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Record W3137465526 · doi:10.1111/febs.15829

Molecular mechanisms regulating Proteinase‐Activated Receptors (PARs)

2021· review· en· W3137465526 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFEBS Journal · 2021
Typereview
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsReceptorEffectorBiologySignal transductionG protein-coupled receptorMechanism (biology)Cell biologyFunction (biology)Protease-activated receptorProteolytic enzymesEnzymeBiochemistryPlateletImmunologyThrombin

Abstract

fetched live from OpenAlex

Proteinase-activated receptors (PARs) are a four-member family of G protein-coupled receptors defined by their irreversible proteolytic mechanism of activation. PARs have emerged as important regulators of various physiological responses and are implicated in numerous pathological conditions. Importantly, PAR1 and PAR4 are critical regulators of platelet function, while PAR2 is well established as a driver of inflammatory responses. PAR-targeted drug development efforts are therefore of great interest. In this review, we provide an overview of recent advances in our understanding of molecular mechanisms underlying PAR activation, effector interaction, and signaling. We also provide an overview of the diverse proteolytic enzymes that are now established as PAR regulators and describe the ability of different enzymes to elicit biased signaling through PARs. Finally, we highlight recent advances in the development of PAR-targeted pharmacological agents and discuss recent structure-activity relationship studies.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.062
GPT teacher head0.339
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it