Role of the NO-cGMP-K<sup>+</sup> channels pathway in the peripheral antinociception induced by α-bisabolol
Bibliographic record
Abstract
The aim of this study was to examine if the peripheral antinociception of α-bisabolol involves the participation of nitric oxide (NO) and cyclic guanosine monophosphate (cGMP) synthesis followed by K+ channel opening in the formalin test. Wistar rats were injected in the dorsal surface of the right hind paw with formalin (1%). Rats received a subcutaneous injection into the dorsal surface of the paw of vehicles or increasing doses of α-bisabolol (100–300 µg/paw). To determine whether the peripheral antinociception induced by α-bisabolol was mediated by either the opioid receptors or the NO-cGMP-K+ channels pathway, the effect of pretreatment (10 min before formalin injection) with the appropriate vehicles, naloxone, naltrexone, NG-nitro-l-arginine methyl ester (L-NAME), 1H-[1,2,4]-oxadiazolo[4,2-a]quinoxalin-1-one (ODQ), glibenclamide, glipizide, apamin, charybdotoxin, tetraethylammonium, or 4-aminopyridine on the antinociceptive effects induced by local peripheral α-bisabolol (300 µg/paw) were assessed. α-Bisabolol produced antinociception during both phases of the formalin test. α-Bisabolol antinociception was blocked by L-NAME, ODQ, and all the K+ channels blockers. The peripheral antinociceptive effect produced by α-bisabolol was not blocked by the opioid receptor inhibitors. α-Bisabolol was able to active the NO-cGMP-K+ channels pathway to produce its antinoceptive effect. The participation of opioid receptors in the peripheral local antinociception induced by α-bisabolol is excluded.
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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".