Intramuscular ketorolac inhibits activation of rat peripheral NMDA receptors
Bibliographic record
Abstract
The nonsteroidal anti-inflammatory drug (NSAID) diclofenac has local anesthetic-like and peripheral N-methyl-d-aspartate (NMDA) receptor antagonist characteristics when administered at higher concentrations to masticatory muscle. It is not known if the ability to inhibit NMDA receptors is unique to diclofenac or shared by other NSAIDs. This study was undertaken to determine whether intramuscular injection of ketorolac or naproxen at concentrations that do not induce local anesthetic-like effects could attenuate jaw-closer muscle nociceptor discharge in anesthetized Sprague-Dawley rats. It was found that ketorolac (5 mM) inhibited hypertonic saline-evoked nociceptor discharge, which suggests that at this concentration, ketorolac has local anesthetic-like properties. A lower concentration of ketorolac (0.5 mM), which did not affect hypertonic saline-evoked discharge, did inhibit nociceptor discharge evoked by NMDA. In contrast, naproxen (5 mM) did not alter hypertonic saline- or NMDA-evoked nociceptor discharge. Subsequent experiments revealed that ketorolac (0.5 mM) had no effect on nociceptor discharge evoked by αβ-methylene ATP, 5-hydroxytryptamine, or AMPA. The inhibitory effect of ketorolac did not appear to be related to cyclooxygenase inhibition, because the concentration of prostaglandin E(2) in the masticatory muscles 10 min after injection of either NSAID was not significantly decreased. The present study indicates that in vivo, ketorolac, but not naproxen, can antagonize NMDA-evoked nociceptor discharge similarly to diclofenac. We speculate that structural similarities between ketorolac and diclofenac could account for the ability of these NSAIDs to inhibit NMDA-evoked nociceptor discharge. These properties may partly explain the analgesic effect of intramuscularly injected ketorolac in the clinic.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".