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Record W3094545640 · doi:10.1126/scitranslmed.aba0624

Aryl hydrocarbon receptor ligand production by the gut microbiota is decreased in celiac disease leading to intestinal inflammation

2020· article· en· W3094545640 on OpenAlexafffund
Bruno Lamas, Leticia Hernández-Galán, Heather J. Galipeau, Marco Constante, Alexandra Clarizio, Jennifer Jury, Natália Martins Breyner, Alberto Caminero, Gaston Rueda, Christina L. Hayes, Justin L. McCarville, Miriam Bermudez Brito, Julien Planchais, Nathalie Rolhion, Joseph A. Murray, Philippe Langella, Linda M. P. Loonen, Jerry M. Wells, Přemysl Berčík, Harry Sokol, Elena F. Verdú

Bibliographic record

VenueScience Translational Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicMicroscopic Colitis
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersCanadian Institutes of Health ResearchUniversity of California, DavisCrohn's and Colitis CanadaAgence Nationale de la RechercheJoint Programming Initiative A healthy diet for a healthy lifeMcMaster University
KeywordsAryl hydrocarbon receptorGut floraLactobacillus reuteriImmunologyIntestinal permeabilityNodBiologyEndocrinologyLactobacillusBiochemistryTranscription factorGeneDiabetes mellitus

Abstract

fetched live from OpenAlex

, or pharmacological stimulation using 6-formylindolo (3,2-b) carbazole (Ficz) decreased immunopathology in NOD/DQ8 mice exposed to gluten. We then determined AhR ligand production by the fecal microbiota and AhR activation in patients with active celiac disease compared to nonceliac control individuals. Patients with active celiac disease demonstrated reduced AhR ligand production and lower intestinal AhR pathway activation. These results highlight gut microbiota-dependent modulation of the AhR pathway in celiac disease and suggest a new therapeutic strategy for treating this disorder.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.293
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations169
Published2020
Admission routes2
Has abstractyes

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