Changes in signalling from faecal neuroactive metabolites following dietary modulation of IBS pain
Bibliographic record
Abstract
OBJECTIVE: Dietary therapies for irritable bowel syndrome (IBS) have received increasing interest but predicting which patients will benefit remains a challenge due to a lack of mechanistic insight. We recently found evidence of a role for the microbiota in dietary modulation of pain signalling in a humanised mouse model of IBS. This randomised cross-over study aimed to test the hypothesis that pain relief following reduced consumption of fermentable carbohydrates is the result of changes in luminal neuroactive metabolites. DESIGN: IBS (Rome IV) participants underwent four trial periods: two non-intervention periods, followed by a diet low (LFD) and high in fermentable carbohydrates for 3 weeks each. At the end of each period, participants completed questionnaires and provided stool. The effects of faecal supernatants (FS) collected before (IBS FS) and after a LFD (LFD FS) on nociceptive afferent neurons were assessed in mice using patch-clamp and ex vivo colonic afferent nerve recording techniques. RESULTS: Total IBS symptom severity score and abdominal pain were reduced by the LFD (N=25; p<0.01). Excitability of neurons was increased in response to IBS FS, but this effect was reduced (p<0.01) with LFD FS from pain-responders. IBS FS from pain-responders increased mechanosensitivity of nociceptive afferent nerve axons (p<0.001), an effect lost following LFD FS administration (p=NS) or when IBS FS was administered in the presence of antagonists of histamine receptors or protease inhibitors. CONCLUSIONS: In a subset of IBS patients with improvement in abdominal pain following a LFD, there is a decrease in pronociceptive signalling from FS, suggesting that changes in luminal mediators may contribute to symptom response.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".