Proteinase-mediated cell signalling: targeting proteinase-activated receptors (PARs) by kallikreins and more
Bibliographic record
Abstract
Serine proteinases, like trypsin, can play a hormone-like role by triggering signal transduction pathways in target cells. In many respects these hormone-like actions of proteinases can now be understood in terms of the pharmacodynamics of the G protein-coupled 'receptor' responsible for the cellular actions of thrombin (proteinase-activated receptor-1, or PAR1). PAR1, like the other three members of this receptor family (PAR2, PAR3 and PAR4), has a unique mechanism of activation involving the proteolytic unmasking of an N-terminally tethered sequence that can activate the receptor. The selective activation of each PAR by short synthetic peptides representing these sequences has demonstrated that PAR1, PAR2 and PAR4 play important roles in regulating physiological responses ranging from vasoregulation and cell growth to inflammation and nociception. We hypothesise that the tissue kallikreins may regulate signal transduction via the PARs. Although PARs can account for many of their biological actions, kallikreins may also cause effects by mechanisms not involving the PARs. For instance, trypsin activates the insulin receptor and thrombin can act via a mechanism involving its non-catalytic domains. Based on the data we summarise, we propose that the kallikreins, like thrombin and trypsin, must now be considered as important 'hormonal' regulators of tissue function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".