Bacterial modulation of visceral sensation: mediators and mechanisms
Bibliographic record
Abstract
The potential role of the intestinal microbiota in modulating visceral pain has received increasing attention during recent years. This has led to the identification of signaling pathways that have been implicated in communication between gut bacteria and peripheral pain pathways. In addition to the well-characterized impact of the microbiota on the immune system, which in turn affects nociceptor excitability, bacteria can modulate visceral afferent pathways by effects on enterocytes, enteroendocrine cells, and the neurons themselves. Proteases produced by bacteria, or by host cells in response to bacteria, can increase or decrease the excitability of nociceptive dorsal root ganglion (DRG) neurons depending on the receptor activated. Short-chain fatty acids generated by colonic bacteria are involved in gut-brain communication, and intracolonic short-chain fatty acids have pronociceptive effects in rodents but may be antinociceptive in humans. Gut bacteria modulate the synthesis and release of enteroendocrine cell mediators, including serotonin and glucagon-like peptide-1, which activate extrinsic afferent neurons. Deciphering the complex interactions between visceral afferent neurons and the gut microbiota may lead to the development of improved probiotic therapies for visceral pain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".