Proteinase-Activated Receptors: Transducers of Proteinase-Mediated Signaling in Inflammation and Immune Response
Bibliographic record
Abstract
Serine proteinases such as thrombin, mast cell tryptase, trypsin, or cathepsin G, for example, are highly active mediators with diverse biological activities. So far, proteinases have been considered to act primarily as degradative enzymes in the extracellular space. However, their biological actions in tissues and cells suggest important roles as a part of the body's hormonal communication system during inflammation and immune response. These effects can be attributed to the activation of a new subfamily of G protein-coupled receptors, termed proteinase-activated receptors (PARs). Four members of the PAR family have been cloned so far. Thus, certain proteinases act as signaling molecules that specifically regulate cells by activating PARs. After stimulation, PARs couple to various G proteins and activate signal transduction pathways resulting in the rapid transcription of genes that are involved in inflammation. For example, PARs are widely expressed by cells involved in immune responses and inflammation, regulate endothelial-leukocyte interactions, and modulate the secretion of inflammatory mediators or neuropeptides. Together, the PAR family necessitates a paradigm shift in thinking about hormone action, to include proteinases as key modulators of biological function. Novel compounds that can modulate PAR function may be potent candidates for the treatment of inflammatory or immune 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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".