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Record W2333061151 · doi:10.1097/fjc.0000000000000196

G Protein–Coupled Receptor Signaling in Cardiac Nuclear Membranes

2015· review· en· W2333061151 on OpenAlexafffund
Ana F. Branco, Bruce G. Allen

Bibliographic record

VenueJournal of Cardiovascular Pharmacology · 2015
Typereview
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsMcGill UniversityUniversité de MontréalMontreal Heart Institute
FundersCanadian Institutes of Health Research
KeywordsCell biologyMembraneReceptorSignal transductionChemistryPharmacologyMedicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

G protein-coupled receptors (GPCRs) play key physiological roles and represent a significant target for drug development. However, historically, drugs were developed with the understanding that GPCRs as a therapeutic target exist solely on cell surface membranes. More recently, GPCRs have been detected on intracellular membranes, including the nuclear membrane, and the concept that intracellular GPCRs are functional is become more widely accepted. Nuclear GPCRs couple to effectors and regulate signaling pathways, analogous to their counterparts at the cell surface, but may serve distinct biological roles. Hence, the physiological responses mediated by GPCR ligands, or pharmacological agents, result from the integration of their actions at extracellular and intracellular receptors. The net effect depends on the ability of a given ligand or drug to access intracellular receptors, as dictated by its structure, lipophilic properties, and affinity for nuclear receptors. This review will discuss angiotensin II, endothelin, and β-adrenergic receptors located on the nuclear envelope in cardiac cells in terms of their origin, activation, and role in cardiovascular function and pathology.

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 score0.003
Threshold uncertainty score0.012

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)0.0030.003

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.

Opus teacher head0.030
GPT teacher head0.323
Teacher spread0.294 · 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

Citations29
Published2015
Admission routes2
Has abstractyes

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