Citral inhibits the nociception in the rat formalin test: effect of metformin and blockers of opioid receptor and the NO-cGMP-K<sup>+</sup> channel pathway
Bibliographic record
Abstract
The objective of the present study was to scrutinize the effect of nitric oxide (NO), cyclic GMP (cGMP), potassium channel blockers, and metformin on the citral-produced peripheral antinociception. The rat paw 1% formalin test was used to assess nociception and antinociception. Rats were treated with local peripheral administration of citral (10–100 µg/paw). The antinociception of citral (100 µg/paw) was evaluated with and without the local pretreatment of naloxone, NG-L-nitro-arginine methyl ester (L-NAME, a NO synthesis inhibitor), 1H-(1,2,4)-oxadiazolo(4,2-a)quinoxalin-1-one (ODQ, a soluble guanylyl cyclase inhibitor), metformin, opioid receptors antagonists, and K+ channel blockers. Injection of citral in the rat paw significantly decreased the nociceptive effect of formalin administration during the two phases of the test. Local pretreatment of the paws with L-NAME and ODQ did not reduced the citral-induced antinociception. Glipizide or glibenclamide (Kir6.1-2; ATP-sensitive K+ channel blockers), tetraethylammonium or 4-aminopyridine (KV; voltage-gated K+ channel blockers), charybdotoxin (KCa1.1; big conductance calcium-activated K+ channel blocker), apamin (KCa2.1-3; small conductance Ca2+-activated K+ channel antagonist), or metformin, but not the opioid antagonists, reduced the antinociception of citral. Citral produced peripheral antinociception during both phases of the formalin test. These effects were due to the activation of K+ channels and a biguanide-dependent mechanism.
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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".