The analgesic action of topical diclofenac may be mediated through peripheral NMDA receptor antagonism
Bibliographic record
Abstract
The analgesic mechanism underlying the efficacy of topical diclofenac in the treatment of musculoskeletal pain is incompletely understood. The present study investigated whether intramuscular injection of diclofenac (0.1mg/ml, approximately 340microM) could attenuate jaw-closer muscle nociceptor discharge and mechanical sensitization induced by activation of peripheral 5-hydroxytryptamine (serotonin) or excitatory amino acid receptors in anesthetized Sprague-Dawley rats. Diclofenac inhibited nociceptor discharge evoked by NMDA, but had no effect on nociceptor discharge evoked by 5-hydroxytryptamine or AMPA. Subsequent experiments revealed that diclofenac-mediated inhibition of NMDA-evoked nociceptor discharge was competitive. Intramuscular injection of 5-hydroxytryptamine, NMDA and AMPA also decreased nociceptor mechanical threshold, however, only the mechanical sensitization produced by NMDA was reversed by diclofenac. Co-administration of the proinflammatory prostaglandin PGE(2) did not alter the ability of diclofenac to significantly attenuate NMDA-evoked nociceptor discharge or NMDA-induced mechanical sensitization. Intramuscular injection of either diclofenac or the competitive NMDA receptor antagonist DL-2-amino-5-phosphonovalerate (50mM) alone could elevate nociceptor mechanical threshold for a 30min period post-injection. The present study indicates that in vivo, diclofenac can exert a selective, competitive inhibition of peripheral NMDA receptors at muscle concentrations achievable after topical administration of diclofenac containing preparations. This property may contribute to the analgesic effect of topical diclofenac when used for muscle pain.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".