The nitric oxide–cGMP signaling pathway plays a significant role in tolerance to the analgesic effect of morphine
Bibliographic record
Abstract
Although the phenomenon of opioid tolerance has been widely investigated, neither opioid nor nonopioid mechanisms are completely understood. The aim of the present study was to investigate the role of the nitric oxide (NO)-cyclic guanosine monophosphate (cGMP) pathway in the development of morphine-induced analgesia tolerance. The study was carried out on male Wistar albino rats (weighing 180-210 g; n = 126). To develop morphine tolerance, animals were given morphine (50 mg/kg; s.c.) once daily for 3 days. After the last dose of morphine was injected on day 4, morphine tolerance was evaluated. The analgesic effects of 3-(5'-hydroxymethyl-2'-furyl)-1-benzylindazole (YC-1), BAY 41-2272, S-nitroso-N-acetylpenicillamine (SNAP), N(G)-nitro-L-arginine methyl ester (L-NAME), and morphine were considered at 15 or 30 min intervals (0, 15, 30, 60, 90, and 120 min) by tail-flick and hot-plate analgesia tests (n = 6 in each study group). The results showed that YC-1 and BAY 41-2272, a NO-independent activator of soluble guanylate cyclase (sGC), significantly increased the development and expression of morphine tolerance, and L-NAME, a NO synthase (NOS) inhibitor, significantly decreased the development of morphine tolerance. In conclusion, these data demonstrate that the nitric oxide-cGMP signal pathway plays a pivotal role in developing tolerance to the analgesic effect of morphine.
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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".