Ex vivo study of human visceral nociceptors
Bibliographic record
Abstract
Objective The development of effective visceral analgesics free of deleterious gut-specific side effects is a priority. We aimed to develop a reproducible methodology to study visceral nociception in human tissue that could aid future target identification and drug evaluation. Design Electrophysiological (single unit) responses of visceral afferents to mechanical (von Frey hair (VFH) and stretch) and chemical (bradykinin and ATP) stimuli were examined. Thus, serosal afferents (putative nociceptors) were used to investigate the effect of tegaserod, and transient receptor potential channel, vanilloid 4 (TRPV4) modulation on mechanical responses. Results Two distinct afferent fibre populations, serosal (n=23) and muscular (n=21), were distinguished based on their differences in sensitivity to VFH probing and tissue stretch. Serosal units displayed sensitivity to key algesic mediators, bradykinin (6/14 units tested) and ATP (4/10), consistent with a role as polymodal nociceptors, while muscular afferents are largely insensitive to bradykinin (0/11) and ATP (1/10). Serosal nociceptor mechanosensitivity was attenuated by tegaserod (−20.8±6.9%, n=6, p<0.05), a treatment for IBS, or application of HC067047 (−34.9±10.0%, n=7, p<0.05), a TRPV4 antagonist, highlighting the utility of the preparation to examine the mechanistic action of existing drugs or novel analgesics. Repeated application of bradykinin or ATP produced consistent afferent responses following desensitisation to the first application, demonstrating their utility as test stimuli to evaluate analgesic activity. Conclusions Functionally distinct subpopulations of human visceral afferents can be demonstrated and could provide a platform technology to further study nociception in human tissue.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".