MétaCan
Menu
Back to cohort
Record W3137465526 · doi:10.1111/febs.15829

Molecular mechanisms regulating Proteinase‐Activated Receptors (PARs)

2021· review· en· W3137465526 on OpenAlexafffund
Arundhasa Chandrabalan, Rithwik Ramachandran

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations84
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueFEBS JournalSame topicBlood Coagulation and Thrombosis MechanismsFrench-language works237,207