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Record W2058107116 · doi:10.3390/ph5050417

Pathophysiology of GPCR Homo- and Heterodimerization: Special Emphasis on Somatostatin Receptors

2012· article· en· W2058107116 on OpenAlexafffund
R.K. Somvanshi, Ujendra Kumar

Bibliographic record

VenuePharmaceuticals · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsUniversity of British Columbia
FundersMichael Smith Health Research BC
KeywordsG protein-coupled receptorReceptorIntracellularExtracellularRhodopsin-like receptorsEffectorBiologyDrug discoverySignal transductionCell biologyCell signalingG proteinNeuroscienceComputational biologyBioinformaticsBiochemistryMetabotropic receptorAgonist

Abstract

fetched live from OpenAlex

G-protein coupled receptors (GPCRs) are cell surface proteins responsible for translating >80% of extracellular reception to intracellular signals. The extracellular information in the form of neurotransmitters, peptides, ions, odorants etc is converted to intracellular signals via a wide variety of effector molecules activating distinct downstream signaling pathways. All GPCRs share common structural features including an extracellular N-terminal, seven-transmembrane domains (TMs) linked by extracellular/intracellular loops and the C-terminal tail. Recent studies have shown that most GPCRs function as dimers (homo- and/or heterodimers) or even higher order of oligomers. Protein-protein interaction among GPCRs and other receptor proteins play a critical role in the modulation of receptor pharmacology and functions. Although ~50% of the current drugs available in the market target GPCRs, still many GPCRs remain unexplored as potential therapeutic targets, opening immense possibility to discover the role of GPCRs in pathophysiological conditions. This review explores the existing information and future possibilities of GPCRs as tools in clinical pharmacology and is specifically focused for the role of somatostatin receptors (SSTRs) in pathophysiology of diseases and as the potential candidate for drug discovery.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.308
Teacher spread0.285 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations27
Published2012
Admission routes2
Has abstractyes

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