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Record W2889220502 · doi:10.1113/jp275396

The interplay between microbes and the immune response in inflammatory bowel disease

2018· review· en· W2889220502 on OpenAlexafffund
Ashleigh Goethel, Kenneth Croitoru, Dana J. Philpott

Bibliographic record

VenueThe Journal of Physiology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsInflammatory bowel diseaseImmune systemInflammatory responseImmunologyDiseaseInflammatory Bowel DiseasesInflammationMedicineMicrobiologyBiologyPathology

Abstract

fetched live from OpenAlex

The aetiology and pathogenesis of inflammatory bowel disease (IBD) remains unclear but involves a complex interplay between genetic risk, environmental exposures, the immune system and the gut microbiota. Nearly two decades ago, the first susceptibility gene for Crohn's disease, NOD2, was identified within the IBD 1 locus. Since then, over 230 genetic risk loci have been associated with IBD and yet NOD2 remains the strongest association to date. As an intracellular innate immune sensor of bacteria, investigations into host-microbe interactions, involving both innate and adaptive immune responses, have become of particular interest in understanding the pathogenesis of IBD. Advancements in sequencing technology have lead to the groundbreaking characterization of the gut microbiota and its role in health and disease. While an altered microbiome has been described for IBD, whether it is a cause or an effect of the intestinal inflammation has yet to be determined. Moreover, the bidirectional relationship between the gut microbiota and the mucosal immune system adds to the multifaceted complexity of intestinal homeostasis. A better understanding of how host genetics, including NOD2, influence immune-microbe interactions and alter susceptibility to IBD is necessary in order to develop therapeutic and preventative treatments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.904
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.290
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations77
Published2018
Admission routes2
Has abstractyes

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