Neutrophils and the kallikrein–kinin system in proteinase‐activated receptor 4‐mediated inflammation in rodents
Bibliographic record
Abstract
1 We evaluated a potential role for proteinase-activated receptor 4 (PAR(4)) in a rodent paw inflammation model, with a focus on two main features of inflammation: (1) oedema and (2) granulocyte recruitment. 2 A PAR(4) antagonist (Pepducin P4pal-10; palmitoyl-SGRRYGHALR-NH(2)) reduced both the oedema and granulocyte recruitment induced by a localized administration of carrageenan in the rat hind paw, pointing to a key role for PAR(4) in this inflammation model. 3 Further, intraplantar injection in the mouse hind paw of a PAR(4) agonist (AYPGKF-NH(2)), but not its standard PAR(4)-inactive peptide control (YAPGKF-NH(2)), caused an inflammatory reaction characterized by oedema (increased paw thickness) and granulocyte recruitment (increased paw myeloperoxidase activity). The PAR(4) agonist-induced effects were inhibited in mice pretreated with pepducin P4pal10. 4 These PAR(4) agonist-mediated effects were not affected by pretreatment with inhibitors of either NO production or prostaglandin release (L-NAME and indomethacin, respectively). 5 However, selective immuno-depletion of neutrophils significantly reduced PAR(4) agonist-induced oedema formation. 6 Moreover, AYPGKF-NH(2)-induced oedema was also reduced by pretreatment with either a kinin B(2) receptor antagonist (icatibant) or a tissue or plasma kallikrein inhibitor (FE999024 and FE999026, respectively), but not with a kinin B(1) receptor antagonist (SSR240612). 7 We conclude: (1) that PAR(4) plays an important role in the inflammatory response as it mediates some of the hallmarks of inflammation and (2) that PAR(4)-mediated oedema is dependent on the recruitment of neutrophils and components of the kallikrein-kinin system.
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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".