The pronociceptive effect of proteinase-activated receptor-4 stimulation in rat knee joints is dependent on mast cell activation
Bibliographic record
Abstract
Proteinase-activated receptor-4 (PAR(4)) is a G-protein-coupled receptor activated by serine proteinases released during tissue repair and inflammation. We have previously shown that PAR(4) activation sensitises articular primary afferents leading to joint pain. This study examined whether mast cells contribute to this PAR(4)-induced sensitisation and consequent heightened pain behaviour. The expression of PAR(4) on synovial mast cells was confirmed with immunofluorescent staining of rat knee joint sections. Electrophysiological recordings were made from joint primary afferents in male Wistar rats during both nonnoxious and noxious rotations of the knee. Afferent firing rate was recorded for 15 minutes after close intra-arterial injection of 10(-9) to 10(-5)mol of the PAR(4) activating peptide, AYPGKF-NH(2), or the inactive peptide, YAPGKF-NH(2) (100-μl bolus). Rats were either naive or pretreated with the mast cell stabilise, cromolyn (20mg/kg). Mechanical withdrawal thresholds were determined using a dynamic planter aesthesiometer and weight bearing determined using an incapacitance tester. These behavioural measurements were taken before and after intra-articular AYPGKF-NH(2), or the inactive peptide, YAPGKF-NH(2) (100μg). Local administration of AYPGKF-NH(2) caused a significant increase in joint primary afferent firing rate and pain behaviour compared with the control peptide YAPGKF-NH(2). These effects were blocked by pretreatment with cromolyn. These data reveal that PAR(4) is expressed on synovial mast cells and the activation of PAR(4) has a pronociceptive effect that is dependent on mast cell activation. Proteinase-activated receptor-4 is expressed on synovial mast cells, and the activation of Proteinase-activated receptor-4 has a pronociceptive effect that is dependent on mast cell activation.
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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 | 0.000 |
| 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".