Role of proteinases and proteinase‐activated receptors in pain pathways
Bibliographic record
Abstract
Proteinase‐activated receptors are G‐protein‐coupled receptors that are activated by the proteolytic cleavage of their N‐terminal domain. PAR‐1, PAR‐3 and PAR‐4 are activated by thrombin, while PAR‐2 is activated by trypsin. Using in vivo models of nociception in response to thermal or mechanical stimuli and Fos immunochemistry, we have shown that PAR‐2 activation by selective peptidic agonists or proteinases (trypsin and tryptase) provoked hyperalgesia and activation of nociceptive neurons. The formalin‐ or compound 48/80‐induced hyperalgesia was significantly decreased in mice deficient for the PAR‐2 gene, compared to wild‐type, showing that PAR‐2 activation contributes to the generation of pain associated with inflammation. In contrast, intraplantar injection of selective PAR‐1 peptidic agonists increased nociceptive threshold and withdrawal latency, leading to mechanical and thermal analgesia, while control peptides had no effect. Intraplantar injection of thrombin also showed analgesic properties in response to mechanical, but not to thermal stimulus. Co‐injection of PAR‐1 selective agonist with carrageenan significantly reduced carrageenan‐induced mechanical and thermal hyperalgesia, while thrombin reduced carrageenan‐induced mechanical but not thermal hyperalgesia. The fact that thrombin is not a selective agonist for PAR‐1 may explain the different effects of thrombin and PAR‐1‐AP. These results identified novel roles for proteinases and their receptors in pain pathways. PAR‐2 antagonists and/or PAR‐1 agonists might then constitute valuable addition to analgesics used in the management of inflammatory 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".