Adherent-invasive <i>Escherichia coli</i> in Crohn’s disease: the 25th anniversary
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".