Proteinases and signalling: pathophysiological and therapeutic implications via PARs and more
Bibliographic record
Abstract
Proteinases like thrombin, trypsin and tissue kallikreins are now known to regulate cell signaling by cleaving and activating a novel family of G-protein-coupled proteinase-activated receptors (PARs 1-4) via exposure of a tethered receptor-triggering ligand. On their own, short synthetic PAR-selective PAR-activating peptides (PAR-APs) mimicking the tethered ligand sequences can activate PARs 1, 2 and 4 and cause physiological responses both in vitro and in vivo. Using the PAR-APs as sentinel probes in vivo, it has been found that PAR activation can affect the vascular, renal, respiratory, gastrointestinal, musculoskeletal and nervous systems (both central and peripheral nervous system) and can promote cancer metastasis and invasion. In general, responses triggered by PARs 1, 2 and 4 are in keeping with an innate immune inflammatory response, ranging from vasodilatation to intestinal inflammation, increased cytokine production and increased or decreased nociception. Further, PARs have been implicated in a number of disease states, including cancer and inflammation of the cardiovascular, respiratory, musculoskeletal, gastrointestinal and nervous systems. In addition to activating PARs, proteinases can cause hormone-like effects by other signalling mechanisms, like growth factor receptor activation, that may be as important as the activation of PARs. We, therefore, propose that the PARs themselves, their activating serine proteinases and their associated signalling pathways can be considered as attractive targets for therapeutic drug development. Thus, proteinases in general must now be considered as 'hormone-like' messengers that can signal either via PARs or other mechanisms.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".