Agonists of proteinase‐activated receptor‐2 affect transendothelial migration and apoptosis of human neutrophils
Bibliographic record
Abstract
Skin is the first barrier preventing microorganism invasion in host. Wounds destroy this defense barrier and, without an appropriate care, may lead to sepsis. Neutrophil activation and immigration plays an important role at the inflammatory stage of wound healing. Neutrophils are known to express proteinase-activated receptors (PARs), which can be activated by serine proteases, also by enzymes involved in wound healing. We previously reported that PAR(2) agonists up-regulate cell adhesion molecule expression and cytokine production by human neutrophils. Here, we demonstrate that PAR(2) agonists (serine proteases as well as synthetic peptides) reduce transendothelial migration of neutrophils and prolong their life in vitro. Synthetic PAR(2) agonist also enhanced protective interferon (IFN)gamma-induced FcgammaRI expression at neutrophil cell surface. Of note, IFNgamma is a cytokine, which was used in clinical trials to reactivate human neutrophil functions during sepsis. Moreover, we observed a significant increase of PAR(2) expression on cell surface of neutrophils from septic patients as compared with healthy volunteers. Together, our results indicate that PAR(2) may be involved in the pathophysiology of neutrophil-endothelial interactions during wound healing or later during sepsis in humans, potentially by affecting neutrophil apoptosis, transendothelial migration and Fcgamma receptor-mediated phagocytosis.
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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) | 0.000 | 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) | 0.003 | 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".