Proteinases as hormones: targets and mechanisms for proteolytic signaling
Bibliographic record
Abstract
Proteinases, such as kallikrein-related peptidases, trypsin and thrombin, can play hormone-like 'messenger roles in vivo. They can regulate cell signaling by cleaving and activating a novel family of G-protein-coupled proteinase-activated receptors (PARs 1-4) by unmasking a tethered receptor-triggering ligand. Short synthetic PAR-derived peptide sequences (PAR-APs) can selectively activate PARs 1, 2 and 4, causing physiological responses in vitro and in vivo. Using the PAR-APs to activate the receptors in vivo, it has been found that PARs, like hormone receptors, can affect the vascular, renal, respiratory, gastrointestinal, musculoskeletal and nervous systems (central and peripheral). PARs trigger responses ranging from vasodilatation to intestinal inflammation, increased cytokine production and increased nociception. These PAR-stimulated responses have been implicated in various disease states, including cancer, atherosclerosis, asthma, arthritis, colitis and Alzheimer's disease. In addition to targeting the PARs, proteinases can also cause hormone-like effects by other signaling mechanisms that may be as important as the activation of PARs. Thus, the PARs themselves, their activating serine proteinases and their signaling pathways can be considered as attractive targets for therapeutic drug development. Further, proteinases can be considered as physiologically relevant 'hormone-like' messengers that can convey signals locally or systemically either via PARs or by other mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".