Attenuation of colitis-induced visceral hypersensitivity and pain by silencing TRPV1-expressing fibers in rat colon
Bibliographic record
Abstract
Abstract Background and Aims Abdominal pain in patients with inflammatory bowel disease (IBD) is common and debilitating. In our study, we aim to utilize transient receptor potential vanilloid 1 (TRPV1) channels, large-pore cation channels expressed on nociceptors, as a drug delivery system to selectively inhibit visceral nociceptors and thus visceral pain in a rodent model of IBD. Methods We induced colitis in rats using intrarectal dinitrobenzene sulfonic acid. Visceral hypersensitivity, spontaneous pain, and responsiveness of the hind paws to noxious heat stimuli were examined before and after the intrarectal application of sodium channel blocker QX-314 alone or together with TRPV1 channel activators or blockers. Results Intrarectal co-application of QX-314 with TRPV1 channel activator capsaicin significantly inhibited colitis-induced gut hypersensitivity. Furthermore, in the model of colitis, but not in naïve rats, QX-314 alone was sufficient to reverse gut hypersensitivity. The blockade of TRPV1 channels prevented this effect of QX-314. Finally, applying QX-314 alone to the inflamed gut inhibited colitis-induced ongoing pain. Conclusions Selective silencing of nociceptors by QX-314 entering via exogenously or endogenously activated TRPV1 channels diminish IBD-induced gut hypersensitivity. These results yet again confirm the central role of TRPV1-expressing nociceptive neurons in IBD pain. The lack of QX-314 effect on naïve rats suggests its selective analgesic effect in IBD pain. Moreover, our results demonstrating the effect of QX-314 alone imply the role of a tonically active TRPV1 channel in the pathophysiology of IBD pain. This approach provides proof-of-concept for using charged activity blockers for selective and effective blockade of 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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".