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 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) | 0.000 | 0.000 |
| 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 | 0.001 | 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".