Proteinase‐activated receptor expression and function in the brain
Bibliographic record
Abstract
Abstract Proteinase‐activated receptors (PARs) represent a novel family of G‐protein coupled receptors that mediate the diverse biologic effects of proteinases on target cells. Four different members of the PAR family have been identified so far: PAR 1 , PAR 3 , and PAR 4 act as receptors for thrombin, and PAR 2 is activated by trypsin/tryptase. It is now known that all four subtypes of PARs are widely expressed in the central nervous system, and there is increasing evidence to suggest roles for proteinases and PARs in development and pathogenesis in the nervous system. Harnessing different G proteins and a variety of signal transduction cascades, PARs can affect neural cell proliferation, morphology, and electrical activities. PARs have also been considered as major players in neuroinflammatory/degenerative processes in which they play both neuroprotective and neuropathogenic roles. The advent of PARs agonistic and antagonistic peptides, which selectively activate their cognate receptor and mediate a broad spectrum of PAR‐executed effects in the nervous system, makes these peptides attractive therapeutic possibilities. Herein we review different aspects of PARs activities in the normal development and function of the brain and address some evidence related to PARs roles in nervous system pathogenesis with a focus on neuroinflammatory/degenerative disorders. Drug Dev. Res. 60:51–57, 2003. © 2003 Wiley‐Liss, Inc.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".