Kallikreins and proteinase-mediated signaling: proteinase-activated receptors (PARs) and the pathophysiology of inflammatory diseases and cancer
Bibliographic record
Abstract
Proteinases such as thrombin and trypsin can affect tissues by activating a novel family of G protein-coupled proteinase-activated receptors (PARs 1-4) by exposing a 'tethered' receptor-triggering ligand (TL). Work with synthetic TL-derived PAR peptide sequences (PAR-APs) that stimulate PARs 1, 2 and 4 has shown that PAR activation can play a role in many tissues, including the gastrointestinal tract, kidney, muscle, nerve, lung and the central and peripheral nervous systems, and can promote tumor growth and invasion. PARs may play roles in many settings, including cancer, arthritis, asthma, inflammatory bowel disease, neurodegeneration and cardiovascular disease, as well as in pathogen-induced inflammation. In addition to activating or disarming PARs, proteinases can also cause hormone-like effects via PAR-independent mechanisms, such as activation of the insulin receptor. In addition to proteinases of the coagulation cascade, recent data suggest that members of the family of kallikrein-related peptidases (KLKs) represent endogenous PAR regulators. In summary: (1) proteinases are like hormones, signaling in a paracrine and endocrine manner via PARs or other mechanisms; (2) KLKs must now be seen as potential hormone-like PAR regulators in vivo; and (3) PAR-regulating proteinases, their target PARs, and their associated signaling pathways appear to be novel therapeutic targets.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".