Local immune response to food antigens drives meal-induced abdominal pain
Bibliographic record
Abstract
Up to 20% of people worldwide develop gastrointestinal symptoms following a meal1, leading to decreased quality of life, substantial morbidity and high medical costs. Although the interest of both the scientific and lay communities in this issue has increased markedly in recent years, with the worldwide introduction of gluten-free and other diets, the underlying mechanisms of food-induced abdominal complaints remain largely unknown. Here we show that a bacterial infection and bacterial toxins can trigger an immune response that leads to the production of dietary-antigen-specific IgE antibodies in mice, which are limited to the intestine. Following subsequent oral ingestion of the respective dietary antigen, an IgE- and mast-cell-dependent mechanism induced increased visceral pain. This aberrant pain signalling resulted from histamine receptor H1-mediated sensitization of visceral afferents. Moreover, injection of food antigens (gluten, wheat, soy and milk) into the rectosigmoid mucosa of patients with irritable bowel syndrome induced local oedema and mast cell activation. Our results identify and characterize a peripheral mechanism that underlies food-induced abdominal pain, thereby creating new possibilities for the treatment of irritable bowel syndrome and related abdominal pain disorders. In mice, oral tolerance to food antigens can break down after enteric infection, and this leads to food-induced pain resembling irritable bowel syndrome in humans.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".