Oxytocin down‐regulates mesenteric afferent sensitivity via the enteric <scp>OTR</scp>/<scp>nNOS</scp>/<scp>NO</scp>/K<sub>ATP</sub> pathway in rat
Bibliographic record
Abstract
BACKGROUND: Oxytocin plays an analgesic role in modulation of nociception and pain. Most work to date has focused on the central mechanisms of oxytocin analgesia, but little is known about whether peripheral mechanisms are also involved. METHODS: The mesenteric afferent discharge was recorded in vitro. The expressions of oxytocin receptor (OTR) and neuronal nitric oxide synthase (nNOS) in longitudinal muscle myenteric plexus (LMMP) was identified by immunofluorescence. KEY RESULTS: Oxytocin per se had no effect on the jejunal mesenteric afferent discharge, however, it markedly attenuated the bradykinin- or distention-evoked increase of mesenteric afferent discharge, which was mimiced by the nitric oxide (NO) donor sodium nitroprusside (SNP). Pretreatment of either NOS inhibitor L-NAME or NPLA largely reduced the inhibitory effect of oxytocin on bradykinin-evoked mesenteric afferent discharge. Such effect, to a large extent, was also alleviated by N-and P-type voltage-dependent calcium channel antagonists or KATP blocker glibenclamide. In addition, immunofluorescence studies show strong colocalization of OTR with nNOS in LMMP of the rat jejunum. CONCLUSIONS & INFERENCES: Oxytocin down-regulates the mesenteric afferent sensitivity through nNOS-NO-KATP pathway. Our findings may reveal a new peripheral mechanism for oxytocin analgesia.
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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".