Adding STORMS checklist

[cmungall]

No description provided.

[caufieldjh]

Running:

ontogpt -vvv pubmed-annotate -t storms --get-pmc --model gpt-4o --limit 1 --max-text-length 100000 -o storms_test.yaml "35173707"

yields:

---
input_text: |-
  Title: Gut Microbiota Composition Is Related to AD Pathology.
  Keywords: Alzheimer’s disease; MRI; P-tau; amyloid beta; gut microbiota; microbiome
  PMID: 35173707
  PMCID: PMC8843078
  ...
full input text here
...
raw_completion_output: |-
  abstract_structured_or_unstructured: Several studies have reported alterations in gut microbiota composition of Alzheimer's disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI), and subjective cognitive decline (SCD). We included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia, 21 with MCI, and 116 with SCD. Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores. Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for demographic factors. Machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Higher abundance of certain SCFA-producing microbes was associated with lower odds of positive amyloid and p-tau status. This study indicates that gut microbiota composition is associated with amyloid and p-tau status in AD patients, providing evidence that lower abundance of SCFA-producing microbes is associated with higher odds of AD pathology. Future studies could further delineate the role of gut microbiota in AD pathology and explore therapeutic implications.
  abstract_study_design: This was an observational study using machine learning models to investigate gut microbiota composition and its associations with AD biomarkers.
  abstract_sequencing_methods: Gut microbiome composition was determined using 16S rRNA sequencing.
  abstract_specimens: Fecal samples were collected from patients.
  introduction_background_and_rationale: Alzheimer’s disease (AD) is the most common cause of dementia and is characterized by amyloid beta plaques and hyperphosphorylated tau tangles, along with chronic neuroinflammation. The gut microbiome interacts with the immune system and has been implicated in neuroinflammation underlying AD pathology. Previous studies show conflicting results on specific microbiota alterations in AD. There is a need to assess the relationship between gut microbiota composition and biomarkers
prompt: |+
  From the text below, extract the following entities in the following format:

  abstract_structured_or_unstructured: <Abstract should include information on background, methods, results, and conclusions in structured or unstructured format.>
  abstract_study_design: <State study design in abstract.>
  abstract_sequencing_methods: <State the strategy used for metagenomic classification.>
  abstract_specimens: <Describe body site(s) studied.>
  introduction_background_and_rationale: <Summarize the underlying background, scientific evidence, or theory driving the current hypothesis as well as the study objectives.>
  introduction_hypotheses: <State the pre-specified hypothesis. If the study is exploratory, state any pre-specified study objectives.>
  methods_study_design: <Describe the study design.>
  methods_participants: <State what the population of interest is, and the method by which participants are sampled from that population.>
  methods_geographic_location: <State the geographic region(s) where participants were sampled from.>
  methods_relevant_dates: <State the start and end dates for recruitment, follow-up, and data collection.>
  methods_eligibility_criteria: <List any criteria for inclusion and exclusion of recruited participants.>
  methods_antibiotics_usage: <List what is known about antibiotics usage before or during sample collection.>
  methods_analytic_sample_size: <Explain how the final analytic sample size was calculated, including the number of cases and controls if relevant, and reasons for dropout at each stage of the study.>
  methods_longitudinal_studies: <For longitudinal studies, state how many follow-ups were conducted, describe sample size at follow-up by group or condition, and discuss any loss to follow-up.>
  methods_matching: <For matched studies, give matching criteria.>
  methods_ethics: <State the name of the institutional review board that approved the study and protocols, protocol number and date of approval, and procedures for obtaining informed consent from participants.>
  methods_laboratory_methods: <State the laboratory/center where laboratory work was done.>
  methods_specimen_collection: <State the body site(s) sampled from and how specimens were collected.>
  methods_shipping: <Describe how samples were stored and shipped to the laboratory.>
  methods_storage: <Describe how the laboratory stored samples, including time between collection and storage and any preservation buffers or refrigeration used.>
  methods_dna_extraction: <Provide DNA extraction method, including kit and version if relevant.>
  methods_human_dna_sequence_depletion_or_microbial_dna_enrichment: <Describe whether human DNA sequence depletion or enrichment of microbial or viral DNA was performed.>
  methods_primer_selection: <Provide primer selection and DNA amplification methods as well as variable region sequenced (if applicable).>
  methods_positive_controls: <Describe any positive controls (mock communities) if used.>
  methods_negative_controls: <Describe any negative controls if used.>
  methods_contaminant_mitigation_and_identification: <Provide any laboratory or computational methods used to control for or identify microbiome contamination from the environment, reagents, or laboratory.>
  methods_replication: <Describe any biological or technical replicates included in the sequencing, including which steps were replicated between them.>
  methods_sequencing_strategy: <Major divisions of strategy, such as shotgun or amplicon sequencing.>
  methods_sequencing_methods: <State whether experimental quantification was used (QMP/cell count based, spike-in based) or whether relative abundance methods were applied.>
  methods_batch_effects: <Detail any blocking or randomization used in study design to avoid confounding of batches with exposures or outcomes. Discuss any likely sources of batch effects, if known.>
  methods_metatranscriptomics: <Detail whether any mRNA enrichment was performed and whether/how retrotranscription was performed prior to sequencing. Provide size range of isolated transcripts. Describe whether the sequencing library was stranded or not. Provide details on sequencing methods and platforms.>
  methods_metaproteomics: <Detail which protease was used for digestion. Provide details on proteomic methods and platforms (e.g. LC-MS/MS, instrument type, column type, mass range, resolution, scan speed, maximum injection time, isolation window, normalised collision energy, and resolution).>
  methods_metabolomics: <Specify the analytic method used (such as nuclear magnetic resonance spectroscopy or mass spectrometry). For mass spectrometry, detail which fractions were obtained (polar and/or non polar) and how these were analyzed. Provide details on metabolomics methods and platforms (e.g. derivatization, instrument type, injection type, column type and instrument settings).>
  results_descriptive_data: <Give characteristics of study participants (e.g. dietary, demographic, clinical, social) and information on exposures and potential confounders.>
  results_microbiome_data: <Report descriptive findings for microbiome analyses with all applicable outcomes and covariates.>
  results_taxonomy: <Identify taxonomy using standardized taxon classifications that are sufficient to uniquely identify taxa.>
  results_differential_abundance: <Report results of differential abundance analysis by the variable of interest and (if applicable) by time, clearly indicating the direction of change and total number of taxa tested.>
  results_other_data_types: <Report other data analyzed--e.g. metabolic function, functional potential, MAG assembly, and RNAseq.>
  results_other_statistical_analysis: <Report any statistical data analysis not covered above.>
  discussion_key_results: <Summarize key results with reference to study objectives.>
  discussion_interpretation: <Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence.>
  discussion_limitations: <Discuss limitations of the study, taking into account sources of potential bias or imprecision.>
  discussion_bias: <Discuss any potential for bias to influence study findings.>
  discussion_generalizability: <Discuss the generalizability (external validity) of the study results.>
  discussion_ongoing_future_work: <Describe potential future research or ongoing research based on the study's findings.>
  other_information_funding: <Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based.>
  other_information_acknowledgements: <Include acknowledgements of those who contributed to the research but did not meet criteria for authorship.>
  other_information_conflicts_of_interest: <Include a conflicts of interest statement.>
  other_information_supplements: <Indicate where supplements may be accessed and what materials they contain.>
  other_information_supplementary_data: <Provide supplementary data files of results with all taxa and all outcome variables analyzed. Indicate the taxonomic level of all taxa.>


  Text:
  Title: Gut Microbiota Composition Is Related to AD Pathology.
  Keywords: Alzheimer’s disease; MRI; P-tau; amyloid beta; gut microbiota; microbiome
  PMID: 35173707
  PMCID: PMC8843078
   ...
full input text here
...

  ===

extracted_object:
  abstract_structured_or_unstructured: Several studies have reported alterations in
    gut microbiota composition of Alzheimer's disease (AD) patients. However, the
    observed differences are not consistent across studies. We aimed to investigate
    associations between gut microbiota composition and AD biomarkers using machine
    learning models in patients with AD dementia, mild cognitive impairment (MCI),
    and subjective cognitive decline (SCD). We included 170 patients from the Amsterdam
    Dementia Cohort, comprising 33 with AD dementia, 21 with MCI, and 116 with SCD.
    Fecal samples were collected and gut microbiome composition was determined using
    16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta
    1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores. Associations
    between gut microbiota composition and dichotomized AD biomarkers were assessed
    with machine learning classification models. The two models with the highest area
    under the curve (AUC) were selected for logistic regression, to assess associations
    between the 20 best predicting microbes and the outcome measures from these machine
    learning models while adjusting for demographic factors. Machine learning prediction
    for amyloid and p-tau from microbiota composition performed best with AUCs of
    0.64 and 0.63. Higher abundance of certain SCFA-producing microbes was associated
    with lower odds of positive amyloid and p-tau status. This study indicates that
    gut microbiota composition is associated with amyloid and p-tau status in AD patients,
    providing evidence that lower abundance of SCFA-producing microbes is associated
    with higher odds of AD pathology. Future studies could further delineate the role
    of gut microbiota in AD pathology and explore therapeutic implications.
  abstract_study_design: This was an observational study using machine learning models
    to investigate gut microbiota composition and its associations with AD biomarkers.
  abstract_sequencing_methods: Gut microbiome composition was determined using 16S
    rRNA sequencing.
  abstract_specimens: Fecal samples were collected from patients.
  introduction_background_and_rationale: Alzheimer’s disease (AD) is the most common
    cause of dementia and is characterized by amyloid beta plaques and hyperphosphorylated
    tau tangles, along with chronic neuroinflammation. The gut microbiome interacts
    with the immune system and has been implicated in neuroinflammation underlying
    AD pathology. Previous studies show conflicting results on specific microbiota
    alterations in AD. There is a need to assess the relationship between gut microbiota
    composition and biomarkers