided percutaneous renal biopsy in China: a retrospective study. 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+ + 1432-0932 + + + 2026 + May + 28 + + + European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society + Eur Spine J + + Tree-based and sparse logistic models for predicting one-month postoperative performance status after surgery for spinal metastases. + 10.1007/s00586-026-09957-3 + + We aimed to develop and internally validate prediction models for one-month postoperative performance status (PS) after surgery for spinal metastases and to identify patients likely to achieve PS 0-2 at one month. + We performed a retrospective analysis of a prospectively collected spine surgery registry. We compared three tree-based models (Random Forest, XGBoost, and CatBoost) with two regularized logistic regression models (ridge-regularized logistic regression and a sparse elastic-net logistic regression model constrained to ≤ 15 predictors). Model development and hyperparameter tuning were performed using nested cross-validation. Missing data were handled using model-specific strategies within the cross-validation pipeline, and a sensitivity analysis excluded the predictor with the highest missingness. Performance was assessed using discrimination and calibration metrics, including the area under the receiver operating characteristic curve (AUC-ROC), accuracy, precision, recall, F1 score, Brier score, calibration intercept, and calibration slope. + The primary analysis included 375 patients with available one-month PS out of 413 enrolled patients. Random Forest achieved the highest discrimination (AUC-ROC 0.811 ± 0.079) and showed calibration measures closest to the ideal among the evaluated models (Brier score 0.168; calibration intercept - 0.024; slope 1.121). The sparse elastic-net model showed good discrimination (AUC-ROC 0.796 ± 0.081) with a limited set of predictors, although its calibration metrics suggested less reliable absolute probability estimates (Brier score 0.217; intercept 0.612; slope 3.228). Excluding the predictor with the highest missingness yielded similar performance for the main models. + Tree-based models, particularly Random Forest, provided the most favorable overall predictive performance for one-month postoperative PS after surgery for spinal metastases, whereas a sparse elastic-net logistic regression model preserved reasonable discrimination with a small predictor set and coefficient-based interpretability. These findings support clinically oriented prediction of early postoperative functional status while highlighting the need to assess calibration before clinical implementation. + © 2026. The Author(s). + + + + Maki + Satoshi + S + + Chiba University, Chiba, Japan. satoshimaki@gmail.com. + + + Center for Frontier Medical Engineering, Chiba University, Chiba, Japan. satoshimaki@gmail.com. + + + + Shiratani + Yuki + Y + + Chiba University, Chiba, Japan. + + + + Orita + Sumihisa + S + + Chiba University, Chiba, Japan. + + + Center for Frontier Medical Engineering, Chiba University, Chiba, Japan. + + + + Suzuki + Akinobu + A + + Osaka Metropolitan University, Osaka, Japan. + + + + Tamai + Koji + K + + Osaka Metropolitan University, Osaka, Japan. + + + + Shimizu + Takaki + T + + Kanazawa University, Kanazawa, Japan. + + + + Kakutani + Kenichiro + K + + Kobe University, Kobe, Japan. + + + + Kanda + Yutaro + Y + + Kobe University, Kobe, Japan. + + + + Tominaga + Hiroyuki + H + + Kagoshima University, Kagoshima, Japan. + + + + Kawamura + Ichiro + I + + Kagoshima University, Kagoshima, Japan. + + + + Ishihara + Masayuki + M + + Kansai Medical University Hospital, Osaka, Japan. + + + + Paku + Masaaki + M + + Kansai Medical University Hospital, Osaka, Japan. + + + + Takahashi + Yohei + Y + + Keio University, Tokyo, Japan. + + + + Funayama + Toru + T + + University of Tsukuba, Tsukuba, Japan. + + + + Miura + Kousei + K + + University of Tsukuba, Tsukuba, Japan. + + + + Shirasawa + Eiki + E + + Kitasato University, Sagamihara, Japan. + + + + Inoue + Hirokazu + H + + Jichi Medical University Hospital, Tochigi, Japan. + + + + Kimura + Atsushi + A + + Jichi Medical University, Shimotsuke, Japan. + + + + Iimura + Takuya + T + + Dokkyo Medical University, Mibu, Japan. + + + + Moridaira + Hiroshi + H + + Dokkyo Medical University, Mibu, Japan. + + + + Nakajima + Hideaki + H + + University of Fukui, Fukui-shi, Japan. + + + + Watanabe + Shuji + S + + University of Fukui, Fukui-shi, Japan. + + + + Akeda + Koji + K + + Mie University, Tsu, Japan. + + + + Takegami + Norihiko + N + + Mie University, Tsu, Japan. + + + + Nakanishi + Kazuo + K + + Kawasaki Medical School, Kurashiki, Japan. + + + + Sawada + Hirokatsu + H + + Nihon University, Tokyo, Japan. + + + + Matsumoto + Koji + K + + Nihon University, Tokyo, Japan. + + + + Funaba + Masahiro + M + + Yamaguchi University, Yamaguchi, Japan. + + + + Suzuki + Hidenori + H + + Yamaguchi University, Yamaguchi, Japan. + + + + Funao + Haruki + H + + International University of Health and Welfare Narita Hospital, Chiba, Japan. + + + + Oshigiri + Tsutomu + T + + Sapporo Medical University, Sapporo, Japan. + + + + Hirai + Takashi + T + + Institute of Science Tokyo, Tokyo, Japan. + + + + Otsuki + Bungo + B + + Kyoto University, Kyoto, Japan. + + + + Kobayakawa + Kazu + K + + Kyushu University, Fukuoka, Japan. + + + + Uotani + Koji + K + + Okayama University Hospital, Okayama, Japan. + + + + Manabe + Hiroaki + H + + Tokushima University, Tokushima, Japan. + + + + Tanishima + Shinji + S + + Tottori University, Yonago, Japan. + + + + Hashimoto + Ko + K + + Tohoku University, Sendai, Japan. + + + + Iwai + Chizuo + C + + Gifu University Hospital, Gifu City, Japan. + + + + Yamabe + Daisuke + D + + Iwate Medical University, Morioka, Japan. + + + + Hiyama + Akihiko + A + + Tokai University, Tokyo, Japan. + + + + Seki + Shoji + S + + University of Toyama, Toyama, Japan. + + + + Kato + Kenji + K + + Nagoya City University, Nagoya, Japan. + + + + Miyazaki + Masashi + M + + Oita University, Ōita, Japan. + + + + Watanabe + Kazuyuki + K + + Fukushima Medical University, Fukushima, Japan. + + + + Nakamae + Toshio + T + + Hiroshima University, Hiroshima, Japan. + + + + Kaito + Takashi + T + + Osaka University, Osaka, Japan. + + + + Nakashima + Hiroaki + H + + Nagoya University, Nagoya, Japan. + + + + Nagoshi + Narihito + N + + Keio University, Tokyo, Japan. + + + + Inoue + Gen + G + + Kitasato University, Sagamihara, Japan. + + + + Imagama + Shiro + S + + Nagoya University, Nagoya, Japan. + + + + Watanabe + Kota + K + + Keio University, Tokyo, Japan. + + + + Kato + Satoshi + S + + Kanazawa University, Kanazawa, Japan. + + + + Ohtori + Seiji + S + + Chiba University, Chiba, Japan. + + + + Furuya + Takeo + T + + Chiba University, Chiba, Japan. + + + + eng + + + JP24K12366 + Japan Society for the Promotion of Science + + + + JP24K12366 + Japan Society for the Promotion of Science + + + + JP24K12366 + Japan Society for the Promotion of Science + + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + Germany + Eur Spine J + 9301980 + 0940-6719 + + IM + + Machine learning + Metastatic spinal tumors + Performance status + Predictive model + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 34 + + + 2025 + 1 + 6 + + + 2026 + 4 + 11 + + + 2026 + 4 + 5 + + + 2026 + 5 + 28 + 11 + 14 + + + aheadofprint + + 42207277 + 10.1007/s00586-026-09957-3 + 10.1007/s00586-026-09957-3 + + + + Santipas B, Veerakanjana K, Ittichaiwong P et al (2024) Development and internal validation of machine-learning models for predicting survival in patients who underwent surgery for spinal metastases. Asian Spine J 18:325–335. https://doi.org/10.31616/asj.2023.0314 + + 10.31616/asj.2023.0314 + 38764230 + 11222881 + + + + Shah AA, Schwab JH (2024) Predictive Modeling for Spinal Metastatic Disease. 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J Orthop Sci 28:874–879. https://doi.org/10.1016/j.jos.2022.06.006 + + 10.1016/j.jos.2022.06.006 + 35811255 + + + + + + + + 42207236 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2509-9280 + + 10 + 1 + + 2026 + May + 28 + + + European radiology experimental + Eur Radiol Exp + + Automated identification of MRI series using a hierarchical modular machine-learning pipeline. + 77 + 10.1186/s41747-026-00740-z + + The volume and diversity of large MR imaging datasets require efficient automated labelling tools for cataloguing MR series, as manual annotation is impractical and costly. However, relying on DICOM header fields alone is unreliable because sequence descriptors are heterogeneous and locally defined, frequently missing or incorrect, and may be altered or removed during anonymisation. + We developed an AI-based modular model to classify MR series. The pipeline comprises five sequential classifiers (Family, Weighting, Fat Suppression, Contrast, and Others) and was trained and tested on 18,181 MRI series from the multicentre PRIMAGE repository. The dataset was split by patient into 80% training/validation and 20% testing; within the training/validation subset, five-fold cross-validation was used. With the exception of contrast classification, all modules used DICOM tag-based machine learning models (CatBoost/Random Forest), while the Contrast classifier incorporated image analysis using a pretrained single-slice ResNet-50. Ethical approval for the study was obtained. + Accuracy was 0.994 for Weighting, 0.984 for Family, 0.959 for Fat suppression and 0.958 for Others; the Contrast classifier recorded 0.841. Overall, the end-to-end classification yielded a weighted F1 of 0.849 (CI: 0.837-0.861) and an accuracy of 0.853 (CI: 0.841-0.865). + The proposed approach provides a reliable and scalable solution for labelling large, heterogeneous MRI datasets across multiple anatomical regions. The pipeline achieved excellent performance for Weighting and Family classification, solid performance for Fat Suppression and 'Others'. However, Contrast classification remains the main limitation and warrants further refinement and/or additional modules. + Reliable, automated MRI sequence labelling enables faster, reproducible cohort selection and protocol harmonisation in large archives, supporting downstream clinical research and AI tools while reducing manual curation effort and error. + Multicentre PRIMAGE cohort (18,181 MR series) enabling evaluation in a heterogeneous setting. Machine learning model combining DICOM metadata and image features. High accuracy in series classification: up to 0.994 on key tasks. A scalable pipeline reduces manual annotation workload for radiologists. + © 2026. The Author(s). + + + + Kujawa + Mariusz J + MJ + 0000-0003-4654-4321 + + Grupo de Investigación Biomédica en Imagen (GIBI230), Instituto de Investigación Sanitaria La Fe, Valencia, Spain. mariusz.kujawa@gumed.edu.pl. + + + 2nd Department of Radiology, Medical University of Gdansk, Gdansk, Poland. mariusz.kujawa@gumed.edu.pl. + + + + Fernández-Patón + Matías + M + 0000-0001-9374-1411 + + Grupo de Investigación Biomédica en Imagen (GIBI230), Instituto de Investigación Sanitaria La Fe, Valencia, Spain. + + + + Cerdá Alberich + Leonor + L + 0000-0002-5567-4278 + + Grupo de Investigación Biomédica en Imagen (GIBI230), Instituto de Investigación Sanitaria La Fe, Valencia, Spain. + + + + Veiga-Canuto + Diana + D + 0000-0002-6048-2940 + + Grupo de Investigación Biomédica en Imagen (GIBI230), Instituto de Investigación Sanitaria La Fe, Valencia, Spain. + + + + Martí-Bonmatí + Luis + L + 0000-0002-8234-010X + + Grupo de Investigación Biomédica en Imagen (GIBI230), Instituto de Investigación Sanitaria La Fe, Valencia, Spain. + + + Área Clínica de Imagen Médica, Hospital Universitario y Politécnico La Fe, Valencia, Spain. + + + + eng + + + 826494 + Horizon 2020 Framework Programme + + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Eur Radiol Exp + 101721752 + 2509-9280 + + IM + + + Magnetic Resonance Imaging + methods + + + Machine Learning + + + Humans + + + Classification Algorithms + + + Image Processing, Computer-Assisted + methods + + + + Artificial intelligence + DICOM + Magnetic resonance imaging + Multicentre study + Series classification + + Declarations. Ethics approval and consent to participate: Ethical approval was obtained from the Ethics Committee for Investigation with medicinal products of the University and Polytechnic La Fe Hospital (ethic code: 2018/0228). Written informed consent was obtained from all participants. Consent for publication: Written informed consent was obtained from all participants. Competing interests: The authors declare no conflicts of interest. + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 2 + 1 + + + 2026 + 4 + 28 + + + 2026 + 3 + 30 + + + 2026 + 5 + 28 + 11 + 4 + + + epublish + + 42207236 + 10.1186/s41747-026-00740-z + 10.1186/s41747-026-00740-z + + + + Liang S, Beaton D, Arnott SR et al (2021) Magnetic resonance imaging sequence identification using a metadata learning approach. Front Neuroinform 15:622951. https://doi.org/10.3389/fninf.2021.622951 + + 10.3389/fninf.2021.622951 + 34867254 + 8635782 + + + + de Mello JPV, Paixão TM, Berriel R et al (2020) Deep learning-based type identification of volumetric MRI sequences. In: Proceedings of the 2020 25th international conference on pattern recognition (ICPR). IEEE, Milan, pp 5674–5681. https://doi.org/10.1109/ICPR48806.2021.9413120 + + + Gauriau R, Bridge C, Chen L et al (2020) Using DICOM metadata for radiological image series categorization: a feasibility study on large clinical brain MRI datasets. J Digit Imaging 33:747–762. https://doi.org/10.1007/s10278-019-00308-x + + 10.1007/s10278-019-00308-x + 31950302 + 7256138 + + + + Mildenberger P, Eichelberg M, Martin E (2002) Introduction to the DICOM standard. Eur Radiol 12:920–927. https://doi.org/10.1007/S003300101100 + + 10.1007/S003300101100 + 11960249 + + + + Gueld MO, Kohnen M, Keysers D et al (2002) Quality of DICOM header information for image categorization. Proc SPIE 4685:280–287. https://doi.org/10.1117/12.467017 + + 10.1117/12.467017 + + + + Gomis-Maya A, Cerdá-Alberich L, Veiga-Canuto D et al (2025) Automatic magnetic resonance imaging series labelling for large repositories. J Big Data 12:39. https://doi.org/10.1186/s40537-025-01086-w + + 10.1186/s40537-025-01086-w + + + + Ranjbar S, Singleton KW, Jackson PR et al (2020) A deep convolutional neural network for annotation of magnetic resonance imaging sequence type. J Digit Imaging 33:439–446. https://doi.org/10.1007/s10278-019-00282-4 + + 10.1007/s10278-019-00282-4 + 31654174 + 7165226 + + + + Pizarro R, Assemlal HE, De Nigris D et al (2019) Using deep learning algorithms to automatically identify the brain MRI contrast: implications for managing large databases. Neuroinformatics 17:115–130. https://doi.org/10.1007/s12021-018-9387-8 + + 10.1007/s12021-018-9387-8 + 29956131 + + + + Baumgärtner GL, Hamm CA, Schulze-Weddige S et al (2023) Metadata-independent classification of MRI sequences using convolutional neural networks: successful application to prostate MRI. Eur J Radiol 166:110964. https://doi.org/10.1016/j.ejrad.2023.110964 + + 10.1016/j.ejrad.2023.110964 + 37453274 + + + + de Almeida JG, Verde ASC, Bilreiro C et al (2025) Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning. Insights Imaging 16:75. https://doi.org/10.1186/s13244-025-01938-2 + + 10.1186/s13244-025-01938-2 + 40146375 + 12187622 + + + + Zhu Z, Mittendorf A, Shropshire E et al (2022) 3D pyramid pooling network for abdominal MRI series classification. IEEE Trans Pattern Anal Mach Intell 44:1688–1698. https://doi.org/10.1109/TPAMI.2020.3033990 + + 10.1109/TPAMI.2020.3033990 + 33112740 + + + + Pan J, Chen Q, Sun C et al (2025) MRISeqClassifier: a deep learning toolkit for precise MRI sequence classification. AMIA Jt Summits Transl Sci Proc 2025:405–413 + + 40502266 + 12150705 + + + + Cerdá-Alberich L, Solana J, Mallol P et al (2023) MAIC–10 brief quality checklist for publications using artificial intelligence and medical images. Insights Imaging 14:11. https://doi.org/10.1186/s13244-022-01355-9 + + 10.1186/s13244-022-01355-9 + 36645542 + 9842808 + + + + Martí-Bonmatí L, Alberich-Bayarri Á, Ladenstein R et al (2020) PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers. Eur Radiol Exp 4:22. https://doi.org/10.1186/s41747-020-00150-9 + + 10.1186/s41747-020-00150-9 + 32246291 + 7125275 + + + + Prokhorenkova L, Gusev G, Vorobev A et al (2018) CatBoost: unbiased boosting with categorical features. Adv Neural Inf Process Syst 31:6638–6648 + + + Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324 + + 10.1023/A:1010933404324 + + + + He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Las Vegas, pp 770–778. https://doi.org/10.1109/CVPR.2016.90 + + + Kim B, Mathai TS, Helm K et al (2025) Automated classification of body MRI sequences using convolutional neural networks. Acad Radiol 32:1192–1203. https://doi.org/10.1016/j.acra.2024.11.046 + + 10.1016/j.acra.2024.11.046 + 39645459 + + + + Bitar R, Leung G, Perng R et al (2006) MR pulse sequences: what every radiologist wants to know but is afraid to ask. Radiographics 26:513–537. https://doi.org/10.1148/rg.262055063 + + 10.1148/rg.262055063 + 16549614 + + + + Nitz WR (1999) MR imaging: acronyms and clinical applications. Eur Radiol 9:979–997. https://doi.org/10.1007/S003300050780 + + 10.1007/S003300050780 + 10370004 + + + + Matta S, Lamard M, Zhang P et al (2024) A systematic review of generalization research in medical image classification. Comput Biol Med 183:109256. https://doi.org/10.1016/j.compbiomed.2024.109256 + + 10.1016/j.compbiomed.2024.109256 + 39427426 + + + + Tóth M, Ruskó L, Csébfalvi B (2016) Automatic recognition of anatomical regions in three-dimensional medical images. Comput Biol Med 76:120–133. https://doi.org/10.1016/j.compbiomed.2016.06.018 + + 10.1016/j.compbiomed.2016.06.018 + 27433991 + + + + Scapicchio C, Gabelloni M, Forte SM et al (2021) DICOM-MIABIS integration model for biobanks: a use case of the EU PRIMAGE project. Eur Radiol Exp 5:20. https://doi.org/10.1186/s41747-021-00214-4 + + 10.1186/s41747-021-00214-4 + 33977357 + 8113005 + + + + + + + + 42207215 + + 2026 + 05 + 28 + +
+ + 2730-6011 + + + 2026 + May + 28 + + + Discover oncology + Discov Oncol + + Up-regulation of SLC26A3 enhances oxaliplatin sensitivity in colorectal cancer. + 10.1007/s12672-026-05285-6 + + The role of SLC26A3 in the sensitivity to oxaliplatin, a widely used chemotherapy drug for colorectal cancer (CRC), remains unclear. This study aimed to explore the association between SLC26A3 expression and oxaliplatin sensitivity. Total 240 differentially expressed mRNAs were identified, with 124 downregulated and 116 upregulated in oxaliplatin-resistant CRC. WGCNA identified a module significantly associated with resistance. GO and KEGG analyses revealed enrichment in fatty acid metabolic processes and organic anion transmembrane transporter activity. PCA and feature gene selection identified a 15-gene signature, with SLC26A3 highlighted by machine learning models as a key discriminator of chemosensitivity. Immunohistochemical staining and survival analysis indicated that higher SLC26A3 expression correlated with better survival outcomes. Additionally, a significant negative correlation between SLC26A3 and AKT1 was observed, suggesting that while SLC26A3 may enhance oxaliplatin sensitivity, its effect may be overshadowed by other factors in resistant cells. SLC26A3 may play a crucial role in oxaliplatin sensitivity in CRC. The identified gene signature could potentially serve as a biomarker for predicting chemosensitivity to oxaliplatin. + © 2026. The Author(s). + + + + Wang + Junyang + J + + Department of Gastrointestinal Nutrition and Hernia Surgery, The Second Hospital of Jilin University, No. 4026, Yatai Street, Nanguan District, Changchun, 130022, China. + + + + Tang + Shaodong + S + + Department of Cardiology, Songyuan Jilin Oilfield Hospital, Songyuan, 138001, China. + + + + Zhang + Min + M + + Department of Gastrointestinal Surgery, Jilin People's Hospital, Changchun, 132001, Jilin, China. + + + + Li + Songhe + S + + Department of Ophthalmology, The First Hospital of Jilin University, No. 1, Xinmin Street, Changchun, 130021, China. songhe@jlu.edu.cn. + + + + Wen + Dacheng + D + + Department of Gastrointestinal Nutrition and Hernia Surgery, The Second Hospital of Jilin University, No. 4026, Yatai Street, Nanguan District, Changchun, 130022, China. wendc@jlu.edu.cn. + + + + eng + + + 20220401073YY + Jilin Province Science and Technology Development Plan Project + + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Discov Oncol + 101775142 + 2730-6011 + + + Colorectal cancer + Gene expression + Machine learning + Oxaliplatin sensitivity + SLC26A3 + + Declarations. Ethics approval and consent to participate: The study was approved by the Ethics Committee of The Second Hospital of Jilin University (SB2021143). The patients or their families signed the informed consent. The study was conducted according to the guidelines of the Declaration of Helsinki. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 28 + 12 + 35 + + + 2026 + 5 + 28 + 12 + 35 + + + 2025 + 12 + 26 + + + 2026 + 5 + 15 + + + 2026 + 5 + 28 + 11 + 4 + + + aheadofprint + + 42207215 + 10.1007/s12672-026-05285-6 + 10.1007/s12672-026-05285-6 + + + + + + 42207158 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 1438-8871 + + 28 + + 2026 + May + 28 + + + Journal of medical Internet research + J Med Internet Res + + Adaptive Fast-Slow Large Language Model Framework for Multidimensional Classification of Prenatal Ultrasound Reports: Comparative Study. + + e91399 + e91399 + + 10.2196/91399 + + Phenotype-driven prenatal diagnosis relies on the precise correlation between ultrasound findings and genetic outcomes; however, this process is hindered by the unstructured nature of clinical ultrasound reports. While large language models (LLMs) hold the potential to address this challenge, their specific application in this domain remains systematically underexplored. + To establish an effective LLM implementation framework for the clinical multidimensional classification of prenatal ultrasound reports, we evaluated the open-source DeepSeek-V3.2 family on real-world anomalous reports-covering both factual and subjective categories-while integrating retrieval-augmented generation (RAG) and chain-of-thought (CoT) reasoning. + From a cohort of 4256 pregnancies, we extracted 254 reports with fetal anomalies. We comprehensively evaluated both the high-speed base model (DeepSeek-V3.2-B) and the reasoning-enhanced model (DeepSeek-V3.2-R) across all 5 classification dimensions, comprising 4 factual extraction tasks-primary classification, standardized terminology, anatomical system, and abnormality count-and 1 subjective severity assessment. We further explicitly evaluated the efficacy of RAG for the subjective tasks. Finally, to validate the clinical utility of this approach, we performed a correlation analysis between the expert-validated multidimensional phenotypic profiles and definitive genetic outcomes derived from amniocentesis. + While V3.2-B achieved high efficiency in factual tasks (accuracy and F1-score >90%), it underperformed in subjective severity grading (56.6% accuracy), exhibiting a recall of 0 for minor anomalies. Crucially, while RAG significantly improved both models' performance on internal retrieval datasets (P<.05), this benefit did not generalize to external test datasets (P>.05). In contrast, the V3.2-R model utilizing CoT reasoning achieved superior robustness (86% accuracy and F1-score=0.75) on external data without RAG; notably, introducing RAG to V3.2-R degraded performance to 81%, suggesting potential noise interference. Clinical validation against amniocentesis outcomes confirmed that accurate multidimensional phenotypic profiles significantly stratified pathogenic genetic risks. + The rapid base models are efficient for factual classification, and RAG enhances performance on data similar to the knowledge base, whereas CoT is indispensable for subjective assessment. Within the constraints of our dataset and current retrieval implementation, CoT proved more robust than RAG for subjective assessment. However, this finding is specifically tied to our experimental setup and should not be generalized as a universal conclusion. We recommend clinically adopting this adaptive "fast-slow" LLM framework to efficiently perform the multidimensional classification of prenatal ultrasound anomalies. This privacy-preserving, locally deployable solution provides a scalable path to accelerate phenotype-genotype research and optimize invasive diagnostic decision-making. + ©Wei Zhong, Huihui Yan, Yifan Liu, Yan Liu, Kai Yang, Huimin Gao, Zhengyang Yao, Wenjing Hao, Yousheng Yan, Chenghong Yin. Originally published in the Journal of Medical Internet Research (https://www.jmir.org). + + + + Zhong + Wei + W + 0000-0001-9823-9500 + + Department of Medical Genetics, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China. + + + + Yan + Huihui + H + 0009-0008-2979-9895 + + Department of Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China. + + + + Liu + Yifan + Y + 0009-0008-7339-4756 + + Department of Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China. + + + + Liu + Yan + Y + 0000-0003-1698-5783 + + Department of Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China. + + + + Yang + Kai + K + 0000-0002-7457-3106 + + Department of Medical Genetics, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China. + + + + Gao + Huimin + H + 0009-0004-8874-6022 + + Department of Medical Genetics, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China. + + + + Yao + Zhengyang + Z + 0009-0009-5761-7136 + + Department of Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China. + + + + Hao + Wenjing + W + 0009-0006-8537-0036 + + Department of Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China. + + + + Yan + Yousheng + Y + 0000-0002-0405-1302 + + Department of Medical Genetics, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, Beijing, China. + + + + Yin + Chenghong + C + 0000-0002-2503-3285 + + Department of Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University. Beijing Maternal and Child Health Care Hospital, No. 251 Yaojiayuan Road, Chaoyang District, Beijing, 100026, China, 86 15572779093. + + + + eng + + Journal Article + Comparative Study + + + 2026 + 05 + 28 + +
+ + Canada + J Med Internet Res + 100959882 + 1438-8871 + + IM + + + Large Language Models + + + Humans + + + Ultrasonography, Prenatal + + + Pregnancy + + + Female + + + + DeepSeek + chain-of-thought + large language models + phenotype-driven diagnosis + prenatal ultrasound + retrieval-augmented generation + + + + + + 2026 + 5 + 28 + 12 + 35 + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 1 + 14 + + + 2026 + 5 + 2 + + + 2026 + 5 + 4 + + + 2026 + 5 + 28 + 10 + 54 + + + epublish + + 42207158 + 10.2196/91399 + v28i1e91399 + + + + + + 42207133 + + 2026 + 05 + 28 + +
+ + 1364-5528 + + + 2026 + May + 28 + + + The Analyst + Analyst + + Current trends in machine learning for surface-enhanced Raman spectroscopy. + 10.1039/d5an01346a + + Surface-enhanced Raman spectroscopy (SERS) is being transformed by the widespread adoption of artificial intelligence across the full methodological spectrum. Conventional machine learning is routinely applied for robust baselines and rapid deployment. Deep learning with convolutional networks, recurrent and transformer architectures, and self-supervised objectives is increasingly used to learn invariant spectral representations from minimally processed data. Generative models (variational, adversarial, diffusion) are being employed for augmentation, denoising, and simulation-to-real transfer, while large language models are leveraged for metadata curation, protocol extraction, and retrieval-augmented decision support. Through these advances, SERS analysis has become more convenient, scalable, and automatable, enabling streamlined applications in medicine, agriculture, food quality assurance, environmental monitoring, and process control. Despite this progress, substantial challenges still remain. Data scarcity persists, characterized by limited sample sizes, heterogeneous acquisition protocols, sparse labels, and restricted public benchmarks, which together constrain generalization and hinder fair comparison. Model explainability also requires improvement, with a need for chemically faithful attributions, standardized reporting of evidential spectra, and rigorous robustness checks to build trust in safety-critical decisions. In this review, current methodologies are surveyed, practical guidelines are summarized, and a path forward is outlined that prioritizes community datasets compliant with Findability, Accessibility, Interoperability, and Reuse (FAIR) principles, transparent evaluation suites, and interpretable, uncertainty-aware models capable of reliable deployment across laboratories and devices. + + + + Luo + Ruihao + R + 0000-0002-8291-4927 + + Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany. dana.cialla-may@leibniz-ipht.de. + + + Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany. + + + + Jiao + Sujia + S + + Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany. dana.cialla-may@leibniz-ipht.de. + + + + Nair + Jyothi B + JB + + Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany. dana.cialla-may@leibniz-ipht.de. + + + + Ghosh + Arna + A + + Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany. dana.cialla-may@leibniz-ipht.de. + + + + Kamiak + Klaudziya + K + 0009-0004-7571-2077 + + Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany. dana.cialla-may@leibniz-ipht.de. + + + + Popp + Jürgen + J + 0000-0003-4257-593X + + Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany. dana.cialla-may@leibniz-ipht.de. + + + Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany. + + + + Cialla-May + Dana + D + 0000-0002-8577-1490 + + Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany. dana.cialla-may@leibniz-ipht.de. + + + Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany. + + + + Bocklitz + Thomas + T + 0000-0003-2778-6624 + + Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany. dana.cialla-may@leibniz-ipht.de. + + + Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 28 + +
+ + England + Analyst + 0372652 + 0003-2654 + + IM + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 10 + 42 + + + aheadofprint + + 42207133 + 10.1039/d5an01346a + + + + + + 42207108 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 1678-2690 + + 98 + 2 + + 2026 + + + Anais da Academia Brasileira de Ciencias + An Acad Bras Cienc + + The use of machine learning approaches for mortality prediction and classification in traumatic brain injury: a literature review. + + e20250585 + e20250585 + + 10.1590/0001-3765202620250585 + S0001-37652026000202601 + + This systematic review analyzed machine learning (ML) techniques for predicting and classifying mortality in traumatic brain injury (TBI) victims. + The PICOS (People, Intervention, Comparison, Outcome, Study) strategy was used for the research question. The included studies were indexed in the Virtual Health Library and the National Library of Medicine, written in English, Portuguese, or Spanish, and focused on TBI-related mortality. Exclusions comprised animal studies, molecular research, reviews, and unavailable full texts. + Of 1,752 screened studies, 36 met inclusion criteria. Most publications emerged in 2023 (n = 10, 27.8%), with the highest number of publications belonging to Asian countries (n = 12, 33.3%). The general mean age of TBI was 50.5 years for male adults and the main causes were motor vehicle accidents and falls. The main predictors of mortality were low Glasgow Coma Scale score (n=17, 12.9%) and age (n=14, 10.6%). The best machine learning techniques to predict mortality were Support Vector Machine, Logistic Regression, Random Forest and eXtreme Gradient Boosting. The areas under the curve (AUC) ranged from 0.757 to 0.968. + Machine learning techniques seem to be useful and promising tools for predicting mortality in TBI victims, and thus help in clinical decisions. + + + + Silva-Sousa + Arthur Afonso + AA + 0000-0001-6032-8487 + + Universidade Federal de Minas Gerais (UFMG), Instituto de Ciências Biológicas, Programa de Pós-graduação em Neurociências, Av. Antônio Carlos, 6627, Pampulha, 31270-901 Belo Horizonte, MG, Brazil. + + + + Camargo + Fernanda Carolina + FC + 0000-0002-1048-960X + + Universidade Federal do Triângulo Mineiro (UFTM), Setor de Pesquisa e Inovação Tecnológica do Hospital de Clínicas, Rua Benjamin Constant, 16, Nossa Senhora da Abadia, 38025-470 Uberaba, MG, Brazil. + + + + Furtado + Leopoldo Mandic F + LMF + 0000-0001-9472-9895 + + Universidade Federal de Minas Gerais (UFMG), Instituto de Ciências Biológicas, Programa de Pós-graduação em Neurociências, Av. Antônio Carlos, 6627, Pampulha, 31270-901 Belo Horizonte, MG, Brazil. + + + + Teixeira + Antonio Lucio + AL + 0000-0002-9621-5422 + + University of Texas Health Science Center at San Antonio, The Glenn Biggs Institute, Floyd Curl Dr., 8300, 78229 San Antonio, TX, USA. + + + + Queiroz + Rafael A B DE + RAB + 0000-0002-3676-8914 + + Universidade Federal de Ouro Preto (UFOP), Departamento de Computação, Instituto de Ciências Exatas e Biológicas, Rua Quatro, s/n, Campus Universitário Morro do Cruzeiro, 35402-136 Ouro Preto, MG, Brazil. + + + + Miranda + Aline Silva DE + AS + 0000-0003-2811-7924 + + Universidade Federal de Minas Gerais (UFMG), Departamento de Morfologia, Instituto de Ciências Biológicas, Av. Antônio Carlos, 6627, Pampulha, 31270-901 Belo Horizonte, MG, Brazil. + + + + eng + + Journal Article + Systematic Review + + + 2026 + 05 + 22 + +
+ + Brazil + An Acad Bras Cienc + 7503280 + 0001-3765 + + IM + + + Brain Injuries, Traumatic + mortality + classification + + + Humans + + + Machine Learning + + + Predictive Learning Models + + + Male + + + Random Forest + + + Classification Algorithms + + + Boosting Machine Learning Algorithms + + + Prediction Algorithms + + + Glasgow Coma Scale + + + + + + + 2026 + 5 + 28 + 12 + 35 + + + 2026 + 5 + 28 + 12 + 34 + + + 2025 + 5 + 27 + + + 2026 + 1 + 3 + + + 2026 + 5 + 28 + 10 + 33 + + + epublish + + 42207108 + 10.1590/0001-3765202620250585 + S0001-37652026000202601 + + + + + + 42207045 + + 2026 + 05 + 28 + +
+ + 1471-2962 + + 384 + 2321 + + 2026 + May + 28 + + + Philosophical transactions. Series A, Mathematical, physical, and engineering sciences + Philos Trans A Math Phys Eng Sci + + Unsupervised machine learning for scientific discovery: workflow and best practices. + 20240602 + 10.1098/rsta.2024.0602 + + Unsupervised machine learning is widely used to mine large, unlabelled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry and more. However, despite its widespread utilization, there is a lack of standardization in unsupervised learning workflows for making reliable and reproducible scientific discoveries. In this paper, we present a structured workflow for using unsupervised learning techniques in science. We highlight and discuss best practices starting with formulating validatable scientific questions, conducting robust data preparation and exploration, using a range of modelling techniques, performing rigorous validation by evaluating the stability and generalizability of unsupervised learning conclusions, and promoting effective communication and documentation of results to ensure reproducible scientific discoveries. To illustrate our proposed workflow, we present a case study from astronomy, seeking to refine globular clusters of Milky Way stars based upon their chemical composition. Our case study highlights the importance of validation and illustrates how the benefits of a carefully designed workflow for unsupervised learning can advance scientific discovery. This article is part of the theme issue 'Statistical workflow'. + © 2026 The Authors. + + + + Chang + Andersen + A + + Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA. + + + + Tang + Tiffany + T + + Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA. + + + + Zikry + Tarek + T + 0000-0003-3360-249X + + Department of Statistics, Columbia University, New York, NY, USA. + + + + Allen + Genevera + G + 0000-0002-6851-4310 + + Department of Statistics, Columbia University, New York, NY, USA. + + + + eng + + + National Science Foundation + + + + + Journal Article + +
+ + England + Philos Trans A Math Phys Eng Sci + 101133385 + 1364-503X + + IM + + astronomy + best practices + clustering + data-driven discovery + dimension reduction + unsupervised learning + workflow + + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 33 + + + 2025 + 6 + 2 + + + 2025 + 9 + 16 + + + 2025 + 10 + 27 + + + 2026 + 5 + 28 + 10 + 23 + + + ppublish + + 42207045 + 10.1098/rsta.2024.0602 + 481927 + + + + + + 42207042 + + 2026 + 05 + 28 + +
+ + 1471-2962 + + 384 + 2321 + + 2026 + May + 28 + + + Philosophical transactions. Series A, Mathematical, physical, and engineering sciences + Philos Trans A Math Phys Eng Sci + + Machine learning workflows in climate modelling: design patterns and insights from case studies. + 20250254 + 10.1098/rsta.2025.0254 + + Machine learning (ML) has been increasingly applied in climate modelling on system emulation acceleration, data-driven parameter inference, forecasting and knowledge discovery, addressing challenges such as physical consistency, multi-scale coupling, data sparsity, robust generalization and integration with scientific workflows. This paper analyses a series of case studies from applied ML research in climate modelling, with a focus on design choices and workflow structure. Rather than reviewing technical details, we aim to synthesize workflow design patterns across diverse projects in ML-enabled climate modelling: from surrogate modelling, ML parameterization and probabilistic programming, to simulation-based inference and physics-informed transfer learning. We unpack how these workflows are grounded in physical knowledge, informed by simulation data and designed to integrate observations. We demonstrate a framework for ensuring rigour in scientific ML through more transparent model development, critical evaluation, informed adaptation and reproducibility, and aim to contribute to lowering the barrier for interdisciplinary collaboration at the interface of data science and climate modelling. This article is part of the theme issue 'Statistical workflow'. + © 2026 The Authors. + + + + Zheng + Tian + T + 0000-0003-4889-0391 + + Department of Statistics, Columbia University in the City of New York, New York, NY, USA. + + + Department of NSF STC Learning the Earth with AI and Physics (LEAP), Columbia University in the City of New York, New York, NY, USA. + + + + Venkatasubramanian + Subashree + S + + Department of NSF STC Learning the Earth with AI and Physics (LEAP), Columbia University in the City of New York, New York, NY, USA. + + + + Li + Shuolin + S + + Department of NSF STC Learning the Earth with AI and Physics (LEAP), Columbia University in the City of New York, New York, NY, USA. + + + Department of Data Science Institute, Columbia University in the City of New York, New York, NY, USA. + + + + Braverman + Amy + A + + Jet Propulsion Laboratory , Pasadena, CA, USA. + + + + Ke + Xinyi + X + + Department of Statistics, Columbia University in the City of New York, New York, NY, USA. + + + Department of NSF STC Learning the Earth with AI and Physics (LEAP), Columbia University in the City of New York, New York, NY, USA. + + + + Hou + Zhewen + Z + + Department of Statistics, Columbia University in the City of New York, New York, NY, USA. + + + + Jin + Peter + P + + Department of Applied Physics and Applied Mathematics, Columbia University in the City of New York, New York, NY, USA. + + + + Agrawal + Samarth + S + + Department of NSF STC Learning the Earth with AI and Physics (LEAP), Columbia University in the City of New York, New York, NY, USA. + + + + eng + + + Directorate for Geosciences + + + + + Journal Article + +
+ + England + Philos Trans A Math Phys Eng Sci + 101133385 + 1364-503X + + IM + + climate modelling + equation discovery + machine learning + probabilistic programming + remote sensing + simulation-based inference + surrogate models + transfer learning + workflow design + + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 33 + + + 2025 + 9 + 5 + + + 2025 + 12 + 3 + + + 2026 + 1 + 7 + + + 2026 + 5 + 28 + 10 + 23 + + + ppublish + + 42207042 + 10.1098/rsta.2025.0254 + 481924 + + + + + + 42207040 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 1471-2962 + + 384 + 2321 + + 2026 + May + 21 + + + Philosophical transactions. Series A, Mathematical, physical, and engineering sciences + Philos Trans A Math Phys Eng Sci + + A four-step simulation-based workflow for ecological analysis and science. + 20250252 + 10.1098/rsta.2025.0252 + + Ecology is a discipline that has faced increasing challenges as the disconnect between its scientific and statistical methods has become more obvious. Growing demands for useful forecasts in an era of intensifying global change requires models that can capture the variability and underlying uncertainty of ecological systems and related data. Yet many ecologists are not trained in current methods to build the flexible robust models needed to address this challenge. Thus, there is often a reliance on a limited set of predefined models combined with null hypothesis testing or a temptation to adopt new approaches without fully understanding their limitations. The result is poor models that lead to incorrect predictions, alongside concerns of a looming replication crisis. Here we show how new advances in workflows can lead to better models and enhance training in ecology. Building on the increasingly computational toolkit of many ecologists, this approach leverages simulation to integrate model building and testing of empirical data more fully with ecological theory. We argue this approach can fit models that are more robust and better-suited to providing new ecological insights and improved predictions, and may provide a blueprint for other fields similarly challenged by complex systems, growing datasets and limited training in how to best approach them. This article is part of the theme issue 'Statistical workflow'. + © 2026 The Author(s). + + + + Wolkovich + E M + EM + 0000-0001-7653-893X + + Forest and Conservation Sciences, University of British Columbia, Vancouver, BCV6T 1Z4, Canada. + + + + Davies + T Jonathan + TJ + 0000-0003-3318-5948 + + Forest and Conservation Sciences, University of British Columbia, Vancouver, BCV6T 1Z4, Canada. + + + Department of Botany, University of British Columbia, Vancouver, BCV6T 1Z4, Canada. + + + + Pearse + William D + WD + + Department of Life Sciences, Imperial College London, AscotSL5 7PY, UK. + + + Alan Turing Institute, British Library, London, NW1 2DB, UK. + + + + Betancourt + Michael + M + + Symplectomorphic, LLC , New York, NY10026, USA. + + + + eng + + Journal Article + +
+ + England + Philos Trans A Math Phys Eng Sci + 101133385 + 1364-503X + + IM + + + Ecology + methods + statistics & numerical data + + + Computer Simulation + + + Workflow + + + Ecosystem + + + + big data + data simulation + machine learning + null hypothesis testing + prediction + scientific workflow + + + + + + 2026 + 5 + 28 + 12 + 36 + + + 2026 + 5 + 28 + 12 + 35 + + + 2025 + 8 + 18 + + + 2025 + 11 + 10 + + + 2025 + 11 + 10 + + + 2026 + 5 + 28 + 10 + 23 + + + ppublish + + 42207040 + 10.1098/rsta.2025.0252 + 481850 + + + + + + 42206980 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2638-6135 + + 8 + 3 + + 2026 + Jun + + + Radiology. Cardiothoracic imaging + Radiol Cardiothorac Imaging + + Fully Automated Quantification of Functional Small Airway Disease at Inspiratory and Expiratory Chest CT Using Deep Learning. + + e250215 + e250215 + + 10.1148/ryct.250215 + + Purpose To evaluate the accuracy and time efficiency of a deep learning (DL)-based tool for automated quantification of functional small airway disease (fSAD) at chest CT compared with a conventional semimanual method. Materials and Methods This retrospective study included paired inspiratory and expiratory chest CT examinations performed from January 2016 to July 2022, with fSAD defined according to established criteria. Semimanual fSAD assessment (fSADman) was performed using commercial postprocessing software with multiple manual steps, whereas automatic fSAD assessment (fSADauto) used a DL-based tool to fully automate the analysis. Time for fSADman was measured by two cardiothoracic radiologists in 20 randomly selected cases. The Wilcoxon signed rank test assessed systematic differences between reader measurements. Agreement between fSADman and fSADauto was evaluated using Spearman correlation and Bland-Altman analysis. Classification accuracy for clinically relevant fSAD (≥28% lung involvement) was also assessed. Results The study included 249 CT examinations from 196 patients (median age, 56 [IQR, 48-65] years; 120 [61.2%] male). Median lung volumes were 5196 mL (IQR, 4315-6192 mL) at inspiration and 2802 mL (IQR, 2401-3451 mL) at expiration. Agreement between fSADman and fSADauto was excellent (Spearman r = 0.93 [95% CI: 0.89, 0.96]; P < .01), with a small bias of -1.7% on Bland-Altman analysis. For detecting fSAD of 28% or greater, fSADauto achieved 100% sensitivity, 96.8% specificity, and 97.2% accuracy. No significant systematic difference was observed between readers (Wilcoxon W = 1; P = .66). Median semimanual analysis times were 3.47 (IQR, 3.06-3.97) minutes and 5.21 (IQR, 3.57-6.65) minutes, respectively. Conclusion DL-based automated quantification of fSAD demonstrated excellent agreement with semimanual assessment and high diagnostic accuracy and may serve as an efficient and reliable alternative to the traditional semimanual method. Keywords: Pulmonary, Lung, Machine Learning, Workflow Optimization, Segmentation Supplemental material is available for this article. © RSNA, 2026. + + + + Gherca + Stefan + S + 0009-0007-9134-0623 + + Department of Radiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, CH-4031 Basel, Switzerland. + + + + Yang + Shan + S + 0000-0002-5209-0576 + + Department of Radiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, CH-4031 Basel, Switzerland. + + + + Bremerich + Jens + J + 0000-0002-1002-8483 + + Department of Radiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, CH-4031 Basel, Switzerland. + + + + Halter + Jörg + J + + Department of Hematology, University Hospital Basel, University of Basel, Basel, Switzerland. + + + + Stolz + Daiana + D + 0000-0003-0099-882X + + Department of Hematology, University Hospital Basel, University of Basel, Basel, Switzerland. + + + Clinic of Respiratory Medicine and Faculty of Medicine, University of Freiburg, Freiburg, Germany. + + + + Winkel + David J + DJ + 0000-0001-7051-8022 + + Department of Radiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, CH-4031 Basel, Switzerland. + + + + Hostettler + Katrin + K + + Clinic of Respiratory Medicine, University Hospital Basel, University of Basel, Basel, Switzerland. + + + + Pradella + Maurice + M + 0000-0003-2449-7835 + + Department of Radiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Petersgraben 4, CH-4031 Basel, Switzerland. + + + + eng + + Journal Article + +
+ + United States + Radiol Cardiothorac Imaging + 101748663 + 2638-6135 + + IM + + + Humans + + + Retrospective Studies + + + Deep Learning + + + Tomography, X-Ray Computed + methods + + + Female + + + Middle Aged + + + Male + + + Aged + + + Radiographic Image Interpretation, Computer-Assisted + methods + + + Inhalation + + + Exhalation + + + Radiography, Thoracic + methods + + + Reproducibility of Results + + + + Lung + Machine Learning + Pulmonary + Segmentation + Workflow Optimization + + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 5 + 28 + 9 + 53 + + + ppublish + + 42206980 + 10.1148/ryct.250215 + + + + + + 42206960 + + 2026 + 05 + 28 + +
+ + 2198-3844 + + + 2026 + May + 28 + + + Advanced science (Weinheim, Baden-Wurttemberg, Germany) + Adv Sci (Weinh) + + Full-Stack Architectures for Intelligent Brain-Computer Interfaces. + + e75838 + e75838 + + 10.1002/advs.75838 + + Brain-computer interfaces (BCIs) have made consistent advances in supporting motor and communication functions; nevertheless, their adoption in everyday environments remains constrained by enduring challenges, including chronic instability at the electrode-tissue interface, motion-induced artifacts, inter-user variability, and strict power and bandwidth limitations. To address these issues, recent work has increasingly focused on system-level innovations encompassing electrode design, wireless communication strategies, and neural decoding algorithms. At the interface level, enhancements in electrochemical performance and mechanical compliance improve long-term electrode-tissue coupling and help maintain signal integrity during naturalistic movement. For signal acquisition and transmission, miniaturized front-end electronics and energy-efficient telemetry architectures enable higher channel counts while minimizing power consumption and optimizing bandwidth utilization. In parallel, decoding approaches have evolved from static, feature-based pipelines toward adaptive machine-learning and deep-learning methods that are more resilient to nonstationary neural signals and capable of supporting low-latency, closed-loop operation. This review consolidates findings from contemporary preclinical and human studies to provide a comprehensive perspective on system-level engineering strategies for practical BCI technologies, emphasizing neural interface architecture and system-design approaches that enhance signal stability and real-world usability, while also identifying emerging design paradigms that may facilitate next-generation BCIs with improved scalability and broader practical impact. + © 2026 The Author(s). Advanced Science published by Wiley‐VCH GmbH. + + + + Lee + Hee Kyu + HK + + Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea. + + + + Kim + Hyun Bin + HB + + Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea. + + + + Park + Sang Uk + SU + + Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea. + + + + Joo + Janghoon + J + + Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea. + + + + Min + Jinhong + J + + Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, Republic of Korea. + + + + Lee + Geumbee + G + + School of Chemical Engineering and Applied Chemistry, Kyungpook National University, Daegu, Republic of Korea. + + + + Kang + Joohoon + J + 0000-0002-6578-2547 + + Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, Republic of Korea. + + + + Jeong + Hyoyoung + H + + Department of Electrical and Computer Engineering, University of California, Davis, CA, USA. + + + + Yoo + Jae-Young + JY + + Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea. + + + Department of Semiconductor Convergence Engineering, Sungkyunkwan University, Suwon, Republic of Korea. + + + + Won + Sang Min + SM + 0000-0002-5750-8628 + + Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea. + + + + eng + + + RS-2024-00427006 + Korea Planning & Evaluation Institute of Industrial Technology + + + + IITP-2025-RS-2020-II201821 + National Research Foundation of Korea (NRF) grant funded by the Korea government + + + + RS-2025-02303342 + National Research Foundation of Korea (NRF) grant funded by the Korea government + + + + RS-2024-00406152 + National Research Foundation of Korea (NRF) grant funded by the Korea government + + + + RS-2024-00406674 + Basic Research Laboratory Project from the National Research Foundation + + + + RS-2024-00418086 + Korea Institute for Advancement of Technology + + + + RS-2024-00435693 + Korea Institute for Advancement of Technology + + + + + Journal Article + Review + + + 2026 + 05 + 28 + +
+ + Germany + Adv Sci (Weinh) + 101664569 + 2198-3844 + + IM + + brain–computer interface + chronic signal stability + closed‐loop BCI + electrode–tissue interface + neural decoding + neural interface + wireless neural recording + + + + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 4 + 28 + + + 2026 + 2 + 27 + + + 2026 + 5 + 16 + + + 2026 + 5 + 28 + 9 + 43 + + + aheadofprint + + 42206960 + 10.1002/advs.75838 + + + + J. Martín‐Fernández, N. Caballero‐Estebaranz, and E. Félez, “Where Are Higher‐Order Cognitive Functions? The Paradox of Non‐Locality in Awake Cognitive Mapping Using a Complex Dynamic System Framework,” Frontiers in Psychology 16 (2025): 1542505, https://doi.org/10.3389/fpsyg.2025.1542505. + + + A. Jackson and T. M. Hall, “Decoding Local Field Potentials for Neural Interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, no. 10 (2016): 1705–1714, https://doi.org/10.1109/TNSRE.2016.2612001. + + + C. T. Nordhausen, E. M. Maynard, and R. A. Normann, “Single Unit Recording Capabilities of a 100 Microelectrode Array,” Brain Research 726, no. 1‐2 (1996): 129–140, https://doi.org/10.1016/0006‐8993(96)00321‐6. + + + Y. Dong, S. Wang, Q. Huang, R. W. Berg, G. Li, and J. He, “Neural Decoding for Intracortical Brain–Computer Interfaces,” Cyborg and Bionic Systems 4 (2023): 0044, https://doi.org/10.34133/cbsystems.0044. + + + K. Shen, O. Chen, J. L. Edmunds, D. K. Piech, and M. M. Maharbiz, “Translational Opportunities and Challenges of Invasive Electrodes for Neural Interfaces,” Nature Biomedical Engineering 7, no. 4 (2023): 424–442, https://doi.org/10.1038/s41551‐023‐01021‐5. + + + L. F. Nicolas‐Alonso and J. Gomez‐Gil, “Brain Computer Interfaces, a Review,” sensors 12, no. 2 (2012): 1211–1279, https://doi.org/10.3390/s120201211. + + + J. R. Wolpaw, “Brain–Computer Interfaces,” in Handbook of Clinical Neurology (Elsevier, 2013), 0072–9752. + + + B. He, H. Yuan, J. Meng, and S. 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+ + 1530-0293 + + + 2026 + May + 28 + + + Critical care medicine + Crit Care Med + + Traumatic Inflammatory-Coagulation Coactivation Occurs Early After Injury and Impacts Mortality. + 10.1097/CCM.0000000000007224 + + Acute traumatic coagulopathy is known to occur early following severe injury. However, the role and impact of both pro- and anti-inflammatory cytokines are unknown. We aimed to investigate the inflammatory (pro- and anti-inflammatory cytokines) and coagulation pathways following injury and their relation to 30-day mortality. + Patients included in this analysis had complete data available for a panel of 48 coagulation and inflammatory biomarkers obtained from initial emergency department blood draws. Principal component analysis (PCA), a dimensionality-reduction machine learning method, was used to identify biomarker phenotypes present. Principal components with variance (eigenvalues) greater than 1 were selected for inclusion in the analysis. Least Absolute Shrinkage and Selection Operator regression was performed to identify independent predictors of 30-day mortality. The critical administration threshold (CAT, > 3 units of RBCs in 1 hr) was used to quantify bleeding. + This is a secondary analysis of the Pragmatic Randomized Optimal Platelet and Plasma Ratio (PROPPR) study. + The PROPPR study was a pragmatic, phase 3, multisite, randomized clinical trial that included 680 severely injured patients at 12 level 1 trauma centers. + None. + Inflammatory and coagulation marker phenotypes were associated with 30-day mortality. Among 286 patients included, 30-day mortality was 24.4% (n = 70). PCA found 14 phenotypes that collectively explained 83% of pathophysiologic variability. The top six principal components contributing to the early pathophysiology changes were a coagulopathy phenotype, followed by two distinct mixed inflammatory phenotypes, a platelet phenotype representing platelet activation integrins, von Willebrand Factor (vWF), and a thromboelastography phenotype representing clot strength and thrombin generation. Controlling for age, sex, injury mechanism, and bleeding (CAT+), mortality was predominantly driven by the coagulopathy phenotype 1 (p = 0.0002) and mixed inflammatory phenotype 3 (p = 0.0684). + These findings demonstrate that coagulation and inflammation are coactivated early after severe injury and are associated with poor outcomes. The activation of these pathways is both complex and integrated, involving inflammatory cytokines, chemokines, growth factors, coagulation factors, platelet activated integrins, vWF, and thrombin. This study uses the largest comprehensive array of coagulation and inflammatory biomarkers to date and identifies key biologic phenotypes linked to mortality after severe trauma. + Copyright © 2026 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. + + + + Riccardi + Julia + J + 0009-0003-4672-440 + + Department of Surgery, University of California Davis, Sacramento, CA. + + + + Robles + Anamaria J + AJ + + Department of Surgery, University of California Davis, Sacramento, CA. + + + + Ross + James T + JT + + Department of Surgery, Case Western Reserve, Cleveland, OH. + + + The Blood, Heart, Lung, and Immunology Research Center at Case Western Reserve University and University Hospitals Cleveland, Cleveland, OH. + + + + Lewis + Carrie + C + + Department of Surgery, University of California Davis, Sacramento, CA. + + + + Bellini + Alyssa R + AR + + Department of Surgery, University of California Davis, Sacramento, CA. + + + + Mell + Matthew + M + + Department of Surgery, University of California Davis, Sacramento, CA. + + + + Holcomb + John B + JB + + Department of Surgery, University of Alabama at Birmingham, Birmingham, AL. + + + + Callcut + Rachael A + RA + + Department of Surgery, University of California Davis, Sacramento, CA. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Crit Care Med + 0355501 + 0090-3493 + + IM + + coagulation + inflammation + inflammatory reaction + injury + trauma + + Dr. Riccardi’s institution received funding from the National Heart, Lung, and Blood Institute (NHLBI; RO1 HL149670; UO1 HL077863). Drs. Riccardi, Robles, Lewis, Mell, and Callcut received support for article research from the National Institutes of Health (NIH). Dr. Bellini’s institution received funding from the National Center for Advancing Translational Sciences (UL1 TR001860). Dr. Holcomb received funding from Aspen Medical, Infrascan, Geneva, QinFlow Chair, Zibrio, Hemostatics, MiNK Therapeutics, Obvius Robotics, and UT Health; he disclosed that he is co-inventor of the Junctional Emergency Tourniquet Tool. Dr. Callcut’s institution received funding from the NHLBI, the NIH, GE Healthcare, CSL Bearing, and Humacyte; she received funding from BeeKeeperAI. Dr. Ross has disclosed that he does not have any potential conflicts of interest. + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 9 + 33 + + + aheadofprint + + 42206924 + 10.1097/CCM.0000000000007224 + 00003246-990000000-00854 + + + + Haagsma JA, Graetz N, Bolliger I, et al.: The global burden of injury: Incidence, mortality, disability-adjusted life years and time trends from the global burden of disease study 2013. Inj Prev 2016; 22:3–18 + + + Rhee P, Joseph B, Pandit V, et al.: Increasing trauma deaths in the United States. Ann Surg 2014; 260:13–21 + + + Xiao W, Mindrinos MN, Seok J, et al.; Inflammation and Host Response to Injury Large-Scale Collaborative Research Program: A genomic storm in critically injured humans. J Exp Med 2011; 208:2581–2590 + + + Hotchkiss RS, Karl IE: The pathophysiology and treatment of sepsis. N Engl J Med 2003; 348:138–150 + + + Darlington DN, Gonzales MD, Craig T, et al.: Trauma-induced coagulopathy is associated with a complex inflammatory response in the rat. Shock 2015; 44:129–137 + + + Savage SA, Zarzaur BL, Fox EE, et al.: Admission thromboelastography at the intersection of traumatic coagulopathy and inflammation: A pragmatic, randomized optimal platelet and plasma ratios (PROPPR) study subanalysis. J Am Coll Surg 2023; 237:259–269 + + + Cardenas JC, Wade CE, Cotton BA, et al.; PROPPR Study Group: TEG lysis shutdown represents coagulopathy in bleeding trauma patients: Analysis of the PROPPR cohort. Shock 2019; 51:273–283 + + + Savage SA, Zarzaur BL, Gaski GE, et al.: Insights into the association between coagulopathy and inflammation: Abnormal clot mechanics are a warning of immunologic dysregulation following major injury. Ann Transl Med 2020; 8:1576 + + + Lenz A, Franklin GA, Cheadle WG: Systemic inflammation after trauma. Injury 2007; 38:1336–1345 + + + Giannoudis P: Current concepts of the inflammatory response after major trauma: An update. Injury 2003; 34:397–404 + + + Keel M, Trentz O: Pathophysiology of polytrauma. Injury 2005; 36:691–709 + + + Xu J, Zhang X, Pelayo R, et al.: Extracellular histones are major mediators of death in sepsis. Nat Med 2009; 15:1318–1321 + + + Zhang Q, Raoof M, Chen Y, et al.: Circulating mitochondrial DAMPs cause inflammatory responses to injury. Nature 2010; 464:104–107 + + + Wei S, Rodriguez EG, Chang R, et al.: Elevated syndecan-1 after trauma and risk of sepsis: A secondary analysis of patients from the pragmatic, randomized optimal platelet and plasma ratios (PROPPR) trial. J Am Coll Surg 2018; 227:587–595 + + + Meledeo MA, Herzig MC, Bynum JA, et al.: Acute traumatic coagulopathy: The elephant in a room of blind scientists. J Trauma Acute Care Surg 2017; 82:S33–S40 + + + McCully BH, Wade CE, Fox EE, et al.; PROPPR study group: Temporal profile of the pro-and anti-inflammatory responses to severe hemorrhage in patients with venous thromboembolism: Findings from the PROPPR trial. J Trauma Acute Care Surg 2021; 90:845–852 + + + Holcomb JB, Tilley BC, Baraniuk S, et al.; PROPPR Study Group: Transfusion of plasma, platelets, and red blood cells in a 1: 1: 1 vs a 1: 1: 2 ratio and mortality in patients with severe trauma: The PROPPR randomized clinical trial. JAMA 2015; 313:471–482 + + + Kunitake RC, Kornblith LZ, Cohen MJ, et al.: Trauma early mortality prediction tool (TEMPT) for assessing 28-day mortality. Trauma Surg Acute Care Open 2018; 3:e000131 + + + Brohi K, Cohen MJ, Ganter MT, et al.: Acute traumatic coagulopathy: Initiated by hypoperfusion: Modulated through the protein C pathway? Ann Surg 2007; 245:812–818 + + + Cohen MJ, Call M, Nelson M, et al.: Critical role of activated protein C in early coagulopathy and later organ failure, infection and death in trauma patients. Ann Surg 2012; 255:379–385 + + + Christiaans SC, Wagener BM, Esmon CT, et al.: Protein C and acute inflammation: A clinical and biological perspective. Am J Physiol Lung Cell Mol Physiol 2013; 305:L455–L466 + + + Jansen JO, Scarpelini S, Pinto R, et al.: Hypoperfusion in severely injured trauma patients is associated with reduced coagulation factor activity. J Trauma 2011; 71:S435–S440 + + + Zulian MC, Chedid MF, Chedid AD, et al.: Low serum factor V level: Early predictor of allograft failure and death following liver transplantation. Langenbecks Arch Surg 2015; 400:589–597 + + + Dobson GP, Letson HL, Sharma R, et al.: Mechanisms of early trauma-induced coagulopathy: The clot thickens or not? J Trauma Acute Care Surg 2015; 79:301–309 + + + Oshiro A, Yanagida Y, Gando S, et al.: Hemostasis during the early stages of trauma: Comparison with disseminated intravascular coagulation. Crit Care 2014; 18:R61 + + + Sawamura A, Hayakawa M, Gando S, et al.: Disseminated intravascular coagulation with a fibrinolytic phenotype at an early phase of trauma predicts mortality. Thromb Res 2009; 124:608–613 + + + Reikerås O, Borgen P: Activation of markers of inflammation, coagulation and fibrinolysis in musculoskeletal trauma. PLoS One 2014; 9:e107881 + + + Sauaia A, Moore FA, Moore EE: Postinjury inflammation and organ dysfunction. Crit Care Clin 2017; 33:167–191 + + + Volpin G, Cohen M, Assaf M, et al.: Cytokine levels (IL-4, IL-6, IL-8 and TGFβ) as potential biomarkers of systemic inflammatory response in trauma patients. 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J Trauma Acute Care Surg 2015; 78:535–542 + + + Jenne CN, Kubes P: Platelets in inflammation and infection. Platelets 2015; 26:286–292 + + + Zarbock A, Polanowska-Grabowska RK, Ley K: Platelet-neutrophil-interactions: Linking hemostasis and inflammation. Blood Rev 2007; 21:99–111 + + + Mezu-Ndubuisi OJ, Maheshwari A: The role of integrins in inflammation and angiogenesis. Pediatr Res 2021; 89:1619–1626 + + + Herter JM, Rossaint J, Zarbock A: Platelets in inflammation and immunity. J Thromb Haemost 2014; 12:1764–1775 + + + Estevez B, Du X: New concepts and mechanisms of platelet activation signaling. Physiology (Bethesda) 2017; 32:162–177 + + + + + + + 42206849 + + 2026 + 05 + 28 + +
+ + 2379-5077 + + + 2026 + May + 28 + + + mSystems + mSystems + + Early-life proteomic and microbiome features signal obesity risk across 26 years of follow-up. + + e0142425 + e0142425 + + 10.1128/msystems.01424-25 + + Childhood obesity is rising globally. Yet, few studies have examined the microbiome and proteome in early childhood in relation to this outcome, and most are cross-sectional by design. Early-life factors in the ABIS birth cohort (n = 16,683) were associated with obesity up to age 26 (mean follow-up 25.3 years, range 23.7-26.5 years): psychosocial stressors, smoking, infections, and diet in the first year. We assessed biomarkers, including cord blood metabolome (n = 290) and proteome (n = 358), by liquid chromatography, mass spectrometry, and Olink. Gut microbial composition at age one (n = 1,743) was assessed using stool samples and 16S rRNA sequencing. In this prospective longitudinal cohort study, significant differences were found in infants with future obesity, including elevated angiopoietin-like 4 (ANGPTL4), follistatin, and hepatocyte growth factor (independently of maternal weight) and reduced isocaproic acid, tryptophan, and oleic acid, with prenatal mediation. Akkermansia, asaccharolytic bacteria (Phascolarctobacterium and Senegalimassiliensia), and equol-producers (Adlercreutzia and Slackia) were depleted. Machine learning models selecting 40 most predictive features showed long-term prediction from birth proteomics and bacterial taxa at age one (area under the curve [AUC] = 0.83 ± .05, n = 1,877) and additional metrics, for example, parental and child body mass index in the first 8 years (AUC = 0.89 ± .02, n = 1,877), suggesting durable biological encoding. Proteomic markers across folds included fibroblast growth factor 19, ANGPTL4, sulfotransferase family 2A member 1, and interleukin 20. These findings suggest clinically relevant biomarkers indicating early-life regulation of bile acid metabolism, lipid storage vs. oxidation, and immune-metabolic signaling and pathways to prospectively prevent childhood- and adult-onset obesity across a 26-year predictive gap. + Understanding the origins of obesity is critical for developing preventive strategies, and early life represents a particularly sensitive window. This study leverages a large, general-population cohort with prospectively collected data, including parental body mass index (BMI), cord blood proteomics, and the gut microbiome at age one, linked to obesity outcomes over 26 years. Using integrated machine learning models, we show that in addition to parental BMI, specific proteomic and microbial markers present in infancy can predict long-term obesity risk, highlighting the role of early metabolic programming. Several key markers point to bile acid signaling as a mechanism connecting early microbiome development with fat accumulation and insulin regulation. By identifying these early-life predictors long before obesity manifests, these results provide new insights into intergenerational risk and suggest measurable targets for preventing obesity and related metabolic disorders from the earliest stages of life. + + + + Ahrens + Angelica P + AP + 0000-0002-1478-7559 + + Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, College of Agricultural and Life Sciences, University of Florida, Gainesville, Florida, USA. + 3463 + https://ror.org/02y3ad647 + + + + Dias + Raquel + R + + Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, College of Agricultural and Life Sciences, University of Florida, Gainesville, Florida, USA. + 3463 + https://ror.org/02y3ad647 + + + + Hyötyläinen + Tuulia + T + + School of Science and Technology, Örebro University, Örebro, Sweden. + 6233 + https://ror.org/05kytsw45 + + + + White + Pär Anderson + PA + + Crown Princess Victoria Children's Hospital and Division of Pediatrics, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden. + 4566 + https://ror.org/05ynxx418 + + + + Orešič + Matej + M + + School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden. + 6233 + https://ror.org/05kytsw45 + + + Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland. + 8058 + https://ror.org/05vghhr25 + + + Department of Life Technologies, University of Turku, Turku, Finland. + 8058 + https://ror.org/05vghhr25 + + + + Triplett + Eric W + EW + 0000-0002-1845-4866 + + Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, College of Agricultural and Life Sciences, University of Florida, Gainesville, Florida, USA. + 3463 + https://ror.org/02y3ad647 + + + + Ludvigsson + Johnny + J + + Crown Princess Victoria Children's Hospital and Division of Pediatrics, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden. + 4566 + https://ror.org/05ynxx418 + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + mSystems + 101680636 + 2379-5077 + + IM + + ANGPTL4 + FGF19 + SULT2A1 + bile acids + carbohydrates + diabetes + environmental toxins + inflammation + machine learning + metabolic disease + metabolome + microbiome + pregnancy + + + + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 5 + 28 + 9 + 2 + + + aheadofprint + + 42206849 + 10.1128/msystems.01424-25 + + + + + + 42206744 + + 2026 + 05 + 28 + +
+ + 1531-8249 + + + 2026 + May + 28 + + + Annals of neurology + Ann Neurol + + Idiopathic Intracranial Hypertension Is Characterized by a Distinct Proteomic Profile. + 10.1002/ana.78261 + + The pathophysiology of idiopathic intracranial hypertension (IIH) is poorly understood and disease-specific biomarkers are lacking. We aimed to shed light on IIH pathophysiology and identify disease-specific biomarkers. + This prospective cross-sectional cohort study included patients with new-onset IIH and age-, body mass index-, and sex-matched healthy controls from 2 tertiary Danish Headache Centers (discovery cohort). Liquid chromatography coupled to tandem mass spectrometry analysis was used to measure reproducible proteomic profiles for paired serum and cerebrospinal fluid (CSF) samples. Results were validated using 3 validation cohorts comprising patients with IIH (pwIIH), cerebral sinus venous thrombosis (pwCSVT), and normal pressure hydrocephalus (pwNPH). + We included 53 pwIIH and 35 controls. PwIIH presented with papilledema and increased median lumbar puncture opening pressure (interquartile range [IQR]) (38.0 [33-46] vs 19.0 [17.0-23.0] cmH2O; p < 0.001) compared to controls. Machine learning analysis identified 20 IIH-predicting proteins for serum and CSF segregating pwIIH, but neither pwCSVT nor pwNPH, from controls with areas under the curve (AUC) of 0.84 and 0.92 (pwIIH, discovery cohort), 0.99 and 0.90 (pwIIH, validation cohort 1), 0.63 (plasma; pwCSVT), and 0.67 (CSF; pwNPH), respectively. Serum carbonic anhydrases (CA) 1 and 2 were upregulated and among the most IIH-predicting proteins as were amyloid precursor protein (APP), S100P, and S100A12. + We identified a panel of independently validated IIH-specific candidate biomarkers. The serum candidate biomarkers pointed toward CA2 driven CSF hypersecretion in IIH providing a possible explanation for the therapeutic efficacy of CA inhibitors. The regulation of markers associated with neuronal impairment (APP, S100P/S100A12) confirmed the non-benign character of IIH. ANN NEUROL 2026. + © 2026 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association. + + + + Bhosale + Santosh D + SD + 0000-0002-3841-452X + + Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark. + + + + Nawrocki + Arkadiusz + A + + Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark. + + + + Korsbæk + Johanne J + JJ + + Danish Headache Center, Department of Neurology, Rigshospitalet-Glostrup, University of Copenhagen, Copenhagen, Denmark. + + + + Hansen + Nadja S + NS + 0000-0002-5488-7992 + + Danish Headache Center, Department of Neurology, Rigshospitalet-Glostrup, University of Copenhagen, Copenhagen, Denmark. + + + + Foettinger + Fabian + F + 0009-0005-7970-5349 + + Department of Neurology, Medical University of Vienna, Vienna, Austria. + + + Medical University of Vienna, Comprehensive Center for Clinical Neurosciences and Mental Health, Vienna, Austria. + + + + Krajnc + Nik + N + 0000-0002-4146-5870 + + Department of Neurology, Medical University of Vienna, Vienna, Austria. + + + Medical University of Vienna, Comprehensive Center for Clinical Neurosciences and Mental Health, Vienna, Austria. + + + + Norvig + Mathias J + MJ + + Department of Neurosurgery, Odense University Hospital, Odense, Denmark. + + + Department of Clinical Research and BRIDGE (Brain Research Inter Disciplinary Guided Excellence), University of Southern Denmark, Odense, Denmark. + + + + Eriksen + Niclas L + NL + + Department of Neurosurgery, Odense University Hospital, Odense, Denmark. + + + Department of Clinical Research and BRIDGE (Brain Research Inter Disciplinary Guided Excellence), University of Southern Denmark, Odense, Denmark. + + + + Macher + Stefan + S + 0000-0001-8068-1528 + + Department of Neurology, Medical University of Vienna, Vienna, Austria. + + + Medical University of Vienna, Comprehensive Center for Clinical Neurosciences and Mental Health, Vienna, Austria. + + + + Pemp + Berthold + B + 0000-0002-0569-0229 + + Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria. + + + + Westgate + Connar S J + CSJ + 0000-0001-5066-8306 + + Danish Headache Center, Department of Neurology, Rigshospitalet-Glostrup, University of Copenhagen, Copenhagen, Denmark. + + + Translational Research Centre, Rigshospitalet, Copenhagen, Denmark. + + + + Pedersen + Christian B + CB + + Department of Neurosurgery, Odense University Hospital, Odense, Denmark. + + + Department of Clinical Research and BRIDGE (Brain Research Inter Disciplinary Guided Excellence), University of Southern Denmark, Odense, Denmark. + + + + Poulsen + Frantz R + FR + + Department of Neurosurgery, Odense University Hospital, Odense, Denmark. + + + Department of Clinical Research and BRIDGE (Brain Research Inter Disciplinary Guided Excellence), University of Southern Denmark, Odense, Denmark. + + + + Munthe + Sune + S + + Department of Neurosurgery, Odense University Hospital, Odense, Denmark. + + + Department of Clinical Research and BRIDGE (Brain Research Inter Disciplinary Guided Excellence), University of Southern Denmark, Odense, Denmark. + + + + Bsteh + Gabriel + G + 0000-0002-0825-0851 + + Department of Neurology, Medical University of Vienna, Vienna, Austria. + + + Medical University of Vienna, Comprehensive Center for Clinical Neurosciences and Mental Health, Vienna, Austria. + + + + Larsen + Martin R + MR + + Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark. + + + + Jensen + Rigmor H + RH + + Danish Headache Center, Department of Neurology, Rigshospitalet-Glostrup, University of Copenhagen, Copenhagen, Denmark. + + + Translational Research Centre, Rigshospitalet, Copenhagen, Denmark. + + + + Beier + Dagmar + D + 0000-0002-3336-157X + + Department of Clinical Research and BRIDGE (Brain Research Inter Disciplinary Guided Excellence), University of Southern Denmark, Odense, Denmark. + + + Department of Neurology, Odense University Hospital, Odense, Denmark. + + + OPEN, Odense Patient data Explorative Network, Odense University Hospital, Odense, Denmark. + + + + eng + + + 21009G.B. + Medical Scientific Fund of the Mayor of the City of Vienna + + + + 276-2018-403 + Lundbeck Foundation + + + + 25-A1320 + Candys foundation and Odense University Hospital and Rigshospitalet + + + + 69-A3346 + Candys foundation and Odense University Hospital and Rigshospitalet + + + + e-fond 177 + Odense University Hospital + + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Ann Neurol + 7707449 + 0364-5134 + + IM + + + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 5 + 6 + + + 2024 + 11 + 6 + + + 2026 + 5 + 11 + + + 2026 + 5 + 28 + 8 + 3 + + + aheadofprint + + 42206744 + 10.1002/ana.78261 + + + + Mollan SP, Davies B, Silver NC, et al. Idiopathic intracranial hypertension: consensus guidelines on management. J Neurol Neurosurg Psychiatry 2018;89:1088–1100. + + + Friedman DI, Liu GT, Digre KB. Revised diagnostic criteria for the pseudotumor cerebri syndrome in adults and children. Neurology 2013;81:1159–1165. + + + Korsbaek JJ, Jensen RH, Hogedal L, et al. Diagnosis of idiopathic intracranial hypertension: a proposal for evidence‐based diagnostic criteria. Cephalalgia 2023;43:3331024231152795. + + + Westgate CS, Botfield HF, Alimajstorovic Z, et al. Systemic and adipocyte transcriptional and metabolic dysregulation in idiopathic intracranial hypertension. JCI Insight 2021;6:e145346. + + + Hornby C, Botfield H, O'Reilly MW, et al. Evaluating the fat distribution in idiopathic intracranial hypertension using dual‐energy X‐ray absorptiometry scanning. Neuroophthalmology 2018;42:99–104. + + + Berdahl JP, Fleischman D, Zaydlarova J, et al. Body mass index has a linear relationship with cerebrospinal fluid pressure. Invest Ophthalmol Vis Sci 2012;53:1422–1427. + + + Fain JN. Release of interleukins and other inflammatory cytokines by human adipose tissue is enhanced in obesity and primarily due to the nonfat cells. Vitam Horm 2006;74:443–477. + + + Sinclair AJ, Ball AK, Burdon MA, et al. Exploring the pathogenesis of IIH: an inflammatory perspective. J Neuroimmunol 2008;201:212–220. + + + O'Reilly MW, Westgate CS, Hornby C, et al. A unique androgen excess signature in idiopathic intracranial hypertension is linked to cerebrospinal fluid dynamics. JCI Insight 2019;4:e125348. + + + Hornby C, Mollan SP, Botfield H, et al. Metabolic concepts in idiopathic intracranial hypertension and their potential for therapeutic intervention. J Neuroophthalmol 2018;38:522–530. + + + Markey K, Mitchell J, Botfield H, et al. 11beta‐Hydroxysteroid dehydrogenase type 1 inhibition in idiopathic intracranial hypertension: a double‐blind randomized controlled trial. Brain Commun 2020;2:fcz050. + + + Lecube A, Poca MA, Colomé N, et al. Proteomic analysis of cerebrospinal fluid from obese women with idiopathic intracranial hypertension: a new approach for identifying new candidates in the pathogenesis of obesity. J Neuroendocrinol 2012;24:944–952. + + + Eide PK, Pripp AH, Ringstad G, Valnes LM. Impaired glymphatic function in idiopathic intracranial hypertension. Brain Commun 2021;3:fcab043. + + + Fargen KM. Idiopathic intracranial hypertension is not idiopathic: proposal for a new nomenclature and patient classification. J Neurointerv Surg 2020;12:110–114. + + + Geyer PE, Wewer Albrechtsen NJ, Tyanova S, et al. Proteomics reveals the effects of sustained weight loss on the human plasma proteome. Mol Syst Biol 2016;12:901. + + + Bader JM, Geyer PE, Muller JB, et al. Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer's disease. Mol Syst Biol 2020;16:e9356. + + + Tao QQ, Cai X, Xue YY, et al. Alzheimer's disease early diagnostic and staging biomarkers revealed by large‐scale cerebrospinal fluid and serum proteomic profiling. Innovation (Camb) 2024;5:100544. + + + Brettschneider J, Hartmann N, Lehmensiek V, et al. Cerebrospinal fluid markers of idiopathic intracranial hypertension: is the renin‐angiotensinogen system involved? Cephalalgia 2011;31:116–121. + + + Pandit AK, Misra S, Sengupta S, et al. Cerebrospinal fluid proteins in idiopathic intracranial hypertension: an exploratory SWATH proteomics analysis. Proteomics Clin Appl 2023;18:2300021. + + + Grech O, Seneviratne SY, Alimajstorovic Z, et al. Nuclear magnetic resonance spectroscopy metabolomics in idiopathic intracranial hypertension to identify markers of disease and headache. Neurology 2022;99:E1702–E1714. + + + Alimajstorovic Z, Mollan SP, Grech O, et al. Dysregulation of amino acid, lipid, and Acylpyruvate metabolism in idiopathic intracranial hypertension: a non‐targeted case control and longitudinal Metabolomic study. J Proteome Res 2023;22:1127–1137. + + + Wang MTM, Bhatti MT, Danesh‐Meyer HV. Idiopathic intracranial hypertension: pathophysiology, diagnosis and management. J Clin Neurosci 2022;95:172–179. + + + Beier D, Korsbæk JJ, Bsteh G, et al. Magnetic resonance imaging signs of idiopathic intracranial hypertension. JAMA Netw Open 2024;7:e2420138. + + + Korsbaek JJ, Beier D, Hagen SM, et al. Psychiatric comorbidities in patients with idiopathic intracranial hypertension: a prospective cohort study. Neurology 2022;99:e199–e208. + + + Molander LD, Hagen SM, Hansen NS, et al. Patterns of retinal damage and visual long‐term consequences in patients with idiopathic intracranial hypertension. Neurology 2025;105:e214335. + + + Polpitiya AD, Qian WJ, Jaitly N, et al. DAnTE: a statistical tool for quantitative analysis of ‐omics data. Bioinformatics 2008;24:1556–1558. + + + Gao B, Zhu J, Negi S, et al. Quickomics: exploring omics data in an intuitive, interactive and informative manner. Bioinformatics 2021;37:3670–3672. + + + Li J, Miao B, Wang S, et al. Hiplot: a comprehensive and easy‐to‐use web service for boosting publication‐ready biomedical data visualization. Brief Bioinform 2022;23:bbac261. + + + Wang S, Zhong Y, Cheng J, Yang H. EnrichVisBox: a versatile and powerful web toolbox for visualizing complex functional enrichment results of omics data. J Comput Biol 2021;28:922–930. + + + Torun FM, Virreira Winter S, Doll S, et al. Transparent exploration of machine learning for biomarker discovery from proteomics and omics data. J Proteome Res 2023;22:359–367. + + + Karayel O, Virreira Winter S, Padmanabhan S, et al. Proteome profiling of cerebrospinal fluid reveals biomarker candidates for Parkinson's disease. Cell Rep Med 2022;3:100661. + + + Smit ER, Kreft IC, Camilleri E, et al. Exploration of the plasma proteomic profile of patients at risk of thromboembolic events. Res Pract Thromb Haemost 2025;9:102713. + + + Zhang X, Shen R, Shu Z, et al. S100A12 promotes inflammation and apoptosis in ischemia/reperfusion injury via ERK signaling in vitro study using PC12 cells. Pathol Int 2020;70:403–412. + + + Pircher A, Montali M, Berberat J, et al. Elevated perioptic lipocalin‐type prostaglandin D synthase concentration in patients with idiopathic intracranial hypertension. Brain Commun 2022;4:fcac240. + + + Damkier HH, Praetorius J. Cerebrospinal fluid pH regulation. Pflugers Arch 2024;476:467–478. + + + Blandina P, Provensi G, Passsani MB, et al. Carbonic anhydrase modulation of emotional memory. Implications for the treatment of cognitive disorders. J Enzyme Inhib Med Chem 2020;35:1206–1214. + + + Cheng K, Wang Y, He Y, et al. Upregulation of carbonic anhydrase 1 beneficial for depressive disorder. Acta Neuropathol Commun 2023;11:59. + + + Korsbaek JJ, Jensen RH, Beier D, et al. Metabolic dysfunction in new‐onset idiopathic intracranial hypertension: identification of novel biomarkers. Ann Neurol 2024;96:595–607. + + + Valderrama‐Mantilla AI, Martin‐Cuevas C, Gomez‐Garrido A, et al. Shared molecular signature in Alzheimer's disease and schizophrenia: a systematic review of the reelin signaling pathway. Neurosci Biobehav Rev 2025;169:106032. + + + Munoz U, Sebal C, Escudero E, et al. Main role of antibodies in demyelination and axonal damage in multiple sclerosis. Cell Mol Neurobiol 2022;42:1809–1827. + + + Cong Y, Cui Y, Wang S, et al. Calcium‐binding protein S100P promotes tumor progression but enhances Chemosensitivity in breast cancer. Front Oncol 2020;10:566302. + + + Cho YE, Latour LL, Kim H, et al. Older age results in differential gene expression after mild traumatic brain injury and is linked to imaging differences at acute follow‐up. Front Aging Neurosci 2016;8:168. + + + Mikkelsen SE, Novitskaya V, Kriajevska M, et al. S100A12 protein is a strong inducer of neurite outgrowth from primary hippocampal neurons. J Neurochem 2001;79:767–776. + + + Qiu SZ, Zheng GR, Ma CY, et al. High serum S100A12 levels predict poor outcome after acute primary intracerebral hemorrhage. Neuropsychiatr Dis Treat 2021;17:3245–3253. + + + Wakisaka Y, Ago T, Kamouchi M, et al. Plasma S100A12 is associated with functional outcome after ischemic stroke: research for biomarkers in ischemic stroke. J Neurol Sci 2014;340:75–79. + + + Nocentini ASC. Human carbonic anhydrases: tissue distribution, physiologic role, and druggability. Amsterdam: Elsevier, 2019:149‐185. + + + Eide PK, Hasan‐Olive MM, Hansson HA, Enger R. Increased occurrence of pathological mitochondria in astrocytic perivascular endfoot processes and neurons of idiopathic intracranial hypertension. J Neurosci Res 2021;99:467–480. + + + Snider S, Albano L, Gagliardi F, et al. Substantially elevated serum glutamate and CSF GOT‐1 levels associated with cerebral ischemia and poor neurological outcomes in subarachnoid hemorrhage patients. Sci Rep 2023;13:5246. + + + Kolanek A, Cemaga R, Maciejczyk M. Role and diagnostic significance of Apolipoprotein D in selected neurodegenerative disorders. Diagnostics (Basel) 2024;14:14. + + + Bandyopadhyay GK, Mahata SK. Chromogranin a regulation of obesity and peripheral insulin sensitivity. Front Endocrinol (Lausanne) 2017;8:20. + + + + + + + 42206627 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 1557-8127 + + 24 + 5 + + 2026 + Jul + + + Assay and drug development technologies + Assay Drug Dev Technol + + Indolizine Compound Selection for HPV Anticancer Active Prediction Using CNN Classifier with ADME Descriptors. + + 381 + 413 + 381-413 + + 10.1177/1540658X261429312 + + + Despite the significant progress made in developing different in silico methodology for structure activity research over the past few decades. The ability to predict correlation structure activity (CSA) from absorption distribution metabolism excretion (ADME) descriptors to select indolizine compounds for human papilloma virus (HPV) anticancer activity continues to pose a challenge. This study employed five machine learning (ML) algorithms for classification, viz., stochastic gradient descent (SGD), random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and logistic regression (LR), to perform the classification based on ADME-related physiochemical descriptors of 8,900 indolizine compounds to predict the CSA. The present study focuses on 26 well-known parameters to optimize the results, which are utilized for ML models SGD, RF, SVM, CNN, and LR for classification. The CNN achieved the best results with the highest overall accuracy and average loss values of 98.33% and 0.16, respectively. On the other hand, the SGD, RF, SVM, and LR recorded the accuracy values of 95.32%, 93.23%, 96.03%, 94.03%, and loss values of 0.046, 0.067, 0.039, and 0.059, respectively. It is stated that from the obtained results, the CNN is performing better compared to other methods. The cross-validation and results are done with the relationship of descriptors, viz., accuracy, correlation, distribution, area under the receiver operating characteristic, area under the precision recall curve, and bootstrap error analysis. This study demonstrated the utility of ML to facilitate early prediction of indolizine compounds for HPV anticancer activity in preclinical development. + + + + + Mahaur + Sangeeta + S + 0000-0003-0527-4230 + + Faculty of Pharmacy, IFTM University, Moradabad, India. + + + + Upadhyay + Sukirti + S + + School of Pharmaceutical Sciences, IFTM University, Moradabad, India. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Assay Drug Dev Technol + 101151468 + 1540-658X + + + + 0 + Antineoplastic Agents + + + 0 + Indolizines + + + 274-40-8 + indolizine + + + IM + + + Antineoplastic Agents + pharmacology + chemistry + + + Indolizines + pharmacology + chemistry + pharmacokinetics + + + Humans + + + Human Papillomavirus Viruses + drug effects + + + Convolutional Neural Networks + + + Prediction Algorithms + + + Classification Algorithms + + + + ADME + CNN + HPV + LR + RF + SGD + SVM + cancer + indolizine + + DISCLOSURE STATEMENTThe authors have declared that no competing interests exist. + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 5 + 28 + 7 + 22 + + + ppublish + + 42206627 + 10.1177/1540658X261429312 + + + + + + 42206593 + + 2026 + 05 + 28 + +
+ + 1549-960X + + + 2026 + May + 28 + + + Journal of chemical information and modeling + J Chem Inf Model + + Are We Underestimating Overfitting? + 10.1021/acs.jcim.6c00518 + + It is a well-established dogma in quantitative structure-activity relationships (QSAR) that parsimonious models generalize best and that overfitting must be avoided. As the number of fitted parameters in a model approaches the number of training examples, the training errors decrease and the external test set errors usually increase substantially. This makes intuitive sense, but recent publications on overfitting and overparameterization, and the dramatic rise in complex but very useful deep learning models, have suggested that formally overparameterized machine learning models may recover their ability to predict external data accurately. This counterintuitive idea is supported by several information theoretic arguments and by modeling of potentially overfitted synthetic and real data. The implication is that supernumerary model parameters contain additional information on SAR that may contribute to model predictive accuracy for unseen data. Here, we discuss the implications of this understanding of overfitting and overparameterization, discuss its implications for QSAR and quantitative structure-property relationship (QSPR) modeling, and provide illustrative examples of the ability of overfitted ML models to predict test set data well. "It is better to be approximately right than precisely wrong" - Warren Buffett. + + + + Winkler + David A + DA + 0000-0002-7301-6076 + + Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria3086, Australia. + + + Monash Institute of Pharmaceutical Sciences, Monash University, Parkville3052, Australia. + + + School of Pharmacy, University of Nottingham, NottinghamNG7 2RD, U.K. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 28 + +
+ + United States + J Chem Inf Model + 101230060 + 1549-9596 + + IM + + + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 5 + 28 + 7 + 3 + + + aheadofprint + + 42206593 + 10.1021/acs.jcim.6c00518 + + + + + + 42206568 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2473-4209 + + 53 + 5 + + 2026 + May + + + Medical physics + Med Phys + + Learning ordinal representation across MRI sequences for liver fibrosis staging. + + e70504 + e70504 + + 10.1002/mp.70504 + + Accurate liver fibrosis staging (LFS) is important for the diagnosis and treatment planning of patients with liver diseases. Noncontrast MRI is suitable for early screening and long-term monitoring in clinical practice. However, due to the lack of contrast-enhanced details, and given the continuous progression of liver fibrosis and the subtle differences between stages, the use of noncontrast MRI for LFS requires further exploration. + Here, we develop and evaluate a fine-grained deep learning pipeline using noncontrast MRI for precise and improved diagnosis. + To reflect variations across progression stages, we propose a fine-grained diagnostic model to capture ordinal representation from noncontrast MRI in 450 cases. We employ a multi-scale learning strategy and a learnable attention mechanism to enhance information utilization across noncontrast MRI sequences. Furthermore, we propose a novel hybrid contrastive triplet learning method and a weighted strategy to address the imbalance across progression stages and improve diagnostic performance. + The proposed fine-grained diagnostic model achieved an area under the curve (AUC) of 0.877, outperforming the existing LFS model, deep learning baselines, and general MRI-based diagnostic models. For identifying cirrhosis as the endpoint of fibrosis, the AUC further increased to 0.930. The combined use of UMAP clustering and gradient-based attribution methods revealed meaningful feature patterns and predictive mechanisms, demonstrating the potential of the proposed approach for fine-grained diagnosis. + The proposed fine-grained deep learning model effectively leverages multisequence noncontrast MRI for precise LFS and demonstrates improved diagnostic performance. + © 2026 American Association of Physicists in Medicine. + + + + Huo + Jinhao + J + + Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China. + + + + Wang + Yutao + Y + + Ningbo No.9 Hospital, Ningbo, China. + + + + Wu + Nan + N + + Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China. + + + + Zhang + Ruixue + R + + The First Affiliated Hospital of Ningbo University, Ningbo, China. + + + + Zhang + Jian + J + + Shanghai Universal Medical Imaging Diagnostic Center, Shanghai University, Shanghai, China. + + + + Jin + Wei + W + + Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China. + + + + eng + + + No.2024Y31 + Ningbo Medical Health and Science Technology Project + + + + No.2022S043,No.202002N3104 + Public Welfare Science and Technology of Ningbo + + + + No.H2022000505 + Cooperation Project between Ningbo University and Affiliated Hospital + + + + No.2020SWSQNGG-06 + Youth Key Health Talents Projects of Ningbo + + + + + Journal Article + +
+ + United States + Med Phys + 0425746 + 0094-2405 + + IM + + + Liver Cirrhosis + diagnostic imaging + pathology + + + Magnetic Resonance Imaging + + + Humans + + + Image Processing, Computer-Assisted + methods + + + Deep Learning + + + + MRI + liver fibrosis staging + ordinal learning + + + + + + 2026 + 5 + 28 + 12 + 34 + + + 2026 + 5 + 28 + 12 + 33 + + + 2026 + 3 + 12 + + + 2025 + 1 + 11 + + + 2026 + 5 + 10 + + + 2026 + 5 + 28 + 6 + 52 + + + ppublish + + 42206568 + 10.1002/mp.70504 + + + + Devarbhavi H, Asrani SK, Arab JP, Nartey YA, Pose E, Kamath PS. Global burden of liver disease: 2023 update. J Hepatol. 2023;79(2):516–537. + + + Friedman SL. Liver fibrosis—from bench to bedside. J Hepatol. 2003;38:38–53. + + + Akkız H, Gieseler RK, Canbay A. Liver fibrosis: from basic science towards clinical progress, focusing on the central role of hepatic stellate cells. Int J Mol Sci. 2024;25(14):7873. + + + Trautwein C, Friedman SL, Schuppan D, Pinzani M. Hepatic fibrosis: concept to treatment. J Hepatol. 2015;62(1):S15–S24. + + + Somnay K, Wadgaonkar P, Sridhar N, Roshni P, Rao N, Wadgaonkar R. Liver fibrosis leading to cirrhosis: basic mechanisms and clinical perspectives. Biomedicines. 2024;12(10):2229. + + + Jain D, Torres R, Celli R, Koelmel J, Charkoftaki G, Vasiliou V. Evolution of the liver biopsy and its future. Transl Gastroenterol Hepatol. 2021;6:20. + + + Addissouky TA, El Agroudy AE, El‐Torgoman A, El Sayed I, Ibrahim E. Efficiency of alternative markers to assess liver fibrosis levels in viral hepatitis B patients. Biomed Res. 2019;30(2):1–6. + + + Addissouky TA, Ali MM, Sayed IETE, Wang Y. Emerging advanced approaches for diagnosis and inhibition of liver fibrogenesis. Egypt J Intern Med. 2024;36(1):19. + + + Li J, Qureshi M, Gupta A, Anderson SW, Soto J, Li B. Quantification of degree of liver fibrosis using fibrosis area fraction based on statistical chi‐square analysis of heterogeneity of liver tissue texture on routine ultrasound images. Acad Radiol. 2019;26(8):1001–1007. + + + Yasaka K, Akai H, Kunimatsu A, Abe O, Kiryu S. Deep learning for staging liver fibrosis on CT: a pilot study. Eur Radiol. 2018;28:4578–4585. + + + Ma X, Qian X, Wang Q, et al. Radiomics nomogram based on optimal VOI of multi‐sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma. Radiol Med. 2023;128(11):1296–1309. + + + Hectors SJ, Kennedy P, Huang KH, et al. Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid–enhanced MRI. Eur Radiol. 2021;31:3805–3814. + + + Campos JT, Sirlin CB, Choi JY. Focal hepatic lesions in Gd‐EOB‐DTPA enhanced MRI: the atlas. Insights Imaging. 2012;3:451–474. + + + Zhang L, Xiao Z, Jiang W, et al. 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Lancet Gastroenterol Hepatol. 2025;10(4):280–282. + + + Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier‐Hein KH. nnU‐Net: a self‐configuring method for deep learning‐based biomedical image segmentation. Nat Methods. 2021;18(2):203–211. + + + Xie S, Girshick R, Dollár P, Tu Z, He K. Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE; 2017:1492–1500. + + + Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning. PMLR; 2019:6105–6114. + + + Liu Z, Lin Y, Cao Y, et al. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. IEEE; 2021:10012–10022. + + + Bien N, Rajpurkar P, Ball RL, et al. Deep‐learning‐assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. 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Diagnostic performance of transient elastography versus two‐dimensional shear wave elastography for liver fibrosis in chronic viral hepatitis: direct comparison and a meta‐analysis. Biomed Res Int. 2022;2022(1):1960244. + + + Liu K, Qin M, Tao K, et al. Identification and external validation of the optimal FIB‐4 and APRI thresholds for ruling in chronic hepatitis B related liver fibrosis in tertiary care settings. J Clin Lab Anal. 2021;35(2):e23640. + + + Shabanian M, Taylor Z, Woods C, et al. Liver fibrosis classification on trichrome histology slides using weakly supervised learning in children and young adults. J Pathol Inform. 2025;16:100416. + + + Rozario R, Ramakrishna B. Histopathological study of chronic hepatitis B and C: a comparison of two scoring systems. J Hepatol. 2003;38(2):223–229. + + + Pozowski P, Bilski M, Bedrylo M, Sitny P, Zaleska‐Dorobisz U. 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+ + 1742-3406 + + + 2026 + May + 28 + + + Radiation protection dosimetry + Radiat Prot Dosimetry + + AI-driven triage classification at the 1 Gy threshold using dietary supplements and portable OSL dosimetry. + ncag053 + 10.1093/rpd/ncag053 + + In radiological emergency scenarios, the rapid distinction between individuals exposed below or above the 1 Gy triage threshold is essential for effective medical sorting and optimized resource allocation. This study proposes a binary classification framework at the 1 Gy threshold that combines Optically Stimulated Luminescence (OSL) measurements of commercial magnesium-based dietary supplements with supervised machine learning (ML) algorithms, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, and XGBoost. A dataset of 355 samples exposed to different radiation doses was analyzed, and model performance was evaluated using cross-validation and multiple statistical metrics. The resulting framework was implemented into a lightweight, browser-based application to provide real-time predictions and support decision-making in field operations. The findings demonstrate that integrating physical dosimetry with ML enables rapid and scalable classification relative to the 1Gy threshold and offers a practical tool to enhance public health response during radiological incidents. + © The Author(s) 2026. Published by Oxford University Press. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com. + + + + Della Monaca + Sara + S + 0000-0002-3109-9344 + + Istituto Superiore di Sanità, Core Facilities, viale Regina Elena 299, 00161, Rome, Italy. + + + + Maltar-Strmečki + Nadica + N + 0000-0003-2416-5451 + + Ruđer Bošković Institute, Division of Physical Chemistry, Laboratory for Electron Spin Spectroscopy, Bijenička c. 54, Zagreb, 10000, HR, Croatia. + + + + Quattrini + Maria Cristina + MC + + Istituto Superiore di Sanità, Core Facilities, viale Regina Elena 299, 00161, Rome, Italy. + + + + Bortolin + Emanuela + E + + Istituto Superiore di Sanità, Core Facilities, viale Regina Elena 299, 00161, Rome, Italy. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Radiat Prot Dosimetry + 8109958 + 0144-8420 + + IM + + + + + 2026 + 5 + 28 + 12 + 35 + + + 2026 + 5 + 28 + 12 + 35 + + + 2025 + 11 + 5 + + + 2026 + 4 + 15 + + + 2026 + 4 + 28 + + + 2026 + 5 + 28 + 6 + 3 + + + aheadofprint + + 42206492 + 10.1093/rpd/ncag053 + 8697280 + + + + + + 42206491 + + 2026 + 05 + 28 + +
+ + 1530-0366 + + + 2026 + May + 27 + + + Genetics in medicine : official journal of the American College of Medical Genetics + Genet Med + + Multimodal Genotype-Phenotype Analysis in SMARCB1-Associated Developmental Disorders. + + 102614 + 102614 + + 10.1016/j.gim.2026.102614 + S1098-3600(26)00932-9 + + Variants in SMARCB1, encoding a core subunit of the BAF chromatin remodeling complex, are associated with intellectual developmental disorders, particularly Coffin-Siris Syndrome (CSS), though the genotype-phenotype spectrum remains incompletely defined. This study aims to assess correlations between SMARCB1 variant location and phenotypic manifestations. + We analyzed 31 individuals with pathogenic or likely pathogenic SMARCB1 variants using multimodal approaches, integrating clinical, structural, and machine learning analyses. We predicted variant effects via 3D protein modelling, assessed facial similarity using GestaltMatcher, and conducted phenotype-driven genotype prediction using machine learning classifiers. + Variants clustered within N-terminal (winged-helix/SNF5) and C-terminal (αC-helix) regions. C-terminal CSS variants were associated with more severe speech delay, microcephaly and cleft palate, exhibiting stronger facial gestalt similarity. XGBoost achieved 96.7% accuracy in classifying variant location from phenotype alone. While gestalt is a key feature delineating variants at the αC helix, overall clinical features have greater predictive power for N-terminal variants. + Using detailed phenotyping and machine learning algorithms we identify differences between individuals with N-terminus and C-terminus SMARCB1 variants. Our study underscores the importance of multi-modal assessments for genotype-phenotype associations, suggesting integrated modelling can provide insights into SMARCB1 variant effects and biological function, with potential for improvement of diagnostic strategies. + Copyright © 2026. Published by Elsevier Inc. + + + + Saad + Ramy + R + + Department of Twin Research & Genetic Epidemiology, King's College London, London, UK; Clinical Genetics Service, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK. + + + + Gigli + Clementina Cobolli + CC + + Neural Stem Cell Biology Lab, The Francis Crick Institute, London, UK; Department of Medical and Molecular Genetics, School of Basic & Medical Biosciences, King's College London, London. + + + + van der Sluijs + Pleuntje J + PJ + + Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands. + + + + Wilson + Jon R + JR + + Structural Biology of Disease Processes Laboratory, The Francis Crick Institute. + + + + Hsieh + Tzung-Chien + TC + + Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany. + + + + McConnell + Vivienne P M + VPM + + Northern Ireland Regional Genetics Service, Belfast City Hospital, Belfast Health & Social Care Trust, Belfast, Northern Ireland. + + + + Bacino + Carlos + C + + Department of Molecular and Human Genetics, Baylor College of Medicine; Genetics Service, Texas Children's Hospital. + + + + Bird + Lynne M + LM + + Department of Pediatrics, University of California San Diego; Division of Dysmorphology/Genetics, Rady Children's Hospital San Diego, San Diego, California, USA. + + + + Adam + Shelin + S + + Department of Medical Genetics and the British Columbia Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada. + + + + Clarke + Lorne + L + + Department of Medical Genetics and the British Columbia Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada. + + + + Cobben + Jan M + JM + + North West Thames Regional Genetics Service, NHS, Northwick Park & St Mark's Hospitals, London, Harrow, UK. + + + + Travessa + André + A + + Serviço de Genética Médica, Centro Hospitalar Universitário Lisboa Norte, Hospital de Santa Maria, Lisbon, Portugal. + + + + Faivre + Laurence + L + + Inserm, UMR1231, Equipe GAD, Bâtiment B3, Université de Bourgogne Franche Comté, Dijon Cedex, France; Centre de Référence Maladies Rares "Anomalies du développement et syndromes malformatifs", Centre de Génétique, FHU-TRANSLAD et Institut GIMI, CHU Dijon Bourgogne, Dijon, France. + + + + Farholt + Stense + S + + Department of Clinical Genetics, Centre for Rare Diseases, Aarhus University Hospital, Aarhus, Denmark. + + + + Gregersen + Pernille + P + + Pediatrics and Adolescent Medicine, Centre for Rare Diseases, Aarhus University Hospital, Aarhus, Denmark. + + + + van Hasselt + Jos + J + + 's Heeren Loo Zorggroep, Advisium, Ermelo, the Netherlands. + + + + Lahiri + Nayana + N + + St George's University Hospitals NHS Foundation Trust & St Georges, University of London, IMBE, London, UK; Department of Molecular and Biomedical Sciences, City St. + + + + Palmer + Elizabeth E + EE + + Discipline of Paediatrics and Child Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales Sydney, New South Wales, Australia; Centre for Clinical Genetics, Sydney Children's Hospitals Network, Randwick, New South Wales, Australia. + + + + Sheffer + Ruth + R + + Department of Human Genetics, Hadassah University Hospital, Jerusalem, Israel. + + + + Clayton-Smith + Jill + J + + Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Saint Mary's Hospital, Oxford Road, Manchester, UK. + + + + Wilnai + Yael + Y + + Genetic Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. + + + + Deshpande + Charu + C + + Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Saint Mary's Hospital, Oxford Road, Manchester, UK; Department of Clinical Genetics, Guy's & St. + + + + Morton + Jenny E V + JEV + + West Midlands Regional Clinical Genetics Service and Birmingham Health Partners, Birmingham Women's and Children's Hospitals NHS Foundation Trust, Birmingham, UK. + + + + Clement + Emma + E + + Clinical Genetics Service, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK. + + + + Santen + Gijs W E + GWE + + Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands. + + + + Dias + Cristina + C + + Clinical Genetics Service, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK; Neural Stem Cell Biology Lab, The Francis Crick Institute, London, UK; Department of Medical and Molecular Genetics, School of Basic & Medical Biosciences, King's College London, London; Department of Clinical Genetics, Guy's & St. Electronic address: cristina.dias@kcl.ac.uk. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + United States + Genet Med + 9815831 + 1098-3600 + + IM + + BAF SWI/SNF complex + Coffin-Siris Syndrome + Genotype-phenotype correlations + Machine learning + SMARCB1 + + + + + + 2025 + 10 + 22 + + + 2026 + 5 + 15 + + + 2026 + 5 + 21 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 5 + 58 + + + aheadofprint + + 42206491 + 10.1016/j.gim.2026.102614 + S1098-3600(26)00932-9 + + + + + + 42206479 + + 2026 + 05 + 28 + +
+ + 1878-3503 + + + 2026 + May + 28 + + + Transactions of the Royal Society of Tropical Medicine and Hygiene + Trans R Soc Trop Med Hyg + + Interpretable machine-learning prognosis of mycetoma from routine clinical data. + trag061 + 10.1093/trstmh/trag061 + + Mycetoma is a chronic tropical infection with limited tools for predicting treatment outcomes. + Routinely collected demographic, clinical, imaging, etiological, treatment, and follow-up data from 1,084 patients at the Mycetoma Research Centre were used to evaluate supervised machine learning models for outcome prediction. Binary and three-class tasks (cured, recurrence, disability) were assessed using stratified five-fold cross-validation with preprocessing, imputation, scaling, and class balancing. Logistic regression, support vector machine, and random forest models were compared. + Treatment mode, treatment duration, disease duration at presentation, adherence, lesion size, and imaging findings were the most important predictors. In eumycetoma, random forest achieved the best binary performance (accuracy 0.760 ± 0.036; ROC-AUC 0.835 ± 0.036). In actinomycetoma, random forest achieved accuracy of 0.719 ± 0.115 and ROC-AUC of 0.779 ± 0.138. For the three-class task, random forest performed best in eumycetoma (accuracy 0.612 ± 0.033; macro-AUC 0.777 ± 0.029), while logistic regression performed best in actinomycetoma (accuracy 0.735 ± 0.097; macro-AUC 0.713 ± 0.092). + Standard clinical and imaging variables can support machine learning-based risk stratification in mycetoma, potentially improving early management and follow-up. External validation and clinical utility assessment are still required. + © The Author(s) 2026. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site-for further information please contact journals.permissions@oup.com. + + + + Yousif + Maha H + MH + + Department of Applied Mathematics, University of Khartoum, Khartoum 11111, Sudan. + + + + Alsammani + Abdallah + A + + Department of Mathematical Science, Delaware State University, Dover, DE 19901, United States. + + + + Abdalla + Mohsin H + MH + + Department of Applied Mathematics, University of Khartoum, Khartoum 11111, Sudan. + + + + Bashier + Eihab B M + EBM + + Department of Applied Mathematics, University of Khartoum, Khartoum 11111, Sudan. + + + + Mohammed + Mohammed A Y + MAY + + Department of Mathematics, Georgia State University, Atlanta, GA, United States. + + + + Saeed + Ali Awadallah + AA + 0000-0003-3524-4825 + + Mycetoma Research Centre, University of Khartoum, Khartoum 11111, Sudan. + + + Faculty of Pharmacy, National University-Sudan, Khartoum 11111, Sudan. + + + + Fahal + Ahmed H + AH + + Mycetoma Research Centre, University of Khartoum, Khartoum 11111, Sudan. + + + + eng + + + MRC + + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Trans R Soc Trop Med Hyg + 7506129 + 0035-9203 + + IM + + actinomycetoma + eumycetoma + machine learning + management + mycetoma + prognosis + treatment outcomes + + + + + + 2025 + 10 + 28 + + + 2026 + 2 + 19 + + + 2026 + 4 + 17 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 5 + 53 + + + aheadofprint + + 42206479 + 10.1093/trstmh/trag061 + 8697273 + + + + + + 42206473 + + 2026 + 05 + 28 + +
+ + 2379-3694 + + + 2026 + May + 28 + + + ACS sensors + ACS Sens + + Matrix-Boosted Electrochemiluminescence Biosensor for Ultrasensitive Exosome Detection and Automated Phenotype Discrimination. + 10.1021/acssensors.6c01482 + + Accurate quantification and phenotypic discrimination of exosomes in complex biological fluids remain technically challenging sensing issues, yet they are essential for early cancer diagnosis. To address this challenge, an ultrasensitive electrochemiluminescence (ECL) biosensor is engineered by integrating a "matrix-boosted" Cp-Pt-Ti3-x + C2T + y + MXene emitter with a proximity-dependent "signal-off-on" aptamer strategy. Upon target recognition, the thermodynamically favored desorption of methylene blue-labeled aptamers triggers a dramatic ECL signal recovery, while the resulting multifeatured sensing data are processed using a support vector machine (SVM) algorithm. The sensing platform achieves a high quenching efficiency of 99.8% and a low limit of detection of 35 particles/μL for MCF-7 exosomes without requiring enzymatic amplification. Furthermore, integrating the SVM model with 3D principal component analysis enables the highly accurate and automated discrimination of varying exosome phenotypes derived from distinct cell lines in clinical serum samples, establishing a smart, AI-empowered paradigm for liquid biopsy. + + + + Shi + Yacheng + Y + + College of Geography and Environmental Sciences, College of Chemistry and Materials Science, Zhejiang Normal University, Jinhua 321004, China. + + + Department of Chemistry, Key Lab of Bioorganic Phosphorus Chemistry and Chemical Biology of Ministry of Education, Beijing Key Laboratory for Analytical Methods and Instrumentation, Tsinghua University, Beijing 100084, China. + + + + Wu + Yang + Y + + Department of Sports Medicine, Peking University Third Hospital, Institute of Sports Medicine of Peking University, Beijing Key Laboratory of Sports Injuries, Engineering Research Center of Sports Trauma Treatment Technology and Devices, Ministry of Education, Beijing 100084, China. + + + + Yan + Zhiyong + Z + + Institute of Analysis and Testing, Beijing Academy of Science and Technology (Beijing Center for Physical and Chemical Analysis), Beijing 100089, China. + + + + Huang + Hongjie + H + + Department of Sports Medicine, Peking University Third Hospital, Institute of Sports Medicine of Peking University, Beijing Key Laboratory of Sports Injuries, Engineering Research Center of Sports Trauma Treatment Technology and Devices, Ministry of Education, Beijing 100084, China. + + + + Liu + Yang + Y + 0000-0003-0042-5183 + + Department of Chemistry, Key Lab of Bioorganic Phosphorus Chemistry and Chemical Biology of Ministry of Education, Beijing Key Laboratory for Analytical Methods and Instrumentation, Tsinghua University, Beijing 100084, China. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + ACS Sens + 101669031 + 2379-3694 + + IM + + MXenes + aptasensor + electrochemiluminescence + exosome + liquid biopsy + machine learning + phenotype discrimination + + + + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 5 + 52 + + + aheadofprint + + 42206473 + 10.1021/acssensors.6c01482 + + + + + + 42206427 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 1741-2811 + + 32 + 2 + + 2026 + Apr-Jun + + + Health informatics journal + Health Informatics J + + Deep survival learning for prognosis prediction in non-metastatic castration-resistant prostate cancer. + + 14604582261456059 + 14604582261456059 + + 10.1177/14604582261456059 + + BackgroundNon-metastatic, castration-resistant prostate cancer (nmCRPC) is an advanced state of prostate cancer with variable prognosis; early identification of patient risk is crucial, so that clinicians can recommend optimal treatment.ObjectiveCompare predictive models in identifying patient risk; evaluate the value of electronic healthcare record (EHR) time-series (TS) information in prediction.MethodsWe evaluated SurvTRACE, Weibull Time to Event Recurrent Neural Network (WTTE-RNN), and traditional Cox proportional hazards (CPH) models' performance on EHR data from 12,819 nmCRPC patients in the Veterans Health Administration, using area under the receiver operating characteristic curve and Brier score.ResultsWTTE-RNN, which intrinsically uses EHR TS information, outperformed the other models without TS information. Feature-engineered TS information improved performances of CPH and especially SurvTRACE; with TS information, SurvTRACE outperformed WTTE-RNN.ConclusionDeep learning methods, whether intrinsically able to handle TS data or enhanced with TS information, can outperform traditional survival analysis in predicting risk. + + + + Li + Chunyang + C + + VA Salt Lake City Health Care System, Salt Lake City, UT, USA. + + + Hematology and Hematologic Malignancy, School of Medicine, University of Utah, Salt Lake City, UT, USA. + + + + Bohman + Julia + J + 0009-0008-3516-6568 + + VA Salt Lake City Health Care System, Salt Lake City, UT, USA. + + + Hematology and Hematologic Malignancy, School of Medicine, University of Utah, Salt Lake City, UT, USA. + + + + Patil + Vikas + V + + VA Salt Lake City Health Care System, Salt Lake City, UT, USA. + + + Hematology and Hematologic Malignancy, School of Medicine, University of Utah, Salt Lake City, UT, USA. + + + + McShinsky + Richard + R + + VA Salt Lake City Health Care System, Salt Lake City, UT, USA. + + + Hematology and Hematologic Malignancy, School of Medicine, University of Utah, Salt Lake City, UT, USA. + + + + Yong + Christina + C + + VA Salt Lake City Health Care System, Salt Lake City, UT, USA. + + + Hematology and Hematologic Malignancy, School of Medicine, University of Utah, Salt Lake City, UT, USA. + + + + Burningham + Zach + Z + + VA Salt Lake City Health Care System, Salt Lake City, UT, USA. + + + Hematology and Hematologic Malignancy, School of Medicine, University of Utah, Salt Lake City, UT, USA. + + + + Halwani + Ahmad + A + + VA Salt Lake City Health Care System, Salt Lake City, UT, USA. + + + Hematology and Hematologic Malignancy, School of Medicine, University of Utah, Salt Lake City, UT, USA. + + + Huntsman Cancer Institute, Salt Lake City, UT, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Health Informatics J + 100883604 + 1460-4582 + + IM + + + Humans + + + Male + + + Prognosis + + + Prostatic Neoplasms, Castration-Resistant + mortality + + + Deep Learning + trends + standards + statistics & numerical data + + + Aged + + + Proportional Hazards Models + + + Predictive Learning Models + + + Survival Analysis + + + Prediction Algorithms + + + ROC Curve + + + United States + + + Electronic Health Records + statistics & numerical data + + + Middle Aged + + + + castration-resistant prostate cancer + deep learning + machine learning + non-metastatic + risk prediction + survival analysis + + Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. + + + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 5 + 33 + + + ppublish + + 42206427 + 10.1177/14604582261456059 + + + + + + 42206405 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2561-1011 + + 10 + + 2026 + May + 22 + + + JMIR cardio + JMIR Cardio + + Extracting Cardiorespiratory Symptoms From Clinical Notes Using Open-Weight Large Language Models: Method Development and Validation Study. + + e89480 + e89480 + + 10.2196/89480 + + Accurate identification of clinical symptoms and signs (S&S) is essential for the early detection of high-burden cardiorespiratory conditions, including lung cancer, chronic obstructive pulmonary disease, and heart failure. Although symptom data play a central role in diagnostic reasoning and predictive modeling, most S&S information remains embedded in unstructured electronic health record notes, limiting their use in automated phenotyping, surveillance, and clinical decision support. Traditional natural language processing systems struggle with domain variability and contextual nuance in clinical text. Recent advances in large language models (LLMs) offer a promising alternative, yet challenges remain in hallucinations, overinference, and safe deployment. This study evaluated whether locally deployed open-source models could reliably extract cardiorespiratory S&S and map them to ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) codes using optimized prompting strategies. + This study aims to assess the accuracy of open-source LLMs in extracting explicitly stated cardiorespiratory S&S from clinical notes and mapping them to ICD-10-CM codes (R00-R09) and to compare performance across 4 prompt-engineering strategies, including a multimodule LLM framework. + A total of 593 clinical notes from the MTSamples database were manually reviewed, with 93 notes used for prompt development and comparison using Llama 3.3-70B, and 500 notes used as testing data for the final best prompt setting using both Llama 3.3-70B and gpt-oss-120B. Four prompting conditions were evaluated: (1) instruction-only, (2) ICD-10-CM definition-based prompts, (3) assumption-free prompts, and (4) a multimodule LLM framework with postprocessing. Performance was measured using precision, recall, and F1-score for both S&S extraction and ICD-10-CM code generation. + Across all prompt strategies, model performance improved as more structure and constraints were added. Instruction-only prompting demonstrated high recall but poor precision. Incorporating ICD-10-CM definitions improved coding accuracy, and assumption-free prompting further balanced precision and recall. The multimodule approach with postprocessing achieved the highest performance during prompt development. On the independent test corpus, entity-level microaveraged evaluation showed that gpt-oss-120B outperformed Llama 3.3-70B in both tasks. For S&S extraction, Llama 3.3-70B achieved a precision of 0.63, a recall of 0.86, and an F1-score of 0.73, whereas gpt-oss-120B achieved a precision of 0.89, a recall of 0.87, and an F1-score of 0.88. For ICD-10-CM code mapping, Llama 3.3-70B achieved a precision of 0.59, a recall of 0.83, and an F1-score of 0.69, whereas gpt-oss-120B achieved a precision of 0.90, a recall of 0.84, and an F1-score of 0.87. + Locally deployed LLMs, when paired with optimized prompting and multimodule orchestration, can accurately extract cardiorespiratory S&S and generate ICD-10-CM codes from unstructured clinical notes. This approach increases the level of data safety by enabling on-premises processing without external data transmission and demonstrates strong potential for scalable, domain-adaptive symptom extraction pipelines in biomedical informatics. Future work should expand datasets and evaluate generalizability across clinical domains. + © Yunbing Bai, Wanting Cui, Joseph Finkelstein. Originally published in JMIR Cardio (https://cardio.jmir.org). + + + + Bai + Yunbing + Y + 0009-0005-4910-4497 + + Arizona Center for Telemedicine and Digital Health, College of Medicine, University of Arizona, 1501 N Campbell Ave AHSL 1156, Tucson, AZ, 85724-5105, United States, 1 520-626-3944. + + + + Cui + Wanting + W + 0000-0001-7341-363X + + Arizona Center for Telemedicine and Digital Health, College of Medicine, University of Arizona, 1501 N Campbell Ave AHSL 1156, Tucson, AZ, 85724-5105, United States, 1 520-626-3944. + + + + Finkelstein + Joseph + J + 0000-0002-8084-7441 + + Arizona Center for Telemedicine and Digital Health, College of Medicine, University of Arizona, 1501 N Campbell Ave AHSL 1156, Tucson, AZ, 85724-5105, United States, 1 520-626-3944. + + + + eng + + Journal Article + Validation Study + + + 2026 + 05 + 22 + +
+ + Canada + JMIR Cardio + 101718325 + 2561-1011 + + IM + + + Large Language Models + + + Humans + + + Electronic Health Records + statistics & numerical data + + + International Classification of Diseases + + + Natural Language Processing + + + Cardiovascular Diseases + diagnosis + + + + NLP + clinical coding + electronic health records + large language models + named entity recognition + natural language processing + prompt engineering + signs and symptoms + + + + + + 2025 + 12 + 11 + + + 2026 + 4 + 28 + + + 2026 + 4 + 28 + + + 2026 + 5 + 28 + 6 + 35 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 5 + 22 + + + epublish + + 42206405 + 10.2196/89480 + v10i1e89480 + + + + + + 42206228 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 1178-2390 + + 19 + + 2026 + + + Journal of multidisciplinary healthcare + J Multidiscip Healthc + + A Bedside Coagulation-Platelet Tipping Point for Mortality in Nontraumatic Subarachnoid Hemorrhage. + + 606643 + 606643 + + 606643 + 10.2147/JMDH.S606643 + + Nontraumatic subarachnoid hemorrhage (ntSAH) is associated with high in-hospital mortality, and current prognostic models often overlook systemic hemostatic disturbances. We aimed to develop a coagulation-platelet index (INR_PLT) for mortality risk, quantify its nonlinear threshold, and construct an externally validated risk model using stacking ensemble learning. + This retrospective study included ntSAH patients from Huizhou Central People's Hospital (n = 287, training cohort) and the MIMIC-IV (n = 621, external validation). INR_PLT was derived by logistic regression: -1.596 + 1.364×INR - 0.008×PLT. Nonlinear relationships and thresholds were identified using restricted cubic splines and segmented regression. Nomograms and machine learning models (LR, SVM, DT, and LGBM) were developed, with a stacking ensemble as the final model. The performance of the models was evaluated by receiver operating characteristic (ROC) curves, calibration plots, and SHAP. + Of the 908 patients, 163 (17.95%) died. The independent predictors included age, GCS score, nimodipine use, and the INR_PLT (OR 2.425). INR_PLT was significantly correlated with antiplatelet therapy (P = 0.006) and mediated 19.8% of diabetes-associated mortality risk. A threshold at INR_PLT = -2.457 markedly increased mortality risk (OR 2.527, P = 0.002). The nomogram (C-index 0.847) and stacking model (AUC 0.80, F1 = 0.763) demonstrated strong performance. + The INR_PLT is a bedside index for identifying a significant mortality threshold in patients with ntSAH, supporting precise risk stratification with external validation. + © 2026 Xia et al. + + + + Xia + Shaohuai + S + 0000-0001-5661-0420 + + Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China. + + + + Wu + Jing + J + + Department of Neuro-Oncology, Beijing Xiaotangshan Hospital, Beijing, 100000, People's Republic of China. + + + + Wang + Ce + C + + Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100000, People's Republic of China. + + + + Li + Wencai + W + + Department of Neurosurgery, Huizhou Central People's Hospital, Huizhou, 516000, People's Republic of China. + + + + Chen + Li + L + + Department of Neurosurgery, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, 350001, People's Republic of China. + + + + Zhang + Xingyu + X + + Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China. + + + + Wang + Junping + J + + Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China. + + + + Yang + Xinyu + X + + Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China. + + + + eng + + Journal Article + + + 2026 + 05 + 22 + +
+ + New Zealand + J Multidiscip Healthc + 101512691 + 1178-2390 + + + coagulation + machine learning + platelet count + prognosis + risk stratification + subarachnoid hemorrhage + + The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. + + + + + 2026 + 3 + 3 + + + 2026 + 5 + 19 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 4 + 45 + + + 2026 + 5 + 22 + + + epublish + + 42206228 + PMC13209147 + 10.2147/JMDH.S606643 + 606643 + + + + Huang YW, Zhang Y, Li ZP, Yin XS. Association between a four-parameter inflammatory index and all-cause mortality in critically ill patients with non-traumatic subarachnoid hemorrhage: a retrospective analysis of the MIMIC-IV database (2012–2019). 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Stroke. 2025;56(5):1224–1231. doi: 10.1161/STROKEAHA.124.049008 + + + 10.1161/STROKEAHA.124.049008 + PMC12037302 + 40109238 + + + + Johnson AEW, Bulgarelli L, Shen L, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10(1):1. doi: 10.1038/s41597-022-01899-x + + + 10.1038/s41597-022-01899-x + PMC9810617 + 36596836 + + + + + + + + 42206146 + + 2026 + 05 + 28 + +
+ + 2522-5839 + + 8 + 5 + + 2026 + + + Nature machine intelligence + Nat Mach Intell + + Platonic representation of foundation machine learning interatomic potentials. + + 830 + 840 + 830-840 + + 10.1038/s42256-026-01235-7 + + Foundation machine learning interatomic potentials (MLIPs) have emerged as powerful tools for atomistic simulation, yet different models encode chemical environments in incompatible latent spaces, limiting direct comparison and interoperability. The platonic representation hypothesis suggests that sufficiently capable models converge towards a shared statistical representation of reality. Here, motivated by this hypothesis, we show that independently developed MLIPs exhibit statistically consistent geometric organization of atomic environments. By projecting embeddings relative to a set of atomic anchors, we unify the latent spaces of seven MLIPs-spanning equivariant, non-equivariant, conservative and non-conservative architectures-into a common latent space that preserves chemical periodicity and structural invariants. This unified framework enables cross-model optimal transport, interpretable embedding arithmetic and the detection of representational biases. Furthermore, we show that deviation in this space provides a ground-truth-free measure for atypical structures, and signals physical prediction failures. Our results suggest that the platonic representation offers a practical route towards interoperable, comparable and interpretable foundation models for materials science. + © The Author(s) 2026. + + + + Li + Zhenzhu + Z + 0000-0002-6669-563X + + Department of Materials, Imperial College London, London, UK. + https://ror.org/041kmwe10 + grid.7445.2 + 0000 0001 2113 8111 + + + Imperial-X, Imperial College London, London, UK. + https://ror.org/041kmwe10 + grid.7445.2 + 0000 0001 2113 8111 + + + Imperial Global Singapore, Singapore, Singapore. + + + + Walsh + Aron + A + 0000-0001-5460-7033 + + Department of Materials, Imperial College London, London, UK. + https://ror.org/041kmwe10 + grid.7445.2 + 0000 0001 2113 8111 + + + + eng + + Journal Article + + + 2026 + 05 + 07 + +
+ + England + Nat Mach Intell + 101740243 + 2522-5839 + + + Materials chemistry + Theory and computation + + Competing interestsA.W. is Chief Scientific Officer at CuspAI. Z.L. declares no competing interests. + + + + + 2025 + 12 + 9 + + + 2026 + 4 + 9 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 4 + 44 + + + 2026 + 5 + 7 + + + ppublish + + 42206146 + PMC13201161 + 10.1038/s42256-026-01235-7 + 1235 + + + + Chen, C. & Ong, S. P. A universal graph deep learning interatomic potential for the periodic table. Nat. Comput. Sci. + 2, 718–728 (2022). + + + 10.1038/s43588-022-00349-3 + 38177366 + + + + Kovács, D. P. et al. Linear atomic cluster expansion force fields for organic molecules: beyond RMSE. J. Chem. Theory Comput. + 17, 7696–7711 (2021). + + + 10.1021/acs.jctc.1c00647 + PMC8675139 + 34735161 + + + + Batatia, I. et al. The design space of E(3)-equivariant atom-centred interatomic potentials. Nat. Mach. 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+ + 2522-5839 + + 8 + 5 + + 2026 + + + Nature machine intelligence + Nat Mach Intell + + Immunotherapy drug target identification using machine learning and patient-derived tumour explant validation. + + 670 + 689 + 670-689 + + 10.1038/s42256-026-01201-3 + + Immunotherapy has revolutionized cancer treatment, yet only a minority of individuals respond clinically, necessitating alternative strategies that can benefit these patients. Novel immuno-oncology targets may achieve this through bypassing resistance mechanisms to standard therapies. We introduce Mining Immunotherapy Drug tArgetS (MIDAS), a multimodal graph neural network system for immuno-oncology target discovery. MIDAS leverages gene interactions, multi-omic patient profiles, immune cell biology, antigen processing, disease associations and phenotypic consequences of genetic perturbations. It generalizes to time-sliced data, outcompetes state-of-the-art baselines (including OpenTargets) and ranks approved targets above those in clinical development. Moreover, MIDAS recovers immunotherapy-response-associated genes in unseen patients, thereby capturing immunotherapy response determinants. Interpretability analyses reveal a reliance on autoimmunity, regulatory networks and immuno-oncology pathways. Functionally perturbing oncostatin M-oncostatin M receptor signalling, a proposed MIDAS target, in TRACERx melanoma-patient-derived explants yielded reduced dysfunctional CD8+ T cells, which associate with immunotherapy response, and reduced CCL4 levels. Furthermore, oncostatin M and oncostatin M receptor expression is associated with altered T cell and macrophage profiles in bulk transcriptomic data from patient samples. These data are consistent with a role for oncostatin M-oncostatin M in modulating the tumour microenvironment towards immunosuppressive, tumour-promoting phenotypes. Our results present a machine learning framework for analysing multimodal data for immuno-oncology target discovery. + © The Author(s) 2026. + + + + Augustine + Marcellus + M + 0000-0003-1909-9883 + + Tumour Immunogenomics and Immunosurveillance (TIGI) Laboratory, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK. + https://ror.org/04tnbqb63 + grid.451388.3 + 0000 0004 1795 1830 + + + Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + Division of Medicine, University College London, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + + Nene + Nuno Rocha + NR + + Tumour Immunogenomics and Immunosurveillance (TIGI) Laboratory, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK. + https://ror.org/04tnbqb63 + grid.451388.3 + 0000 0004 1795 1830 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + Department of Statistical Science, University College London, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + + Fu + Hongchang + H + 0009-0003-6582-8554 + + Tumour Immunogenomics and Immunosurveillance (TIGI) Laboratory, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK. + https://ror.org/04tnbqb63 + grid.451388.3 + 0000 0004 1795 1830 + + + Skin and Renal Unit, Royal Marsden NHS Foundation Trust, London, UK. + https://ror.org/0008wzh48 + grid.5072.0 + 0000 0001 0304 893X + + + + Pinder + Christopher L + CL + 0000-0003-4149-226X + + Tumour Immunogenomics and Immunosurveillance (TIGI) Laboratory, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + + Ligammari + Lorena + L + + Tumour Immunogenomics and Immunosurveillance (TIGI) Laboratory, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + + Simpson + Alexander P + AP + 0000-0003-3439-2236 + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + + Sanz-Fernández + Irene + I + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + + Thakkar + Krupa + K + + Tumour Immunogenomics and Immunosurveillance (TIGI) Laboratory, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + + Qian + Danwen + D + + Tumour Immunogenomics and Immunosurveillance (TIGI) Laboratory, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + + Fitzsimons + Evelyn + E + 0000-0001-9280-9971 + + Tumour Immunogenomics and Immunosurveillance (TIGI) Laboratory, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + + Simpson + Benjamin S + BS + + Tumour Immunogenomics and Immunosurveillance (TIGI) Laboratory, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + + Vendramin + Roberto + R + 0000-0001-7191-4887 + + Tumour Immunogenomics and Immunosurveillance (TIGI) Laboratory, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK. + https://ror.org/04tnbqb63 + grid.451388.3 + 0000 0004 1795 1830 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + + Castro + Andrea + A + + Tumour Immunogenomics and Immunosurveillance (TIGI) Laboratory, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + + Niederer + Heather + H + + Cancer Research Horizons, The Francis Crick Institute, London, UK. + https://ror.org/04tnbqb63 + grid.451388.3 + 0000 0004 1795 1830 + + + + Turajlic + Samra + S + + Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK. + https://ror.org/04tnbqb63 + grid.451388.3 + 0000 0004 1795 1830 + + + Skin and Renal Unit, Royal Marsden NHS Foundation Trust, London, UK. + https://ror.org/0008wzh48 + grid.5072.0 + 0000 0001 0304 893X + + + Cancer Dynamics Laboratory, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK. + https://ror.org/037405c78 + grid.482185.2 + 0000 0000 9151 0233 + + + + Quezada + Sergio A + SA + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + Cancer Research UK City of London Centre, University College London, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + + McGranahan + Nicholas + N + 0000-0001-9537-4045 + + Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + + Watkins + Chris + C + + Department of Computer Science, Royal Holloway, University of London, London, UK. + https://ror.org/04cw6st05 + grid.4464.2 + 0000 0001 2161 2573 + + + + Swanton + Charles + C + 0000-0002-4299-3018 + + Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK. + https://ror.org/04tnbqb63 + grid.451388.3 + 0000 0004 1795 1830 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + Department of Oncology, University College London Hospitals, London, UK. + https://ror.org/00wrevg56 + grid.439749.4 + 0000 0004 0612 2754 + + + + Litchfield + Kevin + K + 0000-0002-3725-0914 + + Tumour Immunogenomics and Immunosurveillance (TIGI) Laboratory, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000 0001 2190 1201 + + + Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK. + https://ror.org/02jx3x895 + grid.83440.3b + 0000000121901201 + + + + eng + + Journal Article + + + 2026 + 05 + 18 + +
+ + England + Nat Mach Intell + 101740243 + 2522-5839 + + + Cancer microenvironment + Machine learning + Tumour immunology + + Competing interestsM.A. reports fees from Neuroute, FutureHouse and Edison Scientific, unrelated to this work. M.A., N.R.N. and C.S. are named as inventors of the patent PCT/EP2025/086701 relating to the use of plasma proteomics for risk prediction of lung cancer (unrelated to this paper). They are also listed as inventors on a patent application (GB) that has been filed but is not related to the method described in this paper. The application is currently unpublished and remains within the priority year. R.V. declares research funding from CRUK TDL–Ono–LifeArc alliance and Genesis Molecular AI. A.C. is a consultant for Tempus Labs. S.T. reports personal fees from Roche, Novartis, AstraZeneca and Ipsen outside the submitted work; and the following patents filed: indel mutations as a therapeutic target and predictive biomarker (PCTGB2018/051892 and PCTGB2018/051893) and clear-cell renal cell carcinoma biomarkers (P113326GB). N.M. has stock options in and has consulted for Achilles Therapeutics and holds a European patent in determining HLA LOH (PCT/GB2018/052004), a patent pending in determining HLA disruption (PCT/EP2023/059039) and is a co-inventor to a patent to identify responders to cancer treatment (PCT/GB2018/051912). C.S. acknowledges grant support from AstraZeneca, Boehringer-Ingelheim, Bristol Myers Squibb, Pfizer, Invitae (previously Archer Dx—collaboration in minimal residual disease sequencing technologies), Ono Pharmaceutical, and Personalis. He is also Co-Chief Investigator of the NHS Galleri trial funded by GRAIL and a paid member of GRAIL’s Scientific Advisory Board. He was Chief Investigator for the AZ MeRmaiD 1 and 2 clinical trials and the Steering Committee Chair. C.S is a paid board member for Novartis from March 2026. He is also a paid board member for Bicycle Therapeutics and is Chair of the Clinical Advisory Group. He receives consultant fees from Genentech, Medicxi, China Innovation Centre of Roche (CICoR) formerly Roche Innovation Centre – Shanghai, Relay Therapeutics (SAB member), Saga Diagnostics (SAB member), and Sarah Cannon Research Institute. He previously received consultant fees from Achilles Therapuetics. C.S has received honoraria from Amgen, AstraZeneca, Bristol Myers Squibb, GlaxoSmithKline, Illumina, MSD, Novartis and Pfizer. C.S. has equity in Bicycle Therapeutics. He has stock options in Novartis, Relay Therapeutics, Saga Diagnostics and Bicycle Therapeutics. He has previously held stock and was co-founder of Achilles Therapeutics. C.S declares a patent application for methods to lung cancer (PCT/US2017/028013); targeting neoantigens (PCT/EP2016/059401); identifying patent response to immune checkpoint blockade (PCT/EP2016/071471); methods for lung cancer detection (US20190106751A1); identifying patients who respond to cancer treatment (PCT/GB2018/051912); determining HLA LOH (PCT/GB2018/052004); predicting survival rates of patients with cancer (PCT/GB2020/050221); methods and systems for tumour monitoring (PCT/EP2022/077987); analysis of HLA alleles transcriptional deregulation (PCT/EP2023/059039); relating to the use of plasma proteomics for risk prediction of lung cancer (PCT/EP2025/086701). C.S. is an inventor on a European patent application (PCT/GB2017/053289) relating to assay technology to detect tumour recurrence. This patent has been licensed to a commercial entity and under their terms of employment C.S is due a revenue share of any revenue generated from such license(s). K.L. has the following disclosures (all unrelated to the current work): patent on indel burden and CPI response pending, patent on ctDNA minimal residual disease calling methods, patent pending on a lung cancer vaccine; speaker fees from Roche Tissue Diagnostics and Ellipses Pharma; research funding from CRUK TDL/Ono/LifeArc alliance and Genesis Therapeutics; and consulting roles with Monopteros Therapeutics, Saga Diagnostics, Kynos Therapeutics and Tempus Labs. Again unrelated to this work, K.L. is currently employed by Isomorphic Labs. The other authors declare no competing interests. + + + + + 2024 + 11 + 21 + + + 2026 + 2 + 4 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 4 + 44 + + + 2026 + 5 + 18 + + + ppublish + + 42206145 + PMC13201160 + 10.1038/s42256-026-01201-3 + 1201 + + + + Sharma, P. et al. The next decade of immune checkpoint therapy. Cancer Discov. + 838, 838–857 (2021). + + 33811120 + + + + Chowell, D. et al. 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+ + 2673-253X + + 8 + + 2026 + + + Frontiers in digital health + Front Digit Health + + Explainable AI in kidney stone detection and segmentation: a mini review. + + 1750411 + 1750411 + + 1750411 + 10.3389/fdgth.2026.1750411 + + Kidney stones are one of the most common renal disorders that can produce severe complications if not diagnosed and treated early. Recently, advances in AI have ensured that deep learning and explainable AI enable the automatic segmentation and detection of kidney stones from medical imaging, thus improving diagnostic efficiency and accuracy. For this review, eighteen representative studies using machine learning, deep learning, and hybrid models for kidney stone segmentation were considered, which were published in the period between 2020 and 2025. The XAI techniques being mainly utilized with the discussed models in the study are SHAP, LIME, Grad-CAM, Layer-wise Relevance Propagation, and EigenCAM. Such approaches tend to enhance clinicians' trust in allowing early diagnosis and supporting clinical decision-making, especially in resource-constrained settings. Regardless of the towering results, this area still suffers due to certain limitations such as lack of diversity in datasets, absence of multimodal integration, and scarcity of real-world validation. All in all, integrating DL with XAI presents a transparent, reliable, and clinically acceptable approach to detecting and segmenting kidney stones. + © 2026 Hossen, Haque, Bannah, Rahman, Ahmed and Noman. + + + + Hossen + Md Jakir + MJ + + Center for Advanced Analytics, COE for Artificial Intelligence Faculty of Engineering & Technology, Multimedia University, Melaka, Malaysia. + + + Elite Research Lab, LLC, New York, NY, United States. + + + + Haque + B M Taslimul + BMT + + Information Systems, Central Michigan University, Mount Pleasant, MI, United States. + + + + Bannah + Hasanul + H + + Faculty of AI and Engineering, Multimedia University Cyberjaya, Cyberjaya, Malaysia. + + + + Rahman + Md Arifur + MA + + College of Graduate and Professional Studies, Trine University, Angola, IN, United States. + + + + Ahmed + Abir + A + + Department of Information Technology, Washington University of Science & Technology, Alexandria, VA, United States. + + + + Noman + Abdullah Al + AA + + Wilmington University, New Castle, DE, United States. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 12 + +
+ + Switzerland + Front Digit Health + 101771889 + 2673-253X + + + clinical decision support + deep learning + explainable AI + grad-CAM + kidney stone segmentation + lime + medical imaging + shap + + Author AA was employed by company ELITE Research Lab LLC. The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2025 + 11 + 20 + + + 2026 + 3 + 26 + + + 2026 + 4 + 8 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 4 + 43 + + + 2026 + 5 + 12 + + + epublish + + 42206076 + PMC13202716 + 10.3389/fdgth.2026.1750411 + + + + Pradhan U, Chattopadhyay H, Aditya MN, Patil M, Baig SAH, Mittal S, et al. Explainable AI for kidney stone prediction: a machine learning-based clinical analysis. 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Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography. Sci Rep. (2022) 12:11440. 10.1038/s41598-022-15634-4 + + 10.1038/s41598-022-15634-4 + PMC9259587 + 35794172 + + + + Villegas-Jimenez A, Flores-Araiza D, Lopez-Tiro F, Ochoa-Ruiz G, Daul C. Causal scoring medical image explanations: A case study on ex-vivo kidney stone images. arXiv [Preprint]. arXiv:2309.01921 (2023). Available online at: +https://arxiv.org/abs/2309.01921 + + + Ahmed F, Abbas S, Athar A, Shahzad T, Khan WA, Alharbi M, et al. Identification of kidney stones in KUB x-ray images using VGG16 empowered with explainable artificial intelligence. Sci Rep. (2024) 14:6173. 10.1038/s41598-024-56478-4 + + 10.1038/s41598-024-56478-4 + PMC10940612 + 38486010 + + + + Bayram AF, Gurkan C, Budak A, Karatas H. A detection and prediction model based on deep learning assisted by explainable artificial intelligence for kidney diseases. Eur J Sci Technol. (2022) 40:67–74. 10.31590/ejosat.1171777 + + 10.31590/ejosat.1171777 + + + + Shikdar OF, Mily AS, Haque T, Islam A, Islam S, Sazeda R. Transfer learning for ultrasound-based kidney stone (urolithiasis) detection with augmented regularization and saliency maps. Proceedings of the 2024 IEEE 3rd International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON); IEEE; (2024). p. 1–8. 10.1109/RAAICON64172.2024.10928447 + + 10.1109/RAAICON64172.2024.10928447 + + + + Dillibabu B, Sreevani M. Automated and explainable kidney abnormality detection from CT images using CNN–LSTM architecture. Int J Comput Sci Eng Res. (2025) 2(3):1–8. 10.63328/IJCSER-V2I3P1 + + 10.63328/IJCSER-V2I3P1 + + + + Batra A, Chatterjee P, Chaki J. Federated learning with AutoAlbum for kidney disease detection: a privacy-preserving approach to medical image analysis. IEEE Access. 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Kidneynet: a novel CNN-based technique for the automated diagnosis of chronic kidney diseases from CT scans. Electronics (Basel). (2024) 13:4981. 10.3390/electronics13244981 + + 10.3390/electronics13244981 + + + + Sharon JJ, Anbarasi LJ. An attention enhanced dilated bottleneck network for kidney disease classification. Sci Rep. (2025) 15:9865. 10.1038/s41598-025-90519-w + + 10.1038/s41598-025-90519-w + PMC11928611 + 40118887 + + + + + + + + 42206072 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2253-5969 + + 13 + + 2026 + + + Journal of hepatocellular carcinoma + J Hepatocell Carcinoma + + Novel Unsupervised Machine Learning Model Using Multi-Parametric Radiomics for Prognostic Stratification of Bifocal Hepatocellular Carcinoma. + + 606780 + 606780 + + 606780 + 10.2147/JHC.S606780 + + To identify radiomics subtypes that reflect tumor heterogeneity in bifocal hepatocellular carcinoma (bHCC) using an unsupervised machine learning approach. Additionally, to develop a preoperative model and a postoperative fusion model aimed at predicting recurrence-free survival (RFS) and overall survival (OS) in bHCC patients following hepatectomy. + This retrospective study included 182 bHCC patients (91 in the training set, 91 in the test set). To capture the overall tumor characteristics, radiomics features were extracted from both lesions across six MR sequences and integrated using a two-lesion fusion approach to represent each patient as a single analytical entity. The similarity network fusion approach was utilized to construct a patient similarity matrix based on multi-sequence radiomic features, aiming to identify distinct subgroups that capture patterns of tumor imaging heterogeneity through spectral clustering. Multivariable Cox regression analysis was conducted to develop prognostic models for RFS and OS. The preoperative radiomics image heterogeneity (RIH) model and postoperative model including pathological features were built to predict prognosis of bHCC patients after hepatectomy. + Unsupervised clustering analysis based on multi-parametric radiomics revealed two subtypes correlated with distinct clinical outcomes, where high-radiomics image heterogeneity (high-RIH) was associated with poorer RFS (Log-rank p = 0.0059) and OS (Log-rank p = 0.0343). The independent predictors of shorter RFS included RIH cluster (HR, 1.782; 95% CI, 1.189-2.670), pathological satellite nodule (HR, 1.946; 95% CI, 1.094-3.460), MVI (HR, 1.714; 95% CI, 1.231-2.386). The independent predictors of shorter OS included RIH cluster (HR, 2.008; 95% CI, 1.119-3.605), radiological satellite nodule (HR, 1.982; 95% CI, 1.008-3.901), MVI (HR, 4.350; 95% CI, 2.358-8.028). + This study identified two different radiomics subtypes in bHCC which could reveal the heterogeneity of bHCC and predict clinical outcomes in post-hepatectomy bHCC patients. + © 2026 Jia et al. + + + + Jia + Xi + X + + Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China. + + + Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China. + + + + Wu + Fei + F + 0000-0001-5169-0417 + + Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China. + + + Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China. + + + + Dai + Haoran + H + + Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China. + + + Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China. + + + + Xiao + Yuyao + Y + + Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China. + + + Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China. + + + + Yang + Chun + C + + Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China. + + + Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China. + + + + Zeng + Mengsu + M + + Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China. + + + Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China. + + + + eng + + Journal Article + + + 2026 + 05 + 19 + +
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+ + 1664-3224 + + 17 + + 2026 + + + Frontiers in immunology + Front Immunol + + Predictive value of thyroid autoantibodies for coronary heart disease severity in individuals with normal thyroid function based on machine learning and SHAP interpretation. + + 1803188 + 1803188 + + 1803188 + 10.3389/fimmu.2026.1803188 + + Assessing the severity of coronary heart disease (CHD) is critical for clinical decision-making. Thyroid autoantibodies are associated with cardiovascular disease, but their ability to predict CHD severity remains unclear. This study aimed to systematically elucidate the predictive value of thyroid autoantibodies for CHD severity using machine learning methods. + This retrospective study included 942 patients hospitalized in the cardiovascular department between January 2024 and June 2025, comprising 590 patients with severe lesions and 352 with nonsevere lesions. Traditional statistical analysis employed correlation analysis and multivariate logistic regression. Eight machine learning models were subsequently constructed and compared: logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), adaptive boosting (AdaBoost), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), and XGBoost (XGB). The optimal model was determined using Shapley additive propagation (SHAP), and the robustness of the core findings was validated through hierarchical analysis and subgroup feature importance comparisons. + Multivariate logistic regression revealed that log anti-TPO was an independent risk factor for severe coronary lesions (OR = 2.19; 95% CI: 1.87-2.57; P<0.001), whereas log anti-Tg shows a negative correlation after adjustment (OR = 0.72; 95% CI: 0.61-0.85; P<0.001). However, given the significant multicollinearity between the two variables (r = 0.55), this negative correlation strongly suggests that it is a statistical artifact. The gradient boosting tree performed best (AUROC:0.855). SHAP analysis consistently confirmed three key predictive features: log anti-TPO, log anti-Tg, and glycated hemoglobin (HbA1c). SHAP dependency plots further revealed a distinct threshold effect for log anti-TPO, while elevated log anti-Tg was associated with reduced risk, However, this negative association is likely a statistical artifact rather than an independent protective effect. Stratified analysis and sex-specific feature importance assessments confirmed that log anti-TPO demonstrated a highly robust and significant predictive value across all the subgroups, ranking as the primary predictor in both the male and female cohorts. + The presence of thyroid autoantibodies represents an independent key predictor of CHD severity. Thyroid peroxidase antibodies (TPO-Ab) serve as a strong risk marker, The gradient boosting tree model demonstrated optimal predictive performance when these biomarkers were integrated. + Copyright © 2026 Liang, Ma, Wang, Meng, Ru, Lv, Li and Qiao. + + + + Liang + Nana + N + + The Second Affiliated Hospital of Harbin Medical University, Harbin, China. + + + + Ma + Huiru + H + + The Second Affiliated Hospital of Harbin Medical University, Harbin, China. + + + + Wang + Xingyue + X + + The Second Affiliated Hospital of Harbin Medical University, Harbin, China. + + + + Meng + Jie + J + + The Second Affiliated Hospital of Harbin Medical University, Harbin, China. + + + + Ru + Zixuan + Z + + The Second Affiliated Hospital of Harbin Medical University, Harbin, China. + + + + Lv + Na + N + + The Second Affiliated Hospital of Harbin Medical University, Harbin, China. + + + + Li + Kerou + K + + The Second Affiliated Hospital of Harbin Medical University, Harbin, China. + + + + Qiao + Hong + H + + The Second Affiliated Hospital of Harbin Medical University, Harbin, China. + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
+ + Switzerland + Front Immunol + 101560960 + 1664-3224 + + + + 0 + Autoantibodies + + + 0 + Biomarkers + + + IM + + + Humans + + + Autoantibodies + blood + immunology + + + Female + + + Male + + + Retrospective Studies + + + Machine Learning + + + Severity of Illness Index + + + Coronary Disease + diagnosis + immunology + blood + + + Middle Aged + + + Predictive Learning Models + + + Boosting Machine Learning Algorithms + + + Aged + + + Thyroid Gland + immunology + + + Predictive Value of Tests + + + Biomarkers + blood + + + Random Forest + + + Classification Algorithms + + + Risk Factors + + + + SHAP + TPOAb + TgAb + cardiovascular disease + machine learning + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 2 + 3 + + + 2026 + 4 + 14 + + + 2026 + 4 + 29 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 4 + 43 + + + 2026 + 5 + 12 + + + epublish + + 42206034 + PMC13201438 + 10.3389/fimmu.2026.1803188 + + + + Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. 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+ + 1664-0640 + + 17 + + 2026 + + + Frontiers in psychiatry + Front Psychiatry + + Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data. + + 1821091 + 1821091 + + 1821091 + 10.3389/fpsyt.2026.1821091 + + Schizophrenia (SZ) is a debilitating mental illness that adversely affects social and family interactions, ranking as a leading contributor to global disability. Existing diagnostic approaches, including MRI, PET, and EEG, underscore the necessity for effective predictive strategies to enhance management and reduce costs. + This review evaluates the application of artificial intelligence (AI) methodologies-specifically Machine Learning (ML) and Deep Learning (DL) in predicting SZ using medical imaging data, while addressing existing challenges and identifying key biomarkers to improve diagnostic accuracy. + A systematic literature review was performed using the databases IEEE, PubMed, ScienceDirect, MDPI, Google Scholar, and Springer from inception until March 31, 2026. The initial search generated 820 records, and after a thorough screening process, 185 studies relevant to disease diagnosis, model selection across various neuroimaging modalities, including biomarker identification, were identified. The review protocol has been registered with PROSPORO registration: CRD420251131635. The studies were selected based on different medical imaging data related to SZ. + This review presents a thorough examination of advancements in SZ detection via AI methodologies. It highlights not only providing existing predictive techniques, identifies research gaps, biomarkers identification and assessment, and underscores the potential of AI-based ML and DL methods to facilitate early and accurate diagnosis of SZ. Five unimodal and various combinations of multimodal data were examined, along with the AI models' performance metrics from multiple studies. + The review provides a comprehensive assessment of AI algorithms relevant to both unimodal and multimodal data, biomarkers of neuroimaging modalities with ROIs, challenges, and limitations of ML and DL models, and future directions of prediction for clinical diagnosis, thereby supporting timely interventions for individuals affected by SZ. + https://www.crd.york.ac.uk/PROSPERO/view, identifier CRD420251131635. + Copyright © 2026 Palpandi, Palanigurupackiam, Almatar, Alduhayan, Alsomaie and Almazroa. + + + + Palpandi + Suresh Babu + SB + + Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India. + + + + Palanigurupackiam + Nagaraj + N + + Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India. + + + + Almatar + Hessa + H + + AI and Data Management, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia. + + + King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia. + + + + Alduhayan + Reema + R + + AI and Data Management, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia. + + + King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia. + + + + Alsomaie + Barrak + B + + King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia. + + + Research Operation Department, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia. + + + + Almazroa + Ahmed + A + + AI and Data Management, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia. + + + King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia. + + + + eng + + Journal Article + Systematic Review + + + 2026 + 05 + 12 + +
+ + Switzerland + Front Psychiatry + 101545006 + 1664-0640 + + + artificial intelligence + biomarker + deep learning + machine learning + multimodal data + neuroimaging + schizophrenia + unimodal data + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 3 + 2 + + + 2026 + 4 + 20 + + + 2026 + 4 + 21 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 4 + 43 + + + 2026 + 5 + 12 + + + epublish + + 42206005 + PMC13202723 + 10.3389/fpsyt.2026.1821091 + + + + Coiera W. Artificial intelligence in medicine: The challenges ahead. J Am Med Inf Assoc. (1996) 3:363–6. doi:  10.1136/jamia.1996.97084510. PMID: + + 10.1136/jamia.1996.97084510 + PMC116321 + 8930853 + + + + +World Health Organization and Obsessive-Compulsive Disorder . 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+ + 2296-858X + + 13 + + 2026 + + + Frontiers in medicine + Front Med (Lausanne) + + Identification and preliminary clinical validation of type 2 diabetes signature genes through machine learning analysis of scRNA-seq data. + + 1801737 + 1801737 + + 1801737 + 10.3389/fmed.2026.1801737 + + Type 2 diabetes (T2DM) is a highly prevalent metabolic disorder with substantial molecular heterogeneity, and traditional bulk transcriptomic approaches often fail to capture cell-specific changes critical to disease pathogenesis. This study aims to identify and validate key signature genes for T2DM by integrating single-cell RNA sequencing (scRNA-seq) with machine learning, providing new insights into disease mechanisms and potential biomarkers. + We analyzed scRNA-seq data to characterize cellular heterogeneity across 10 distinct cell types. Differential expression analysis identified 455 candidate genes, which were refined using LASSO regression. The diagnostic potential of identified genes was evaluated using ROC curve analysis on an independent dataset. Functional enrichment and cell communication analyses were performed to elucidate biological processes and intercellular signaling networks. Finally, expression changes of the candidate genes were validated in peripheral blood from a separate clinical cohort (15 T2DM patients, 20 controls) using qRT-PCR. + Four core genes (PNLIP, BUB1, CTSB, NAMPT) were identified as candidate signature genes. ROC analysis showed AUC values of 0.819, 0.931, 0.882, and 0.694, respectively, suggesting promising but variable diagnostic accuracy. Enrichment analyses indicated these genes participate in processes including extracellular matrix remodeling, digestion/absorption, and signal transduction. Cell communication analysis suggested a potential central role of Alpha and Beta cells in diabetic signaling networks, with the MK and SPP1 pathways showing complementary expression patterns. In addition, qRT-PCR confirmed significantly up-regulated expression of PNLIP, BUB1, and CTSB along with down-regulated NAMPT in T2DM patients, supporting their potential as circulating candidate biomarkers. + This study integrates machine learning with scRNA-seq to identify PNLIP, BUB1, CTSB, and NAMPT as potential T2DM signature genes. These findings offer candidate diagnostic biomarkers and provide preliminary mechanistic insights into disease-associated pathways. + Copyright © 2026 Tang, Zuo, Wang, Sun, Cheng and Wu. + + + + Tang + Fang + F + + Department of Endocrinology, Shenzhen Third People's Hospital, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, China. + + + + Zuo + Xin + X + + Department of Endocrinology, Shenzhen Third People's Hospital, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, China. + + + + Wang + Weiyan + W + + Department of Endocrinology, Shenzhen Third People's Hospital, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, China. + + + + Sun + Rui + R + + Department of Endocrinology, Shenzhen Third People's Hospital, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, China. + + + + Cheng + Heng + H + + Department of Endocrinology, Shenzhen Third People's Hospital, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, China. + + + + Wu + Weihua + W + + Department of Endocrinology, Shenzhen Third People's Hospital, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, China. + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
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Visceral fat lipolysis by pancreatic lipases worsens heart failure. +Cell Rep Med. (2025) 6:102147. 10.1016/j.xcrm.2025.102147 + + + + 10.1016/j.xcrm.2025.102147 + PMC12208342 + 40460832 + + + + Masson E, Berthet S, Le Gac G, Le Rhun M, Ka C, Autret S, et al. Identification of protease-sensitive but not misfolding PNLIP variants in familial and hereditary pancreatitis. +Pancreatology. (2023) 23:507–11. 10.1016/j.pan.2023.05.011 + + + + 10.1016/j.pan.2023.05.011 + 37270400 + + + + Hoffka G, Mhana S, Vas M, Toldi V, Motyan JA, Tozser J, et al. Modeling protease-sensitive human pancreatic lipase mutations in the mouse ortholog. +J Biol Chem. (2024) 300:107763. 10.1016/j.jbc.2024.107763 + + + + 10.1016/j.jbc.2024.107763 + PMC11489347 + 39265662 + + + + Cassidy BM, Jiang F, Lin J, Chen JM, Curry GE, Ma GX, et al. Exploring the enigmatic association between PNLIP variants and risk of chronic pancreatitis in a large Chinese cohort. +Pancreatology. 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Cathepsin B deteriorates diabetic cardiomyopathy induced by streptozotocin via promoting NLRP3-mediated pyroptosis. +Mol Ther Nucleic Acids. (2022) 30:198–207. 10.1016/j.omtn.2022.09.019 + + + + 10.1016/j.omtn.2022.09.019 + PMC9554743 + 36250207 + + + + + + + + 42205824 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2296-858X + + 13 + + 2026 + + + Frontiers in medicine + Front Med (Lausanne) + + Relationship of the Endothelial Activation and Stress Index with 28-day mortality in urosepsis patients: a retrospective two-cohort investigation. + + 1761104 + 1761104 + + 1761104 + 10.3389/fmed.2026.1761104 + + The Endothelial Activation and Stress Index (EASIX) is a novel biomarker for assessing endothelial dysfunction. This study aimed to evaluate its prognostic value for 28-day mortality in patients with urosepsis. + We conducted a retrospective study of patients with urosepsis admitted to the ICU using data from the MIMIC-IV database and West China Hospital. Restricted cubic spline (RCS) regression, multivariable Cox regression, and Kaplan-Meier analysis were used to assess the association between EASIX and short-term mortality. Four machine learning feature selection methods (LASSO-COX, Boruta, random forest, and gradient boosting) were applied to identify key prognostic features and develop a predictive model, which was evaluated using ROC curve analysis and validated in an external cohort. + A total of 2,593 patients were included. The 28-day ICU and in-hospital mortality rates were 16.0 and 17.2%, respectively. Higher EASIX scores were significantly associated with increased mortality across quartiles (ICU mortality: 10.5% in Q1 to 30.4% in Q4, p < 0.001). In the fully adjusted model, each unit increase in EASIX was associated with a 7% higher risk of ICU mortality (HR 1.07, 95% CI 1.05-1.11, p < 0.001), and patients in Q4 had a 57% higher risk than those in Q1 (HR 1.57, 95% CI 1.09-2.26, p = 0.016). RCS analysis revealed a non-linear relationship between EASIX and mortality. The predictive model incorporating EASIX, Charlson Comorbidity Index, RDW, and SAPS II achieved AUC values of 0.70-0.73 across training, internal validation, and external validation cohorts, demonstrating improved performance compared to traditional severity scores. + EASIX is independently associated with short-term mortality in patients with urosepsis and may serve as a valuable tool for risk stratification following further validation. + Copyright © 2026 Tang, Li, Zhang, Cao and Nie. + + + + Tang + Jiaqi + J + + Emergency Department, West China Hospital of Sichuan University, Chengdu, China. + + + + Li + Hu + H + + Emergency Department, West China Hospital of Sichuan University, Chengdu, China. + + + + Zhang + Zhuo + Z + + Emergency Department, West China Hospital of Sichuan University, Chengdu, China. + + + + Cao + Jiaxin + J + + Emergency Department, West China Hospital of Sichuan University, Chengdu, China. + + + + Nie + Hu + H + + Emergency Department, West China Hospital of Sichuan University, Chengdu, China. + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
+ + Switzerland + Front Med (Lausanne) + 101648047 + 2296-858X + + + Endothelial Activation and Stress Index + adverse outcomes + biomarker + risk stratification + urosepsis + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2025 + 12 + 5 + + + 2026 + 4 + 17 + + + 2026 + 4 + 20 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 4 + 41 + + + 2026 + 5 + 12 + + + epublish + + 42205824 + PMC13201423 + 10.3389/fmed.2026.1761104 + + + + McAteer J, Lee JH, Cosgrove SE, Dzintars K, Fiawoo S, Heil EL, et al. Defining the optimal duration of therapy for hospitalized patients with complicated urinary tract infections and associated bacteremia. Clin Infect Dis. (2023) 76:1604–12. doi: 10.1093/cid/ciad009, + + 10.1093/cid/ciad009 + PMC10411929 + 36633559 + + + + Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. 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(2024) 76:153–65. doi: 10.1002/art.42681, + + 10.1002/art.42681 + 37610060 + + + + Baaten CCFMJ, Nagy M, Bergmeier W, Spronk HMH, van der Meijden PEJ. Platelet biology and function: plaque erosion vs. rupture. Eur Heart J. (2024) 45:18–31. doi: 10.1093/eurheartj/ehad720, + + 10.1093/eurheartj/ehad720 + PMC10757869 + 37940193 + + + + Qiao X, Yin J, Zheng Z, Li L, Feng X. Endothelial cell dynamics in sepsis-induced acute lung injury and acute respiratory distress syndrome: pathogenesis and therapeutic implications. Cell Commun Signal. (2024) 22:241. doi: 10.1186/s12964-024-01620-y, + + 10.1186/s12964-024-01620-y + PMC11046830 + 38664775 + + + + Sun R, Huang J, Sun B. Mobilization of endothelial progenitor cells in sepsis. Inflamm Res. (2020) 69:1–9. doi: 10.1007/s00011-019-01299-9, + + 10.1007/s00011-019-01299-9 + 31758219 + + + + Evans CE, Zhao YY. Impact of thrombosis on pulmonary endothelial injury and repair following sepsis. Am J Physiol Lung Cell Mol Physiol. (2017) 312:L441–51. doi: 10.1152/ajplung.00441.2016, + + 10.1152/ajplung.00441.2016 + PMC5407094 + 28130261 + + + + Cha JK, Kim HS, Kim EJ, Lee ES, Lee JH, Song IA. Effect of early nutritional support on clinical outcomes of critically ill patients with sepsis and septic shock: a single-center retrospective study. Nutrients. (2022) 14:2318. doi: 10.3390/nu14112318, + + 10.3390/nu14112318 + PMC9182793 + 35684117 + + + + Li Q, Shang N, Gao Q, Guo S, Yang T. Prevalence of sarcopenia and its association with frailty and malnutrition among older patients with sepsis-a cross-sectional study in the emergency department. BMC Geriatr. (2025) 25:377. doi: 10.1186/s12877-025-06060-y, + + 10.1186/s12877-025-06060-y + PMC12107726 + 40426064 + + + + Merz A, Germing U, Kobbe G, Kaivers J, Jauch A, Radujkovic A, et al. EASIX for prediction of survival in lower-risk myelodysplastic syndromes. Blood Cancer J. (2019) 9:85. doi: 10.1038/s41408-019-0247-z, + + 10.1038/s41408-019-0247-z + PMC6848148 + 31712595 + + + + Finke D, Hund H, Frey N, Luft T, Lehmann LH. EASIX (endothelial activation and stress index) predicts mortality in patients with coronary artery disease. Clin Res Cardiol. (2025) 114:1008–18. doi: 10.1007/s00392-024-02534-y, + + 10.1007/s00392-024-02534-y + PMC12283470 + 39256221 + + + + He Y, Li Y, Xiaojin Y, Wu D, Jiang W, Xie X. Endothelial activation and stress index for prediction of mortality in asthma. Front Med (Lausanne). (2025) 12:1622944. doi: 10.3389/fmed.2025.1622944, + + 10.3389/fmed.2025.1622944 + PMC12283289 + 40703256 + + + + Ulrich H, Behrend P, Wiedekopf J, Drenkhahn C, Kock-Schoppenhauer AK, Ingenerf J. Hands on the medical informatics initiative Core data set- lessons learned from converting the MIMIC-IV. Stud Health Technol Inform. (2021) 283:119–26. doi: 10.3233/SHTI210549, + + 10.3233/SHTI210549 + 34545827 + + + + Joffre J, Hellman J, Ince C, Ait-Oufella H. Endothelial responses in sepsis. Am J Respir Crit Care Med. (2020) 202:361–70. doi: 10.1164/rccm.201910-1911TR, + + 10.1164/rccm.201910-1911TR + 32101446 + + + + Zhang H, Wang Y, Qu M, Li W, Wu D, Cata JP, et al. Neutrophil, neutrophil extracellular traps and endothelial cell dysfunction in sepsis. Clin Transl Med. (2023) 13:e1170. doi: 10.1002/ctm2.1170, + + 10.1002/ctm2.1170 + PMC9832433 + 36629024 + + + + Tang F, Zhao XL, Xu LY, Zhang JN, Ao H, Peng C. Endothelial dysfunction: pathophysiology and therapeutic targets for sepsis-induced multiple organ dysfunction syndrome. Biomed Pharmacother. (2024) 178:117180. doi: 10.1016/j.biopha.2024.117180, + + 10.1016/j.biopha.2024.117180 + 39068853 + + + + Joffre J, Hellman J. Oxidative stress and endothelial dysfunction in sepsis and acute inflammation. Antioxid Redox Signal. (2021) 35:1291–307. doi: 10.1089/ars.2021.0027, + + 10.1089/ars.2021.0027 + 33637016 + + + + Zhou Y, Qi M, Yang M. Current status and future perspectives of lactate dehydrogenase detection and medical implications: a review. Biosensors (Basel). (2022) 12:1145. doi: 10.3390/bios12121145, + + 10.3390/bios12121145 + PMC9775244 + 36551112 + + + + Vincent JL, Francois B, Zabolotskikh I, Daga MK, Lascarrou JB, Kirov MY, et al. Effect of a recombinant human soluble thrombomodulin on mortality in patients with sepsis-associated coagulopathy: the SCARLET randomized clinical trial. JAMA. (2019) 321:1993–2002. doi: 10.1001/jama.2019.5358, + + 10.1001/jama.2019.5358 + PMC6547077 + 31104069 + + + + Chang W, Zhao Z, Ma L, Lu L, Liu C, Hu M, et al. Relationship between endothelial activation and stress index and all-cause mortality in rheumatoid arthritis patients: a moderating effect of gamma-glutamyl transferase. Front Nutr. (2025) 12:1554429. doi: 10.3389/fnut.2025.1554429 + + 10.3389/fnut.2025.1554429 + PMC12055536 + 40336962 + + + + Estler B, Fröhlich H, Täger T, Hund H, Frey N, Luft T, et al. Endothelial dysfunction assessed by the endothelial activation and stress index (EASIX) predicts risk of mortality in chronic heart failure patients. Int J Cardiol. (2025) 438:133566. doi: 10.1016/j.ijcard.2025.133566, + + 10.1016/j.ijcard.2025.133566 + 40617481 + + + + + + + + 42205821 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2296-858X + + 13 + + 2026 + + + Frontiers in medicine + Front Med (Lausanne) + + A new direction in personalized medicine: multimodal joint prediction of hepatic encephalopathy risk post-TIPS. + + 1816396 + 1816396 + + 1816396 + 10.3389/fmed.2026.1816396 + + Overt hepatic encephalopathy (OHE) is a common complication after transjugular intrahepatic portosystemic shunt (TIPS), adversely affecting quality of life. This study aimed to develop a predictive model integrating manual imaging, radiomics, and clinical data to forecast OHE within 1 year post-TIPS. + This retrospective study included 338 patients who underwent TIPS between November 2015 and January 2022, divided into training and validation sets (7:3). Feature selection was performed using Chi-square test, t-test, least absolute shrinkage and selection operator, and logistic regression. Three models were built using manual CT features (Model M), radiomics (Model R), and clinical data (Model C), respectively. A combined model (Model MRC) integrated all three. Model performance was evaluated via ROC curves, calibration plots, and decision curve analysis. The primary endpoint was OHE occurrence within 1 year post-TIPS. + Within 1 year after TIPS, 79 (33.4%) participants in the training group and 34 (33.3%) participants in validation group developed OHE. Three independent models and one combined model were established and evaluated in terms of their performance. The areas under the ROC curve of Model M, Model R, Model C, and Model MRC were 0.858 (95% CI: 0.809-0.907), 0.744 (95% CI: 0.681-0.808), 0.757 (95% CI: 0.692-0.821), and 0.902 (95% CI: 0.863-0.941), respectively. F1 scores were 0.861, 0.765, 0.797, and 0.891, respectively. Model MRC demonstrated superior performance compared to the other three models. + Model MRC exhibited a considerable predictive ability for OHE within the first year after TIPS. + Copyright © 2026 Zhou, Tang, Shen, Zhang, Li, Tao and Zhu. + + + + Zhou + Lin-Feng + LF + + Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China. + + + Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. + + + + Tang + Hao-Huan + HH + + Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China. + + + Department of Interventional Radiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China. + + + + Shen + Jian + J + + Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China. + + + + Zhang + Shuai + S + + Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China. + + + + Li + Wan-Ci + WC + + Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China. + + + + Tao + Jun + J + + Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. + + + + Zhu + Xiao-Li + XL + + Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China. + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
+ + Switzerland + Front Med (Lausanne) + 101648047 + 2296-858X + + + cirrhosis + hepatic encephalopathy + machine learning + radiomics + transjugular intrahepatic portosystemic shunt + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer FZ declared a past co-authorship with the author X-LZ to the handling editor. + + + + + 2026 + 2 + 24 + + + 2026 + 4 + 14 + + + 2026 + 4 + 17 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 4 + 41 + + + 2026 + 5 + 12 + + + epublish + + 42205821 + PMC13201119 + 10.3389/fmed.2026.1816396 + + + + Rajesh S, George T, Philips C, Ahamed R, Kumbar S, Mohan N, et al. Transjugular intrahepatic portosystemic shunt in cirrhosis: an exhaustive critical update. +World J Gastroenterol. 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(2017) 42:2069–78. 10.1007/s00261-017-1096-5 + + + + 10.1007/s00261-017-1096-5 + 28314916 + + + + Liu F, Ning Z, Liu Y, Liu D, Tian J, Luo H, et al. Development and validation of a radiomics signature for clinically significant portal hypertension in cirrhosis (CHESS1701): a prospective multicenter study. +EBioMedicine. (2018) 36:151–8. 10.1016/j.ebiom.2018.09.023 + + + + 10.1016/j.ebiom.2018.09.023 + PMC6197722 + 30268833 + + + + Wang C, Huang Y, Liu C, Liu F, Hu X, Kuang X, et al. Diagnosis of clinically significant portal hypertension using CT- and MRI-based vascular model. +Radiology. (2023) 307:e221648. 10.1148/radiol.221648 + + + + 10.1148/radiol.221648 + 36719293 + + + + + + + + 42205789 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2297-055X + + 13 + + 2026 + + + Frontiers in cardiovascular medicine + Front Cardiovasc Med + + Pericoronary adipose tissue radiomics enhances prediction of major adverse cardiovascular events beyond CCTA-derived functional parameters in coronary atherosclerosis. + + 1833189 + 1833189 + + 1833189 + 10.3389/fcvm.2026.1833189 + + To explore the predictive value of a combined model integrating perivascular coronary adipose tissue (PCAT) radiomics features and coronary computed tomography angiography (CCTA)-derived functional parameters for major adverse cardiovascular events (MACE) in patients with Coronary Atherosclerosis (CAS). + This retrospective study enrolled 171 CAS patients who underwent CCTA at Datong Third People's Hospital between November 2020 and September 2022 and stratified them into a MACE-positive group (n = 72) and a MACE-negative group (n = 99) based on the occurrence of MACE. Using support vector machine (SVM) and Gaussian process regression (GPR) algorithms, we constructed four MACE prediction models: two models relying on CCTA-derived functional parameters (stenosis severity and CT-FFR), and two combined models integrating these parameters with the radiomics score (Rad-score). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1-score, Delong test, calibration curves, and decision curve analysis (DCA). + Among the CCTA-derived functional parameters, CT-derived fractional flow reserve (CT-FFR) and coronary stenosis severity emerged as independent predictors of MACE in patients with CAS (both P < 0.05). Models integrating CCTA-derived functional parameters with the radiomics score (Rad-score) demonstrated superior predictive performance compared with models relying solely on CCTA-derived functional parameters. Specifically, the mean AUC for SVM and GPR models based exclusively on CCTA-derived functional parameters were 0.742 and 0.737, respectively. In contrast, the mean AUCs for the corresponding combined SVM and GPR models both increased to 0.803. Notably, the combined GPR model achieved the highest mean F1-score (0.686). The DeLong test confirmed that the combined models significantly outperformed the CCTA-only models in both the training and testing sets (all P < 0.05). Calibration curves revealed the best goodness-of-fit for the combined GPR model, and DCA indicated that this model provided the greatest net clinical benefit across a broad range of decision thresholds. + PCAT radiomics features can enhance the predictive performance of models based on CCTA-derived functional parameters for MACE in CAS patients. Notably, the combined GPR model exhibits optimal predictive accuracy and clinical utility. + © 2026 Wang, Wu, Cao, Zhao, Zhang, Wu, Zhang, Ning, Zhang, Wang, Yin, Wang and Xu. + + + + Wang + Zhenye + Z + + Department of Radiology, The Third People's Hospital of Datong, Datong, China. + + + First Clinical Medical College, Graduate School, Changzhi Medical College, Changzhi, China. + + + + Wu + Zhijing + Z + + Department of Radiology, The Third People's Hospital of Datong, Datong, China. + + + First Clinical Medical College, Graduate School, Changzhi Medical College, Changzhi, China. + + + + Cao + Milan + M + + Department of Science and Education, The Third People's Hospital of Datong, Datong, China. + + + + Zhao + Lili + L + + Department of Radiology, The Third People's Hospital of Datong, Datong, China. + + + + Zhang + Guojiang + G + + Department of Cardiology, The Third People's Hospital of Datong, Datong, China. + + + + Wu + Shan + S + + Department of Radiology, Shanxi Bethune Hospital, Taiyuan, China. + + + + Zhang + Xiong + X + + Department of Electronic and Information Engineering, Taiyuan University of Science and Technology, Taiyuan, China. + + + + Ning + Jiwei + J + + Department of Clinical Laboratory, The Third People's Hospital of Datong, Datong, China. + + + + Zhang + Yanhua + Y + + Department of Science and Education, The Third People's Hospital of Datong, Datong, China. + + + + Wang + Junqin + J + + Department of Science and Education, The Third People's Hospital of Datong, Datong, China. + + + + Yin + Lei + L + + Department of Radiology, The Third People's Hospital of Datong, Datong, China. + + + + Wang + Qiang + Q + + Department of Computer and Network Engineering, Shanxi Datong University, Datong, China. + + + + Xu + Zhigao + Z + + Department of Radiology, The Third People's Hospital of Datong, Datong, China. + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
+ + Switzerland + Front Cardiovasc Med + 101653388 + 2297-055X + + + coronary atherosclerosis + machine learning + major adverse cardiovascular events + pericoronary adipose tissue + radiomics + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 3 + 20 + + + 2026 + 4 + 22 + + + 2026 + 4 + 22 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 4 + 40 + + + 2026 + 5 + 12 + + + epublish + + 42205789 + PMC13201203 + 10.3389/fcvm.2026.1833189 + + + + World Health Organization. Cardiovascular diseases (CVDs). Fact sheet. (2023) Available online at: +https://www.who.int/zh/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) +(Accessed April 29, 2026). + + + Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. 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(2020) 22(1):2. 10.1007/s11936-020-0803-7 + + 10.1007/s11936-020-0803-7 + PMC7012056 + 31938934 + + + + Alabdullah AA, Marey A, Li Y, Jha S, Rogers C, Abdulla J, et al. Clinical value and cost effectiveness of FFR-CT in guiding revascularization and predicting major adverse cardiac events: a meta-analysis. Clin Imaging. (2026) 129:110659. 10.1016/j.clinimag.2025.110659 + + 10.1016/j.clinimag.2025.110659 + 41202647 + + + + Xaplanteris P, Fournier S, Pijls NHJ, Fearon WF, Barbato E, Tonino PAL, et al. Five-Year outcomes with PCI guided by fractional flow reserve. N Engl J Med. (2018) 379(3):250–9. 10.1056/NEJMoa1803538 + + 10.1056/NEJMoa1803538 + 29785878 + + + + Smolka S, Fava A, Moshage M, Marwan M, Desai MY, Achenbach S. CT-FFR in predicting future cardiovascular events. Eur Heart J. (2022) 43:206. 10.1093/eurheartj/ehac544.206 + + 10.1093/eurheartj/ehac544.206 + + + + Yang F, Pang Z, Cui S, Ma Y, Li Y, Wang Y, et al. 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(2024) 54(7):382–94. 10.4070/kcj.2023.0288 + + 10.4070/kcj.2023.0288 + PMC11252635 + 38767442 + + + + + + + + 42205787 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2297-055X + + 13 + + 2026 + + + Frontiers in cardiovascular medicine + Front Cardiovasc Med + + Applications of artificial intelligence in cardiovascular risk detection and prediction among adults with obesity: a scoping review. + + 1802647 + 1802647 + + 1802647 + 10.3389/fcvm.2026.1802647 + + Obesity is a chronic and multifactorial disease that substantially increases cardiovascular risk, the leading cause of mortality worldwide. Conventional cardiovascular risk prediction tools are largely derived from general populations and often fail to capture the metabolic heterogeneity and complex pathophysiology associated with obesity. Artificial intelligence (AI) has been proposed as an approach to improve cardiovascular risk detection and prediction through the integration of large and heterogeneous clinical datasets. + To map and characterise the available evidence on the application of artificial intelligence for cardiovascular risk detection, prediction, and stratification in adults with obesity. + scoping review was conducted following the Joanna Briggs Institute methodology and reported according to the PRISMA ScR guidelines. PubMed, Scopus, Web of Science, LILACS, and IEEE Xplore were searched for studies published between January 2015 and January 2026. Eligible studies included observational designs and investigations describing the development or validation of AI based models applied to cardiovascular risk assessment in adults with obesity. Data were synthesised using narrative and tabulated approaches. + Thirty studies were included, most of which were retrospective and characterised by heterogeneous populations. Tree based ensemble methods, particularly Random Forest and gradient boosting algorithms, were most frequently used, followed by support vector machines and artificial neural networks. Outcomes mainly focused on cardiovascular risk stratification and disease detection, whereas prediction of incident cardiovascular events and mortality was less common. External validation was infrequently reported, and model performance was generally moderate when longitudinal outcomes were assessed. + Artificial intelligence shows potential as a complementary tool for cardiovascular risk assessment in adults with obesity. However, methodological heterogeneity, limited external validation, and inconsistent outcome definitions currently limit clinical implementation. Future research should prioritise prospective designs, robust external validation, and standardised outcomes to define the clinical value of AI based cardiovascular risk models in obesity. + © 2026 Torres Torres, Garcés, Cárdenas Montoya, Concha Fernández and Hernández Rincón. + + + + Torres Torres + Mario Andrés + MA + + Primary Care Physician, School of Medicine, Universidad de La Sabana, Chía, Colombia. + + + + Garcés + Mariana González + MG + + Primary Care Physician, School of Medicine, Universidad de La Sabana, Chía, Colombia. + + + + Cárdenas Montoya + Jerónimo + J + + Primary Care Physician, Faculty of Medicine, Universidad de La Sabana, Chía, Colombia. + + + + Concha Fernández + Valeria + V + + Primary Care Physician, School of Medicine, Universidad de La Sabana, Chía, Colombia. + + + + Hernández Rincón + Erwin Hernando + EH + + Department of Family Medicine and Public Health, Universidad de La Sabana, Chía, Colombia. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 12 + +
+ + Switzerland + Front Cardiovasc Med + 101653388 + 2297-055X + + + artificial intelligence + cardiovascular diseases + machine learning + obesity + risk assessment + risk prediction + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 2 + 3 + + + 2026 + 3 + 19 + + + 2026 + 4 + 20 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 4 + 40 + + + 2026 + 5 + 12 + + + epublish + + 42205787 + PMC13201141 + 10.3389/fcvm.2026.1802647 + + + + World Health Organization. Obesity and overweight. WHO (2024). + + + World Health Organization. One in eight people are now living with obesity. WHO (2024). + + + World Obesity Federation. World Obesity Atlas 2025. London: WOF; (2025). + + + World Health Organization. Cardiovascular diseases (CVDs). 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+ + 2297-055X + + 13 + + 2026 + + + Frontiers in cardiovascular medicine + Front Cardiovasc Med + + Integrating untargeted metabolomics and deep learning approaches to identify specific metabolic signatures and new mechanisms in unstable plaques. + + 1646067 + 1646067 + + 1646067 + 10.3389/fcvm.2026.1646067 + + Unstable carotid artery plaques are an important risk factor for ischemic stroke, and their clinical prognosis is poor. The present study to systematically investigate the metabolic changes of carotid plaques and use machine learning methods to identify and screen metabolic biomarkers in unstable carotid plaques for helping diagnosis of stroke risk caused by unstable plaques. + A non-targeted metabolomics analysis was performed on 67 cases (40 stable and 27 unstable) of carotid artery plaques. Specific metabolic signatures were identified in unstable plaques. Four machine learning algorithms, including random forest (RF), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and logistic regression (LR), were used to construct feature analysis models for unstable carotid artery plaques and predict the associated metabolic biomarkers. + A total of 98 metabolites significantly differentially associated with unstable plaques were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that the cGMP-PKG signaling pathway, glucagon signaling pathway, central carbon metabolism in cancer, and lipolysis regulation in adipocytes are metabolic pathways significantly associated with unstable plaques. The network diagram of metabolites and metabolic pathways revealed the relationship between 43 metabolites and their corresponding pathways. Furthermore, some metabolites that may serve as biomarkers for unstable plaques were screened. + Different metabolite patterns associated with unstable plaque tissue were identified and characterized. This study identified some potential metabolic biomarkers significantly associated with unstable carotid artery plaques, which can predict metabolic products and further improve the prediction of stroke risk in unstable plaques. + © 2026 Liu, Ma, Wang, Qu, Zhang, Song, Gao, Wang, Zheng, Fang, Qu, Shen and Liu. + + + + Ma + Jia-Qi + JQ + + Department of Neurosurgery, Tangdu Hospital, Airforce Military Medical University, Xi'an, Shaanxi, China. + + + + Wang + Lu + L + + Department of Neurosurgery, Tangdu Hospital, Airforce Military Medical University, Xi'an, Shaanxi, China. + + + College of Life Sciences, Northwest University, Xi'an, Shaanxi, China. + + + + Qu + Xiao-Peng + XP + + Department of Neurosurgery, Tangdu Hospital, Airforce Military Medical University, Xi'an, Shaanxi, China. + + + + Zhang + Yue + Y + + Department of Neurosurgery, Tangdu Hospital, Airforce Military Medical University, Xi'an, Shaanxi, China. + + + + Song + Li-Jia + LJ + + Department of Pediatrics, Tangdu Hospital, Airforce Military Medical University, Xi'an, China. + + + + Gao + Guo-Dong + GD + + Department of Neurosurgery, Tangdu Hospital, Airforce Military Medical University, Xi'an, Shaanxi, China. + + + + Wang + Chao + C + + Department of Neurosurgery, Tangdu Hospital, Airforce Military Medical University, Xi'an, Shaanxi, China. + + + + Zheng + Long-Long + LL + + Department of Neurosurgery, Tangdu Hospital, Airforce Military Medical University, Xi'an, Shaanxi, China. + + + + Fang + Qi-Xing + QX + + Department of Neurosurgery, Tangdu Hospital, Airforce Military Medical University, Xi'an, Shaanxi, China. + + + + Qu + Yan + Y + + Department of Neurosurgery, Tangdu Hospital, Airforce Military Medical University, Xi'an, Shaanxi, China. + + + + Shen + Liang-Liang + LL + + Department of Biochemistry and Molecular Biology, Basic Medical Science Academy, Airforce Military Medical University, Xi'an, Shaanxi, China. + + + + Liu + Bei + B + + Department of Neurosurgery, Tangdu Hospital, Airforce Military Medical University, Xi'an, Shaanxi, China. + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
+ + Switzerland + Front Cardiovasc Med + 101653388 + 2297-055X + + + biomarkers + carotid artery + machine learning + metabolomics + unstable plaque + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2025 + 6 + 12 + + + 2026 + 3 + 18 + + + 2026 + 4 + 22 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 4 + 40 + + + 2026 + 5 + 12 + + + epublish + + 42205783 + PMC13201192 + 10.3389/fcvm.2026.1646067 + + + + Abdulsalam M, Feng J. The composition of vulnerable plaque and its effect on arterial waveforms. J Mech Behav Biomed Mater. (2021) 119:104491. 10.1016/j.jmbbm.2021.104491 + + 10.1016/j.jmbbm.2021.104491 + 33901965 + + + + Tan L, Xu Q, Shi R, Zhang G. Bioinformatics analysis reveals the landscape of immune cell infiltration and immune-related pathways participating in the progression of carotid atherosclerotic plaques. 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(2016) 23(6):952–61. 10.1038/cdd.2016.7 + + 10.1038/cdd.2016.7 + PMC4987739 + 26891692 + + + + Kurano M, Kano K, Dohi T, Matsumoto H, Igarashi K, Nishikawa M, et al. Different origins of lysophospholipid mediators between coronary and peripheral arteries in acute coronary syndrome. J Lipid Res. (2017) 58(2):433–42. 10.1194/jlr.P071803 + + 10.1194/jlr.P071803 + PMC5282959 + 28007846 + + + + Nishikawa M, Kurano M, Ikeda H, Aoki J, Yatomi Y. Lysophosphatidylserine has bilateral effects on macrophages in the pathogenesis of atherosclerosis. J Atheroscler Thromb. (2015) 22(5):518–26. 10.5551/jat.25650 + + 10.5551/jat.25650 + 25445889 + + + + Bellini F, Bruni A. Role of a serum phospholipase A1 in the phosphatidylserine-induced T cell inhibition. FEBS Lett. (1993) 316(1):1–4. 10.1016/0014-5793(93)81724-E + + 10.1016/0014-5793(93)81724-E + 8422930 + + + + Barnes MJ, Li CM, Xu Y, An J, Huang Y, Cyster JG. The lysophosphatidylserine receptor GPR174 constrains regulatory T cell development and function. J Exp Med. (2015) 212(7):1011–20. 10.1084/jem.20141827 + + 10.1084/jem.20141827 + PMC4493414 + 26077720 + + + + Sirianni AC, Jiang J, Zeng J, Mao LL, Zhou S, Sugarbaker P, et al. N-acetyl-l-tryptophan, but not N-acetyl-d-tryptophan, rescues neuronal cell death in models of amyotrophic lateral sclerosis. J Neurochem. (2015) 134(5):956–68. 10.1111/jnc.13190 + + 10.1111/jnc.13190 + 26031348 + + + + Yan C, Wu D, Gan L, Wang J, Yang W, Xu B. Significant metabolic alterations in non-small cell lung cancer patients by epidermal growth factor receptor-targeted therapy and PD-1/PD-L1 immunotherapy. Front Pharmacol. (2022) 13:949745. 10.3389/fphar.2022.949745 + + 10.3389/fphar.2022.949745 + PMC9403486 + 36034789 + + + + Shim K, Gulhar R, Jialal I. Exploratory metabolomics of nascent metabolic syndrome. J Diabetes Complications. (2019) 33(3):212–6. 10.1016/j.jdiacomp.2018.12.002 + + 10.1016/j.jdiacomp.2018.12.002 + 30611573 + + + + Jiang X, Stockwell BR, Conrad M. Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol. (2021) 22(4):266–82. 10.1038/s41580-020-00324-8 + + 10.1038/s41580-020-00324-8 + PMC8142022 + 33495651 + + + + Park JS, Kim D-H, Choi H-I, Kim CS, Bae EH, Ma SK, et al. 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) induces cell death through ferroptosis and acts as a trigger of apoptosis in kidney cells. Cell Death Dis. (2023) 14(2):78. 10.1038/s41419-023-05601-w + + 10.1038/s41419-023-05601-w + PMC9894909 + 36732325 + + + + Prentice KJ, Luu L, Allister EM, Liu Y, Jun LS, Sloop KW, et al. +The furan fatty acid metabolite CMPF is elevated in diabetes and induces β cell dysfunction. Cell Metab. (2014) 19(4):653–66. 10.1016/j.cmet.2014.03.008 + + + 10.1016/j.cmet.2014.03.008 + 24703697 + + + + Liu Y, Prentice K, Eversley J, Hu C, Batchuluun B, Leavey K, et al. Rapid elevation in CMPF may act as a tipping point in diabetes development. Cell Rep. (2016) 14(12):2889–900. 10.1016/j.celrep.2016.02.079 + + 10.1016/j.celrep.2016.02.079 + 26997281 + + + + Paolisso P, Foà A, Bergamaschi L, Angeli F, Fabrizio M, Donati F, et al. Impact of admission hyperglycemia on short and long-term prognosis in acute myocardial infarction: mINOCA versus MIOCA. Cardiovasc Diabetol. (2021) 20(1):192. 10.1186/s12933-021-01384-6 + + 10.1186/s12933-021-01384-6 + PMC8464114 + 34560876 + + + + + + + + 42205775 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2352-3409 + + 66 + + 2026 + Jun + + + Data in brief + Data Brief + + + The stack overflow recommendations dataset (SORD) - A large-scale curated dataset of recommendations related stack overflow questions, answers and comments. + + 112818 + 112818 + + 112818 + 10.1016/j.dib.2026.112818 + + Developer discussions particularly on programming related questions answering (Q&A) sites, contain useful information, which, if mined and analysed carefully can be transformed into insightful recommendations for developers about which software to use or prefer over others, matching with one's requirements for different software development activities. However, there is a long way to go before such a system can be realized. As a first step in this direction, we empirically explored the developers' discussions on Stack Overflow, one of the most popular Q&A sites among developers, with a particular emphasis on mining software recommendations related insights. We considered the Stack Overflow data dump published in October 2025 containing complete data of the site since 2008. The data extraction process started with the conversion of the gigantic XML files to SQL Server database table records. We then applied a keywords-based filtering approach to identify potential recommendation related queries in questions, and recommendations in answers and comments related to any aspect of software development. The set of keywords comprised of 19 keywords including the term 'recommend' along with its 14 synonyms and 4 antonyms. The extracted dataset contains 73.9k (0.31%) questions containing such terms in the question title, 1.1 M (4.69%) questions containing them in the question body, 2.2 M (6.18%) answers and 1.9 M (2.08%) comments because of exact keyword matching. The results further increase in case of substring matching. When enriched with additional metadata e.g. Users, Badges, Votes and Tags (which are available in the Stack Overflow data dump), the raw dataset presented in this paper can become highly useful for the empirical software engineering and machine learning research community for training models and developing recommendation systems for software engineering. The extracted answers and comments can be mined to extract implicit developer preferences from which ratings can be inferred whereas the extracted recommendation related questions with accepted answers can be transformed into a benchmark for retrieval evaluation of software recommendation systems for developers. The dataset is hosted on Figshare using DOI 10.6084/m9.figshare.30948506 and can be accessed via https://doi.org/10.6084/m9.figshare.30948506 or the GitHub repository. + © 2026 The Author(s). + + + + Fatima + Arjumand + A + + Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan. + + + + Maqbool + Onaiza + O + + Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan. + + + + eng + + + figshare + + 10.6084/m9.figshare.30948506 + + + + + Journal Article + + + 2026 + 05 + 06 + +
+ + Netherlands + Data Brief + 101654995 + 2352-3409 + + + Community question answering sites + Crowdsourced knowledge sharing + Human factors + Q&A websites + Recommendation systems for software engineering + Software information sites + Software recommendations + Stack exchange + + + + + + 2025 + 12 + 28 + + + 2026 + 4 + 27 + + + 2026 + 4 + 30 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 6 + 30 + + + 2026 + 5 + 28 + 4 + 40 + + + 2026 + 5 + 6 + + + epublish + + 42205775 + PMC13207349 + 10.1016/j.dib.2026.112818 + S2352-3409(26)00370-7 + + + + How do I access a data dump for a Stack Exchange site? (https://stackoverflow.com/help/data-dumps), Accessed: 2025-11-27. + + + Fatima A., Maqbool O. Scalable conversion of Stack Exchange network-based question answering sites’ Data dump to SQL server database–A case of stack overflow data dump. Array. 2026;30 + + + What topics can I ask about here? | Stack Overflow (https://stackoverflow.com/help/on-topic), Accessed: 2025-12-24. + + + Robillard M., Walker R., Zimmermann T. Recommendation systems for software engineering. IEEe Softw. 2009;27(4):80–86. + + + Kelly A. John Wiley & Sons; 2008. Changing Software development: Learning to Become Agile. + + + Bacchelli A., Ponzanelli L., Lanza M. 2012 Third International Workshop on Recommendation Systems for Software Engineering (RSSE) IEEE; 2012. Harnessing stack overflow for the ide. + + + Ponzanelli L., Bacchelli A., Lanza M. 2013 35th International Conference on Software Engineering (ICSE) IEEE; 2013. Seahawk: stack overflow in the ide. + + + Novielli N., Calefato F., Lanubile F. Proceedings of the 6th international workshop on social software engineering. 2014. Towards discovering the role of emotions in stack overflow. + + + Correa D., Sureka A. Proceedings of the 23rd international conference on World wide web. 2014. Chaff from the wheat: characterization and modeling of deleted questions on stack overflow. + + + Ponzanelli L., Mocci A., Lanza M. 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories. IEEE. 2015. Stormed: stack overflow ready made data. + + + Baltes S., Treude C., Diehl S. 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR) IEEE; 2019. Sotorrent: studying the origin, evolution, and usage of stack overflow code snippets. + + + Kou B., et al. Proceedings of the 19th International Conference on Mining Software Repositories. 2022. SOSum: a dataset of stack overflow post summaries. + + + Software Recommendations Stack Exchange, (https://softwarerecs.stackexchange.com/), Accessed: 2025-12-24. + + + Stack Overflow – Where Developers Learn, Share & Build Careers, (https://stackoverflow.com/), Accessed: 2025-12-24. + + + Ask Ubuntu, (https://askubuntu.com/), Accessed: 2025-12-24. + + + Server Fault, (https://serverfault.com/), Accessed: 2025-12-24. + + + Super User, (https://superuser.com/), Accessed: 2025-12-24. + + + Fatima A., Maqbool O. On the sustainability of software recommendations: analyzing the least-answered site on the Stack Exchange network. Data (Basel) 2026;11(3):58. + + + AI Assist (https://stackoverflow.com/ai-assist), Accessed: 2025-12-10. + + + Database schema documentation for the public data dump and SEDE (https://meta.stackexchange.com/questions/2677/database-schema-documentation-for-the-public-data-dump-and-sede), Accessed: 2026-January. + + + How to split large text file in windows? [closed] (https://stackoverflow.com/questions/31786287/how-to-split-large-text-file-in-windows), Accessed: 2025-11-27. + + + Recommend – English Meaning Cambridge Dictionary (https://dictionary.cambridge.org/dictionary/english/recommend), Accessed: 2025-11-28. + + + Recommend – definition and meaning Merriam Webster (https://www.merriam-webster.com/dictionary/recommend), Accessed: 2025-11-28. + + + Recommend – Definition and Meaning | Britannica Dictionary (https://www.britannica.com/dictionary/recommend), Accessed: 2025-11-28. + + + Recommend – definition of recommend by the Free Dictionary (https://www.thefreedictionary.com/recommend), Accessed: 2025-11-28. + + + What is Full Text Search versus Like (https://stackoverflow.com/questions/224714/what-is-full-text-search-vs-like), Accessed: 2025-12-24. + + + Full Text Search | SQL Server | Microsoft Learn (https://learn.microsoft.com/en-us/sql/relational-databases/search/full-text-search?view=sql-server-ver17), Accessed: 2025-12-24. + + + Import Flat File to SQL – SQL Server | Microsoft Learn (https://learn.microsoft.com/en-us/sql/relational-databases/import-export/import-flat-file-wizard?view=sql-server-ver17), Accessed: 2026-4-11. + + + What is the limitation of SQL Server express free edition for shared database in network (In terms of production deployment)? (https://learn.microsoft.com/en-us/answers/questions/1098023/what-is-the-limitation-of-sql-server-express-free), Accessed: 2025-11-27. + + + Which tools and technologies are used to build the Stack Exchange Network? (https://meta.stackexchange.com/questions/10369/which-tools-and-technologies-are-used-to-build-the-stack-exchange-network), Accessed: 2026-04-16. + + + Andrea V.A., Panjaya J., Jingga K. Performance comparison of relational database management systems for processing large amount of text data. Procedia Comput. Sci. 2025;269:150–160. + + + Scholar, P.G. ``Comparative performance analysis of MySQL and SQL Server relational database management systems in Windows environment.'' + + + Isinkaye F.O., Folajimi Y.O., Ojokoh B.A. Recommendation systems: principles, methods and evaluation. Egypt. inform. j. 2015;16(3):261–273. + + + Da'u A., et al. Recommendation system exploiting aspect-based opinion mining with deep learning method. Inf Sci (Ny) 2020;512:1279–1292. + + + Al-Ghuribi S.M., Noah S.A.M. Multi-criteria review-based recommender system–the state of the art. IEEe Access. 2019;7:169446–169468. + + + Lin B., et al. Opinion mining for software development: a systematic literature review. ACM Trans. Softw. Eng. Methodol. (TOSEM) 2022;31(3):1–41. + + + + + + + 42205772 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2352-3409 + + 66 + + 2026 + Jun + + + Data in brief + Data Brief + + FishNet: A dataset of freshwater fish from Bangladesh for deep learning-based fish species classification. + + 112799 + 112799 + + 112799 + 10.1016/j.dib.2026.112799 + + The fisheries sector plays a vital role in the economy and food security of Bangladesh. Bangladesh is one of the leading countries in inland fish production. Bangladesh gains sustainable economic benefits from aquaculture and fisheries. This sector made a significant contribution to the GDP and ensures employment for approximately 18 million people. Fish is one of the primary sources of protein for the population, accounting for >60 % of the country's animal protein intake. Efficient fish species identification is relevant to sustainable fisheries management, smart aquaculture, and food authenticity. This dataset includes 2455 clear images of seven frequently consumed freshwater fish in Bangladesh: Shrimp, Prawn, Mola Carplet, Dwarf Gourami, Swamp Barb, Stinging Catfish, and Mystus Catfish. All data were collected from the fish-rich areas in Bangladesh-Netrokona and Bogura. Data samples were collected from ponds, rivers, and fish markets, both natural and commercial sources. The diverse environment provides variation in lighting, background, and orientations, which highlights the real-world complexity for image classification. Each species is classified as scientific, local, and English names for accurate recognition. The dataset is suitable for research in smart aquaculture, including fish identification and species recognition. The collected data allows building machine learning models for image classification and allows fine-tuning previous models for local applications. The dataset includes real-world variability, which may support the generalization and robustness of machine learning models. This dataset provides a strong foundational resource for academic research and practical implementation in smart aquaculture. This dataset aims to contribute to sustainable fisheries and similar ecosystem development. + © 2026 Published by Elsevier Inc. + + + + Biswas + Bishal + B + + MARS Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh. + + + + Rabbi + Rakibul Haque + RH + + MARS Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh. + + + + Masuk + Md Mohtashim + MM + + MARS Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh. + + + + Siddiquee + Shah Md Tanvir + SMT + + MARS Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh. + + + + eng + + Journal Article + + + 2026 + 04 + 22 + +
+ + Netherlands + Data Brief + 101654995 + 2352-3409 + + + Computer vision + Fish identification + Fisheries + Freshwater fish + Image dataset + + + + + + 2025 + 7 + 23 + + + 2026 + 4 + 20 + + + 2026 + 4 + 20 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 4 + 40 + + + 2026 + 4 + 22 + + + epublish + + 42205772 + PMC13208109 + 10.1016/j.dib.2026.112799 + S2352-3409(26)00352-5 + + + + Munappy A., Bosch J., Olsson H.H., Arpteg A., Brinne B. Proc. 2019 45th Euromicro Conf. Softw. Eng. Adv. Appl. (SEAA) 2019. Data management challenges for deep learning; pp. 140–147. + + 10.1109/seaa.2019.00030 + + + + Das P.K., Kawsar Md.A., Paul P.B., Hridoy Md.A., Hossain Md.S., Niloy S. BD-freshwater-fish: an image dataset from Bangladesh for AI-powered automatic fish species classification and detection toward Smart Aquaculture. Data Brief. 2024;57 doi: 10.1016/j.dib.2024.111132. + + 10.1016/j.dib.2024.111132 + PMC11648155 + 39687362 + + + + Li X., Han T., Zheng S., Wu G. In: Wu G., editor. Vol. 1285. 2021. Nutrition and functions of amino acids in aquatic crustaceans; pp. 169–198. (Adv. Exp. Med. Biol.). + + 10.1007/978-3-030-54462-1_9 + 33770407 + + + + Ahamed F., Fulanda B.M., Siddik S.M., Hossain M.Y. An overview of freshwater prawn fishery in Bangladesh: present status and future prospect. J. Coast. Life Med. 2014;2(7):580–588. doi: 10.12980/JCLM.2.201414J31. + + 10.12980/JCLM.2.201414J31 + + + + Roos N., Leth T., Jakobsen J., Thilsted S.H. High vitamin A content in some small indigenous fish species in Bangladesh: perspectives for food-based strategies to reduce vitamin A deficiency. Int. J. Food Sci. Nutr. 2002;53:425–437. doi: 10.1080/0963748021000044778. + + 10.1080/0963748021000044778 + 12396468 + + + + C.-Y. Huang, H.-C. Lin, The effect of acidity on Gill variations in the aquatic air-breathing fish, Trichogaster Lalius, Comp. Biochem. Physiol. A: Mol. Integr. Physiol.. 158 (2011) 61–71. doi:10.1016/j.cbpa.2010.09.004. + + 10.1016/j.cbpa.2010.09.004 + 20840871 + + + + Sit G., Jana A., Chanda A., Sahu S. Dose selection for induced breeding and larval development of indigenous ornamental fish Puntius Chola (Hamilton, 1822) Aquat. Sci. Eng. 2023;38:62–67. doi: 10.26650/ase20221177106. + + 10.26650/ase20221177106 + + + + Kohinoor A., Khan M., Yeasmine S., Mandol P., Islam M. Effects of stocking density on growth and production performance of indigenous stinging catfish, heteropneustes fossilis (Bloch) Int. J. Agric. Res. Innov. Technol. 2013;2:9–14. doi: 10.3329/ijarit.v2i2.14009. + + 10.3329/ijarit.v2i2.14009 + + + + Darshan A., Mahanta P.C., Barat A., Kumar P. Redescription of the striped catfish mystus tengara (Hamilton, 1822) (Siluriformes: bagridae) India, J. Threat. Taxa. 2013;5:3536–3541. doi: 10.11609/jott.o2813.842. + + 10.11609/jott.o2813.842 + + + + Shamsuzzaman Md.M., Mozumder M.M.Hoque, Mitu S.J., Ahamad A.F., Bhyuian Md.S. The economic contribution of fish and fish trade in Bangladesh. Aquac. Fish. 2020;5:174–181. doi: 10.1016/j.aaf.2020.01.001. + + 10.1016/j.aaf.2020.01.001 + + + + Zendel O., Murschitz M., Humenberger M., Herzner W. How good is my test data? Introducing safety analysis for computer vision. Int. J. Comput. Vis. 2017;125:95–109. doi: 10.1007/s11263-017-1020-z. + + 10.1007/s11263-017-1020-z + + + + + + + + 42205771 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2352-3409 + + 66 + + 2026 + Jun + + + Data in brief + Data Brief + + A 2000-2023 dataset for measuring China's biodiversity risk. + + 112650 + 112650 + + 112650 + 10.1016/j.dib.2026.112650 + + The increasing frequency of extinctions of biological populations has important implications for related sectors. Consequently, the risks associated with biodiversity are receiving increasing attention and are being recognized as entirely new risk factors. To understand the drivers of biodiversity risk, it is crucial to measure biodiversity risk at multiple levels, especially in developing countries. Using machine learning and text mining methods, we measure the biodiversity risk of the Chinese market from 2000 to 2023 from the perspectives of macro-government, meso‑industry, and micro-firms, by analysing official news media texts, related fund-holding data, and listed firms' annual reports. We construct a macro-level index derived from the sentiment and frequency of biodiversity-related discourse in official media, including the China Environment News and the CCTV News, from 2013 to 2023. We also construct a meso-level industry risk exposure indicator, calculated as the deviation of biodiversity-themed public funds' holdings from market portfolio weights across 58 sectors. In addition, we develop micro-level firm-specific metrics, based on the frequency and sentiment analysis of biodiversity sentences in the annual reports of 5606 A-share listed firms, 2000-2023, using an improved BERT model for Chinese text. These indicators provide researchers, policymakers, and financial practitioners with a foundational resource for empirically investigating the economic and financial implications of biodiversity risk in China. + © 2026 Published by Elsevier Inc. + + + + Chen + Zhang-Hangjian + ZH + + School of Economics, Anhui University, Hefei 230601, China. + + + + Gao + Xiang + X + + Research Center of Finance, Shanghai Business School, Shanghai 200235, China. + + + + Yang + Jin Rui + JR + + School of Economics, Anhui University, Hefei 230601, China. + + + + Yarovaya + Larisa + L + + Southampton Business School, University of Southampton, Southampton, SO17 1BJ, UK. + + + + eng + + Journal Article + + + 2026 + 05 + 06 + +
+ + Netherlands + Data Brief + 101654995 + 2352-3409 + + + BERT model + Biodiversity risk + Chinese market + Machine learning + + + + + + 2024 + 12 + 5 + + + 2026 + 2 + 24 + + + 2026 + 2 + 27 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 4 + 40 + + + 2026 + 5 + 6 + + + epublish + + 42205771 + PMC13208078 + 10.1016/j.dib.2026.112650 + S2352-3409(26)00203-9 + + + + Flammer C., Giroux T., Heal G. Biodiversity finance. J. Financ. Econ. 2025;164 doi: 10.1016/j.jfineco.2024.103987. + + 10.1016/j.jfineco.2024.103987 + + + + Giglio S., Kuchler T., Stroebel J., Zeng X. Biodiversity risk. Rev. Financ. 2025 doi: 10.1093/rof/rfaf063. + + 10.1093/rof/rfaf063 + + + + W. Yin, B. Liu, H. Zhou. (2023). Biodiversity risk exposure and investment efficiency: evidence from 10-k. Available at SSRN 4629771. + + + Garel A., Romec A., Sautner Z., Wagner A.F. Do investors care about biodiversity? Rev Financ. 2024;28(4):1151–1186. doi: 10.1093/rof/rfae010. + + 10.1093/rof/rfae010 + + + + Kurth T., Wübbels G., Portafaix A., Meyer zum Felde A., Zielcke S. Boston Consulting Group; Boston, MA, USA: 2021. The Biodiversity Crisis is a Business Crisis.https://web-assets.bcg.com/fb/5e/74af5531468e9c1d4dd5c9fc0bd7/bcg-the-biodiversity-crisis-is-a-business-crisis-mar-2021-rr.pdf + + + Engle R.F., Giglio S., Kelly B., Lee H., Stroebel J. Hedging climate change news. Rev. Financ. Stud. 2020;33(3):1184–1216. doi: 10.1093/rfs/hhz072. + + 10.1093/rfs/hhz072 + + + + Cui Y., Che W., Liu T., Qin B., Yang Z. Pre-training with whole word masking for Chinese BERT. IEEE/ACM Trans. Audio Speech Lang. Process. 2021;29:3504–3514. doi: 10.1109/TASLP.2021.3124365. + + 10.1109/TASLP.2021.3124365 + + + + Chen Z.H., Gao X., Koedijk K.G., Wei Q. Biodiversity scores and corporate profitability. Int. J. Finance Econ. 2025 doi: 10.1002/ijfe.70086. + + 10.1002/ijfe.70086 + + + + Chen Z.H. Biodiversity risk and the cost of equity capital: implications for sustainable development. Sustain. Dev. 2025 doi: 10.1002/sd.70449. + + 10.1002/sd.70449 + + + + Li J., Jin Y., Zhou P. Do banks price firms’ biodiversity risk? Evidence from the Kunming declaration. Int. Rev. Financ. Anal. 2025 doi: 10.1016/j.irfa.2025.104557. + + 10.1016/j.irfa.2025.104557 + + + + He F., Wei C., Lucey B., Hao J. Beyond greenwashing: how does firm-level biodiversity disclosure affect corporate sustainability strategy? Pac.-Basin Finance J. 2025 doi: 10.1016/j.pacfin.2025.102787. + + 10.1016/j.pacfin.2025.102787 + + + + Steindl T., Küster S., Hartlieb S. Pricing firms’ biodiversity risk exposure: empirical evidence from audit fees. J. Ind. Ecol. 2025 doi: 10.1111/jiec.70014. + + 10.1111/jiec.70014 + + + + Zhou C., Chen Y., Ji Q., Zhang D. Does public attention to biodiversity matter to stock markets? Int. Rev. Financ. Anal. 2025;98 doi: 10.1016/j.irfa.2025.103925. + + 10.1016/j.irfa.2025.103925 + + + + Giglio S., Kuchler T., Stroebel J., Wang O. AEA Papers and Proceedings. Vol. 115. American Economic Association; 2025. Nature loss and climate change: the twin-crises multiplier; pp. 409–414. 2014 Broadway, Suite 305, Nashville, TN 37203. + + 10.1257/pandp.20251074 + + + + Silva F.D.D.S., Carvalheiro L.G., Aguirre-Gutiérrez J., Lucotte M., Guidoni-Martins K., Mertens F. Virtual pollination trade uncovers global dependence on biodiversity of developing countries. Sci. Adv. 2021;7(11) doi: 10.1126/sciadv.abe6636. + + 10.1126/sciadv.abe6636 + PMC7946370 + 33692110 + + + + Gao X. The harmonious coexistence of creatures and firms: a review of quantifying biodiversity contribution for corporate performance evaluation. J. Transit. Econ. Finance. 2025;1(1) doi: 10.1142/S3082844925300017. + + 10.1142/S3082844925300017 + + + + + + + + 42205768 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2352-3409 + + 66 + + 2026 + Jun + + + Data in brief + Data Brief + + Quantitative sensory testing and classical pain model dataset in 127 healthy volunteers. + + 112842 + 112842 + + 112842 + 10.1016/j.dib.2026.112842 + + Experimental pain models are integral to human pain research and the development of analgesic drugs. Of many options available, it is possible to identify pain models that reliably predict clinical analgesic efficacy in cost-effective laboratory settings. However, translating experimental findings to clinical practice is imperfect, and laboratory models can only partially replicate specific clinical pain conditions. The present dataset originates from a study that addressed this gap between experimental and clinical pain. The broader study investigated whether patterns of clinical neuropathic pain could be induced, at least to some degree, in healthy human subjects using topical capsaicin application to induce hypersensitization. This dataset provides the baseline control measurements from untreated skin that were obtained as part of this research. The study enrolled 127 healthy volunteers who underwent comprehensive sensory testing. A quantitative sensory testing (QST) battery developed for clinical diagnosis by the German Research Network on Neuropathic Pain was utilized to characterize baseline sensory function and pain sensitivity. The dataset is provided in both its original (raw) and transformed formats. Key pain data are presented as a 127×23 matrix (127 cases and 23 QST variables). Transformations include sign inversion for certain variables to align pain sensitivity, directionality, unit conversions, and log transformations based on psychophysical principles. A separate metadata file documents participant characteristics, such as age, sex, test site allocation (hand or foot), and body side tested. This dataset has significant potential for reuse. Researchers can use it to benchmark new pain assessment tools, validate experimental pain models, and develop or test statistical and computational methods for analyzing sensory and pain data. The inclusion of demographic variables allows for the investigation of how age and sex influence pain perception. The structure of the dataset supports secondary analyses, integration into machine learning workflows as independent validation data, and use as a reference in future pain research. All data are anonymized and formatted to facilitate reproducibility and secondary use. + © 2026 The Author(s). + + + + Lötsch + Jörn + J + + Goethe University, Institute of Clinical Pharmacology, Faculty of Medicine, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany. + + + University of Helsinki, Faculty of Medicine, Haartmaninkatu 8, P.O. Box 63, Helsinki 00014 Finland. + + + Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany. + + + + Dimova + Violeta + V + + Goethe University, Institute of Clinical Pharmacology, Faculty of Medicine, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany. + + + + Lieb + Isabel + I + + Goethe University, Institute of Clinical Pharmacology, Faculty of Medicine, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany. + + + + Kringel + Dario + D + + Goethe University, Institute of Clinical Pharmacology, Faculty of Medicine, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany. + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
+ + Netherlands + Data Brief + 101654995 + 2352-3409 + + + Experimental pain + Human + Pain research + Psychophysics + + + + + + 2025 + 6 + 11 + + + 2026 + 5 + 6 + + + 2026 + 5 + 8 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 4 + 40 + + + 2026 + 5 + 12 + + + epublish + + 42205768 + PMC13208079 + 10.1016/j.dib.2026.112842 + S2352-3409(26)00394-X + + + + Lötsch J., Dimova V., Ultsch A., Lieb I., Zimmermann M., Geisslinger G., Oertel B.G. A small yet comprehensive subset of human experimental pain models emerging from correlation analysis with a clinical quantitative sensory testing protocol in healthy subjects. Eur. J. Pain. 2016;20(5):777–789. + + 26492152 + + + + Lötsch J., Dimova V., Hermens H., Zimmermann M., Geisslinger G., Oertel B.G., Ultsch A. Pattern of neuropathic pain induced by topical capsaicin application in healthy subjects. Pain. 2015;156(3):405–414. + + 25687540 + + + + Lötsch J., Oertel B.G., Ultsch A. Human models of pain for the prediction of clinical analgesia. Pain. 2014;155(10):2014–2021. + + 25020003 + + + + Oertel B.G., Lötsch J. Clinical pharmacology of analgesics assessed with human experimental pain models: bridging basic and clinical research. Br. J. Pharmacol. 2013;168(3):534–553. + + PMC3579278 + 23082949 + + + + Rolke R., Baron R., Maier C., Tolle T.R., Treede R.D., Beyer A., Binder A., Birbaumer N., Birklein F., Botefur I.C., Braune S., Flor H., Huge V., Klug R., Landwehrmeyer G.B., Magerl W., Maihofner C., Rolko C., Schaub C., Scherens A., Sprenger T., Valet M., Wasserka B. Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): standardized protocol and reference values. Pain. 2006;123(3):231–243. + + 16697110 + + + + Rolke R., Magerl W., Campbell K.A., Schalber C., Caspari S., Birklein F., Treede R.D. Quantitative sensory testing: a comprehensive protocol for clinical trials. Eur. J. Pain. 2006;10(1):77–88. + + 16291301 + + + + Lötsch J., Mayer B., Kringel D. Machine learning analysis predicts a person's sex based on mechanical but not thermal pain thresholds. Sci. Rep. 2023;13(1):7332. + + PMC10163041 + 37147321 + + + + Fechner G.T. Breitkopf and Härtel; Leipzig: 1860. Elemente Der Psychophysik. + + + Hummel T., Kobal G. Chemosensory event-related potentials to trigeminal stimuli change in relation to the interval between repetitive stimulation of the nasal mucosa. Eur Arch. Otorhinolaryngol. 1999;256(1):16–21. + + 10065380 + + + + Kobal G. Pain-related electrical potentials of the human nasal mucosa elicited by chemical stimulation. Pain. 1985;22(2):151–163. + + 4047701 + + + + Tarun A.S., Bryant B., Zhai W., Solomon C., Shusterman D. Gene expression for carbonic anhydrase isoenzymes in human nasal mucosa. Chem. Senses. 2003;28(7):621–629. + + 14578124 + + + + Hummel T., Mohammadian P., Marchl R., Kobal G., Lötsch J. Pain in the trigeminal system: irritation of the nasal mucosa using short- and long-lasting stimuli. Int. J. Psychophysiol. 2003;47(2):147–158. + + 12568945 + + + + Pfau D.B., Krumova E.K., Treede R.-D., Baron R., Toelle T., Birklein F., Eich W., Geber C., Gerhardt A., Weiss T., Magerl W., Maier C. Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): reference data for the trunk and application in patients with chronic postherpetic neuralgia. Pain. 2014;155(5):1002–1015. + + 24525274 + + + + Pfau D., Klein T., Blunk J.A., Geber C., Krumova E., Limbeck C., Magerl W., Maier C., Westermann A., Schuh-Hofer S., Tiede W., Treede R.D. In: Lehrstuhl Für Neurophysiologie. Rolke R., Andrews A., Magerl W., Treede R.D., editors. Universitätsmedizin Mannheim; 2010. QST quantitative sensorische testung, handanweisung für den untersucher, eine standardisierte testbatterie für die Quantitative sensorische testung nach den Regeln des Deutschen Forschungsverbundes Neuropathischer Schmerz (DFNS) editors. + + + Magerl W., Krumova E.K., Baron R., Tolle T., Treede R.D., Maier C. Reference data for quantitative sensory testing (QST): refined stratification for age and a novel method for statistical comparison of group data. Pain. 2010;151(3):598–605. + + 20965658 + + + + Maier C., Baron R., Tölle T.R., Binder A., Birbaumer N., Birklein F., Gierthmühlen J., Flor H., Geber C., Huge V., Krumova E.K., Landwehrmeyer G.B., Magerl W., Maihöfner C., Richter H., Rolke R., Scherens A., Schwarz A., Sommer C., Tronnier V., Uçeyler N., Valet M., Wasner G., Treede R.-D. Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): somatosensory abnormalities in 1236 patients with different neuropathic pain syndromes. Pain. 2010;150(3):439–450. + + 20627413 + + + + + + + + 42205754 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2234-943X + + 16 + + 2026 + + + Frontiers in oncology + Front Oncol + + 3D intratumoral heterogeneity-based quantitative score from chest CT for preoperative prediction of visceral pleural invasion in lung adenocarcinoma: a multicenter study. + + 1837845 + 1837845 + + 1837845 + 10.3389/fonc.2026.1837845 + + Visceral pleural invasion (VPI) is a critical adverse prognostic factor in lung adenocarcinoma (LUAD). This study aimed to develop a stacking ensemble model that integrates three-dimensional intratumoral heterogeneity (3D ITH) scores with clinicoradiologic features to achieve accurate preoperative prediction of VPI in LUAD. + This multicenter retrospective study included 1,301 patients with LUAD from three medical centers. Patients from Centers 1 and 2 were assigned to the development cohort, whereas those from Center 3 constituted the fixed external validation cohort. To calculate the 3D ITH score, we integrated local radiomic descriptors with global pixel distribution characteristics derived from whole tumor CT volumes. Clinicoradiologic features and 3D ITH scores were then used to construct six base machine learning models and a final stacking ensemble classifier. Model performance was primarily assessed using receiver operating characteristic analysis and the area under the curve (AUC). SHapley Additive exPlanations (SHAP) were used to quantify feature contributions and to interpret the final model. + The stacking ensemble classifier achieved the highest AUC for preoperative prediction of VPI in LUAD (AUC = 0.878), whereas XGBoost showed competitive performance on several threshold dependent metrics. SHAP analysis identified the 3D ITH score as the most influential predictor, followed by nodule size and CT density. Comparative experiments further showed that the stacking ensemble model outperformed the conventional radiomics signature (AUC = 0.841) and the clinicoradiologic comparative model (AUC = 0.776). + The model integrating 3D ITH scores with clinicoradiologic features showed strong discrimination for preoperative prediction of VPI in LUAD. This approach may serve as a useful adjunct for preoperative risk stratification and individualized treatment planning. + Copyright © 2026 Ouyang, Wu, Zhou, Qi, Zhang, Zhao and Zuo. + + + + Ouyang + Qunzhi + Q + + Department of Radiology, Ningyuan County People's Hospital, Yongzhou, Hunan, China. + + + + Wu + Yanping + Y + + Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, China. + + + + Zhou + Liuhan + L + + Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, China. + + + + Qi + Wanyin + W + + Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China. + + + + Zhang + Sanhong + S + + Department of Radiology, Liuyang Traditional Chinese Medicine Hospital, Changsha, Hunan, China. + + + + Zhao + Yan + Y + + Department of Radiology, The Fifth People's Hospital of Xiangtan City, Xiangtan, Hunan, China. + + + + Zuo + Jingyi + J + + School of Medicine and Life Sciences, Zhangjiajie College, Zhangjiajie, Hunan, China. + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
+ + Switzerland + Front Oncol + 101568867 + 2234-943X + + + 3D ITH score + lung adenocarcinoma + multicenter study + stacking ensemble learning + visceral pleural invasion + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 3 + 24 + + + 2026 + 4 + 23 + + + 2026 + 4 + 23 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 4 + 40 + + + 2026 + 5 + 12 + + + epublish + + 42205754 + PMC13201124 + 10.3389/fonc.2026.1837845 + + + + Siegel RL, Miller KD, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. (2023) 73:17–48. doi:  10.3322/caac.21763. PMID: + + 10.3322/caac.21763 + 36633525 + + + + Zuo Z, Zhang G, Song P, Yang J, Li S, Zhong Z, et al. Survival nomogram for stage IB non-small-cell lung cancer patients, based on the SEER database and an external validation cohort. Ann Surg Oncol. (2021) 28:3941–50. doi:  10.1245/s10434-020-09362-0. PMID: + + 10.1245/s10434-020-09362-0 + 33249521 + + + + De Giglio A, Di Federico A, Gelsomino F, Ardizzoni A. Prognostic relevance of pleural invasion for resected NSCLC patients undergoing adjuvant treatments: a propensity score-matched analysis of SEER database. Lung Cancer. (2021) 161:18–25. doi:  10.1016/j.lungcan.2021.08.017. PMID: + + 10.1016/j.lungcan.2021.08.017 + 34492552 + + + + Qi M, Bian D, Zhang J, Zhu X, Zhou C, Zhang L. The modification of T description according to visceral pleural invasion and tumor size from 3.1 cm to 4.0 cm in non-small cell lung cancer: a retrospective analysis based on the SEER database. Lung Cancer. (2021) 158:47–54. doi:  10.1016/j.lungcan.2021.06.003. PMID: + + 10.1016/j.lungcan.2021.06.003 + 34119932 + + + + Wang S, Zhang B, Qian J, Qiao R, Xu J, Zhang L, et al. Proposal on incorporating lymphovascular invasion as a T-descriptor for stage I lung cancer. Lung Cancer. (2018) 125:245–52. doi:  10.1016/j.lungcan.2018.09.024. PMID: + + 10.1016/j.lungcan.2018.09.024 + 30429028 + + + + Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WEE, et al. The IASLC lung cancer staging project: proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. J Thorac Oncol. (2016) 11:39–51. doi:  10.1016/j.jtho.2015.09.009. PMID: + + 10.1016/j.jtho.2015.09.009 + 26762738 + + + + Yu Y, Huang R, Wang P, Wang S, Ling X, Zhang P, et al. Sublobectomy versus lobectomy for long-term survival outcomes of early-stage non-small cell lung cancer with a tumor size ≤2 cm accompanied by visceral pleural invasion: a SEER population-based study. J Thorac Dis. (2020) 12:592–604. doi:  10.21037/jtd.2019.12.121. PMID: + + 10.21037/jtd.2019.12.121 + PMC7138986 + 32274125 + + + + Wang Y, Lyu D, Fan L, Liu S. Research progress in predicting visceral pleural invasion of lung cancer: a narrative review. 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+ + 2296-9144 + + 13 + + 2026 + + + Frontiers in robotics and AI + Front Robot AI + + Editorial: Reinforcement learning for real-world robot navigation. + + 1861947 + 1861947 + + 1861947 + 10.3389/frobt.2026.1861947 + + + Wang + Pengqin + P + + Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology, Hong Kong SAR, China. + + + + Li + Xiaocong + X + + Agency for Science, Technology and Research (A*STAR), Singapore, Singapore. + + + + Zhu + Meixin + M + + School of Transportation, Southeast University, Nanjing, China. + + + + Ma + Jun + J + + Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. + + + + eng + + Editorial + + + 2026 + 05 + 12 + +
+ + Switzerland + Front Robot AI + 101749350 + 2296-9144 + + + deep learning + machine learning + motion planning + reinforcement learning + robot navigation + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 4 + 21 + + + 2026 + 4 + 29 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 4 + 40 + + + 2026 + 5 + 12 + + + epublish + + 42205719 + PMC13201120 + 10.3389/frobt.2026.1861947 + 1861947 + + + + + + 42205681 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 1178-7074 + + 19 + + 2026 + + + International journal of general medicine + Int J Gen Med + + Evolocumab Alters Transcriptomic Signatures and Identifies Inflammatory Biomarkers in Brain-Heart Syndrome with Coronary Heart Disease History. + + 603395 + 603395 + + 603395 + 10.2147/IJGM.S603395 + + A history of coronary heart disease (CHD) increases the risk of Brain-Heart Syndrome (BHS) after acute stroke, partly through heightened inflammatory responses. Evolocumab, a PCSK9 inhibitor, has anti-inflammatory properties, but its transcriptomic effects in BHS patients with CHD remain unclear. This study aims to identify evolocumab-associated transcriptomic changes and inflammation-related biomarkers in this population. + Blood samples from 24 BHS patients with CHD history (12 receiving rosuvastatin alone, 12 receiving rosuvastatin plus evolocumab) underwent transcriptomic sequencing. Candidate biomarkers were identified via differential expression and machine learning, with functional enrichment and immune infiltration analyses conducted. + Four candidate biomarkers were identified: WHRN (DFNB31), IL12A, and ASB14 were upregulated, while TMED7-TICAM2 was downregulated in the evolocumab combination group. These genes were enriched in pathways related to cell metabolism, signal transduction, and immune regulation. Immune infiltration analysis showed modest but detectable changes in B-cell subsets. External validation confirmed differential expression of these candidate biomarkers in CAD patients. + This pilot study provides preliminary insights into the molecular mechanisms of evolocumab in treating Brain-Heart Syndrome with a coronary heart disease history, identifying four inflammation-related biomarkers. These findings suggest potential targets for future investigation; however, given the exploratory nature and small sample size, further experimental and clinical validation is required before any therapeutic application. + © 2026 Dong et al. + + + + Dong + Huijie + H + + Department of Cardiology, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, People's Republic of China. + + + + Gong + Xing + X + + Department of Neurology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, People's Republic of China. + + + + Zhao + Zhenrong + Z + + Department of Cardiology, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, People's Republic of China. + + + + Joyama + Yuki + Y + + Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan. + + + + Ji + Xiaofei + X + 0000-0002-6104-1952 + + Department of Neurology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, People's Republic of China. + + + + Qu + Peng + P + + Institute of Heart and Vessel Diseases, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, People's Republic of China. + + + + eng + + Journal Article + + + 2026 + 05 + 22 + +
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+ + 2753-4294 + + 4 + 2 + + 2026 + + + BMJ public health + BMJ Public Health + + Can artificial intelligence bridge critical gaps in hypertension prediction and risk assessment in low- and middle-income countries? A scoping review. + + e003435 + e003435 + + e003435 + 10.1136/bmjph-2025-003435 + + According to the WHO, as of 2025, 1.4 billion adults between the ages 30-79 were living with hypertension, with approximately two-thirds of that number from low- and middle-income countries (LMICs). By 2025, the number of individuals with hypertension is expected to have reached 1.5 billion, with sub-Saharan Africa alone projected to have 74.7 million cases. In recent years, artificial intelligence (AI) has demonstrated the potential to improve the accuracy of risk assessment tools for predicting, managing and diagnosing diseases before they escalate. This paper aims to conduct a scoping review to identify current research, innovations and developments in the application of AI-based tools for hypertension prediction and risk assessment, specifically in LMICs. + This scoping review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines to ensure proper representation and organisation of the literature. A search was conducted across multiple databases, including PubMed, Elsevier, Google Scholar, Scopus, IEEE Xplore and the ACM Digital Library. Two independent reviewers used the search terms in Appendix A to identify relevant literature. Initial scans were followed by a detailed evaluation based on well-structured and clear inclusion and exclusion criteria. A third, more experienced reviewer resolved any selection discrepancies flagged from the first two reviews. + The literature that would be included within this study will primarily be sourced from the following scientific literature repositories: PubMed, Scopus, Elsevier, Google Scholar, IEEE and ACM Digital Library. + The inclusion and exclusion criteria defined and used to guide the literature selection process within this review included the following:The literature should primarily be about the study of the deployment of AI algorithms or tools in the diagnosis, detection or risk assessment of hypertension in an LMIC.The data used in the literature to train and assess any implemented AI algorithms should be obtained from either an LMIC.The literature should have been published within the last 10 years, that is, 2014-2025. + The scoping review process followed the steps first outlined by Arksey and O'Malley in 2005. These steps are:Identifying the research question.Identifying the relevant studies within the literature.Selecting the studies within the relevant studies that most align with the research question.Charting the data that is present within these selected studies to get a deeper understanding of them.Collating, summarising and reporting the information found in the selected literature.From an initial result of 1371 papers, 5 were selected based on predefined inclusion and exclusion criteria. These studies solely focused on AI-based risk assessment and prediction of hypertension in LMICs. + The review identified significant research on AI applications for hypertension risk assessment and prediction in LMICs. Notable studies include an Ethiopian study on using machine learning algorithms, achieving an accuracy of 88.81%, precision of 89.62% and recall of 97.04% in hypertension risk prediction. Another research study involving hypertensive patients from South Asian countries (Bangladesh, Nepal, India) employed non-invasive information and machine learning tools for accurate hypertension prediction. The reported prediction accuracy of the studies found ranged from 78% to 97% depending on dataset size and AI model used. Algorithms discussed in these papers include logistic regression, decision trees and naïve Bayes. + The reviewed studies highlight the promising potential of AI to enhance hypertension prevention and management in LMICs. Successful implementation of AI tools requires the development of locally contextualised models and the availability of validated local data. AI can significantly impact hypertension outcomes in LMICs, provided these conditions are met. + © Author(s) (or their employer(s)) 2026. Re-use permitted under CC BY. Published by BMJ Group. + + + + Sasu + David + D + + Ashesi University, Berekuso, Ghana. + + + + Agbettor + Tsatsu + T + 0009-0003-4735-0841 + + Academic Affair, Ashesi University, Berekuso, Ghana. + + + + Owusu Ansah + Angela + A + + Ashesi University, Berekuso, Ghana. + + + + Annoh + William + W + + Ashesi University, Berekuso, Ghana. + + + + Ackerson + Lecia + L + 0009-0005-0068-1621 + + Ashesi University, Berekuso, Ghana. + + + + Ohene-Adu + Ayeyi Adjoa Domeyo + AAD + 0009-0006-3341-5933 + + Ashesi University, Berekuso, Ghana. + + + + Sackitey-Matey + Roselyn + R + 0009-0006-9247-2962 + + Academic Affair, Ashesi University, Berekuso, Ghana. + + + + eng + + Journal Article + + + 2026 + 05 + 22 + +
+ + England + BMJ Public Health + 9918697578906676 + 2753-4294 + + + Digital Technology + Public Health + Risk Assessment + + Competing interests: None declared. + + + + + 2025 + 6 + 25 + + + 2026 + 4 + 1 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 4 + 38 + + + 2026 + 5 + 22 + + + epublish + + 42205617 + PMC13202181 + 10.1136/bmjph-2025-003435 + bmjph-2025-003435 + + + + Hypertension. Available: https://www.who.int/news-room/fact-sheets/detail/hypertension [Accessed 19 Mar 2024]. + + + Moloro AH, Seid AA, Jaleta FY. A systematic review and meta-analysis protocol on hypertension prevalence and associated factors among bank workers in Africa. SAGE Open Med 2023;11:20503121231172001. 10.1177/20503121231172001 + + 10.1177/20503121231172001 + PMC10170600 + 37181276 + + + + Bosu WK, Reilly ST, Aheto JMK, et al. Hypertension in older adults in Africa: A systematic review and meta-analysis. 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+ + 2001-8525 + + 13 + 1 + + 2026 + + + European clinical respiratory journal + Eur Clin Respir J + + Breath-by-breath: unmasking cardio-respiratory conditions through capnography waveforms - the general breathing record study. + + 2661529 + 2661529 + + 2661529 + 10.1080/20018525.2026.2661529 + + In an increasingly global comorbid population, there are significant challenges to diagnosing the cause of breathlessness. Furthermore, once chronic respiratory conditions have been diagnosed, there is considerable difficulty in detecting deterioration early enough to provide timely, effective intervention. Current methods of diagnosing and monitoring conditions that cause breathlessness, such as asthma and chronic heart failure, can be extensive and difficult to perform. + This observational, proof-of-concept study explored the potential of high-resolution capnography to differentiate respiratory and cardiac conditions causing breathlessness. Using an early model of the N-Tidal device, we analysed capnography waveforms from participants with severe asthma, chronic heart failure, and pneumonia, and compared them to healthy baseline controls. + A moderate correlation was observed between alpha angle and spirometry metrics in asthma (r2 = 0.48 for FEV1/FVC), along with a moderate association between alpha angle and left ventricular ejection fraction in heart failure (r2 = 0.46). Pneumonia recovery was marked by a 12.6% median increase in end-tidal CO2. + These results suggest that high-resolution capnography may offer promise for non-invasive diagnosis and monitoring of cardiorespiratory conditions. + NCT03356288 (registered on 7 September 2017). + © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. + + + + Neville + Daniel M + DM + + Respiratory Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK. + + + + Lim + Rui Hen + RH + 0009-0004-2284-7410 + + Machine Learning / Hardware / Clinical Departments, TidalSense Limited, Cambridge, UK. + + + + Hawke + Elizabeth + E + + Machine Learning / Hardware / Clinical Departments, TidalSense Limited, Cambridge, UK. + + + + Talker + Leeran + L + + Machine Learning / Hardware / Clinical Departments, TidalSense Limited, Cambridge, UK. + + + + Broomfield + Henry + H + + Machine Learning / Hardware / Clinical Departments, TidalSense Limited, Cambridge, UK. + + + + Dogan + Cihan + C + + Machine Learning / Hardware / Clinical Departments, TidalSense Limited, Cambridge, UK. + + + + Selim + Ahmed B + AB + + Machine Learning / Hardware / Clinical Departments, TidalSense Limited, Cambridge, UK. + + + + Lambert + Gabriel + G + + Machine Learning / Hardware / Clinical Departments, TidalSense Limited, Cambridge, UK. + + + + Carter + Julian C + JC + + Machine Learning / Hardware / Clinical Departments, TidalSense Limited, Cambridge, UK. + + + + Begum + Selina + S + + Respiratory Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK. + + + + De Vos + Ruth + R + + Respiratory Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK. + + + + Kalra + Paul + P + + Respiratory Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK. + + + + Quint + Matthew + M + + Respiratory Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK. + + + + Adeniji + Kayode + K + + Respiratory Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK. + + + + Brown + Thomas P + TP + + Respiratory Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK. + + + + Patel + Ameera X + AX + + Machine Learning / Hardware / Clinical Departments, TidalSense Limited, Cambridge, UK. + + + + Chauhan + Anoop J + AJ + + Respiratory Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK. + + + + eng + + + ClinicalTrials.gov + + NCT03356288 + + + + + Journal Article + + + 2026 + 05 + 25 + +
+ + United States + Eur Clin Respir J + 101662134 + 2001-8525 + + + Capnography + asthma + breathlessness + chronic heart failure + pneumonia + + RHL, EH, LT, CD, HB, ABS, GL, JCC, and AXP are currently employed, or were employed/funded at the time of the research, by TidalSense Limited. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, UKRI or the Department of Health and Social Care. + + + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 4 + 37 + + + 2026 + 5 + 25 + + + epublish + + 42205504 + PMC13202639 + 10.1080/20018525.2026.2661529 + 2661529 + + + + National Institute for Health and Care Excellence . Prevalence | Background information | Asthma | CKS | NICE [Internet]. 2024. [cited 2024 Dec +14]. Available from: +https://cks.nice.org.uk/topics/asthma/background-information/prevalence/ + + + British Heart Foundation . Cardiovascular disease statistics 2017 [Internet]. 2017. [cited 2024 Dec +14]. Available from: +https://www.bhf.org.uk/what-we-do/our-research/heart-statistics/heart-statistics-publications/cardiovascular-disease-statistics-2017 + + + National Institute for Health and Care Excellence . Overview | Pneumonia in adults: diagnosis and management | Guidance | NICE [Internet]. 2014. [cited 2024 Dec +14]. Available from: +https://www.nice.org.uk/guidance/cg191 + + + Gupta R, Sheikh A, Strachan DP, et al. Burden of allergic disease in the UK: secondary analyses of national databases. Clin Exp Allergy. 2004;34(4):520–10. doi: 10.1111/j.1365-2222.2004.1935.x + + 10.1111/j.1365-2222.2004.1935.x + 15080802 + + + + Vos T, Barber RM, Bell B, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386(9995):743. + + PMC4561509 + 26063472 + + + + Hubbard R. The burden of lung disease. Thorax. 2006;61(7):557–558. doi: 10.1136/thx.2006.066050 + + 10.1136/thx.2006.066050 + PMC2104658 + 16807390 + + + + Côté J, Cartier A, Malo JL, et al. Compliance with peak expiratory flow monitoring in home management of asthma. Chest. 1998;113(4):968–972. doi: 10.1378/chest.113.4.968 + + 10.1378/chest.113.4.968 + 9554633 + + + + Bate SR, Jugg B, Rutter S, et al. N-tidal C: a portable, hand held device for assessing respiratory performance and injury. Am J Respir Crit Care Med. 2018;197:A2371. + + + Neville DM, Rupani H, Kalra PR, et al. Exploring the waveform characteristics of tidal breathing carbon dioxide, measured using the N-Tidal C device in different breathing conditions (the general breathing record study): protocol for an observational, longitudinal study. JMIR Res Protoc. 2018;7(5):e140. doi: 10.2196/resprot.9767 + + 10.2196/resprot.9767 + PMC5992452 + 29798833 + + + + Herry CL, Townsend D, Green GC, et al. Segmentation and classification of capnograms: application in respiratory variability analysis. Physiol Meas. 2014;35(12):2343–2358. doi: 10.1088/0967-3334/35/12/2343 + + 10.1088/0967-3334/35/12/2343 + 25389703 + + + + Kean TT, Teo AH, Malarvili MB. Feature extraction of capnogram for asthmatic patient. 2010 Second Int Conf Comput Eng Appl. 2010;2:251–255. + + + Pertzov B, Ronen M, Rosengarten D, et al. Use of capnography for prediction of obstruction severity in non-intubated COPD and asthma patients. Respir Res. 2021;22(1):1–9. doi: 10.1186/s12931-021-01747-3 + + 10.1186/s12931-021-01747-3 + PMC8138110 + 34020637 + + + + Abid A, Mieloszyk RJ, Verghese GC, et al. Model-based estimation of respiratory parameters from capnography, with application to diagnosing obstructive lung disease. IEEE Trans Biomed Eng. 2017;64(12):2957–2967. + + 28475040 + + + + Talker L, Neville D, Wiffen L, et al. Machine diagnosis of chronic obstructive pulmonary disease using a novel fast-response capnometer. Respir Res. 2023;24(1):1–11. doi: 10.1186/s12931-023-02460-z + + 10.1186/s12931-023-02460-z + PMC10239171 + 37268935 + + + + Talker L, Dogan C, Neville D, et al. Diagnosis and severity assessment of COPD using a novel fast-response capnometer and interpretable machine learning. COPD J Chronic Obstr Pulm Dis. 2024;21(1). doi: 10.1080/15412555.2024.2321379 + + 10.1080/15412555.2024.2321379 + 38655897 + + + + Cundrle I, Olson LJ, Johnson BD. Pulmonary limitations in heart failure. Clin Chest Med. 2019;40(2):439–448. doi: 10.1016/j.ccm.2019.02.010 + + 10.1016/j.ccm.2019.02.010 + PMC6541018 + 31078220 + + + + + + + + 42205499 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 1662-4548 + + 20 + + 2026 + + + Frontiers in neuroscience + Front Neurosci + + Multimodal CT radiomics-clinical ensemble machine learning model effectively predicts futile recanalization after endovascular treatment of acute ischemic stroke. + + 1838675 + 1838675 + + 1838675 + 10.3389/fnins.2026.1838675 + + Futile recanalization (FR) poses a significant challenge in endovascular treatment and there is a lack of reliable predictive models for assessing treatment outcomes in stroke. The aim of this study is to develop a robust CT radiomics-clinical ensemble model that predicts FR in patients with acute ischemic stroke (AIS) following endovascular treatment (EVT) utilizing machine learning techniques. + This study enrolled 101 patients diagnosed with AIS who underwent successful EVT. A total of 946 radiomics features were, respectively, extracted from non-contrast CT (NCCT), contrast-enhanced CT (CECT), and various CT perfusion maps (CBF, CBV, MTT, and TTP) using PyRadiomics prior to the endovascular intervention. Demographic characteristics, along with baseline clinical, laboratory, and angiographic variables, were incorporated as clinical features in the model analysis. Feature engineering was performed using SelectKBest. Five traditional machine learning algorithms were employed for modeling. The dataset was randomly split into a training cohort (n = 71, 70%) and an internal validation cohort (n = 30, 30%). Receiver operating characteristic (ROC) curves were utilized to evaluate the performance of each model. + Among the 101 patients, FR occurred in 66 individuals (65%), as determined by the modified Rankin Scale (mRS) at 90 days. The ensemble model integrating clinical data, NCCT, and CBV achieved the highest performance, with an area under the curve (AUC) of 0.918 using the CatBoost algorithm. + The multimodal CT radiomics-clinical ensemble machine learning model demonstrated excellent predictive capability for identifying FR in AIS patients with large vessel occlusion prior to EVT. + Copyright © 2026 Wang, Gao, Xu, Wu, Li, Huang, Deng, Xu, Wei and Li. + + + + Wang + Zhenxiong + Z + + Department of Radiology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China. + + + + Gao + Yidong + Y + + Department of Radiology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China. + + + + Xu + Pan + P + + Department of Neurology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China. + + + + Wu + Di + D + + Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. + + + + Li + Wuying + W + + Department of Neurology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China. + + + + Huang + Huameng + H + + Department of Neurology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China. + + + + Deng + Weihua + W + + Department of Neurology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China. + + + + Xu + Honggang + H + + Department of Radiology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China. + + + + Wei + Xinhua + X + + Department of Radiology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China. + + + + Li + Xing + X + + Department of Neurology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China. + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
+ + Switzerland + Front Neurosci + 101478481 + 1662-453X + + + CT radiomics + futile recanalization + machine learning + predictive model + stroke + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 3 + 25 + + + 2026 + 4 + 13 + + + 2026 + 4 + 21 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 4 + 37 + + + 2026 + 5 + 12 + + + epublish + + 42205499 + PMC13201450 + 10.3389/fnins.2026.1838675 + + + + Brugnara G., Neuberger U., Mahmutoglu M. A., Foltyn M., Herweh C., Nagel S., et al. (2020). Multimodal predictive modeling of endovascular treatment outcome for acute ischemic stroke using machine-learning. Stroke 51, 3541–3551. doi: 10.1161/STROKEAHA.120.030287, + + 10.1161/STROKEAHA.120.030287 + 33040701 + + + + Chen T., Guestrin C., (2016) XGBoost: A Scalable Tree Boosting System. 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Methods Prog. Biomed. 263:108701. doi: 10.1016/j.cmpb.2025.108701, + + 10.1016/j.cmpb.2025.108701 + 40049030 + + + + + + + + 42205493 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2090-8083 + + 2026 + + 2026 + + + Parkinson's disease + Parkinsons Dis + + Identification of Gene Signatures and Molecular Mechanisms for Diagnosing Parkinson's Disease and Nonalcoholic Fatty Liver Disease Using Machine Learning. + + 8731032 + 8731032 + + 8731032 + 10.1155/padi/8731032 + + Parkinson's disease (PD) is a prevalent neurodegenerative disease, whereas nonalcoholic fatty liver disease (NAFLD) is a common metabolic liver disorder. Growing evidence suggests that NAFLD may affect the central nervous system through the liver-brain axis, potentially contributing to PD, although the underlying molecular mechanisms remain unclear. + To identify differentially expressed genes (DEGs), transcriptomic data for NAFLD and PD were sourced from the GEO database. Key common candidate genes were screened using protein-protein interaction (PPI) networks, machine learning approaches (LASSO, neural networks, and random forest), and functional enrichment analyses, including GO, KEGG, GSEA, and GSVA. Immune infiltration, TF-miRNA regulatory networks, and single-cell RNA sequencing analyses were applied to investigate gene function, immune regulation, and cellular distribution. Candidate drugs were predicted using bioinformatic approaches and validated through molecular docking. + CASP1, CCNA2, and INHBE were identified as three core common candidate genes that may be associated with NAFLD and PD. Involvement of these genes includes inflammatory responses, regulation of the cell cycle, metabolic pathways, and immune microenvironment remodeling. The analysis of the TF-miRNA network suggested possible regulation by transcription factors CEBPB, BRD4, FOS, and miRNAs such as hsa-miR-29b-1-5p and hsa-miR-128-3p. Drug prediction and molecular docking identified ethinyl estradiol, mesalamine, and seliciclib as candidate therapeutics, showing strong binding affinity to the core targets. + This study offers a comprehensive elucidation of the molecular ties between NAFLD and PD. The identified core genes and candidate drugs offer theoretical support for potential candidate biomarkers and therapeutic targets in comorbid NAFLD and PD. + Copyright © 2026 Xuan Chen et al. Parkinson’s Disease published by John Wiley & Sons Ltd. + + + + Chen + Xuan + X + 0009-0000-2309-0684 + + Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China, sxmu.edu.cn. + + + Department of Neurology, Taiyuan City Central Hospital, Peking University First Hospital Taiyuan Branch, The Ninth Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi, China. + + + + Wu + Yulin + Y + 0009-0002-8799-9664 + + Changzhi Medical College, Changzhi, Shanxi, China, czmc.com. + + + + Zhang + Yonglai + Y + 0000-0002-3105-9182 + + School of Software, North University of China, Taiyuan, Shanxi, China, nuc.edu.cn. + + + + Hou + Yuli + Y + 0000-0002-4464-6212 + + Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China, sxmu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 26 + +
+ + United States + Parkinsons Dis + 101539877 + 2042-0080 + + + CASP1 + CCNA2 + INHBE + NAFLD + Parkinson’s disease + immune regulation + machine learning + + The authors declare no conflicts of interest. + + + + + 2026 + 1 + 27 + + + 2026 + 3 + 23 + + + 2026 + 4 + 22 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 4 + 37 + + + 2026 + 5 + 26 + + + epublish + + 42205493 + PMC13202728 + 10.1155/padi/8731032 + PADI8731032 + + + + Pouwels S., Sakran N., Graham Y. et al., Non-Alcoholic Fatty Liver Disease (NAFLD): A Review of Pathophysiology, Clinical Management and Effects of Weight Loss, BMC Endocrine Disorders. (2022) 22, no. 1, 10.1186/s12902-022-00980-1. + + 10.1186/s12902-022-00980-1 + PMC8919523 + 35287643 + + + + Brunt E. M., Wong V. W., Nobili V. et al., Nonalcoholic Fatty Liver Disease, Nature Reviews Disease Primers. (2015) 1, 10.1038/nrdp.2015.80, 2-s2.0-85017106465. + + 10.1038/nrdp.2015.80 + 27188459 + + + + Ahmed A., Wong R. 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+ + 2277-9531 + + 15 + + 2026 + + + Journal of education and health promotion + J Educ Health Promot + + Prediction of polycystic ovary syndrome using machine learning models: Addressing class imbalance and high dimensionality. + + 174 + 174 + + 174 + 10.4103/jehp.jehp_280_25 + + Polycystic ovary syndrome (PCOS) is a hormonal disorder that affects fertility and long-term health issues such as metabolic syndrome. Class imbalance is prevalent in PCOS datasets and leads to bias toward the majority class, resulting in poor identification of the minority class. This study evaluated the impact of varying levels of class imbalance and high dimensionality on the performance of various machine learning (ML) models through a simulation study. + This study involved an imbalanced PCOS dataset consisting of 41 clinical and physical parameters of 541 patients gathered from 10 hospitals across Kerala, India. This study evaluated the performance of ML models, namely k-nearest neighbor, decision tree (DT), random forest (RF), support vector machine, and XGBoost, under varying imbalance ratios (IRs). Stratified resampling was used to generate multiple imbalanced scenarios. Model performance was assessed using accuracy, sensitivity, specificity, precision, and F1 score. + For IRs up to 3, RF outperformed other methods, based on performance measures. For IR >3, XGBoost emerged as the top-performing method, followed by DT. In the case of IR exceeding 5, the accuracy of the minority class fell below 50%. Based on simulation results, RF was used to identify significant variables. + For original data (IR = 2.03), the RF model is a better model for predicting PCOS, with a sensitivity of 94% and a specificity of 81%. This study contributes to making informed decisions on selecting the most appropriate classification method based on a specific proportion of imbalance in healthcare datasets. + Copyright: © 2026 Journal of Education and Health Promotion. + + + + Acharya + Sachin + S + + Department of Applied Statistics and Data Science, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India. + + + + Poojari + Satyanarayana + S + + Department of Applied Statistics and Data Science, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India. + + + + Kamath + Asha + A + + Department of Applied Statistics and Data Science, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India. + + + + eng + + Journal Article + + + 2026 + 05 + 07 + +
+ + India + J Educ Health Promot + 101593794 + 2277-9531 + + + Infertility + polycystic ovary syndrome + random forest + support vector machine + + There are no conflicts of interest. + + + + + 2025 + 2 + 11 + + + 2025 + 9 + 2 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 4 + 36 + + + 2026 + 5 + 7 + + + epublish + + 42205370 + PMC13210020 + 10.4103/jehp.jehp_280_25 + JEHP-15-174 + + + + Selvaraj V, Vanitha J, Dhanaraj FM, Sekar P, Babu AR. Impact of yoga and exercises on polycystic ovarian syndrome risk among adolescent schoolgirls in South India. Health Sci Rep. 2020;3:e212. + + PMC7717472 + 33305014 + + + + Ganie MA, Chowdhury S, Malhotra N, Sahay R, Bhattacharya PK, Agrawal S, et al. Prevalence, phenotypes, and comorbidities of polycystic ovary syndrome among Indian women. JAMA Netw Open. 2024;7:e2440583. + + PMC11581580 + 39441596 + + + + Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. 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+ + 1176-9351 + + 25 + + 2026 + + + Cancer informatics + Cancer Inform + + A Machine Learning Approach to Prognostication in Oral Cancer: Analysis of the Surveillance, Epidemiology, and End Results Database. + + 11769351261442875 + 11769351261442875 + + 11769351261442875 + 10.1177/11769351261442875 + + To develop and validate machine learning models to predict 5-year survival in oral cancer using a large population-based registry and to rank prognostic factors. + We analyzed Surveillance, Epidemiology, and End Results (SEER) data from 1992 to 2020. After applying inclusion criteria, 39 904 patients with complete data on 21 variables were included from 53 611 cases (25.6% exclusion). Selection bias was assessed by comparing included and excluded patients. The outcome was binary 5-year survival. Four models were trained and evaluated using nested fivefold cross-validation: XGBoost, LASSO, Random Forest, and logistic regression. Performance was assessed using the Brier score, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Calibration was evaluated using slope and intercept with 95% confidence intervals. Model explainability used permutation feature importance and SHAP values. + Random Forest showed the best discrimination (AUC 77.6%, accuracy 71.4%, Brier 0.186) and was selected for risk stratification. However, it overestimated risk in lower deciles. Logistic regression and LASSO showed better calibration, with slopes near 1.0, but slightly lower discrimination (AUCs 75.5% and 76.9%). SHAP analysis identified localized stage as the strongest protective factor (importance 100.0), followed by age (91.1) and chemotherapy (29.3). Excluded patients had more unstaged tumors (3.9% vs 1.7%, P < .001). + Random Forest provides strong risk stratification, but miscalibration limits its use for absolute risk prediction. Logistic regression and LASSO may be preferable when accurate probabilities are needed. External validation is required before clinical use. + © The Author(s) 2026. + + + + Okeagu + Philips + P + 0009-0003-2021-6052 + + Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. + + + Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, Boston, MA, USA. + + + + Murukutla + Saranya + S + + Department of Oral Health Sciences, Temple University Kornberg School of Dentistry, Philadelphia, PA, USA. + + + + Iwuchukwu + Afamdi + A + + Department of Comprehensive Care, Fairfield Clinic, Tufts University School of Dental Medicine, Boston, MA, USA. + + + + Ogwo + Chukwuebuka + C + + Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, Boston, MA, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 25 + +
+ + United States + Cancer Inform + 101258149 + 1176-9351 + + + SEER database + machine learning + oral cancer + prognostic factors + survival prediction + + The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. + + + + + 2026 + 1 + 2 + + + 2026 + 3 + 30 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 6 + 30 + + + 2026 + 5 + 28 + 4 + 35 + + + 2026 + 5 + 25 + + + epublish + + 42205306 + PMC13201935 + 10.1177/11769351261442875 + 10.1177_11769351261442875 + + + + +SEER. Cancer of the oral cavity and pharynx. Cancer stat facts. 2024. Accessed July 15, 2024. https://seer.cancer.gov/statfacts/html/oralcav.html + + + +American Cancer Society. Oral cavity & oropharyngeal cancer key statistics. Published 2021. Accessed May 24, 2024. https://www.cancer.org/cancer/types/oral-cavity-and-oropharyngeal-cancer/about/key-statistics.html + + + Fellows JL, Atchison KA, Chaffin J, Chávez EM, Tinanoff N. 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+ + 2046-2069 + + 16 + 30 + + 2026 + May + 20 + + + RSC advances + RSC Adv + + Precision oncology at the nanoscale: nano-optical biosensors for early cancer detection. + + 27358 + 27373 + 27358-27373 + + 10.1039/d6ra02425d + + Cancer remains one of the foremost global health challenges, accounting for approximately 10 million deaths annually worldwide. Early and accurate diagnosis is pivotal in improving patient survival rates and enabling curative therapeutic interventions. Over the past two decades, nano-optical biosensors have emerged as transformative diagnostic tools that exploit the unique optical properties of nanomaterials-including localized surface plasmon resonance (LSPR), surface-enhanced Raman scattering (SERS), fluorescence enhancement, and photonic crystal phenomena-to detect cancer biomarkers at ultralow concentrations. This comprehensive review critically examines the state-of-the-art advances in nano-optical biosensing platforms designed for the early diagnosis of diverse cancer types, including breast, colorectal, lung, ovarian, and prostate cancers. We systematically cover the fundamental design principles governing plasmonic nanostructures, quantum dot-based sensors, nanophotonic waveguides, SERS-active substrates, and lab-on-chip integrated devices. Special emphasis is placed on the clinical translation challenges, including selectivity in complex biomatrices, reproducibility, stability, and regulatory pathways. We also discuss emerging strategies such as machine learning-assisted signal processing, multiplexed biomarker detection, and CRISPR-coupled optical readouts. Comparative performance metrics across platforms are presented through structured tables, and representative fabrication and sensing mechanisms are illustrated. The review concludes with a critical assessment of future directions and unmet needs in the field, aiming to provide a comprehensive resource for researchers and clinicians working at the interface of nanophotonics and oncology. + This journal is © The Royal Society of Chemistry. + + + + Hassan + Youssef M + YM + 0009-0000-2826-0726 + + Department of Zoology, Faculty of Science, Ain Shams University Abbassia 11566 Cairo Egypt youssefmuhammedbio@gmail.com. + + + + El-Tantawi + Hala + H + + Department of Zoology, Faculty of Science, Ain Shams University Abbassia 11566 Cairo Egypt youssefmuhammedbio@gmail.com. + + + + Ali + Ibrahim Rabie + IR + + Department of Immunology and Treatment Evaluation, Theodore Bilharz Research Institute Giza Egypt. + + + + Attia + Mohamed S + MS + 0000-0002-6696-8538 + + Chemistry Department, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11623 Saudi Arabia mgaber@imamu.edu.sa. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 22 + +
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+ + 1664-2392 + + 17 + + 2026 + + + Frontiers in endocrinology + Front Endocrinol (Lausanne) + + Identification of candidate biomarkers for NAFLD through bioinformatics analysis and machine learning based on circulating insulin degradation-associated genes. + + 1774997 + 1774997 + + 1774997 + 10.3389/fendo.2026.1774997 + + Non-alcoholic fatty liver disease (NAFLD) has become as a metabolic disorder posing a significant threat to public health, with no presently available effective treatment. Circulating insulin degradation constitutes a pivotal process regulating insulin concentration and biological activity in the bloodstream, and its capacity is closely associated with hyperinsulinaemia and hepatic lipid accumulation. Hepatic lipid accumulation represents a key pathophysiological mechanism in NAFLD. Therefore, targeting the circulating insulin degradation pathway may represent a significant therapeutic opportunity for NAFLD. This study employed a multi-omics strategy, incorporating pertinent datasets from the Gene Expression Omnibus (GEO) collection, to investigate the function of circulating insulin degradation in NAFLD. We employed systems biology informatics approaches, including weighted gene co-expression network analysis (WGCNA) and machine learning models, to identify four hub biomarkers: MYO7A, AGTR1, IL1RN, and IGFBP2. We applied Shapley Additive Explanations (SHAP) to interpret the contribution of each gene to the machine learning model. The expression patterns and potential relevance of these hub genes were further assessed in external datasets, cellular models, and animal models. Overall, this hypothesis-generating study identified four candidate genes potentially associated with NAFLD and provided additional insights into the molecular mechanisms underlying disease progression. + Copyright © 2026 Guo, Lou, Song, Gao, Wang, Ma, Wang and Wang. + + + + Guo + Mingjie + M + + School of Basic Medical Sciences, Huaihe Hospital (Zhongzhou Laboratory for Integrative Biology), Henan University, Kaifeng, Henan, China. + + + + Lou + Wei + W + + School of Basic Medical Sciences, Huaihe Hospital (Zhongzhou Laboratory for Integrative Biology), Henan University, Kaifeng, Henan, China. + + + + Song + Xin + X + + School of Basic Medical Sciences, Huaihe Hospital (Zhongzhou Laboratory for Integrative Biology), Henan University, Kaifeng, Henan, China. + + + + Gao + Dongxin + D + + School of Basic Medical Sciences, Huaihe Hospital (Zhongzhou Laboratory for Integrative Biology), Henan University, Kaifeng, Henan, China. + + + + Wang + Guoan + G + + School of Basic Medical Sciences, Huaihe Hospital (Zhongzhou Laboratory for Integrative Biology), Henan University, Kaifeng, Henan, China. + + + + Ma + Hanyu + H + + School of Basic Medical Sciences, Huaihe Hospital (Zhongzhou Laboratory for Integrative Biology), Henan University, Kaifeng, Henan, China. + + + + Wang + Wenlei + W + + School of Basic Medical Sciences, Huaihe Hospital (Zhongzhou Laboratory for Integrative Biology), Henan University, Kaifeng, Henan, China. + + + + Wang + Yongliang + Y + + School of Basic Medical Sciences, Huaihe Hospital (Zhongzhou Laboratory for Integrative Biology), Henan University, Kaifeng, Henan, China. + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
+ + Switzerland + Front Endocrinol (Lausanne) + 101555782 + 1664-2392 + + + + 0 + Biomarkers + + + 0 + Insulin + + + IM + + + Non-alcoholic Fatty Liver Disease + genetics + metabolism + blood + diagnosis + + + Machine Learning + + + Biomarkers + metabolism + blood + + + Humans + + + Animals + + + Computational Biology + methods + + + Insulin + metabolism + blood + + + Gene Expression Profiling + + + + WGCNA + bioinformatic analysis + biomarkers + circulating insulin degradation + machine learning + non-alcoholic fatty liver disease + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2025 + 12 + 24 + + + 2026 + 4 + 17 + + + 2026 + 4 + 27 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 4 + 35 + + + 2026 + 5 + 12 + + + epublish + + 42205256 + PMC13201111 + 10.3389/fendo.2026.1774997 + + + + Stefan N, Yki-Jarvinen H, Neuschwander-Tetri BA. 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+ + 2055-2076 + + 12 + + 2026 + Jan-Dec + + + Digital health + Digit Health + + Implication of machine learning models versus traditional models for the prediction of suicidal thoughts or ideation in west of Iran; data mining approaches on a population-based cross-sectional study. + + 20552076261415932 + 20552076261415932 + + 20552076261415932 + 10.1177/20552076261415932 + + To identify the effective factors in suicidal thoughts or ideations by comparing several classification data mining methods and logistic regression (LR). + This was a secondary data analysis conducted on data from a cross-sectional study involving 1500 individuals selected using multi-stage stratified cluster random sampling in the urban area of Ilam City during 2023. The data was collected by a standardized questionnaire. Five classification methods, including decision tree (DT), random forest (RF), support vector machine (SVM), neural networks, and LR, were used to identify the effective factors in the suicide thought or ideation. + Data from 1370 individuals were analyzed. The SVM model outperformed others in most indicators, with 77.9% sensitivity, 95.3% negative predictive value, and the highest balanced accuracy (79.3%). Its precision-recall AUC, along with LR, was about 60% higher than other models. In contrast, the DT model showed superior specificity (99.9%), positive predictive value (50%), positive likelihood ratio (5.60), and negative likelihood ratio (0.99). Across DT, RF, and SVM, the main predictors of suicidal ideation were suicide attempt history, tiredness of life, BMI, and age. + AI models specifically SVM and DT outperform traditional ones for detecting suicidal ideation. Key predictors include a history of suicide attempts, being tired of life, depression, and anxiety, highlighting areas for health policymakers to focus on in prevention strategies. + © The Author(s) 2026. + + + + Sarmand + Arezoo + A + + Department of Health Information Management, School of Allied Medicine, Tehran University of Medical Sciences, Tehran, Iran. + 48439 + + + + Raiszadeh + Mohammad + M + + President of the Islamic Republic of Iran Medical Council, Tehran, Iran. + 430696 + + + + Najafi-Ghobadi + Khadijeh + K + + Department of Biostatistics, School of Health, Ilam University of Medical Sciences, Ilam, Iran. + 48443 + + + + Malekabadi + Ebadallah Shiri + ES + + Department of Epidemiology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran. + 48439 + + + + Shekarchi + Babak + B + + Department of Radiology, School of Medicine, AJA University of Medical Sciences, Tehran, Iran. + 162996 + + + + Pakzad + Reza + R + 0000-0001-8133-3664 + + Department of Epidemiology, Faculty of Health, Ilam University of Medical Sciences, Ilam, Iran. + 48443 + + + Student Research Committee, Ilam University of Medical Sciences, Ilam, Iran. + 48443 + + + + Mohajeri Irvani + Mojgan + M + + Department of Anesthesiology and Critical Care, Paramedical Faculty, AJA University of Medical Sciences, Tehran, Iran. + 162996 + + + + Afrah + Ramin + R + + School of advanced technologies in medical sciences, Isfahan university of medical sciences, Isfahan, Iran. + 48455 + + + + eng + + Journal Article + + + 2026 + 05 + 25 + +
+ + United States + Digit Health + 101690863 + 2055-2076 + + + Suicide thought + decision tree + logistic regression + neural networks + random forest + suicide ideation + support vector machine + + The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. + + + + + 2025 + 6 + 29 + + + 2025 + 12 + 31 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 4 + 35 + + + 2026 + 5 + 25 + + + epublish + + 42205210 + PMC13201945 + 10.1177/20552076261415932 + 10.1177_20552076261415932 + + + + Asadiyun M, Daliri S. Suicide attempt and suicide death in Iran: a systematic review and meta-analysis study. Iran J Psychiatry 2023; 18: 191. + + PMC10293692 + 37383956 + + + + Darabi F, Ziapour A, Ahmadinia H. Motivations for suicide attempts and risk factors among youths in western Iran: a cross-sectional study. 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A machine learning approach to identifying suicide risk among text-based crisis counseling encounters. Front Psychiatry 2023; 14: 1110527. + + PMC10076638 + 37032952 + + + + Bae SM. The prediction model of suicidal thoughts in Korean adults using decision tree analysis: a nationwide cross-sectional study. PloS One 2019; 14: e0223220. + + PMC6785128 + 31596870 + + + + Haghish E, Laeng B, Czajkowski N. Are false positives in suicide classification models a risk group? Evidence for “true alarms” in a population-representative longitudinal study of Norwegian adolescents. Front Psychol 2023; 14: 1216483. + + PMC10540433 + 37780152 + + + + Fazel S, Vazquez-Montes MD, Molero Y, et al. Risk of death by suicide following self-harm presentations to healthcare: development and validation of a multivariable clinical prediction rule (OxSATS). BMJ Ment Health 2023; 26: e300673. DOI: https://doi.org/10.1136/bmjment-2023-300673. + + PMC10335583 + 37385664 + + + + Kim AM, Jeon SW, Cho SJ, et al. Comparison of the factors for suicidal ideation and suicide attempt: a comprehensive examination of stress, view of life, mental health, and alcohol use. Asian J Psychiatr 2021; 65: 102844. + + 34571412 + + + + Kim E, Yi JS. Factors related to suicidal ideation and prediction of high-risk groups among youngest-old adults in South Korea. Int J Environ Res Public Health 2022; 19: 10028. + + PMC9408520 + 36011664 + + + + Qiu T, Klonsky ED, Klein DN. Hopelessness predicts suicide ideation but not attempts: a 10-year longitudinal study. Suicide Life Threat Behav 2017; 47: 718–722. + + PMC5540846 + 28150463 + + + + Beghi M, Butera E, Cerri CG, et al. Suicidal behaviour in older age: a systematic review of risk factors associated to suicide attempts and completed suicides. Neurosci Biobehav Rev 2021; 127: 193–211. + + 33878336 + + + + Ellis JD, Rabinowitz JA, Strickland JC, et al. Predictors of suicidal ideation during residential substance use treatment. 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BMC Med Res Methodol 2023; 23: 157. + + PMC10318777 + 37403100 + + + + Pakzad R, Nedjat S, Yaseri M, et al. Effect of smoking on breast cancer by adjusting for smoking misclassification bias and confounders using a probabilistic bias analysis method. Clin Epidemiol 2020; 12: 557–568. + + PMC7266328 + 32547245 + + + + Cao Y, Dai J, Wang Z, et al. Machine learning approaches for depression detection on social Media: a systematic review of biases and methodological challenges. J Behav Data Sci 2025; 5: 67–102. + + + Ding Z, Wang Z, Zhang Y, et al. Trade-offs between machine learning and deep learning for mental illness detection on social media. Sci Rep 2025; 15: 14497. + + PMC12032126 + 40281061 + + + + Zhang Y, Wang Z, Ding Z, et al. Employing machine learning and deep learning models for mental illness detection. Computation 2025; 13: 186. + + + + + + + 42205168 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2772-5723 + + 5 + 7 + + 2026 + + + Gastro hep advances + Gastro Hep Adv + + A Clustering-Based Machine Learning Approach for Mortality Prediction in Gastrointestinal Bleeding: Development and Validation. + + 100985 + 100985 + + 100985 + 10.1016/j.gastha.2026.100985 + + Gastrointestinal bleeding (GIB) is a life-threatening emergency with considerable morbidity and mortality. Traditional risk scores like AIMS65 and Glasgow-Blatchford Score (GBS) are limited in capturing nonlinear clinical interactions. We developed and externally validated a machine learning model to predict 30-day mortality in GIB patients. + We retrospectively analyzed 5453 emergency department patients with GIB from the Medical Information Mart for Intensive Care IV-Emergency Department database for model development, with external validation using 7166 patients from Jefferson Health. Sixteen clinical and laboratory variables were selected based on a literature review and clinical relevance. The development cohort was divided into training (80%) and internal validation (20%) sets. Survivors were partitioned into 24 clusters using K-means, with separate random forest models trained on each cluster, combined with all deceased cases. Performance was evaluated using the area under the receiver-operating characteristic curve, sensitivity, and specificity on the external validation set, then benchmarked against AIMS65 and the GBS. + The model achieved an area under the receiver-operating characteristic curve of 0.884 (95% confidence interval: 0.863-0.905) on internal validation and 0.882 (95% confidence interval: 0.863-0.900) on external validation, significantly outperforming AIMS65 (0.737) and GBS (0.768) (P < .001). At the optimal threshold, the model achieved 87.9% sensitivity and 74.3% specificity on the external validation cohort. At maximum sensitivity thresholds, the model maintained higher specificity (54.4%) than AIMS65 (29.7%) and GBS (17.0%) (P < .001). Clustering identified distinct phenotypes with mortality ranging from 0.6% to 15.3%. SHapley Additive exPlanations analysis identified age, albumin, hemodynamic parameters, and presenting hemoglobin and platelet count as key predictors. + Our model provides superior risk stratification for 30-day mortality in GIB compared to conventional scores, with validated generalizability and potential for integration into electronic health record systems. + © 2026 The Author(s). + + + + Alomari + Laith + L + + Department of Medicine, Jefferson Einstein Philadelphia Hospital, Philadelphia, Pennsylvania. + + + + Al-Fakhouri + Zaid + Z + + Department of Medicine, The MetroHealth System, Case Western Reserve University, Cleveland, Ohio. + + + + Jaradat + Jaber + J + + Faculty of Medicine, Mu'tah University, Al-Karak, Jordan. + + + + Simadibrata + Daniel + D + + Department of Medicine, The MetroHealth System, Case Western Reserve University, Cleveland, Ohio. + + + + Al-Riyalat + Ahmad + A + + Department of Medicine, Jefferson Einstein Philadelphia Hospital, Philadelphia, Pennsylvania. + + + + Lam + Justin + J + + Department of Medicine, Jefferson Einstein Philadelphia Hospital, Philadelphia, Pennsylvania. + + + + Otabor + Emmanuel + E + + Department of Medicine, Jefferson Einstein Philadelphia Hospital, Philadelphia, Pennsylvania. + + + + Jarrar + Yaman + Y + + Department of Medicine, Lehigh Valley Health Network, Allentown, Pennsylvania. + + + + Massad + Abdallah + A + + Department of Medicine, University of Texas Medical Branch, Galveston, Texas. + + + + Alomari + Jana + J + + Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan. + + + + Abdel-Jalil + Ala + A + + Division of Gastroenterology and Hepatology, MetroHealth Medical Center, Case Western Reserve University, Cleveland, Ohio. + + + + Ezaz + Ghideon + G + + Division of Gastroenterology and Hepatology, Department of Medicine, Jefferson Einstein Philadelphia Hospital, Philadelphia, Pennsylvania. + + + + eng + + Journal Article + + + 2026 + 04 + 24 + +
+ + Netherlands + Gastro Hep Adv + 9918350485906676 + 2772-5723 + + + Ensemble Model + Gastrointestinal Bleeding + Machine Learning + Mortality Prediction + Risk Stratification + + + + + + 2025 + 12 + 21 + + + 2026 + 4 + 17 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 4 + 34 + + + 2026 + 4 + 24 + + + epublish + + 42205168 + PMC13202545 + 10.1016/j.gastha.2026.100985 + S2772-5723(26)00106-8 + + + + Zheng N.S., Tsay C., Laine L., et al. Trends in characteristics, management, and outcomes of patients presenting with gastrointestinal bleeding to emergency departments in the United States from 2006 to 2019. Aliment Pharmacol Ther. 2022;56(11–12):1543–1555. + + PMC9669230 + 36173090 + + + + Almadi M.A., Barkun A.N. Patient presentation, risk stratification, and initial management in acute lower gastrointestinal bleeding. Gastrointest Endosc Clin N Am. 2018;28(3):363–377. + + 29933781 + + + + Stanley A.J., Laine L., Dalton H.R., et al. Comparison of risk scoring systems for patients presenting with upper gastrointestinal bleeding: international multicentre prospective study. BMJ. 2017;356 + + PMC5217768 + 28053181 + + + + Shung D., Simonov M., Gentry M., et al. Machine learning to predict outcomes in patients with acute gastrointestinal bleeding: a systematic review. Dig Dis Sci. 2019;64(8):2078–2087. + + 31055722 + + + + Shung D.L., Au B., Taylor R.A., et al. Validation of a machine learning model that outperforms clinical risk scoring systems for upper gastrointestinal bleeding. Gastroenterology. 2019;158(1):160–167. + + PMC7004228 + 31562847 + + + + Shung D.L., Chan C.E., You K., et al. Validation of an electronic health record–based machine learning model compared with clinical risk scores for gastrointestinal bleeding. Gastroenterology. 2024;167(6):1198–1212. + + PMC11493512 + 38971198 + + + + Johnson A., Bulgarelli L., Pollard T., et al. MIMIC-IV-ED (version 2.2) PhysioNet. 2023 + + + Johnson A., Bulgarelli L., Pollard T., et al. MIMIC-IV (version 3.1) PhysioNet. 2024 + + + Deshmukh F., Merchant S.S. Explainable machine learning model for predicting GI bleed mortality in the intensive care unit. Am J Gastroenterol. 2020;115(10):1657–1668. + + 32341266 + + + + Hoffmann V., Neubauer H., Heinzler J., et al. A novel easy-to-use prediction scheme for upper gastrointestinal bleeding: Cologne-WATCH (C-WATCH) risk score. Medicine (Baltimore) 2015;94(38) + + PMC4635768 + 26402828 + + + + Yang J., Han S., Nah S., et al. A novel predictive model for Intensive Care Unit admission in Emergency Department patients with upper gastrointestinal bleeding. Medicine (Baltimore) 2024;103(47) + + PMC11596417 + 39809218 + + + + Moledina S.M., Komba E. Risk factors for mortality among patients admitted with upper gastrointestinal bleeding at a tertiary hospital: a prospective cohort study. BMC Gastroenterol. 2017;17(1):165. + + PMC5738843 + 29262794 + + + + Chalasani N., Patel K., Clark W.S., et al. The prevalence and significance of leukocytosis in upper gastrointestinal bleeding. Am J Med Sci. 1998;315(4):233–236. + + 9537636 + + + + Laursen S.B., Oakland K., Laine L., et al. ABC score: a new risk score that accurately predicts mortality in acute upper and lower gastrointestinal bleeding: an international multicentre study. Gut. 2020;70(4):707–716. + + 32723845 + + + + Hassanat A., Altarawneh G., Alkhawaldeh I.M., et al. 2022 IEEE Symposium on Computers and Communications (ISCC) 2023. The jeopardy of learning from over-sampled class-imbalanced medical datasets; pp. 1–7. + + + Tarawneh A.S., Hassanat A.B., Altarawneh G.A., et al. Stop oversampling for class imbalance learning: a review. IEEE Access. 2022;10:47643–47660. + + + Hassanat A.B., Tarawneh A.S., Abed S.S., et al. RDPVR: random data partitioning with voting rule for machine learning from class-imbalanced datasets. Electronics. 2022;11(2):228. + + + Lundberg S.M., Lee S.I. A unified approach to interpreting model predictions. arXiv. 2017 + + + Lundberg S.M., Erion G., Chen H., et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2(1):56–67. + + PMC7326367 + 32607472 + + + + Liu L., Bi B., Cao L., et al. Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation. Front Endocrinol (Lausanne) 2024;15 + + PMC10933094 + 38481447 + + + + Khan W., Zaki N., Ghenimi N., et al. Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women. PLoS One. 2023;18(12) + + PMC10752564 + 38150456 + + + + Yang F., Li C., Yang W., et al. Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis. Brief Bioinform. 2024;25(6a) + + PMC11446601 + 39358034 + + + + Luo H., Xiang C., Zeng L., et al. SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation. Sci Rep. 2024;14(1) + + PMC11291677 + 39085442 + + + + Raghareutai K., Kaosombatwattana U. Reassessing the inputs for a machine learning model in gastrointestinal bleeding risk stratification. Gastroenterology. 2025;168:1038–1039. + + 39884468 + + + + Sengupta N., Feuerstein J.D., Jairath V., et al. Management of patients with acute lower gastrointestinal bleeding: an updated ACG guideline. Am J Gastroenterol. 2022;118(2):208–231. + + 36735555 + + + + Laine L., Barkun A.N., Saltzman J.R., et al. Correction to: ACG clinical guideline: upper gastrointestinal and ulcer bleeding. 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BMC Gastroenterol. 2022;22(1):337. + + PMC9277905 + 35820868 + + + + Afessa B. Systemic inflammatory response syndrome in patients hospitalized for gastrointestinal bleeding. Crit Care Med. 1999;27(3):554–557. + + 10199536 + + + + Adler-Milstein J., DesRoches C.M., Kralovec P., et al. Electronic health record adoption in US hospitals: progress continues, but challenges persist. Health Aff. 2015;34(12):2174–2180. + + 26561387 + + + + Henry K.E., Hager D.N., Pronovost P.J., et al. A targeted real-time early warning score (TREWScore) for septic shock. Sci Transl Med. 2015;7(299) + + 26246167 + + + + Hyland S.L., Faltys M., Hüser M., et al. Early prediction of circulatory failure in the intensive care unit using machine learning. Nat Med. 2020;26(3):364–373. + + 32152583 + + + + Obermeyer Z., Powers B., Vogeli C., et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447–453. + + 31649194 + + + + Lapp L., Roper M., Kavanagh K., et al. Dynamic prediction of patient outcomes in the intensive care unit: a scoping review of the state-of-the-art. J Intensive Care Med. 2023;38(7):575–591. + + PMC10302367 + 37016893 + + + + Sung J.J.Y., Laine L., Kuipers E.J., et al. Towards personalised management for non-variceal upper gastrointestinal bleeding. Gut. 2021;70(5):818–824. + + 33649044 + + + + Keefer L., Palsson O.S., Pandolfino J.E. Best practice update: incorporating psychogastroenterology into management of digestive disorders. Gastroenterology. 2018;154(5):1249–1257. + + 29410117 + + + + + + + + 42205092 + + 2026 + 05 + 28 + +
+ + 2040-3372 + + + 2026 + May + 28 + + + Nanoscale + Nanoscale + + Machine learning assisted stability and CO2 reduction reaction activity prediction of single atom alloys. + 10.1039/d6nr00618c + + Single-atom alloy catalyst design requires a synergistic understanding of stability and activity. Herein, density functional theory (DFT) and machine learning (ML) were integrated to investigate the stability and CO2 reduction activity of 1131 SAA configurations across the periodic table. XGBoost regression model was developed to predict SAA stability, achieving performance with R + 2 of 0.93 and MAE of 0.21 eV. Recursive feature elimination (RFE) and Shapley additive exPlanations (SHAP) analysis identified the bulk cohesive energy difference (ΔCEbulk), electronegativity difference (Δχ), and atomic radius difference (Δr) as key stability descriptors, where Δr quantifies geometric compatibility, Δχ modulates electronic distribution, and ΔCEbulk directly reflects the combined effect of geometric and electronic factors, aligning with the Hume-Rothery theory. For CO2RR activity investigation, 514 CO adsorption energy (ΔE + CO) data points were collected, and Random Forest Regression (RFR) model was built with R + 2 = 0.97 and MAE = 0.09 eV. Cohesive energy of the dopant atom (CEbulk_b), C-O bond length (l + C-O), and C-dopant bond length (l + C-N) were identified as dominant descriptors for ΔE + CO. Guided by the Sabatier principle (ΔE + CO∈[-0.77, -0.57 eV] and ΔE + H∉[-0.47, -0.07] eV), 26 promising SAA candidates were screened. DFT validation confirmed Al1/Cu (111) and Au1/Pd (111) reduce the potential-determining step barrier of CO2RR to CH4/CH3OH, enhancing the reaction kinetics. This work provides a comprehensive framework for the efficient screening of stable and active SAA catalysts for CO2RR. + + + + Zhu + Qiuyan + Q + 0009-0004-8586-4277 + + School of Chemistry, State Key Laboratory of Fine Chemicals, , Dalian University of Technology, No. 2 Linggong Road, Dalian City, Liaoning Province, 116024, P. R. China. tiandx@dlut.edu.cn. + + + + Tian + Dongxu + D + 0000-0002-8126-2787 + + School of Chemistry, State Key Laboratory of Fine Chemicals, , Dalian University of Technology, No. 2 Linggong Road, Dalian City, Liaoning Province, 116024, P. R. China. tiandx@dlut.edu.cn. + + + + Du + Xiaru + X + + Dalian Kaiteli Catalytic Engineering Technology Co. Ltd, No. 19 Huangpu Road, Dalian City, Liaoning Province, 116024, P. R. China. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Nanoscale + 101525249 + 2040-3364 + + IM + + + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 4 + 22 + + + aheadofprint + + 42205092 + 10.1039/d6nr00618c + + + + + + 42205041 + + 2026 + 05 + 28 + +
+ + 1362-4962 + + + 2026 + May + 28 + + + Nucleic acids research + Nucleic Acids Res + + PLATE-VS: a web server for protein-ligand assay curation and cross-target virtual screening datasets. + gkag509 + 10.1093/nar/gkag509 + + PLATE-VS (Protein-Ligand Affinity-based Target Evaluation-Virtual Screening, https://www.drugbench.org/) is a free, openly accessible web server that integrates protein structural information, ligand activity data, and property-matched decoys to produce training-ready datasets for virtual screening and molecular machine learning. Unlike structure-only or assay-only resources, PLATE-VS addresses the bottleneck in obtaining clean protein-ligand datasets with principled train/test splits, spanning a range of difficulty, from those allowing higher levels of similarity with known ligand-receptor pairs to more challenging cross-target generalization. The server enables queries and inspection of data in various formats, returning interactive assay summaries with harmonized activities, curated metadata, a stratified panel of protein-ligand complexes, and downloadable split tables. These data and the associated Application Programming Interface can be used to facilitate the study of binding activity relationships in protein-ligand interactions. + &amp;#x00A9; The Author(s) 2026. Published by Oxford University Press. + + + + Xu + Ao + A + + Department of Computer Science, University of Southern California, Los Angeles, 90089,United States. + + + + Hong + Yongchan + Y + + Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, 90089,United States. + + + + Lam + Jordy Homing + JH + 0000-0002-5496-6228 + + Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, 90089,United States. + + + + Katritch + Vsevolod + V + 0000-0003-3883-4505 + + Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, 90089,United States. + + + + eng + + + R35GM153437 + NH + NIH HHS + United States + + + Croucher Foundation of Hong Kong + + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Nucleic Acids Res + 0411011 + 0305-1048 + + IM + + + + + 2026 + 3 + 20 + + + 2026 + 4 + 29 + + + 2026 + 5 + 6 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 3 + 53 + + + aheadofprint + + 42205041 + 10.1093/nar/gkag509 + 8697227 + + + + + + 42204930 + + 2026 + 05 + 28 + +
+ + 1532-4311 + + + 2026 + May + 27 + + + Immunological investigations + Immunol Invest + + Identification of BMX as a Potential Biomarker for Rheumatoid Arthritis Based on WGCNA, Machine Learning, and Experimental Validation. + + 1 + 18 + 1-18 + + 10.1080/08820139.2026.2674702 + + Rheumatoid arthritis (RA) is a chronic autoimmune disease needing reliable biomarkers. This study aimed to identify potential RA biomarkers using WGCNA and machine learning, and analyze their correlation with disease activity and STAT3. + GEO datasets were used. WGCNA and three machine learning methods (LASSO, SVM, Boruta) screened common core genes. Correlation with STAT3 was analyzed. RT-qPCR validated core gene expression in RA patients. Correlation with clinical parameters (DAS28, CCP, RF, ESR) and ROC curves were assessed. + WGCNA identified 11 modules; the black module correlated most with RA. LASSO, SVM, and Boruta identified 16, 35, and 46 key genes respectively, with one overlapping core gene: BMX. BMX expression positively correlated with STAT3. RT-qPCR confirmed BMX upregulation in RA, which positively correlated with DAS28 and RF. ROC analysis gave an AUC of 0.789. + BMX expression is upregulated in RA patients, correlates with disease activity, and represents a potential diagnostic biomarker. The BMX‑STAT3 correlation suggests its possible involvement in RA pathogenesis, but the regulatory relationship requires further functional validation. + + + + Huang + Xinmin + X + + Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China. + + + + Cai + Xu + X + + Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China. + + + + Yan + Zhenbo + Z + + Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China. + + + + Wu + Xian + X + + Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China. + + + + Chen + Xinpeng + X + + Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China. + + + + Xiao + Jianwei + J + + Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + Immunol Invest + 8504629 + 0882-0139 + + IM + + BMX + Rheumatoid arthritis + STAT3 + WGCNA + machine learning + + + + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 2 + 53 + + + aheadofprint + + 42204930 + 10.1080/08820139.2026.2674702 + + + + + + 42204865 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 1537-744X + + 2026 + 1 + + 2026 + + + TheScientificWorldJournal + ScientificWorldJournal + + Integrating Business Intelligence and CRM Systems With a Machine Learning Approach for Predictive Customer Retention in E-Commerce. + + e1946904 + e1946904 + + 10.1155/tswj/1946904 + + In the rapidly evolving e-commerce landscape, retaining existing customers has become more cost-effective and strategically important than acquiring new ones. This study proposes a data-driven framework that integrates business intelligence (BI) tools, machine learning, and customer relationship management (CRM) decision support to improve predictive customer retention. The framework was developed using the publicly available Brazilian E-Commerce Public Dataset (Olist), which contains more than 100,000 orders and includes transactional, payment, delivery, product, and customer-review information. After SQL-based integration and feature engineering, customer segmentation was performed using K-means clustering on recency, frequency, monetary (RFM) variables, identifying three behavioral groups: loyal, at-risk, and occasional customers. For churn prediction, Random Forest and XGBoost classifiers were trained on customer-level behavioral, satisfaction, and service-related features. XGBoost achieved the best overall performance, with accuracy = 0.81, precision = 0.79, recall = 0.83, F1 - score = 0.81, and AUC = 0.85, outperforming Random Forest (accuracy = 0.76, precision = 0.74, recall = 0.71, F1 - score = 0.72, and AUC = 0.76). The resulting segmentation and churn scores were then exposed through Power BI dashboards and mapped into a proof-of-concept CRM decision framework for retention planning. Unlike studies that treat BI, machine learning, or CRM in isolation, this research presents an end-to-end analytical pipeline that links data preparation, predictive modeling, dashboard-based decision support, and scenario-level CRM action design. The framework provides a reproducible basis for e-commerce retention analytics and a practical foundation for future live deployment and A/B-tested CRM validation. + Copyright © 2026 Mohammad Zeinali et al. The Scientific World Journal published by John Wiley & Sons Ltd. + + + + Zeinali + Mohammad + M + 0009-0001-5215-5691 + + Faculty of Engineering, University of Isfahan, Isfahan, Iran, ui.ac.ir. + + + + Ramezani Asli + Leila + L + 0009-0002-9819-5063 + + Department of Industrial Engineering, Tafresh University, Tafresh, Iran, tafreshu.ac.ir. + + + + Khalili + Mohammad Amin + MA + 0000-0001-9671-761X + + Department of Earth, Environmental and Resource Sciences, Monte Sant'Angelo Campus, Federico II University of Naples, Naples, Italy, unina.it. + + + School of Geography, Geology and the Environment, University of Leicester, Leicester, UK, le.ac.uk. + + + + eng + + Journal Article + +
+ + United States + ScientificWorldJournal + 101131163 + 1537-744X + + IM + + + Machine Learning + + + Commerce + + + Consumer Behavior + + + Boosting Machine Learning Algorithms + + + Random Forest + + + Prediction Algorithms + + + Predictive Learning Models + + + Data Analytics + + + Humans + + + Classification Algorithms + + + + business intelligence (BI) + churn prediction + customer relationship management (CRM) + customer segmentation + e-commerce data analytics + + + + + + 2026 + 4 + 21 + + + 2025 + 9 + 27 + + + 2026 + 5 + 11 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 2 + 22 + + + ppublish + + 42204865 + 10.1155/tswj/1946904 + + + + R L. S. and Manasa N., Role of Digital Marketing Tactics in Enhancing Financial Performance in E-Commerce Enterprises, Artificial Intelligence in Peace, Justice, and Strong Institutions, 2025, IGI Global Scientific Publishing, 257–282, https://doi.org/10.4018/979-8-3693-9395-6.ch012. + + + Kumar S., Bajpai V. 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(2025) 2, no. 2, 182–201, https://doi.org/10.62411/faith.3048-3719-110. + + + Prashanthan A., An Integrated Framework for Optimizing Customer Retention Budget Using Clustering, Classification, and Mathematical Optimization, Journal of Computing Theories and Applications. (2025) 3, no. 1, 45–63, https://doi.org/10.62411/jcta.13194. + + + + + + + 42204811 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 1943-7730 + + 57 + 3 + + 2026 + Apr + 03 + + + Laboratory medicine + Lab Med + + Measurement uncertainty in laboratory medicine: a comprehensive review of frameworks, analytical performance specifications, and emerging technologies. + lmag032 + 10.1093/labmed/lmag032 + + Measurement uncertainty is a fundamental component of laboratory quality assurance and a key requirement of International Organization for Standardization (ISO) 15189. Despite its recognized importance, routine implementation of measurement uncertainty in clinical laboratories remains inconsistent due to methodologic complexity and lack of harmonized approaches. We sought to summarize current methodologic frameworks, analytical performance specifications (APS), and practical strategies for estimation and implementation of measurement uncertainty in medical laboratories. + A structured literature review was conducted covering the period 2015 to 2025 using databases such as PubMed and Scopus, along with resources from the European Federation of Clinical Chemistry and Laboratory Medicine. The review focused on international standards (ISO 15189, Clinical and Laboratory Standards Institute), biological variation-based APS (including the Milan Consensus), patient-based real-time quality control (QC), and emerging artificial intelligence and machine learning applications. + The findings demonstrate a convergence between traditional total allowable error concepts and contemporary measurement uncertainty frameworks, with biological variation serving as a key foundation for APS. Both bottom-up approaches based on uncertainty budgeting and top-down approaches using routine internal QC and external quality assessment data are widely applied. Six Sigma metrics provide a robust tool for prioritizing analytical performance improvement. Integration of patient-based real-time QC with artificial intelligence/machine learning techniques, including anomaly detection and deep learning, enhances the detection of analytical drift and sporadic errors beyond conventional QC systems. + Measurement uncertainty should be integrated within laboratory quality management systems rather than treated as an isolated requirement. Future directions include harmonization of measurement uncertainty estimation practices, adaptation to emerging analytical technologies, and improving clinician understanding and interpretation of uncertainty to enhance clinical decision-making. + © The Author(s) (2026). Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com. + + + + Pawade + Yogesh + Y + + Department of Biochemistry, All India Institute of Medical Sciences, Nagpur, India. + + + + Bhoyar + Apurva + A + + Department of Biochemistry, All India Institute of Medical Sciences, Nagpur, India. + + + + eng + + Journal Article + Review + +
+ + England + Lab Med + 0250641 + 0007-5027 + + IM + + + Uncertainty + + + Quality Control + + + Humans + + + Laboratories, Clinical + standards + + + Clinical Laboratory Techniques + standards + + + + analytical performance specifications + biological variation + clinical decision-making + laboratory medicine + measurement uncertainty + quality control + + + + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 1 + 33 + + + ppublish + + 42204811 + 10.1093/labmed/lmag032 + 8696685 + + + + + + 42204785 + + 2026 + 05 + 28 + +
+ + 1872-969X + + + 2026 + May + 27 + + + Annals of the ICRP + Ann ICRP + + Automated dicentric scoring system in Singapore nuclear research and safety initiative. + + 1466453251411985 + 1466453251411985 + + 10.1177/01466453251411985 + + The dicentric chromosome assay (DCA) serves as the gold standard for quantifying ionising radiation exposure in individuals. However, the conventional manual scoring method for DCA is both time-consuming and mentally demanding. Consequently, numerous research groups and commercial entities are actively pursuing the integration of artificial intelligence to automate this process. In this study, we present methodologies and techniques employed at the Singapore Nuclear Research and Safety Initiative (SNRSI) for the development and optimisation of an in-house automated dicentric chromosome scoring tool. Our approach involves the utilisation of thresholding and watershed methods to identify chromosomes within a metaphase. Subsequently, these identified chromosomes are fed into a trained convolutional neural network (CNN) for classification and centromere number assignment. The cumulative centromere count is then calculated to determine the acceptance or rejection of the metaphase for scoring. This integration of advanced image processing techniques and machine learning algorithms would streamline and enhance the efficiency of the dicentric chromosome scoring at SNRSI. + + + + Yeo + J J W + JJW + + Singapore Nuclear Research and Safety Institute, National University of Singapore, Singapore, Singapore; e-mail: snrccel@nus.edu.sg. + + + + Goh + V S T + VST + + Singapore Nuclear Research and Safety Institute, National University of Singapore, Singapore, Singapore; e-mail: snrccel@nus.edu.sg. + + + + Teo + S X + SX + + Singapore Nuclear Research and Safety Institute, National University of Singapore, Singapore, Singapore; e-mail: snrccel@nus.edu.sg. + + + + Chew + Z H + ZH + + Singapore Nuclear Research and Safety Institute, National University of Singapore, Singapore, Singapore; e-mail: snrccel@nus.edu.sg. + + + + Chua + C E L + CEL + + Singapore Nuclear Research and Safety Institute, National University of Singapore, Singapore, Singapore; e-mail: snrccel@nus.edu.sg. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + Ann ICRP + 7708044 + 0146-6453 + + IM + + Artificial intelligence + Deep learning + Dicentric chromosome assay + Dose estimation + + + + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 1 + 3 + + + aheadofprint + + 42204785 + 10.1177/01466453251411985 + + + + + + 42204783 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 2473-4209 + + 53 + 5 + + 2026 + May + + + Medical physics + Med Phys + + Efficient vision mamba for MRI super-resolution via hybrid selective scanning. + + e70508 + e70508 + + 10.1002/mp.70508 + + High-resolution MRI is essential for accurate diagnosis and treatment planning, but its clinical acquisition is often constrained by long scanning times, which increase patient discomfort and reduce scanner throughput. While super-resolution (SR) techniques offer a post-acquisition solution to enhance resolution, existing deep learning approaches face trade-offs between reconstruction fidelity and computational efficiency, limiting their clinical applicability. + This study aims to develop an efficient and accurate deep learning framework for MRI SR that preserves fine anatomical detail while maintaining low computational overhead, enabling practical integration into clinical workflows. + We propose a novel SR framework based on multi-head selective state-space models (MHSSM) integrated with a lightweight channel multilayer perceptron (MLP). The model employs 2D patch extraction with hybrid scanning strategies (vertical, horizontal, and diagonal) to capture long-range dependencies while mitigating pixel forgetting. Each MambaFormer block combines MHSSM, depthwise convolutions, and gated channel mixing to balance local and global feature representation. The framework was trained and evaluated on two distinct datasets: 7T brain T1 MP2RAGE maps (142 subjects) and 1.5T prostate T2w MRI (334 subjects). Performance was compared against multiple baselines including Bicubic interpolation, GAN-based (CycleGAN, Pix2pix, SPSR), transformer-based (SwinIR), Mamba-based (MambaIR), and diffusion-based (I2SB, Res-SRDiff) methods. + The proposed model demonstrated superior performance across all evaluation metrics while maintaining exceptional computational efficiency. On the 7T brain dataset, our method achieved the highest structural similarity (SSIM: + + + 0.951 + ± + 0.021 + + $0.951 \pm 0.021$ + + ) and peak signal-to-noise ratio (PSNR: + + + 26.90 + ± + 1.41 + + $26.90 \pm 1.41$ + + dB), along with the best perceptual quality scores (LPIPS: + + + 0.076 + ± + 0.022 + + $0.076 \pm 0.022$ + + ; GMSD: + + + 0.083 + ± + 0.017 + + $0.083 \pm 0.017$ + + ). These results represented statistically significant improvements over all baselines ( + + + p + < + 0.001 + + $p < 0.001$ + + ), including a 2.1% SSIM gain over SPSR and a 2.4% PSNR improvement over Res-SRDiff. For the prostate dataset, the model similarly outperformed competing approaches, achieving SSIM of + + + 0.770 + ± + 0.049 + + $0.770 \pm 0.049$ + + , PSNR of + + + 27.15 + ± + 2.19 + + $27.15 \pm 2.19$ + + dB, LPIPS of + + + 0.190 + ± + 0.095 + + $0.190 \pm 0.095$ + + , and GMSD of + + + 0.087 + ± + 0.013 + + $0.087 \pm 0.013$ + + . Notably, our framework accomplished these results with only 0.9 million parameters and 57 GFLOPs, representing reductions of 99.8% in parameters and 97.5% in computational operations compared to Res-SRDiff, while also substantially outperforming SwinIR and MambaIR in both accuracy and efficiency metrics. + The proposed framework provides a computationally efficient yet accurate solution for MRI SR, delivering well-defined anatomical details and improved perceptual fidelity across anatomically distinct datasets. By significantly reducing computational demands while maintaining state-of-the-art performance, the model offers strong potential for feasibility toward clinical translation and scalable integration into future imaging workflows. + © 2026 American Association of Physicists in Medicine. + + + + Safari + Mojtaba + M + + Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA. + + + Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois, USA. + + + + Wang + Shansong + S + + Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA. + + + Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois, USA. + + + + Wildman + Vanessa L + VL + + Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA. + + + + Hu + Mingzhe + M + + Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA. + + + + Eidex + Zach + Z + + Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA. + + + + Chang + Chih-Wei + CW + + Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA. + + + + Middlebrooks + Erik H + EH + + Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA. + + + + Qiu + Richard L J + RLJ + + Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA. + + + + Patel + Pretesh + P + + Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA. + + + + Jani + Ashesh B + AB + + Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA. + + + + Mao + Hui + H + + Department of Radiology and Imaging Science and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA. + + + + Tian + Zhen + Z + + Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois, USA. + + + + Yang + Xiaofeng + X + + Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA. + + + Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois, USA. + + + + eng + + + R56EB033332 + NH + NIH HHS + United States + + + R01CA272991 + NH + NIH HHS + United States + + + + Journal Article + +
+ + United States + Med Phys + 0425746 + 0094-2405 + + IM + + + Magnetic Resonance Imaging + methods + + + Image Processing, Computer-Assisted + methods + + + Multilayer Perceptrons + + + Humans + + + Deep Learning + + + Signal-To-Noise Ratio + + + Prostate + diagnostic imaging + + + + MRI + SSM + deep learning + state‐space model + super‐resolution + ultra‐high field MRI + + + + + + 2026 + 5 + 1 + + + 2025 + 12 + 22 + + + 2026 + 5 + 13 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 1 + 3 + + + ppublish + + 42204783 + 10.1002/mp.70508 + + + + Park CKS, Warner NS, Kaza E, Sudhyadhom A. Optimization and validation of low‐field MP2RAGE T1 mapping on 0.35 T MR‐Linac: toward adaptive dose painting with hypoxia biomarkers. Med Phys. 2024;51(11):8124‐8140. + + + Epel B, Maggio MC, Barth ED, et al. Oxygen‐guided radiation therapy. Int J Radiat Oncol Biol Phys. 2019;103(4):977‐984. + + + Barrett T, Rajesh A, Rosenkrantz AB, Choyke PL, Turkbey B. PI‐RADS version 2.1: one small step for prostate MRI. Clin Radiol. 2019;74(11):841‐852. + + + Safari M, Eidex Z, Qiu RLJ, Goette M, Wang T, Yang X. Systematic review and meta‐analysis of AI‐driven MRI motion artifact detection and correction. 2025. + + + Safari M, Eidex Z, Chang CW, Qiu RL, Yang X. Advancing MRI reconstruction: a systematic review of deep learning and compressed sensing integration. Biomed Signal Process Control. 2026;111:108291. + + + Yu M, Xu Z, Lukasiewicz T. A general survey on medical image super‐resolution via deep learning. Comput Biol Med. 2025;193:110345. + + + Khateri M, Vasylechko S, Ghahremani M, et al. MRI super‐resolution with deep learning: a comprehensive survey. arXiv preprint arXiv:2511.16854. 2025. + + + Qiu D, Cheng Y, Wang X. Medical image super‐resolution reconstruction algorithms based on deep learning: a survey. Comput Methods Programs Biomed. 2023;238:107590. + + + Lim B, Son S, Kim H, Nah S, Mu Lee K. Enhanced deep residual networks for single image super‐resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2017:136‐144. + + + Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y. Image super‐resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV). 2018:286‐301. + + + Isola P, Zhu JY, Zhou T, Efros AA. Image‐to‐image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:1125‐1134. + + + Zhu JY, Park T, Isola P, Efros AA. Unpaired image‐to‐image translation using cycle‐consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision. 2017:2223‐2232. + + + Huang S, Liu X, Tan T, et al. TransMRSR: transformer‐based self‐distilled generative prior for brain MRI super‐resolution. 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Vmamba: visual state space model. Adv Neural Inf Process Syst. 2024;37:103031‐103063. + + + Zhu L, Liao B, Zhang Q, Wang X, Liu W, Wang X. Vision mamba: efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09417. 2024. + + + Lin YC, Xu YS, Chen HW, Kuo HK, Lee CY. EAMamba: Efficient all‐around vision state space model for image restoration. arXiv preprint arXiv:2506.22246. 2025. + + + Xu R, Yang S, Wang Y, Cai Y, Du B, Chen H. Visual mamba: a survey and new outlooks. arXiv preprint arXiv:2404.18861. 2024. + + + Tolstikhin IO, Houlsby N, Kolesnikov A, et al. Mlp‐mixer: an all‐mlp architecture for vision. Adv Neural Inf Process Syst. 2021;34:24261‐24272. + + + Chen L, Chu X, Zhang X, Sun J. Simple baselines for image restoration. In: European Conference on Computer Vision. Springer; 2022:17‐33. + + + Smith JT, Warrington A, Linderman SW. Simplified state space layers for sequence modeling. arXiv preprint arXiv:2208.04933. 2022. + + + Ma C, Rao Y, Cheng Y, Chen C, Lu J, Zhou J. Structure‐preserving super resolution with gradient guidance. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020:7769‐7778. + + + Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13(4):600‐612. + + + Xue W, Zhang L, Mou X, Bovik AC. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans Image Process. 2013;23(2):684‐695. + + + Sheikh HR, Bovik AC. Image information and visual quality. IEEE Trans Image Process. 2006;15(2):430‐444. + + + Middlebrooks EH, Patel V, Zhou X, et al. 7 T lesion‐attenuated magnetization‐prepared gradient echo acquisition for detection of posterior fossa demyelinating lesions in multiple sclerosis. Invest Radiol. 2024;59(7):513‐518. + + + Armato III SG, Huisman H, Drukker K, et al. PROSTATEx challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J Med Imaging. 2018;5(4):044501‐044501. + + + Smith SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17(3):143‐155. + + + Yaniv Z, Lowekamp BC, Johnson HJ, Beare R. SimpleITK image‐analysis notebooks: a collaborative environment for education and reproducible research. J Digit Imaging. 2018;31(3):290‐303. + + + Mao L, Zhang X, Chen T, Li Z, Yang J. High‐resolution reduced field‐of‐view diffusion‐weighted magnetic resonance imaging in the diagnosis of cervical cancer. Quant Imaging Med Surg. 2023;13(6):3464. + + + Wang S, Safari M, Li Q, et al. Triad: vision foundation model for 3D magnetic resonance imaging. 2025. + + + Wang S, Jin Z, Hu M, et al. Unifying biomedical vision‐language expertise: towards a generalist foundation model via multi‐CLIP knowledge distillation. 2025. + + + + + + + 42204737 + + 2026 + 05 + 28 + +
+ + 1466-609X + + + 2026 + May + 27 + + + Critical care (London, England) + Crit Care + + Machine-learning algorithms identifies sTREM1 has a key biomarker for outcome prediction in critically ill. + 10.1186/s13054-026-06092-9 + + Prognostic assessment in critically ill patients traditionally relies on severity scores or single biomarkers, each with limited ability to capture the biological heterogeneity of critical illness. + To compare the prognostic performance of multiple biomarkers, individually and in combination with clinical variables, using machine learning approaches for the prediction of mortality and kidney-related outcomes. + We performed a post-hoc analysis of the FROG-ICU cohort, a prospective observational study of patients admitted to ICUs. The study included critically ill patients who required invasive mechanical ventilation or a vasoactive agent for more than 24 h. The primary outcome was day-90 mortality, secondary outcome was major adverse kidney event (MAKE) in ICU. A total of 15 plasma biomarkers were evaluated using multiparametric approach. ML models involved Random Forest (RF) and LASSO regression. Mean decrease in accuracy was used to determine variable importance in RF model. External validation was performed in the MARS cohort which involved ICU patients admitted for sepsis and septic shock. + Among 2,061 patients in the FROG-ICU day-90 mortality was 30.1%. Machine learning models achieved AUCs of 0.74, outperforming severity scores (AUC 0.64, p < 0.001). Variable importance analysis consistently identified sTREM-1 as the strongest predictor. When evaluated alone, sTREM-1 demonstrated high prognostic performance (AUC 0.72), comparable to ML models. These findings were confirmed in the MARS cohort. Similar results were observed for MAKE prediction. + sTREM-1 is a robust biomarker associated with mortality and kidney-related outcomes in critically ill patients. Its predictive performance were comparable to multiparametric machine learning models and superior to severity scores. + © 2026. The Author(s). + + + + de Roquetaillade + Charles + C + + Université Paris Cité, Inserm U942 MASCOT, Paris, F-75006, France. charles.de-roquetaillade@aphp.fr. + + + Department of Anesthesiology and Critical Care, Hôpital Lariboisière, AP- HP, Paris, France. charles.de-roquetaillade@aphp.fr. + + + U942 MASCOT, INSERM-Université de Paris, Hôpital Lariboisière, 43 boulevard de la Chapelle, Paris Cedex 10, 75475, France. charles.de-roquetaillade@aphp.fr. + + + + Blot + Pierre-Louis + PL + + Université Paris Cité, Inserm U942 MASCOT, Paris, F-75006, France. + + + Department of Anesthesiology and Critical Care, Hôpital Lariboisière, AP- HP, Paris, France. + + + + Uhel + Fabrice + F + + Université Paris Cité, INSERM UMR-S1151, CNRS UMR-S8253, Institut Necker-Enfants Malades, Paris, F- 75015, France. + + + Intensive Care Unit, Hôpital Louis Mourier, DMU ESPRIT, AP-HP, Paris, France. + + + + Boutin + Louis + L + + Université Paris Cité, Inserm U942 MASCOT, Paris, F-75006, France. + + + Department of Anesthesiology and Critical Care, HEGP, AP-HP, Paris, France. + + + + Cartailler + Jérôme + J + + Université Paris Cité, Inserm U942 MASCOT, Paris, F-75006, France. + + + + Van Der Poll + Tom + T + + Centre of Infection and Molecular Medicine, Division of Infectious Diseases, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands. + + + + Gayat + Etienne + E + + Université Paris Cité, Inserm U942 MASCOT, Paris, F-75006, France. + + + Department of Anesthesiology and Critical Care, Hôpital Lariboisière, AP- HP, Paris, France. + + + + Mebazaa + Alexandre + A + + Université Paris Cité, Inserm U942 MASCOT, Paris, F-75006, France. + + + Department of Anesthesiology and Critical Care, Hôpital Lariboisière, AP- HP, Paris, France. + + + + Chousterman + Benjamin + B + + Université Paris Cité, Inserm U942 MASCOT, Paris, F-75006, France. + + + Department of Anesthesiology and Critical Care, Hôpital Lariboisière, AP- HP, Paris, France. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + Crit Care + 9801902 + 1364-8535 + + IM + + Conserved immune dysregulation + ICU + MAKE + Machine learning + Sepsis, mortality + Treatment response prediction + + Declarations. Ethical approval and consent to participate: The original FROG-ICU study was conducted in France and Belgium in accordance with Good Clinical Practice (Declaration of Helsinki 2002) and Ethical Committee approvals (Comité de Protection des Personnes—Ile de France IV, IRB n°00003835 and Commission d’éthique biomédicale hospitalo-facultaire de l’hôpital de Louvain, IRB n°. Consent for publication: All authors have read the paper and gave their consent for publication. Competing interests: The authors declare no competing interests. + + + + + 2026 + 1 + 8 + + + 2026 + 5 + 16 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 0 + 49 + + + aheadofprint + + 42204737 + 10.1186/s13054-026-06092-9 + 10.1186/s13054-026-06092-9 + + + + + + 42204706 + + 2026 + 05 + 28 + +
+ + 1472-6955 + + + 2026 + May + 27 + + + BMC nursing + BMC Nurs + + Importance ranking and predictive model construction of WMSDs risk factors among shift-working nurses based on multiple machine-learning algorithms. + 10.1186/s12912-026-04803-9 + + Nurses are essential for safeguarding public health, and their physical condition directly affects care quality and patient safety. Work-related musculoskeletal disorders (WMSDs) are highly prevalent in this workforce and may be amplified by circadian disruption. We therefore integrated individual and environmental risk factors, with special attention to night-shift characteristics, to identify and rank determinants of WMSDs among shift-working nurses. Seven machine-learning algorithms were compared to generate a comprehensive, validated prediction tool that enables managers and nurses to implement targeted, proactive interventions and reduce occupational injury. + This study is a cross-sectional study. The general information, lifestyle, psychosocial data, working environment and shift characteristics of shift nurses were collected, and the influencing factors of WMSDs were analyzed. The mean square error increase and residual sum of squares are calculated, and the importance of influencing factors is sorted respectively. The independent influencing factors of WMSDs in shift nurses were included. After screening variables again by Lasso regression, seven prediction models of LDA, PLS, RDA, GLM, RF, SVM-Radial and SVM-Linear were established by machine learning. The AUC, accuracy and specificity median were used to evaluate the prediction efficiency, and the best prediction model was obtained and the accuracy of the prediction factors was verified. + Among 1 080 shift-working nurses, the WMSD prevalence at any body site was 85.19%. The top-ranked determinants were perceived control, perceived social support, Pittsburgh Sleep Quality Index (PSQI) score, chronotype, frequent bending over, night-shift nap duration, friend support, work support, family support, prolonged neck flexion and years in nursing. The LASSO-selected predictor set comprised dairy intake frequency, shift pattern, monthly night shifts, number of nurses on night duty, post-night-shift recovery days, post-night-shift catch-up sleep, frequent trunk flexion, prolonged neck flexion, PSQI, chronotype and perceived control. Random forest achieved the highest predictive performance (median AUC = 0.919). + Individual characteristics, lifestyle, physical condition, occupational features, shift schedule, biomechanical load and psychosocial factors collectively influence WMSD occurrence in nurses. Random forest outperformed the other algorithms and should be carried out in conjunction with various factors in the model. + © 2026. The Author(s). + + + + Lai + Li-Chong + LC + + The Second Affiliated Hospital of Guangxi Medical University, No. 166 Daxue East Road, Xixiangtang District, Nanning City, Guangxi, 530007, China. + + + + Wu + Hai-Chen + HC + + The Second Affiliated Hospital of Guangxi Medical University, No. 166 Daxue East Road, Xixiangtang District, Nanning City, Guangxi, 530007, China. + + + + Cao + Xiao-Ying + XY + + The Second Affiliated Hospital of Guangxi Medical University, No. 166 Daxue East Road, Xixiangtang District, Nanning City, Guangxi, 530007, China. + + + + Liao + Yi-Fen + YF + + The Second Affiliated Hospital of Guangxi Medical University, No. 166 Daxue East Road, Xixiangtang District, Nanning City, Guangxi, 530007, China. + + + + Tao + Pin-Yue + PY + + The Second Affiliated Hospital of Guangxi Medical University, No. 166 Daxue East Road, Xixiangtang District, Nanning City, Guangxi, 530007, China. + + + + Pan + Xiao + X + + The Second Affiliated Hospital of Guangxi Medical University, No. 166 Daxue East Road, Xixiangtang District, Nanning City, Guangxi, 530007, China. + + + + Lu + Shu-Yu + SY + + The Second Affiliated Hospital of Guangxi Medical University, No. 166 Daxue East Road, Xixiangtang District, Nanning City, Guangxi, 530007, China. + + + + Pan + Qi-Ni + QN + + The Second Affiliated Hospital of Guangxi Medical University, No. 166 Daxue East Road, Xixiangtang District, Nanning City, Guangxi, 530007, China. + + + + Huang + Dong-Mei + DM + + The Second Affiliated Hospital of Guangxi Medical University, No. 166 Daxue East Road, Xixiangtang District, Nanning City, Guangxi, 530007, China. + + + + Li + Cai-Li + CL + + The Second Affiliated Hospital of Guangxi Medical University, No. 166 Daxue East Road, Xixiangtang District, Nanning City, Guangxi, 530007, China. + + + + Dong + Peng-Xin + PX + + The Second Affiliated Hospital of Guangxi Medical University, No. 166 Daxue East Road, Xixiangtang District, Nanning City, Guangxi, 530007, China. + + + + Zhou + Dong-Na + DN + + The Second Affiliated Hospital of Guangxi Medical University, No. 166 Daxue East Road, Xixiangtang District, Nanning City, Guangxi, 530007, China. 183444472@qq.com. + + + + Huang + Hui-Qiao + HQ + + The Second Affiliated Hospital of Guangxi Medical University, No. 166 Daxue East Road, Xixiangtang District, Nanning City, Guangxi, 530007, China. hhq@sr.gxmu.edu.cn. + + + + eng + + + 2025FNFN96726 + Guangxi Science and Technology Program: Key Research and Development Project + + + + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + BMC Nurs + 101088683 + 1472-6955 + + + Nurse + Predictive model + Shift work + Work-related musculoskeletal disorders + + Declarations. Ethics approval and consent to participate: The study was approved by the Ethics Committee of The Second Affiliated Hospital of Guangxi Medical University (Approval No. 2023-KY-0941) and conducted in accordance with the ethical standards set forth in the appropriate version of the Declaration of Helsinki. Informed consent was obtained from all participants prior to their enrollment in this study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 1 + 9 + + + 2026 + 5 + 18 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 0 + 47 + + + aheadofprint + + 42204706 + 10.1186/s12912-026-04803-9 + 10.1186/s12912-026-04803-9 + + + + + + 42204687 + + 2026 + 05 + 28 + +
+ + 1472-6947 + + + 2026 + May + 27 + + + BMC medical informatics and decision making + BMC Med Inform Decis Mak + + Machine learning models for predicting metabolic syndrome to support clinical decision-making in ART-treated adults living with HIV. + 10.1186/s12911-026-03589-9 + + Metabolic syndrome (MetS) is an emerging complication among people living with HIV (PLHIV) receiving long-term antiretroviral therapy (ART), particularly with protease and integrase inhibitor regimens. Early identification of high-risk individuals remains challenging, and predictive tools are limited in African settings. This study evaluated nine machine learning (ML) algorithms for predicting MetS in ART-treated adults. + We analysed a retrospective cohort of 1,027 PLHIV without baseline MetS, followed for 144 weeks; 854 with complete data were included. Nine ML algorithms, including logistic regression, support vector machines, random forest, and XGBoost, were trained on 70% of the data using stratified repeated 10-fold cross-validation with hyperparameter tuning. Performance was assessed on a 30% test set using discrimination (AUC), calibration, and predictor importance. + Age, sex, viral load, and alcohol use were the strongest predictors of metabolic syndrome in ART-treated individuals. Discrimination was modest across models (AUC 0.49-0.64), with radial SVM and logistic regression performing best (AUC ≈ 0.64). Calibration was generally acceptable (Brier score 0.16-0.19). Sensitivity-specificity trade-offs varied: XGBoost favored sensitivity (85.2%) but had low specificity (34.4%), whereas logistic regression and random forest achieved higher specificity (~ 75%). Overall, complex models offered limited gains over logistic regression. + Although internally validated ML models demonstrated acceptable calibration and modest discrimination, predictive performance remains insufficient for standalone clinical deployment and should be considered supportive rather than definitive for risk stratification in HIV care. Logistic regression and random forest provided the most consistent balance of discrimination and calibration, while complex approaches offered limited gains. Age, sex, viral load, and alcohol use emerged as key predictors. External validation and prospective evaluation are essential to establish generalisability, clinical impact, and feasibility before integration into routine practice. + © 2026. The Author(s). + + + + Siwingwa + Mpanji + M + + School of Health Sciences, Department of Biomedical Sciences, University of Zambia, Lusaka, Zambia. mpanjisiwingwa@gmail.com. + + + Adult Infectious Disease Center, University Teaching Hospital, Lusaka, Zambia. mpanjisiwingwa@gmail.com. + + + + Mutale + Wilbroad + W + + School of Public Health, Department of Policy, University of Zambia, Lusaka, Zambia. + + + Vanderbilt Institute for Global Health, Vanderbilt University Medical Center, Nashville, TN, USA. + + + + Munsaka + Sody Mweetwa + SM + + School of Health Sciences, Department of Biomedical Sciences, University of Zambia, Lusaka, Zambia. + + + + Kayamba + Violet + V + + School of Medicine, Department of Internal Medicine, University of Zambia, Lusaka, Zambia. + + + + Heimburger + Douglas C + DC + + Vanderbilt Institute for Global Health, Vanderbilt University Medical Center, Nashville, TN, USA. + + + + Hazelhurst + Scott + S + + School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South Africa. + + + Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa. + + + + Sivile + Suilanji + S + + Adult Infectious Disease Center, University Teaching Hospital, Lusaka, Zambia. + + + + Mweemba + Aggrey + A + + Adult Infectious Disease Center, University Teaching Hospital, Lusaka, Zambia. + + + + Mbewe + Nyuma + N + + Adult Infectious Disease Center, University Teaching Hospital, Lusaka, Zambia. + + + Zambia National Public Health Institute, Lusaka, Zambia. + + + + Mulenga + Lloyd B + LB + + School of Medicine, Department of Internal Medicine, University of Zambia, Lusaka, Zambia. + + + Vanderbilt Institute for Global Health, Vanderbilt University Medical Center, Nashville, TN, USA. + + + Adult Infectious Disease Center, University Teaching Hospital, Lusaka, Zambia. + + + + Sinkala + Musalula + M + + School of Health Sciences, Department of Biomedical Sciences, University of Zambia, Lusaka, Zambia. + + + Computational Biology Division, Department of Integrative Biomedical Sciences,Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + BMC Med Inform Decis Mak + 101088682 + 1472-6947 + + IM + + Antiretroviral therapy + Calibration + HIV + Machine learning + Metabolic syndrome + Predictive modelling + Sub-Saharan Africa + + Declarations. Ethics approval and consent to participate: This study was approved by the University of Zambia Biomedical Research Ethics Committee (UNZABREC; Ref. 4887–2024) and the National Health Research Authority (NHRA; Ref. NHRA1124/15/04/2024). Written permission was obtained from the principal investigator of the VISEND study for secondary data use. All participants provided written informed consent prior to enrolment. All methods were conducted in accordance with relevant ethical guidelines and regulations. The VISEND trial itself was approved by UNZABREC (Ref. 004 07 18), registered with the Pan African Clinical Trials Registry (PACTR201904781300573). All procedures involving human participants were conducted in accordance with the ethical standards of the relevant institutional and national research committees and with the principles of the Declaration of Helsinki and its subsequent amendments. Consent for publication: Not applicable. No individual-level data or identifiable personal information are included in this manuscript. Competing interests: The authors declare no competing interests. + + + + + 2025 + 12 + 19 + + + 2026 + 5 + 15 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 0 + 46 + + + aheadofprint + + 42204687 + 10.1186/s12911-026-03589-9 + 10.1186/s12911-026-03589-9 + + + + + + 42204678 + + 2026 + 05 + 28 + +
+ + 1471-2288 + + + 2026 + May + 27 + + + BMC medical research methodology + BMC Med Res Methodol + + Quality and performance of machine learning versus logistic regression for predicting IVIG resistance in Kawasaki disease: a PROBAST+AI systematic comparison. + 10.1186/s12874-026-02893-2 + + This study aimed to systematically compare the predictive performance and methodological quality of logistic regression (LR) and machine learning (ML) models for intravenous immunoglobulin (IVIG) resistance in Kawasaki disease (KD) using the PROBAST + AI framework. + We searched PubMed, Embase, and Web of Science to identify studies on prediction models for IVIG resistance in KD published between January 1, 2006, and July 31, 2025. We assessed methodological rigour, risk of bias, and applicability using PROBAST + AI. A meta-analysis was performed using random-effects models with logit-transformed area under the receiver operating characteristic curve (AUC) values. Subgroup, sensitivity, and publication bias analyses were additionally conducted. + We identified 52 eligible studies (40 LR and 12 ML). In external validation, pooled AUCs were similar between ML and LR models (0.76 [95% CI 0.64-0.86] vs. 0.75 [95% CI 0.68-0.81]). In internal validation, ML showed a slightly higher pooled AUC than LR (0.86 [95% CI 0.78-0.92] vs. 0.76 [95% CI 0.72-0.79]), although no statistically significant differences were observed. All studies were judged to be at high risk of bias, mainly due to retrospective single-centre designs, inadequate handling of missing data and continuous predictors, and poor reporting of calibration and clinical utility. No study reported sample size calculations. + Given the limited external validation and substantial heterogeneity across studies, ML does not consistently outperform LR in predicting IVIG resistance in KD. Future studies should prioritise rigorous external validation and adherence to TRIPOD + AI and PROBAST + AI. + © 2026. The Author(s). + + + + Zhang + Jiaying + J + + Department of Cardiology, Children's Hospital of Soochow University, 92 Zhongnan Street, Suzhou, Jiangsu, China. + + + + Wang + Difan + D + + Institute of Pediatric Research, Children's Hospital of Soochow University, Suzhou, Jiangsu, China. + + + + Dong + Jinfeng + J + + Department of Hematology, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China. + + + + He + Ying + Y + + Department of Pediatrics, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, 134 Dong Street, Fuzhou, Fujian, China. + + + + Liu + Ying + Y + + Institute of Pediatric Research, Children's Hospital of Soochow University, Suzhou, Jiangsu, China. + + + + You + Tingjiao + T + + Institute of Pediatric Research, Children's Hospital of Soochow University, Suzhou, Jiangsu, China. + + + + Li + Jing + J + + Department of Cardiology, Children's Hospital of Soochow University, 92 Zhongnan Street, Suzhou, Jiangsu, China. + + + + Li + Lizhi + L + + Department of Pediatric Surgery, Fujian Provincial Hospital, Fujian Provincial Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China. + + + + Wu + Xiaodan + X + + Department of Anesthesiology, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China. + + + + Tang + Qiuyu + Q + + Pediatric Intensive Care Unit, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Children's Hospital, Fujian Medical University, Fuzhou, Fujian, China. + + + + Ma + Shurong + S + + Department of Endocrine, Children's Hospital of Soochow University, Suzhou, Jiangsu, China. + + + + Liu + Panpan + P + + Department of Cardiology, Children's Hospital of Soochow University, 92 Zhongnan Street, Suzhou, Jiangsu, China. + + + + Lv + Haitao + H + + Department of Cardiology, Children's Hospital of Soochow University, 92 Zhongnan Street, Suzhou, Jiangsu, China. haitaosz@163.com. + + + + Huang + Hongbiao + H + + Department of Pediatrics, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, 134 Dong Street, Fuzhou, Fujian, China. 403032197@qq.com. + + + + eng + + + KYCX24_3342 + the Postgraduate Research & Practice Innovation Program of Jiangsu Province + + + + 2025J01748 + the Fujian Provincial Natural Science Foundation + + + + 2025J01076 + the Fujian Provincial Natural Science Foundation + + + + GSWS2024029 + Suzhou Program of Gusu Medical Talent + + + + KJXW2022019 + the Youth Talents in Science and Education Program of Suzhou + + + + 82470523 + National Natural Science Foundation of China + + + + 2024GGA010 + the Program for the Middle-aged and Young Key Talents in the Health System of Fujian Province + + + + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + BMC Med Res Methodol + 100968545 + 1471-2288 + + IM + + IVIG resistance + Kawasaki disease + Logistic regression + Machine learning + PROBAST + AI + Systematic review + + Declarations. Ethics approval and consent to participate: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 1 + 1 + + + 2026 + 5 + 25 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 6 + 31 + + + 2026 + 5 + 28 + 0 + 45 + + + aheadofprint + + 42204678 + 10.1186/s12874-026-02893-2 + 10.1186/s12874-026-02893-2 + + + + + + 42204620 + + 2026 + 05 + 28 + +
+ + 1749-799X + + + 2026 + May + 27 + + + Journal of orthopaedic surgery and research + J Orthop Surg Res + + Machine-learning-model based tool for screening bone metastases from lung cancer patients in primary care practice. + 10.1186/s13018-026-06979-x + + This study aimed to use the machine-learning methods to predict bone metastasis (BM) in patients with lung cancer. + This study included 8,612 patients with lung cancer, from whom we collected baseline characteristics and hematological data. BM was diagnosed based on imaging results. We normalized metric data using Z-scores and divided the patients into training and validation sets in a 7:3 ratio. After under-sampling of the training set to relieve the interlocking imbalance, the key variables were then selected using the LASSO method. Nine distinct machine-learning models were developed on the training set, and their performance was evaluated on the validation set. Finally, we created an online tool to support real-time analysis of BM in lung cancer patients. + Among 8,612 lung cancer patients, 947 (11%) were diagnosed with BM. Nine key indicators associated with BM were identified, including ALP, LYM%, HCT, FBG, TT, TBIL, smoking status, DBIL, and D-dimer. Among the evaluated models, the eXtreme Gradient Boosting (XGBoost) model showed consistently strong performance, achieving AUCs of 0.8423 and 0.7871; accuracies of 0.7655 and 0.7309; balanced accuracies of 0.76645 and 0.72385; specificities of 0.7652 and 0.7329; and sensitivities of 0.7677 and 0.7148 in the training and validation sets, respectively, along with higher Youden index and F1 score. Consequently, we identified XGBOOST as the optimal predictive model and developed an online platform based on this model to facilitate prediction of BM in lung cancer patients. + A machine learning model was developed to diagnose BM in lung cancer based on hematological indicators, along with a real-time website. This tool cloud guide further diagnostic evaluations and intervention for high-risk patients. + © 2026. The Author(s). + + + + Zhou + Yang + Y + + Department of Orthopedic Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China. + + + Jiangxi Provincial Key Laboratory of Spine and Spinal Cord Diseases, Nanchang, China. + + + + Cai + Tianpan + T + + Department of Information, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China. + + + + Lan + Chunyu + C + + Department of Information, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China. + + + + Feng + Shuang + S + + Department of Orthopaedics and Traumatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, 999077, Hong Kong, China. + + + + Liu + Zhili + Z + + Department of Orthopedic Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China. + + + Jiangxi Provincial Key Laboratory of Spine and Spinal Cord Diseases, Nanchang, China. + + + + Liu + Jiaming + J + + Department of Information, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China. + + + + Cao + Lei + L + + Medical Big Data Research Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China. ndyfy01955@ncu.edu.cn. + + + + Nie + Jiangbo + J + + Department of Orthopedic Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China. niejiangbo@alu.ncu.edu.cn. + + + Jiangxi Provincial Key Laboratory of Spine and Spinal Cord Diseases, Nanchang, China. niejiangbo@alu.ncu.edu.cn. + + + Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China. niejiangbo@alu.ncu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + J Orthop Surg Res + 101265112 + 1749-799X + + IM + + Bone metastasis + Lung cancer + Machine-learning models + Serum indicators + + Declarations. Ethics approval and consent to participate: This retrospective study was approved by the Ethics Committee of the First Affiliated Hospital of Nanchang University (No. IITS2024738). The requirement for informed consent was waived due to the retrospective nature of the study. Competing interests: The authors declare no competing interests. + + + + + 2025 + 10 + 13 + + + 2026 + 5 + 18 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 0 + 42 + + + aheadofprint + + 42204620 + 10.1186/s13018-026-06979-x + 10.1186/s13018-026-06979-x + + + + + + 42204598 + + 2026 + 05 + 28 + +
+ + 1531-8249 + + + 2026 + May + 27 + + + Annals of neurology + Ann Neurol + + Frequency- and Network-Specific Changes in Functional Connectivity Reflect Pathophysiological Mechanisms across Parkinson's Disease Stages. + 10.1002/ana.78262 + + Parkinson's disease (PD) is increasingly conceptualized as a disorder of large-scale brain networks, yet whether and how frequency-specific functional connectivity reorganizes across stages remains poorly understood. In this study, we used high-density electroencephalography (EEG) to characterize cortico-cortical functional connectivity across the clinical spectrum of PD. + We performed high-density EEG in a cross-sectional cohort of 140 PD patients spanning early, intermediate, and advanced stages and 57 healthy controls. Cortico-cortical functional connectivity was reconstructed in source space across multiple frequency bands and analyzed using network-based statistics combined with machine-learning models to identify stage-dependent network alterations and evaluate their diagnostic and prognostic relevance. + We detected 3 distinct large-scale networks showing divergent trajectories across disease stages. An α-band network involving prefrontal and parieto-temporal regions exhibited progressive hypoconnectivity and was associated with cognitive and axial impairment. A β-band sensorimotor network showed progressive hyperconnectivity, paralleling bradykinesia severity. A high-γ network demonstrated increased connectivity in early PD, followed by a progressive connectivity breakdown, and was inversely associated with motor complications. Multiband integration achieved near-perfect discrimination between early PD and healthy controls and robust stratification across disease stages. Band-specific networks also predicted clinical milestones of disease progression, with preserved α connectivity identifying patients at lower risk for cognitive and axial impairment, and stronger high-γ connectivity indicating reduced vulnerability to motor complications. + Together, these results identify frequency-specific cortical networks as markers of disease stage and clinical vulnerability and support high-density EEG connectivity as a scalable systems-level biomarker for diagnosis, staging, and risk stratification in PD. ANN NEUROL 2026. + © 2026 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association. + + + + Conti + Matteo + M + 0000-0003-2879-2209 + + Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy. + + + + D'Onofrio + Valentina + V + 0000-0002-5898-0967 + + Padova Neuroscience Center, University of Padua, Padua, Italy. + + + + Lorenzon + Luca + L + + Department of Neuroscience, University of Padua, Padua, Italy. + + + + Grassi + Laura Ludovica + LL + + Department of Neuroscience, University of Padua, Padua, Italy. + + + + Mascioli + Davide + D + + Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy. + + + + Ferrari + Valerio + V + + Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy. + + + Neurology Unit, IRCCS Neuromed, Pozzilli, Italy. + + + + Simonetta + Clara + C + + Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy. + + + + Pavan + Sofia + S + + Department of Neuroscience, University of Padua, Padua, Italy. + + + + Di Giuliano + Francesca + F + + Neuroradiology Unit, Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy. + + + + Pierantozzi + Mariangela + M + + Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy. + + + Neurology Unit, IRCCS Neuromed, Pozzilli, Italy. + + + + Schirinzi + Tommaso + T + 0000-0002-2517-6278 + + Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy. + + + Neurology Unit, IRCCS Neuromed, Pozzilli, Italy. + + + + Centonze + Diego + D + 0000-0002-8390-8545 + + Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy. + + + Neurology Unit, IRCCS Neuromed, Pozzilli, Italy. + + + + Corbetta + Maurizio + M + + Padova Neuroscience Center, University of Padua, Padua, Italy. + + + Department of Neuroscience, University of Padua, Padua, Italy. + + + + Antonini + Angelo + A + + Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy. + + + IRCCS San Camillo Hospital, Venice, Italy. + + + + Stefani + Alessandro + A + + Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy. + + + Parkinson Center, Tor Vergata University Hospital, Rome, Italy. + + + + Guerra + Andrea + A + + Padova Neuroscience Center, University of Padua, Padua, Italy. + + + Department of Neuroscience, University of Padua, Padua, Italy. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
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+ + 1749-8546 + + 21 + 1 + + 2026 + May + 27 + + + Chinese medicine + Chin Med + + Epigenetic precision diagnostics of traditional Chinese medicine (TCM) syndrome differentiation: a pilot study of atrial fibrillation with qi-yin deficiency syndrome based on 5-hydroxymethylcytosine signatures in extracellular vesicle DNA from plasma. + 147 + 10.1186/s13020-025-01267-y + + Syndrome differentiation in Traditional Chinese Medicine (TCM) is pivotal to clinical practice and dictates the efficacy of medicinal treatments. However, precision diagnostic models for TCM syndromes, constructed from biomarkers such as metabolites and proteins, have failed to achieve high precision. Recent studies have highlighted a strong link between TCM and epigenetics, an area that remains largely unexplored in TCM diagnosis. Taking atrial fibrillation (AF) with Qi-Yin deficiency syndrome (QYDS) as an example, we utilized a type of epigenetic sequencing technology called 5hmC-Seal and integrated it with various machine learning models to develop an Epigenetic Differential Syndrome (Epi-DS) technology for identifying epigenetic biomarkers. This approach is crucial for developing more accurate diagnostic models for traditional Chinese medicine syndromes and for advancing the modernization of traditional Chinese medicine. + In this study, we conducted a single-center, prospective study involving two independent cohorts (cohort 1 and cohort 2) in AF, including QYDS and non-Qi-Yin deficiency syndrome (NQYDS). Next, we utilized 5hmC-Seal to obtain the patients' 5hmC genome-wide profiles in plasma extracellular vesicles DNAs (evDNAs). Meanwhile, a variety of sophisticated machine learning algorithms were employed across three datasets-training, validation, and external cohorts (the training and validation sets constituting cohort 1 and the external cohort constituting cohort 2) to construct and validate QYDS diagnosis model. + Based on the hydroxymethylation profile of the QYDS in AF, we have successfully constructed a disease-phenotype-molecule biological network for AF. At the molecular level, we identified nine characteristic 5hmC markers for the QYDS in AF and successfully established a diagnostic model for this syndrome. In Cohort 1's training set, the area under the receiver operating characteristic curve (AUC) was as high as 0.984, with a sensitivity of 0.976 and a specificity of 1.000. In validation set, the AUC was 0.949, with a sensitivity of 0.952 and a specificity of 0.952. In the independent external validation cohort 2, the AUC was as high as 0.934, with a sensitivity of 0.886 and a specificity of 0.919. Moreover, the diagnostic model we built based on symptoms and molecular markers achieved an AUC value of 0.864 in an independent external cohort. + A novel precision diagnostic approach of TCM Syndrome Differentiation was established based on Epi-DS. The disease-phenotype-molecule network we have constructed reveals the epigenetic foundation of TCM and has identified molecular diagnostic markers for the QYDS in AF. This provides an example for understanding the molecular basis of TCM syndrome differentiation and for integrated traditional Chinese and Western medicine diagnosis. + © 2026. The Author(s). + + + + Fan + Shaowei + S + + Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China. + + + + Chen + Haoyu + H + + School of Graduate, Hebei University of Chinese Medicine, Xinshi South Road No. 326, Qiaoxi District, Shijiazhuang, 050091, Hebei, China. + + + + Chen + Hangyu + H + + Department of Pharmacy, Peking University Third Hospital, Beijing, 100191, China. + + + + Du + Bai + B + + Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China. + + + + Zhen + Baixin + B + + College of Pharmacy, Xinjiang Medical University, Urumqi, 830011, China. + + + + Chen + Xianglong + X + + School of Information and Intelligent Engineering, University of Sanya, Sanya, 572022, Hainan, China. + + + + Zhang + Lei + L + + Department of Pharmacy, Peking University Third Hospital, Beijing, 100191, China. + + + + Li + Xiaxuan + X + + School of Information and Communication Engineering, Hainan University, Haikou, 570228, Hainan, China. + + + + Duolikun + Maimaitiyasen + M + + Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, 570100, China. + + + + Chen + Long + L + + Department of Pharmacy, Peking University Third Hospital, Beijing, 100191, China. + + + Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, 100871, China. + + + Peking University Third Hospital Cancer Center, Beijing, 100191, China. + + + + Gao + Han + H + + Department of Pharmacy, Peking University Third Hospital, Beijing, 100191, China. + + + + Shi + Shuqing + S + + Department of Internal Medicine, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China. + + + + Zhang + Xiaohan + X + + Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China. + + + + Wang + Yangang + Y + + School of Graduate, Hebei University of Chinese Medicine, Xinshi South Road No. 326, Qiaoxi District, Shijiazhuang, 050091, Hebei, China. piwei001@163.com. + + + Beijing University of Chinese Medicine Third Affiliated Hospital, Anwai Xiaoguan Street No.51, Chaoyang District, Beijing, 100029, China. piwei001@163.com. + + + + Hu + Yuanhui + Y + + Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China. huiyuhui55@sohu.com. + + + + Lin + Jian + J + + Department of Pharmacy, Peking University Third Hospital, Beijing, 100191, China. linjian@pku.edu.cn. + + + Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou, 570100, China. linjian@pku.edu.cn. + + + Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, 100871, China. linjian@pku.edu.cn. + + + Peking University Third Hospital Cancer Center, Beijing, 100191, China. linjian@pku.edu.cn. + + + + eng + + + 82205096 + National Natural Science Foundation of China + + + + 82274034 + National Natural Science Foundation of China + + + + HLCMHPP2023082 + Central High-Level Chinese Medicine Hospital Promotion Project + + + + 2022-1-4153 + Capital's Funds for Health Improvement and Research + + + + CI2021A00918 + Technological Innovation Project of the China Academy of Chinese Medical Sciences + + + + + Journal Article + + + 2026 + 05 + 27 + +
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+ + 1477-7819 + + + 2026 + May + 27 + + + World journal of surgical oncology + World J Surg Oncol + + Machine learning-based prognostic model integrating preoperative HALP score and lactate dehydrogenase for predicting postoperative recurrence of prostate cancer. + 10.1186/s12957-026-04423-2 + + Postoperative biochemical recurrence (BCR) of prostate cancer (PCa) remains a major clinical challenge, and traditional risk assessment systems show suboptimal predictive performance for PCa recurrence. This study aimed to develop and validate interpretable machine learning (ML) models for predicting PCa postoperative recurrence by integrating multi-dimensional clinical features, and to construct a simplified and practical prognostic model for individualized risk stratification. + A total of 320 PCa patients (125 recurrences vs. 195 non-recurrences) who underwent laparoscopic radical prostatectomy (LRP) at the primary center were retrospectively enrolled as the internal cohort, and 144 patients (50 recurrences vs. 94 non-recurrences) from another campus were included as the external validation cohort. Ten ML algorithms were used to construct prediction models with clinicopathological, preoperative hematological and nutrition-inflammation features. Stratified sampling and ten-fold cross-validation were used for model training and validation, and SHAP analysis was adopted for feature importance evaluation and model interpretability. Recursive feature inclusion was performed to optimize the model, and clinical cutoffs of key indicators were determined. + The gradient boosting machine (GBM) model achieved the best predictive performance in the internal cohort with an AUC of 0.891, which was significantly superior to the UCSF-CAPRA score (AUC = 0.703) and the D'Amico classification (AUC = 0.610). A simplified 5-feature GBM model [positive surgical margin, preoperative hemoglobin-albumin-lymphocyte-platelet (HALP) score, postoperative Gleason score, preoperative maximum prostate specific antigen, preoperative lactate dehydrogenase (LDH)] achieved an AUC of 0.912 in the internal cohort and 0.895 in the external cohort, with excellent calibration and higher net clinical benefit. The optimal cutoffs were 41.31 for preoperative HALP score and 182.61 U/L for preoperative LDH. Low HALP was associated with shorter recurrence-free survival (HR = 0.30, P < 0.0001), and high LDH indicated increased recurrence risk (HR = 1.37, P = 0.083). A three-tier risk stratification system was established based on the cutoff values to predict postoperative recurrence risk. + The ML model integrating preoperative HALP score, LDH and core clinicopathological features has high accuracy and good clinical applicability for predicting PCa postoperative recurrence. Validated successfully in both internal and external cohorts, the 5-feature simplified model can serve as a practical tool for individualized recurrence risk assessment, facilitating optimized clinical management of PCa patients. + © 2026. The Author(s). + + + + Wang + Hao + H + + Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China. + + + + Qiu + Xuemeng + X + + Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China. + + + + Li + Zhen + Z + + Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China. + + + + Wu + Jiyue + J + + Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China. + + + + Gan + Lijian + L + + Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China. + + + + Xie + Dawei + D + + Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China. + + + + Wei + Yirui + Y + + Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China. + + + + Wang + Jianwen + J + + Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China. wjianw999@163.com. + + + + Wang + Wei + W + + Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China. weiwang0920@163.com. + + + + eng + + + grant No. 2020-2-2033 + The Capital Health Research and Development of Special Fund, Beijing, China + + + + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + World J Surg Oncol + 101170544 + 1477-7819 + + IM + + Biochemical recurrence + HALP score + Lactate dehydrogenase + Machine learning + Prognostic prediction + Prostate cancer + + Declarations. Ethics approval and consent to participate: The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Beijing Chaoyang Hospital (2020-science-299-1). Due to the retrospective nature of the study, Ethics Committee of Beijing Chaoyang Hospital waived the need of obtaining informed consent in the manuscript. Competing interests: The authors declare no competing interests. + + + + + 2026 + 3 + 13 + + + 2026 + 5 + 18 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 0 + 38 + + + aheadofprint + + 42204546 + 10.1186/s12957-026-04423-2 + 10.1186/s12957-026-04423-2 + + + + + + 42204501 + + 2026 + 05 + 28 + +
+ + 1471-2407 + + + 2026 + May + 27 + + + BMC cancer + BMC Cancer + + A machine learning-based interpretable model for predicting pancreatic cancer in chronic pancreatitis patients with focal pancreatic lesions. + 10.1186/s12885-026-16234-5 + + Pancreatic cancer (PC) is deadly and distinguishing it from inflammatory conditions in chronic pancreatitis (CP) patients is challenging. We aimed to develop machine learning models to predict PC in CP patients with focal pancreatic lesions. + For this bicentric retrospective study, CP patients with indeterminate focal pancreatic lesions discovered through contrast-enhanced computed tomography scans were enrolled. Final diagnosis of focal pancreatic lesions was established by surgical pathology or follow-up outcomes. We used Boruta algorithm for feature screening, and conducted six machine learning models (Logistic Regression, Random Forest, eXtreme Gradient Boosting, Light Gradient Boosting Machine, K-Nearest Neighbors and Naive Bayes). The input data this study used were clinical information and laboratory data. Finally, SHAP was employed for interpretation. Receiver operating characteristic curve, area under curve (AUC), accuracy, sensitivity and specificity were used to evaluate model performance. + A total of 187 participants were enrolled, and 44 patients (23.5%) were diagnosed as PC. Six important features were identified by the Boruta algorithm. Among the six machine learning models based on these features, Logistic Regression had the best diagnostic performance (AUC: 0.875 (95% CI: 0.801-0.949) in the training set and 0.908 (95% CI: 0.818-0.997) in the testing set). The ranking of SHAP variables importance from highest to lowest were carbohydrate antigen 19 - 9, hemoglobin, alanine transaminase, carcinoembryonic antigen, aspartate transaminase and the maximum diameter of focal pancreatic lesions. + Six features-based machine learning models, especially the Logistic Regression, had satisfactory performance in predicting PC in CP patients with focal pancreatic lesions. This approach was crucial for enhancing early detection rate and reducing mortality associated with PC. + © 2026. The Author(s). + + + + Yi + Jin-Hui + JH + + School of Medicine, NanKai University, No. 94 Weijin Road, Nankai District, Tianjin, 300071, China. + + + Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Fuxing Road 28, Haidian District, Beijing, 100853, China. + + + Department of Gastroenterology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. + + + + Wang + Teng + T + + Department of Gastroenterology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. + + + Shanghai Institute of Pancreatic Diseases, 168 Changhai Road, Shanghai, 200433, China. + + + Changhai Clinical Research Unit, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. + + + + Bi + Ya-Wei + YW + + Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Fuxing Road 28, Haidian District, Beijing, 100853, China. + + + + Xu + Jin-Jie + JJ + + Department of Gastroenterology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. + + + Department of Gastroenterology, The Hospital of 32521 Troops of Chinese People's Liberation Army, Zhenjiang, Jiangsu, 212400, China. + + + + Liu + Miao + M + + Department of Gastroenterology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. + + + Shanghai Institute of Pancreatic Diseases, 168 Changhai Road, Shanghai, 200433, China. + + + Changhai Clinical Research Unit, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. + + + + Chen + Guang-Ming + GM + + Department of Gastroenterology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. + + + Shanghai Institute of Pancreatic Diseases, 168 Changhai Road, Shanghai, 200433, China. + + + Changhai Clinical Research Unit, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. + + + + Li + Zhao-Shen + ZS + + Department of Gastroenterology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. zhaoshen-li@hotmail.com. + + + Shanghai Institute of Pancreatic Diseases, 168 Changhai Road, Shanghai, 200433, China. zhaoshen-li@hotmail.com. + + + Changhai Clinical Research Unit, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. zhaoshen-li@hotmail.com. + + + + Wang + Dan + D + + Department of Gastroenterology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. danwang@smmu.edu.cn. + + + Shanghai Institute of Pancreatic Diseases, 168 Changhai Road, Shanghai, 200433, China. danwang@smmu.edu.cn. + + + Changhai Clinical Research Unit, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. danwang@smmu.edu.cn. + + + + Chai + Ning-Li + NL + + School of Medicine, NanKai University, No. 94 Weijin Road, Nankai District, Tianjin, 300071, China. chainingli@vip.163.com. + + + Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Fuxing Road 28, Haidian District, Beijing, 100853, China. chainingli@vip.163.com. + + + + Hu + Liang-Hao + LH + + Department of Gastroenterology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. lianghao-hu@smmu.edu.cn. + + + Shanghai Institute of Pancreatic Diseases, 168 Changhai Road, Shanghai, 200433, China. lianghao-hu@smmu.edu.cn. + + + Changhai Clinical Research Unit, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai, 200433, China. lianghao-hu@smmu.edu.cn. + + + + eng + + + 2023ZD0500904 + the Noncommunicable Chronic Diseases-National Science and Technology Major Project + + + + 82370657 + National Natural Science Foundation of China + + + + 82270679 + National Natural Science Foundation of China + + + + 2023ZD0500900 + Noncommunicable Chronic Diseases-National Science and Technology Major Project + + + + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + BMC Cancer + 100967800 + 1471-2407 + + IM + + Chronic pancreatitis + Machine learning + Pancreatic cancer + SHAP + + Declarations. Ethics approval and consent to participate: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of ChangHai Hospital. Consent for publication: Not Applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 3 + 24 + + + 2026 + 5 + 20 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 6 + 32 + + + 2026 + 5 + 28 + 0 + 35 + + + aheadofprint + + 42204501 + 10.1186/s12885-026-16234-5 + 10.1186/s12885-026-16234-5 + + + + + + 42204441 + + 2026 + 05 + 28 + + + 2026 + 05 + 28 + +
+ + 1525-6049 + + 48 + 1 + + 2026 + Dec + + + Renal failure + Ren Fail + + Time-updated explainable machine learning predicts short-term mortality in peritoneal dialysis patients. + + 2666955 + 2666955 + + 10.1080/0886022X.2026.2666955 + + + Objective: This study aimed to develop and validate a time-updated, explainable machine learning (ML) early-warning system for short-term mortality risk in patients with continuous ambulatory peritoneal dialysis (CAPD). Methods: Multiple supervised ML techniques were validated to stratify individuals at high risk of 6-month death, classifiers were trained, internally validated and independently temporally validated in this retrospective study of 1,484 CAPD patients in our PD center. We collected time-updated markers such as patient demographics, clinical characteristics, and laboratory data to inform the ML models and the performance of eight ML models was assessed via the area under the curve (AUC) and accuracy. The SHapley Additive exPlanation (SHAP) method was selected to further interpret the predictive models and link findings to clinical actionability. Results: In internal validation cohort, the light gradient boosting machine (lightGBM) model demonstrated best performance with AUC of 0.888 and highest accuracy of 0.879. In temporal validation cohort, the lightGBM model also exhibited good classification performance, achieving AUC of 0.850 and good accuracy of 0.874. Moreover, SHAP analysis identified several key features that contributed to the accurate prediction of the lightGBM model. Conclusions: The time-updated lightGBM early-warning system helps clinicians to early identify CAPD patients with high risk of death within six months based on easily accessible and time-updated data and provides a basis for personalized treatment. + + + + Wang + Quan + Q + + Department of Nephrology, Wuhan No.1 Hospital, Wuhan, China. + + + + Ding + Yanqiong + Y + + Department of Nephrology, Wuhan No.1 Hospital, Wuhan, China. + + + + Luo + Qing + Q + + Department of Nephrology, Wuhan No.1 Hospital, Wuhan, China. + + + + Wan + Sheng + S + + Department of Nephrology, Wuhan No.1 Hospital, Wuhan, China. + + + + Zhang + Yanmin + Y + + Department of Nephrology, Wuhan No.1 Hospital, Wuhan, China. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + Ren Fail + 8701128 + 0886-022X + + IM + + + Humans + + + Female + + + Peritoneal Dialysis, Continuous Ambulatory + mortality + + + Retrospective Studies + + + Predictive Learning Models + + + Boosting Machine Learning Algorithms + + + Male + + + Middle Aged + + + Kidney Failure, Chronic + mortality + therapy + + + Risk Assessment + methods + + + Machine Learning + + + Aged + + + Area Under Curve + + + Prediction Algorithms + + + Risk Factors + + + + We developed and validated a time-updated, explainable ML early-warning system that accurately predicts 6-month all-cause mortality in CAPD patients using routinely, available laboratory data.The LightGBM model achieved excellent discriminative performance, with an AUC of 0.888 in internal validation and 0.850 in independent temporal validation.This model provides a scalable, low-cost early-warning tool for routine CAPD care, with a standardized risk-stratified intervention framework to guide timely, targeted care and advance care planning. + + + Continuous ambulatory peritoneal dialysis + electronic health records + explainable artificial intelligence + machine learning models + mortality risk prediction + + + + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 0 + 23 + + + ppublish + + 42204441 + 10.1080/0886022X.2026.2666955 + + + + + + 42204406 + + 2026 + 05 + 28 + +
+ + 1520-5851 + + + 2026 + May + 27 + + + Environmental science & technology + Environ Sci Technol + + Correction to "Correspondence on "A Novel Framework for Airshed Delineation and PM2.5 Estimation across India Using Machine Learning and Spatial Clustering"". + 10.1021/acs.est.6c07081 + + + Qing + Lina + L + 0009-0002-4861-8304 + + School of Business, Macau University of Science and Technology, Taipa 999078, Macau, China. + + + + eng + + Published Erratum + + + 2026 + 05 + 27 + +
+ + United States + Environ Sci Technol + 0213155 + 0013-936X + + IM + + + Environ Sci Technol. 2026 Mar 24;60(11):8894-8895. doi: 10.1021/acs.est.5c14377. + 41788083 + + + + + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 6 + 33 + + + 2026 + 5 + 28 + 0 + 3 + + + aheadofprint + + 42204406 + 10.1021/acs.est.6c07081 + + + + + + 42204395 + + 2026 + 05 + 28 + +
+ + 1528-1140 + + + 2026 + May + 28 + + + Annals of surgery + Ann Surg + + Deriving Clavien-Dindo Classification from Administrative Data: Development and External Validation in Hepatobiliary Surgery. + 10.1097/SLA.0000000000007105 + + Routine electronic health records (HER)/administrative data could enable time- and cost-efficient, real-time surveillance and health-economic evaluation of hepatobiliary postoperative complications, yet no validated algorithm currently exists. We aimed to develop and externally validate an interpretable procedure-code-based, internationally portable algorithm classifying 30-day complications by Clavien-Dindo (CDC) grades and to compare its performance to machine learning (ML) approaches. + A retrospective cohort study was conducted across two French tertiary hepatobiliary centers (Cochin for development; Beaujon for validation) from 2021-2023. Gold-standard CDC grades (≤II, III-IV, V) were assigned by an independent hepatobiliary surgeon blinded to algorithmic rules. The algorithm was expert-derived using 311 procedure codes mapped to 168 WHO's International Classification codes (ICHI), applying a temporal rule to ICU-related codes (≥POD4 for CDC-IV). ML comparators included RandomForest, ElasticNet and XGBoost trained using repeated cross-validation with hyperparameter tuning. Primary outcome was agreement with the gold-standard CDC classification; metrics included macro-F1-score, macro-balanced accuracy (MBA), sensitivity/specificity, weighted-kappa, and Ranked Probability Score (RPS) with 2000-bootstrap 95%CIs. + Among 959 liver resections (development: 476; validation: 488), major complications occurred in 18%, with 2.6% mortality. In validation, the expert algorithm achieved macro-F1-score: 0.962 (0.946-0.977), MBA: 0.974 (0.963-0.985), sensitivity: 0.950 (0.901-0.988), specificity 0.971 (0.951-0.985), weighted-κ: 0.928 (0.900-0.962) and RPS 0.016 (0.009-0.025). ML pipelines underperformed in all metrics. Misclassification (3.2%) was mainly due to ICU timing or incomplete coding. + An interpretable therapeutic-act-based algorithm accurately reproduced CDC grading from routine data and outperformed ML approaches. ICHI mapping supports international portability for real-time complication surveillance, quality benchmarking and policy evaluation. + Copyright © 2026 Wolters Kluwer Health, Inc. All rights reserved. + + + + Tzedakis + Stylianos + S + + Service de chirurgie digestive, hépatobiliaire et endocrinienne, AP-HP Centre, Groupe Hospitalier Cochin Port Royal, Paris, France. + + + Université Paris Cité, Inria, Inserm, HeKA, F-75015, Paris, France. + + + + Romengas + Louis + L + + Université Paris Cité, Inria, Inserm, HeKA, F-75015, Paris, France. + + + AP-HP Centre, Hôpital Européen Georges-Pompidou, Service d'Épidémiologie et de Biostatistiques, Université Paris Cité, Paris, France. + + + + Berzan + Diana + D + + Service de chirurgie digestive, hépatobiliaire et endocrinienne, AP-HP Centre, Groupe Hospitalier Cochin Port Royal, Paris, France. + + + + Ronde-Roupie + Charlotte + C + + Service de chirurgie digestive, hépatobiliaire et endocrinienne, AP-HP Centre, Groupe Hospitalier Cochin Port Royal, Paris, France. + + + Université Paris Cité, Inria, Inserm, HeKA, F-75015, Paris, France. + + + + Jeddou + Heithem + H + + Service de chirurgie digestive, hépatobiliaire et transplantation, Hôpital Pontchaillou, Université Rennes 1, Rennes, France. + + + + Dhote + Alix + A + + Service de chirurgie digestive, hépatobiliaire et endocrinienne, AP-HP Centre, Groupe Hospitalier Cochin Port Royal, Paris, France. + + + Université Paris Cité, Inria, Inserm, HeKA, F-75015, Paris, France. + + + + De Ponthaud + Charles + C + + Université Paris Cité, Inria, Inserm, HeKA, F-75015, Paris, France. + + + Service de chirurgie hépato-bilio-pancréatique et transplantation hépatique, Hôpital Beaujon, Université Paris Cité, Clichy, France. + + + + Lazzati + Andrea + A + + Université Paris Cité, Inria, Inserm, HeKA, F-75015, Paris, France. + + + Service de chirurgie digestive, bariatrique et endocrinienne, AP-HP, Hôpital Avicennes, Bobigny, France. + + + + Guilloux + Agathe + A + + Université Paris Cité, Inria, Inserm, HeKA, F-75015, Paris, France. + + + + Dokmak + Safi + S + + Service de chirurgie hépato-bilio-pancréatique et transplantation hépatique, Hôpital Beaujon, Université Paris Cité, Clichy, France. + + + + Lesurtel + Mickaël + M + + Service de chirurgie hépato-bilio-pancréatique et transplantation hépatique, Hôpital Beaujon, Université Paris Cité, Clichy, France. + + + + Fuks + David + D + + Service de chirurgie digestive, Centre Hospitalier Universitaire Vaudois (CHUV), Université de Lausanne, 1005 Lausanne, Suisse. + + + + Katsahian + Sandrine + S + + Université Paris Cité, Inria, Inserm, HeKA, F-75015, Paris, France. + + + AP-HP Centre, Hôpital Européen Georges-Pompidou, Service d'Épidémiologie et de Biostatistiques, Université Paris Cité, Paris, France. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Ann Surg + 0372354 + 0003-4932 + + IM + + Clavien-Dindo classification + PMSI database + administrative data + electronic health records + hepatobiliary surgery + international classification of health interventions + machine-learning + morbidity + mortality + postoperative complication + severe complication + supervised learning + + Declaration of interest statement: The authors have no conflicts of interest to disclose. + + + + + 2025 + 12 + 14 + + + 2026 + 5 + 19 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 6 + 34 + + + 2026 + 5 + 28 + 0 + 2 + + + aheadofprint + + 42204395 + 10.1097/SLA.0000000000007105 + 00000658-990000000-01632 + + + + + + 42204360 + + 2026 + 05 + 27 + +
+ + 1546-1696 + + + 2026 + May + 27 + + + Nature biotechnology + Nat Biotechnol + + Accurate quantification in proteomics with QuantUMS. + 10.1038/s41587-026-03131-2 + + In mass-spectrometry-based proteomics it remains challenging to ensure the accuracy of protein quantities. Here we introduce QuantUMS (quantification using an uncertainty-minimizing solution), a machine learning-based method that dynamically tunes the quantification algorithm to minimize quantitative errors. When applied to data-independent acquisition proteomics, QuantUMS increases accuracy and precision, ameliorates ratio compression bias and enhances differential expression analysis. It further reports an uncertainty measure enabling quality control of individual quantities. + © 2026. The Author(s). + + + + Grossmann + Justus L + JL + 0009-0002-7094-1888 + + Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + + Kistner + Franziska + F + 0009-0009-8080-8643 + + Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + + Sinn + Ludwig R + LR + 0000-0003-4692-0681 + + Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + + Szyrwiel + Lukasz + L + + Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + + Rappsilber + Juri + J + 0000-0001-5999-1310 + + Bioanalytics, Institute of Biotechnology, Technical University of Berlin, Berlin, Germany. + + + Si-M/"Der Simulierte Mensch", Technical University of Berlin and Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + + Demichev + Vadim + V + 0000-0002-2424-9412 + + Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. vadim.demichev@gmail.com. + + + + eng + + + 161L0221 + Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) + + + + + Journal Article + + + 2026 + 05 + 27 + +
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+ + 1559-064X + + + 2026 + May + 27 + + + Journal of exposure science & environmental epidemiology + J Expo Sci Environ Epidemiol + + Incorporating geospatial environmental exposure indicators in individual hypertension risk prediction: a multi-stage machine learning pipeline. + 10.1038/s41370-026-00915-1 + + Environmental exposures are known contributors to chronic disease but are rarely incorporated into risk prediction models. + We demonstrate a staged machine learning approach to incorporating geospatially measured neighborhood social and ambient environmental exposure measures into hypertension risk prediction. + We analyzed data from 10,491 adults in the Gulf Long-Term Follow-Up (GuLF) Study. Hypertension was defined by measured blood pressure and medication use. We assessed incremental predictive performance across three stages: Stage 1 (age, sex, race, BMI); Stage 2 (Stage 1 + neighborhood social factors); Stage 3 (Stage 2 + ambient exposures). Variable selection combined Boruta and bootstrapped area under the precision-recall curve (AUPRC). Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGB) were trained and evaluated for discrimination, calibration, and classification. SHAP (Shapley Additive Explanations) was used to interpret variable contributions. + Participants had a mean age of 43.6 years with a standard deviation of 13.02 years; 78.4% were male, 52.3% were non-Hispanic White, and 35.5% had hypertension. Model-selected environmental predictors included neighborhood disadvantage, community resilience, social vulnerability (Stage 2), vegetation, PM₂.₅, NO₂, and formaldehyde (Stage 3). AUC and AUPRC showed minimal change across stages; in the XGB model, sensitivity was 0.775 (Stage 1), increased to 0.797 (Stage 2), and was 0.784 (Stage 3), with a corresponding precision trade-off (0.525→0.517→0.524). SHAP identified vegetation, social vulnerability, area deprivation, PM₂.₅, formaldehyde, and community resilience scores as leading environmental contributors. + Environmentalexposures have been linked to an increased risk of hypertension but have rarely been incorporated into risk prediction models. Leveraging data from a large prospective cohort, we developed an interpretable machine learning pipeline that screens and incorporates relevant geospatial socioeconomic and environmental exposures to individual-level risk prediction models. While adding these selected exposures provided modest improvements in model sensitivity, improved sensitivity identified additional hypertension cases that would have been missed. Even small gains in sensitivity can translate to earlier identification of additional at-risk individuals who might benefit from interventions, which can lead to public health improvements. + © 2026. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. + + + + Hu + Yi-Han + YH + + Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Baltimore, MD, USA. + + + + Jamal + Harris + H + + Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Baltimore, MD, USA. + + + Medical Research Scholars Program, National Institute of Health, Bethesda, MD, USA. + + + + Deng + Xinlei + X + + Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA. + + + + Lawrence + Kaitlyn G + KG + + Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA. + + + + Werder + Emily J + EJ + + Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA. + + + + Launer + Lenore J + LJ + + Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Baltimore, MD, USA. launerl@nia.nih.gov. + + + + Sandler + Dale P + DP + + Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA. Dale.Sandler@nih.gov. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
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+ + England + Nature + 0410462 + 0028-0836 + + IM + + Biotechnology + Databases + Machine learning + Proteomics + + + + + + 2026 + 5 + 28 + 0 + 29 + + + 2026 + 5 + 28 + 0 + 29 + + + 2026 + 5 + 27 + 23 + 35 + + + aheadofprint + + 42204326 + 10.1038/d41586-026-01686-3 + 10.1038/d41586-026-01686-3 + + + + Candido, S. et al. Preprint (2026). + + + Yeo, J. et al. Preprint at bioRxiv https://doi.org/10.1101/2025.04.23.650224 (2025). + + + + + + + 42204299 + + 2026 + 05 + 27 + +
+ + 1745-7254 + + + 2026 + May + 27 + + + Acta pharmacologica Sinica + Acta Pharmacol Sin + + The solute carrier transporter SLCO5A1 as a novel therapeutic target for the treatment of myocardial infarction-induced heart failure. + 10.1038/s41401-026-01827-4 + + Heart failure (HF) following myocardial infarction (MI) remains a major threat to health worldwide. While transcriptomics has revealed numerous genes whose expression is altered in HF, distinguishing therapeutic targets remains challenging. In this study, we aimed to identify novel therapeutic targets for HF and explore potential pharmacological interventions. We integrated human HF datasets with weighted gene coexpression network analysis (WGCNA) and machine learning (LASSO/SVM-RFE) to screen for candidate genes and applied Mendelian randomization (MR) to assess causality. SLCO5A1 emerged as a prioritized candidate, as it showed a genetically supported protective association with HF and was consistently downregulated in the ischemic failing myocardium. In mice, cardiomyocyte-targeted SLCO5A1 overexpression attenuated post-MI systolic dysfunction and pathological remodeling. Using drug-gene signature mining followed by biophysical and cellular validation, we identified 3-iodothyronamine (T1AM) as a small molecule that directly binds to SLCO5A1 and increases SLCO5A1 protein levels. Pharmacological administration of T1AM increased post-MI survival, improved cardiac function and reduced fibrosis; these benefits were markedly weakened by cardiomyocyte-specific SLCO5A1 knockdown, supporting a functional requirement for SLCO5A1. Mechanistically, SLCO5A1 reduced cardiomyocyte transforming growth factor beta 1 (TGF‑β1) secretion, thereby limiting fibroblast Smad3 activation and myofibroblast marker expression in conditioned-medium assays. In conclusion, our findings demonstrate that SLCO5A1 is a cardioprotective regulator of cardiomyocyte-fibroblast communication in post-MI HF and support a pharmacological increase in SLCO5A1 levels as a potential therapeutic strategy. SLCO5A1 serves as a novel therapeutic target for heart failure. Enhancing SLCO5A1 expression protects against MI-induced HF by inhibiting TGF-β1/Smad3-mediated cardiomyocyte-fibroblast crosstalk. + © 2026. The Author(s). + + + + Liu + Ke-Yu + KY + + Department of Anesthesiology, The Second Affiliated Hospital of Guangdong Medical University, Guangdong Medical University, Zhanjiang, 524003, China. + + + + Gong + Ke + K + + School of Obstetrics and Pediatrics, Guangdong Medical University, Zhanjiang, 524023, China. + + + + Fang + Fang + F + + Translational Medicine Center, The Second Affiliated Hospital of Guangdong Medical University, Guangdong Medical University, Zhanjiang, 524003, China. + + + + Chen + Qing-Hua + QH + + Translational Medicine Center, The Second Affiliated Hospital of Guangdong Medical University, Guangdong Medical University, Zhanjiang, 524003, China. + + + Medical Interdisciplinary Science Research Center of Western Guangdong, The Second Affiliated Hospital of Guangdong Medical University, Guangdong Medical University, Zhanjiang, 524003, China. + + + Key Laboratory for Research on Organ Interactions in Major Diseases, The Second Affiliated Hospital of Guangdong Medical University, Guangdong Medical University, Zhanjiang, 524003, China. + + + + Xu + Jin-Rong + JR + + Department of Cardiovascular Medicine, The Second Affiliated Hospital of Guangdong Medical University, Guangdong Medical University, Zhanjiang, 524003, China. zjeyxjr@163.com. + + + + Zhang + Liang-Qing + LQ + + Department of Anesthesiology, The Second Affiliated Hospital of Guangdong Medical University, Guangdong Medical University, Zhanjiang, 524003, China. Zhangliangqing@gdmu.edu.cn. + + + + Chen + Wen-Liang + WL + 0000-0001-5141-325X + + Translational Medicine Center, The Second Affiliated Hospital of Guangdong Medical University, Guangdong Medical University, Zhanjiang, 524003, China. Chenwl@gdmu.edu.cn. + + + Medical Interdisciplinary Science Research Center of Western Guangdong, The Second Affiliated Hospital of Guangdong Medical University, Guangdong Medical University, Zhanjiang, 524003, China. Chenwl@gdmu.edu.cn. + + + Key Laboratory for Research on Organ Interactions in Major Diseases, The Second Affiliated Hospital of Guangdong Medical University, Guangdong Medical University, Zhanjiang, 524003, China. Chenwl@gdmu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + United States + Acta Pharmacol Sin + 100956087 + 1671-4083 + + IM + + 3‑iodothyronamine + SLCO5A1 + TGF beta 1 + heart failure + myocardial infarction + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 1 + 29 + + + 2026 + 4 + 19 + + + 2026 + 5 + 28 + 0 + 29 + + + 2026 + 5 + 28 + 0 + 29 + + + 2026 + 5 + 27 + 23 + 35 + + + aheadofprint + + 42204299 + 10.1038/s41401-026-01827-4 + 10.1038/s41401-026-01827-4 + + + + 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. + + 35150240 + 10.1093/cvr/cvac013 + + + + Li H, Wang X, Zhang Y, Yang Y, Zhang JZ, Zhou B. SLC39A13 regulates heart function via mitochondrial iron homeostasis maintenance. Circ Res. 2025;137:e144–e56. + + 40726388 + 10.1161/CIRCRESAHA.125.326201 + + + + Yao D, Bao L, Wang S, Tan M, Xu Y, Wu T, et al. 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Processing of transforming growth factor beta 1 precursor by human furin convertase. J Biol Chem. 1995;270:10618–24. + + 7737999 + 10.1074/jbc.270.18.10618 + + + + + + + + 42204286 + + 2026 + 05 + 27 + +
+ + 2398-6352 + + + 2026 + May + 27 + + + NPJ digital medicine + NPJ Digit Med + + Deep chemical structure graph learning deciphers the lipotoxicity code of hypertriglyceridemic pancreatitis. + 10.1038/s41746-026-02792-2 + + The discrepancy between serum triglyceride levels and the clinical severity of hyperlipidemic acute pancreatitis (HLAP) complicates risk stratification. Traditional lipidomics, which primarily rely on linear abundance, often fail to distinguish the HLAP-specific lipidome from the metabolic background of hypertriglyceridemia (HTG). To overcome this limitation, DeepLipiDecipher was developed as a knowledge-guided graph neural network framework that integrates lipid chemical structures with metabolic topology to identify latent lipotoxic features. In a retrospective cohort of 433 subjects, DeepLipiDecipher demonstrated robust classification performance (AUC = 0.810), effectively distinguishing the HLAP phenotype and outperforming conventional machine learning models. Interpretability analysis revealed that HLAP susceptibility correlated with a distinct structural lipid profile, marked by the synergistic enrichment of polyunsaturated and ether-linked phospholipids, rather than total lipid mass. Moreover, computational causal inference implicated a pathogenic mechanism wherein SMPD3-mediated ceramide accumulation induced basal cytotoxicity, and PTGS2 hyperactivation promoted the peroxidation of these vulnerable lipids, triggering systemic inflammation. These results highlight the value of incorporating network topology into lipidomic analysis and suggest the network-inferred SMPD3-Ceramide-PTGS2 immunometabolic axis as a potential therapeutic target for preventing the progression from metabolic dysfunction to acute organ injury. + © 2026. The Author(s). + + + + Huang + Anliang + A + + Clinical Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China. + + + + Yuan + Qihang + Q + + Clinical Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China. + + + + Chen + Junhong + J + + Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China. + + + + Wang + Lei + L + + Department of Vascular Surgery, First Affiliated Hospital of Dalian Medical University, Dalian, China. + + + + Yu + Yanlong + Y + + Department of Hepatobiliary Surgery, Chifeng Municipal Hospital, Chifeng, Inner Mongolia, China. + + + + Zhang + Yunshu + Y + + Clinical Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China. + + + + Ma + Shurong + S + + Clinical Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China. + + + + Liu + Kai + K + + Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China. + + + + Wu + Zeming + Z + + iPhenome Biotechnology Inc. Dalian (Yun Pu Kang), Dalian, Liaoning, China. + + + + Cao + Shengji + S + + Department of Clinical Laboratory Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, China. jicharles@126.com. + + + + Liu + Tianyi + T + + Department of Otorhinolaryngology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China. rm003860@whu.edu.cn. + + + + Shang + Dong + D + + Clinical Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China. shangdongdalian@163.com. + + + + Yin + Peiyuan + P + + Clinical Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China. yinperry@126.com. + + + + eng + + + 82374248 + National Natural Science Foundation of China + + + + 2025JH2/101800026 + Science and Technology Plan of Liaoning Province (Key Research and Development Program Project) + + + + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + NPJ Digit Med + 101731738 + 2398-6352 + + Competing interests: Z.W. is co-founder of iPhenome Biotechnology Inc. Dalian (Yun Pu Kang). Other authors have declared that no competing interests exist. + + + + + 2025 + 11 + 12 + + + 2026 + 5 + 14 + + + 2026 + 5 + 28 + 0 + 29 + + + 2026 + 5 + 28 + 0 + 29 + + + 2026 + 5 + 27 + 23 + 34 + + + aheadofprint + + 42204286 + 10.1038/s41746-026-02792-2 + 10.1038/s41746-026-02792-2 + + + + + + 42204283 + + 2026 + 05 + 27 + +
+ + 2045-2322 + + + 2026 + May + 27 + + + Scientific reports + Sci Rep + + Association of triglyceride-cholesterol-body weight index with in-hospital mortality in critically ill patients with heart failure. + 10.1038/s41598-026-54399-y + + Malnutrition and metabolic abnormalities are common in patients with heart failure (HF) requiring intensive care unit (ICU) admission and are associated with poor outcomes. This study evaluated the association between triglyceride-cholesterol-body weight index (TCBI) and in-hospital mortality in critically ill patients with HF and developed a machine learning-based model to assess its predictive value. This multicenter retrospective study included 1,537 HF patients in a derivation cohort and 3,537 patients in an external validation cohort. Kaplan-Meier, Cox regression, and restricted cubic spline analyses were used to assess the association and nonlinearity. An XGBoost model integrating TCBI and clinical variables was developed for risk prediction. Among 1,537 patients in the derivation cohort, 295 (19.2%) died during hospitalization. Mortality decreased across increasing TCBI tertiles (22.6% vs. 18.4% vs. 16.6%, P = 0.043). Patients in the highest tertile had lower mortality risk than those in the lowest (HR 0.58, 95% CI 0.39-0.85). A nonlinear inverse association was observed (P for nonlinearity = 0.001) and confirmed in the external cohort (HR 0.75, 95% CI 0.59-0.94). The model achieved area under the curve values of 0.791, 0.744, and 0.709 in the training, internal validation, and external validation cohorts, outperforming SAPS II and SOFA. Decision curve analysis indicated superior net clinical benefit across a range of threshold probabilities. Lower TCBI was independently associated with higher in-hospital mortality in critically ill patients with HF. Incorporating TCBI improved risk prediction and may aid early risk stratification in this high-acuity population. + © 2026. The Author(s). + + + + Xie + Yachen + Y + + Department of Pain Management, The People's Hospital of Yubei District of Chongqing, Chongqing, China. + + + + Tong + Ke + K + + Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China. 1020305558@qq.com. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + + Heart failure + In-hospital mortality + Machine learning prediction + Nutritional and metabolic status + Triglyceride–cholesterol–body weight index + + Declarations. Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: The data used in this study were obtained from the publicly available MIMIC-IV database and eICU Collaborative Research Database. Both databases contain de-identified patient information and have received ethical approval for research use. As this study analyzed anonymized retrospective data, the requirement for informed consent was waived. All procedures were conducted in accordance with relevant guidelines and the Declaration of Helsinki. + + + + + 2026 + 3 + 27 + + + 2026 + 5 + 19 + + + 2026 + 5 + 28 + 0 + 30 + + + 2026 + 5 + 28 + 0 + 30 + + + 2026 + 5 + 27 + 23 + 34 + + + aheadofprint + + 42204283 + 10.1038/s41598-026-54399-y + 10.1038/s41598-026-54399-y + + + + + + 42204253 + + 2026 + 05 + 27 + +
+ + 2398-6352 + + + 2026 + May + 27 + + + NPJ digital medicine + NPJ Digit Med + + Reassessing negative 24 h pH impedance tests for hidden gastroesophageal reflux disease using multi feature anomaly detection. + 10.1038/s41746-026-02796-y + + Gastroesophageal reflux disease (GERD) diagnosis traditionally relies on acid exposure time (AET) obtained from 24-h multichannel intraluminal impedance-pH (MII-pH) monitoring, the gold standard for GERD diagnosis. However, a negative result (AET < 4%) does not always exclude GERD, as the limited 24-h monitoring window may fail to capture reflux events in patients with intermittent or low-frequency reflux. To address this limitation, we proposed a complementary machine learning-based framework targeting exclusively patients with negative MII-pH results (AET < 4%) to identify potential false-negative cases within this cohort, by integrating statistical and waveform-derived features from pH signals to enhance anomaly detection. Using one-class support vector machine and support vector data description models trained on real-world MII-pH datasets, the framework achieved an + + + F + + + 3 + + + score of approximately 0.9 and identified potential anomalies undetected by the conventional AET criteria. Explainable AI techniques using Shapley additive explanations showed that features such as kurtosis and peak-to-peak amplitude contributed significantly to the identification of subtle reflux patterns within this cohort. These anomalies may indicate additional candidates for clinical reassessment within the AET-negative cohort. This complementary approach, operating downstream of the conventional MII-pH diagnostic system, could help identify potential false-negative cases among patients with negative MII-pH results, potentially assisting in their proper clinical management. + © 2026. The Author(s). + + + + Lee + Songho + S + + Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. + + + + Lee + Junhyeong + J + + Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. + + + + Park + Donggeun + D + + Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. + + + + Lee + Sang Kil + SK + + Department of Internal Medicine, Yonsei Institute of Gastroenterology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea. + + + + Cho + Jae Hee + JH + + Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, 211, Eonju-ro Gangnam-gu, Seoul, Republic of Korea. + + + + Lee + Kyoung G + KG + + Center for NanoBio Development, National NanoFab Center, Daejeon, Republic of Korea. kglee@nnfc.re.kr. + + + + Kim + Hee Man + HM + + Division of Gastroenterology, Department of Internal Medicine, Severance Hospital, and Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea. loverkorea2009@gmail.com. + + + + Ryu + Seunghwa + S + + Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. ryush@kaist.ac.kr. + + + KAIST InnoCORE PRISM-AI Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. ryush@kaist.ac.kr. + + + + eng + + + RS-2024-00438316 + Ministry of Science and ICT, South Korea + + + + RS-2024-00438316 + Ministry of Science and ICT, South Korea + + + + RS-2024-00438316 + Ministry of Science and ICT, South Korea + + + + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + NPJ Digit Med + 101731738 + 2398-6352 + + Competing interests: The authors declare no competing interests. + + + + + 2025 + 9 + 16 + + + 2026 + 5 + 15 + + + 2026 + 5 + 28 + 0 + 30 + + + 2026 + 5 + 28 + 0 + 30 + + + 2026 + 5 + 27 + 23 + 33 + + + aheadofprint + + 42204253 + 10.1038/s41746-026-02796-y + 10.1038/s41746-026-02796-y + + + + \ No newline at end of file