Nitric oxide and potassium channels but not opioid and cannabinoid receptors mediate tramadol-induced peripheral antinociception in rat model of paw pressure withdrawal
Bibliographic record
Abstract
Tramadol, an analgesic classified as an "atypical opioid", exhibits both opioid and non-opioid mechanisms of action. This study aimed to explore these mechanisms, specifically the opioid-, cannabinoid-, nitric oxide-, and potassium channel-based mechanisms, which contribute to the peripheral antinociception effect of tramadol, in an experimental rat model. The nociceptive threshold was determined using paw pressure withdrawal. To examine the mechanisms of action, several substances were administered intraplantarly: naloxone, a non-selective opioid antagonist (50 µg/paw); AM251 (80 µg/paw) and AM630 (100 µg/paw) as the selective antagonists for types 1 and 2 cannabinoid receptors, respectively; nitric oxide synthase inhibitors L-NOArg, L-NIO, L-NPA, and L-NIL (24 µg/paw); and the enzyme inhibitors of guanylatocyclase and phosphodiesterase of cGMP, ODQ, and zaprinast. Additionally, potassium channel blockers glibenclamide, tetraethylammonium, dequalinium, and paxillin were used. The results showed that opioid and cannabinoid receptor antagonists did not reverse tramadol's effects. L-NOarg, L-NIO, and L-NPA partially reversed antinociception, while ODQ completely reversed, and zaprinast enhanced tramadol's antinociception effect. Notably, glibenclamide blocked tramadol's antinociception in a dose-dependent manner. These findings suggest that tramadol's peripheral antinociception effect is likely mediated by the nitrergic pathway and sensitive ATP potassium channels, rather than the opioid and cannabinoid pathways.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".