Targeting Proteinase Activated Receptor-4 Reduces Mechanonociception During the Acute Inflammatory Phase but not the Chronic Neuropathic Phase of Osteoarthritis in Rats
Bibliographic record
Abstract
Serine proteases are elevated in arthritic joints where they can cleave protease activated receptors (PARs) to modulate pain and inflammation. Activation of protease-activated receptor 4 (PAR4) has been implicated in inflammatory joint pain. Whether PAR4 is involved in osteoarthritis (OA) pain has not yet been explored. The aim of this study was to compare the role of PAR4 in modulating early versus late stage OA pain using two models of OA viz. monoiodoacetate (MIA) and medial meniscal transection (MMT). G-ratio calculation and electron microscopy analysis revealed saphenous nerve demyelination and structural damage during late stage but not early OA in both models. Using immunohistochemistry, neuronal expression of PAR4 was higher in early versus late OA. Systemic administration of the PAR4 antagonist pepducin P4pal10 reduced both secondary allodynia (von Frey hair algesiometry) and joint nociceptor firing (single unit recordings) in MMT and MIA animals compared to vehicle-treated animals in early OA. The PAR4 antagonist was ineffective at altering pain or joint afferent firing in post-inflammatory OA. During the acute phase of the models, joint inflammation as determined by laser speckle contrast analysis and intravital microscopy could be partially blocked by pepducin P4pal10. Compared to late-stage disease, inflammatory cytokines were elevated in early MIA and MMT rats. These findings suggest that PAR4 may be a viable target to treat the pain of early onset OA or during episodic inflammatory flares.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".