Neutrophil elastase induces inflammation and pain in mouse knee joints via activation of proteinase‐activated receptor‐2
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Neutrophil elastase plays a crucial role in arthritis. Here, its potential in triggering joint inflammation and pain was assessed, and whether these effects were mediated by proteinase-activated receptor-2 (PAR2). EXPERIMENTAL APPROACH: Neutrophil elastase (5 μg) was injected into the knee joints of mice and changes in blood perfusion, leukocyte kinetics and paw withdrawal threshold were assessed. Similar experiments were performed in animals pretreated with the neutrophil elastase inhibitor sivelestat, the PAR2 antagonist GB83, the p44/42 MAPK inhibitor U0126 and in PAR2 receptor knockout (KO) mice. Neutrophil elastase activity was also evaluated in arthritic joints by fluorescent imaging and sivelestat was assessed for anti-inflammatory and analgesic properties. KEY RESULTS: Intra-articular injection of neutrophil elastase caused an increase in blood perfusion, leukocyte kinetics and a decrease in paw withdrawal threshold. Sivelestat treatment suppressed this effect. The PAR2 antagonist GB83 reversed neutrophil elastase-induced synovitis and pain and these responses were also attenuated in PAR2 KO mice. The MAPK inhibitor U0126 also blocked neutrophil elastase-induced inflammation and pain. Active neutrophil elastase was increased in acutely inflamed knees as shown by an activatable fluorescent probe. Sivelestat appeared to reduce neutrophil elastase activity, but had only a moderate anti-inflammatory effect in this model. CONCLUSIONS AND IMPLICATIONS: Neutrophil elastase induced acute inflammation and pain in knee joints of mice. These changes are PAR2-dependent and appear to involve activation of a p44/42 MAPK pathway. Blocking neutrophil elastase, PAR2 and p44/42 MAPK activity can reduce inflammation and pain, suggesting their utility as therapeutic targets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".