Thrombin receptor: An endogenous inhibitor of inflammatory pain, activating opioid pathways
Bibliographic record
Abstract
Serine proteases such as thrombin, trypsin and mast cell tryptase can act on different cell types through protease-activated receptors (PARs). These receptors have been shown to be implicated in several phenomena such as inflammation, platelet activation, immune response and atherosclerosis. Several studies recently reported PARs expression on neurons and some of them demonstrated that these receptors could interfere with nociception. The contribution of PAR(1) to inflammatory pain and the mechanism involved in this phenomenon were investigated. Intraplantar injection of PAR(1) agonist increased withdrawal latency and reduced response frequency to von Frey filaments, thus inhibiting nociceptive response to both mechanical and thermal stimuli in mice. PAR(1) agonist also reduced carrageenan-induced inflammatory hyperalgesia. The anti-nociceptive effects of PAR(1) agonist were mediated by endogenous opioids, as this effect was inhibited by local injection of naloxone methiodide, and because intraplantar injection of PAR(1) agonist increased mRNA expression of the endogenous opioid precursor proenkephalin. However, PAR(1) agonist was not able to inhibit calcium signals in isolated sensory neurons exposed to pro-nociceptive agents. Finally, despite similar inflammatory parameters, PAR(1)-deficient mice showed a strong potentiation of inflammatory hyperalgesia induced by the intraplantar injection of either formalin or carrageenan, or in the chronic model of collagen-induced arthritis, compared to wild-type mice. This study highlights a previously unknown endogenous mechanism of analgesia, showing a central role for the thrombin receptor PAR(1) in the regulation of inflammatory pain and as an activator of opioid pathways.
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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) | 0.003 | 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".