Activation of Peripheral NMDA Receptors Contributes to Human Pain and Rat Afferent Discharges Evoked by Injection of Glutamate into the Masseter Muscle
Bibliographic record
Abstract
Peripheral N-methyl-d-aspartate (NMDA) receptors are found in deep tissues and may play a role in deep tissue pain. Injection of the endogenous NMDA receptor agonist glutamate into the masseter muscle excites deep craniofacial afferent fibers in rats and evokes pain in human subjects. It is not clear whether peripheral NMDA receptors play a role in these effects of glutamate. Accordingly, the effect of NMDA on afferent activity as well as the effect of locally administered NMDA receptor antagonists on glutamate-evoked afferent discharges in acutely anesthetized rats and muscle pain in human subjects was examined. Injection of NMDA into the masseter muscle evoked afferent discharges in a concentration-related manner. It was found that the NMDA receptor antagonists 2-amino-5-phosphonvalerate (APV, 10 mM), ketamine (10 mM), and dextromethorphan (40 mM) significantly decreased glutamate-evoked afferent discharges. The effects of APV and ketamine, but not dextromethorphan, were selective for glutamate-evoked afferent discharges and did not affect hypertonic saline-evoked afferent discharges. In human experiments, it was found that 10 mM ketamine decreased glutamate-evoked muscle pain but had no effect on hypertonic saline-evoked muscle pain. These results indicate that injection of glutamate into the masseter muscle evokes afferent discharges in rats and muscle pain in humans in part through activation of peripheral NMDA receptors. It is conceivable that activation of peripheral NMDA receptors may contribute to masticatory muscle pain and that peripherally acting NMDA receptor antagonists could prove to be effective analgesics for this type of 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) | 0.000 | 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 | 0.000 |
| 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".