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Record W2165175562 · doi:10.1210/me.2014-1062

Minireview: New Molecular Mediators of Glucocorticoid Receptor Activity in Metabolic Tissues

2014· review· en· W2165175562 on OpenAlexafffund
Rucha Patel, Jasmine Williams‐Dautovich, Carolyn L. Cummins

Bibliographic record

VenueMolecular Endocrinology · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsDiabetes CanadaUniversity of Toronto
FundersLeslie Dan Faculty of Pharmacy, University of TorontoNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsGlucocorticoidGlucocorticoid receptorBiologyAdipose tissueInsulin resistanceEndocrinologyInflammationReceptorEndogenyInternal medicineWastingNuclear receptorHormoneInsulinImmunologyMedicineGeneTranscription factorBiochemistry

Abstract

fetched live from OpenAlex

The glucocorticoid receptor (GR) was one of the first nuclear hormone receptors cloned and represents one of the most effective drug targets available today for the treatment of severe inflammation. The physiologic consequences of endogenous or exogenous glucocorticoid excess are well established and include hyperglycemia, insulin resistance, fatty liver, obesity, and muscle wasting. However, at the molecular and tissue-specific level, there are still many unknown protein mediators of glucocorticoid response and thus, much remains to be uncovered that will help determine whether activation of the GR can be tailored to improve therapeutic efficacy while minimizing unwanted side effects. This review summarizes recent discoveries of tissue-selective modulators of glucocorticoid signaling that are important in mediating the unwanted side effects of therapeutic glucocorticoid use, emphasizing the downstream molecular effects of GR activation in the liver, adipose tissue, muscle, and pancreas.

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)0.001
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 score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.007

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 head0.011
GPT teacher head0.302
Teacher spread0.291 · 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

Citations108
Published2014
Admission routes2
Has abstractyes

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