TNFα Mechanically Sensitizes Masseter Muscle Nociceptors by Increasing Prostaglandin E<sub>2</sub> Levels
Bibliographic record
Abstract
TNFα induces mechanical sensitization of rat masseter muscle nociceptors, which takes 2-3 h to manifest and is mediated through activation of P55 and P75 receptors. This study was undertaken to determine whether TNFα induces nociceptor mechanical sensitization through the release of other algogenic substances such as glutamate, prostaglandin E(2) (PGE(2)), and/or nerve growth factor (NGF), which have been shown to induce mechanical sensitization of muscle nociceptors. Masseter muscle homogenate levels of PGE(2) and NGF were measured 3 h after injection of TNFα (1 μg) or vehicle control using commercially available kits. Interstitial glutamate concentration was measured after injection of TNFα or vehicle control using a glutamate-selective biosensor probe. Diclofenac, a cyclooxygenase inhibitor that blocks the synthesis of PGE(2), D-2-amino-5-phosphonovaleric acid (APV), a competitive N-methyl-D-aspartate (NMDA) receptor antagonist, and a tyrosine kinase A (TrkA) receptor antibody, which blocks NGF-induced masseter muscle nociceptor sensitization, were used to assess the contribution of PGE(2), glutamate, and NGF to TNFα-induced nociceptor sensitization. PGE(2) and glutamate concentrations were significantly elevated 3 h after TNFα injection into the masseter muscle. Injection of diclofenac partially reversed the TNFα-induced decreases in the mechanical threshold (MT) of masseter muscle nociceptors, whereas vehicle control, APV, and TrkA antibody did not significantly alter nociceptor MT. These results suggest that TNFα-induced mechanical sensitization of masseter muscle nociceptors is mediated in part by increased PGE(2) levels. The findings of this study support the hypothesis that TNFα induces a delayed mechanical sensitization of masseter muscle nociceptors indirectly by the release of PGE(2).
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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) | 0.000 | 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".