Contribution of spinal 5-HT<sub>5A</sub> receptors to the antinociceptive effects of systemically administered cannabinoid agonist WIN 55,212-2 and morphine
Bibliographic record
Abstract
The antinociceptive effects of cannabinoids and opioids have been known for centuries. Serotonin and its receptors are also known to play important roles in nociception. However, the contribution of spinal 5-HT5A receptors in antinociceptive effects of cannabinoids and opioids has not been studied. We conducted this study to clarify spinal mechanisms of the actions of the antinociceptive effects of cannabinoids and opioids. Hot plate and tail flick tests were used to assess the antinociceptive activity in Balb/c mice. WIN 55,212-2, a nonselective CB1 and CB2 agonist, and morphine exerted significant antinociceptive effects at 1, 3, and 10 mg/kg doses administered intraperitoneally in both hot plate and tail flick tests. The selective 5-HT5A receptor antagonist SB-699551 (10 nmol/mouse) was administered intrathecally 10 min before the agonists. SB-699551 significantly reduced the antinociceptive effect of both WIN 55,212-2 and morphine. In the rotarod test, WIN 55,212-2 disrupted the motor coordination at a dose of 10 mg/kg, while morphine did not affect this function at any dose. Our findings show that spinal 5-HT5A receptors are involved in the antinociceptive effects of WIN 55,212-2 and morphine.
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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) | 0.001 | 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".