Proteinase-Activated Receptor 2 (PAR2): A Challenging New Target for Treatment of Vascular Diseases
Bibliographic record
Abstract
Proteinase-activated Receptor 2 (PAR2) is a potential target for the design of drug treatments for vascular diseases. Its unique mechanism of activation by serine proteinases, questions regarding the identities of endogenous agonists and its apparent multiple activities in the vasculature contribute to complex pharmacology. The progress of the pursuit to understand the function of PAR2 relies on the design of short specific peptides as selective agonists for PAR2 in receptor-selective cultured cell expression systems and is limited by the lack of any PAR2 antagonists. Fortunately, the utilization of transgenic PAR2-deficient mice enables the identification of the actions of selective PAR2-derived activating peptides attributed to activation solely of PAR2 in more physiologically complex systems. Of multiple pharmacological responses, PAR2-derived peptide agonists reduce vascular tone, and therefore increase blood flow, via nitric oxide-dependent and -independent paracrine actions of the endothelium upon the underlying vascular smooth muscle cells of blood vessels. PAR2-mediated endothelial-dependent relaxation and hyperpolarization of vascular smooth muscle in select arterial vascular beds via a nitric oxide/cyclooxygenases-independent mechanism suggests a strategy for correction of endothelium-based vascular dysfunction. Vascular tissues respond to progression of vascular diseases such as atherosclerosis or to injury with variable changes of PAR2 expression. With further research and drug development, PAR2 agonists and antagonists may become a basis for a new class of therapeutic agents for treatment of vascular diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".