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Record W4411095315 · doi:10.1136/gutjnl-2025-335331

Adherent-invasive <i>Escherichia coli</i> in Crohn’s disease: the 25th anniversary

2025· review· en· W4411095315 on OpenAlexaff
Nicolas Barnich, Janelle C. Arthur, Anthony Buisson, Barry J. Campbell, Franck Carbonnel, Benoît Chassaing, Brian K. Coombes, Jérémy Denizot, Belgin Dogan, Jeremiah J. Faith, Nobuhiko Kamada, Randy Longman, Margarita Martínez-Medina, Claire O’Brien, R. Balfour Sartor, Jean‐Frédéric Colombel, Kenneth W. Simpson

Bibliographic record

VenueGut · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMcMaster University
FundersCollege of Veterinary Medicine, Cornell UniversityLeona M. and Harry B. Helmsley Charitable Trust
KeywordsBiologyEscherichia coliMicrobiomeDiseaseMicrobiologyVirulenceCrohn's diseaseMedical microbiologyBioinformaticsGeneticsPathologyGeneMedicine

Abstract

fetched live from OpenAlex

In 1998, Arlette Darfeuille-Michaud, Christel Neut and Jean-Frederic Colombel discovered a novel pathovar of Escherichia coli , adherent and invasive Escherichia coli (AIEC), in the ileum of patients with Crohn’s disease (CD), that was genetically distinct from diarrheagenic E. coli , could adhere to and invade intestinal epithelial cells and survive in macrophages. The consistent association between AIEC and CD (approximately 30% across the world), their ability to exploit CD-associated genetic traits, and virulence in preclinical colitis models but not healthy hosts spurred global research to elucidate their pathogenicity. Research focused on integrating AIEC with the microbiome, metabolome, metagenome, host response and the impact of diet and antimicrobials has linked the luminal microenvironment and AIEC metabolism to health and disease. This deeper understanding has led to therapeutic trials and precision medicine targeting AIEC-colonised patients. In November 2023, prominent members of the AIEC research community met to present and discuss the many facets of basic, translational and clinical AIEC fields at ‘AIEC: past, present and future’ in NYC. This review is a summary of this international meeting highlighting the history of AIEC, knowledge accumulated over the past 25 years about its pathogenic properties and proposes a standardised approach for screening patients for AIEC.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.745
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.278
Teacher spread0.264 · 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.

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

Citations15
Published2025
Admission routes1
Has abstractyes

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