Searching for the physiological role and therapeutic potential of vascular proteinase‐activated receptor‐2 (PAR<sub>2</sub>)
Bibliographic record
Abstract
Abstract The intercellular interactions of endothelial and vascular smooth muscle cells are interesting to many scientists who seek to develop new drugs to treat cardiovascular diseases. Of particular interest is the regulation of blood flow by the paracrine actions of the endothelium on the underlying vascular smooth muscle cells of blood vessels. The development and further understanding of drugs that mimic or potentiate endothelial‐derived factors, in particular vasodilators such as nitric oxide (NO), have proved to be of therapeutic benefit (e.g., sildenafil, nitroglycerin). On the other hand, endothelial‐derived proinflammatory substances are released in response to tissue insult or during the progression of vascular diseases such as atherosclerosis. Proteinase‐activated receptor 2 (PAR2) represents a novel target for vascular biology because of 1) its unique mechanism of activation by proteinases, 2) questions regarding the identity of the endogenous agonist(s), and 3) its apparent multiple activities in the vasculature. Whether it will be agonists or antagonists of PAR2 that will serve as the basis for a new class of therapeutic agents for the treatment of vascular diseases is an open question for further research and drug development. Drug Dev. Res. 60:14–19, 2003. © 2003 Wiley‐Liss, Inc.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".