Effect of nitric oxide–cyclic GMP–K<sup>+</sup> channel pathway blockers, naloxone and metformin, on the antinociception induced by the diuretic pamabrom
Bibliographic record
Abstract
Pamabrom is a diuretic that is effective in treating premenstrual syndrome and primary dysmenorrhea. The aim of this study was to examine the effect of metformin and modulators of the opioid receptor–nitric oxide (NO)–cyclic guanosine monophosphate (cGMP)–K+ channel pathway on the local antinociception induced by pamabrom. The rat paw 1% formalin test was used to assess the effects. Rats were treated with local administration of pamabrom (200–800 µg/paw) or indomethacin (200–800 µg/paw). The antinociception of pamabrom or indomethacin was evaluated with and without the local pretreatment of the blockers. Local administration of pamabrom and indomethacin produced dose-dependent antinociception during the second phase of the test. Local pretreatment of the paws with naloxone (50 µg/paw), l-nitro-arginine methyl ester (10–100 µg/paw), or 1 H-(1,2,4)-oxadiazolo[4,2- a]quinoxalin-1-one (10–100 µg/paw) reverted the antinociception induced by local pamabrom, but not of indomethacin. Similarly, the K+ channel blockers glibenclamide, glipizide, 4-aminopyridine, tetraethylammonium, charybdotoxin, or apamin reverted the pamabrom-induced antinociception, but not of indomethacin. Metformin significantly blocked the antinociception of pamabrom and indomethacin. Our data suggest that pamabrom could activate the opioid receptor–NO–cGMP–K+ channel pathway to produce its peripheral antinociception in the formalin test. Likewise, a biguanide-dependent mechanism could be activated by pamabrom and indomethacin to generate antinociception.
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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) | 0.002 | 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".