Participation of the opioid receptor – nitric oxide – cGMP – K<sup>+</sup> channel pathway in the peripheral antinociceptive effect of nalbuphine and buprenorphine in rats
Bibliographic record
Abstract
The aim of this study was to examine if the peripheral antinociceptive effects of the opioid agonist/antagonist nalbuphine and buprenorphine involve the sequential participation of nitric oxide (NO) and cyclic guanosine monophosphate (cGMP) synthesis followed by K+ channel opening in the formalin test. Wistar rats (180–220 g) were injected in the dorsal surface of the right hind paw with formalin (1%). Rats received a subcutaneous (s.c.) injection into the dorsal surface of the paw of vehicles or increasing doses of nalbuphine (50–200 μg/paw) or buprenorphine (1–5 μg/paw) 20 min before formalin injection into the paw. Nalbuphine antinociception was reversed by the s.c. injection into the paw of the inhibitor of NO synthesis (NG-nitro-l-arginine methyl ester (L-NAME)), by the inhibitor of guanylyl cyclase (1H-[1,2,4]oxadiazolo[4,3-a]quinoxalin-1-one (ODQ)), by the Kir6.1–2, ATP-sensitive K+ channel inhibitors (glibenclamide and glipizide), by the KCa2.1–3, small conductance Ca2+-activated K+ channel blocker (apamin), by the KCa1.1, large conductance Ca2+-activated K+ channel blocker (charybdotoxin), and by the KV, voltage-dependent K+ channel inhibitors (4-aminopyridine (4-AP) and tetraethylammonium chloride (TEA)). The antinociceptive effect produced by buprenorphine was blocked by the s.c. injection of 4-AP and TEA but not by L-NAME, ODQ, glibenclamide, glipizide, apamin, or charybdotoxin. The present results provide evidence for differences in peripheral mechanisms of action between these opioid drugs.
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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) | 0.000 | 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".