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Record W3009911095 · doi:10.3390/ijms21051645

Estrogen-Related Receptor Alpha: An Under-Appreciated Potential Target for the Treatment of Metabolic Diseases

2020· review· en· W3009911095 on OpenAlexfundno aff
Madhulika Tripathi, Paul M. Yen, Brijesh Kumar Singh

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsnot available
FundersMedical Research CouncilMcGill UniversityWeill Cornell Medical CollegeNational Medical Research CouncilUniversité de LyonMinistry of HealthSchool of Medicine, Duke University
KeywordsEstrogen-related receptor alphaNuclear receptorBiologyEstrogen receptor alphaEstrogen receptorTranscription factorEstrogenEstrogen-related receptor gammaAutophagyReceptorEstrogen receptor betaCell biologyGeneEndocrinologyBiochemistryCancerGeneticsBreast cancerApoptosis

Abstract

fetched live from OpenAlex

The estrogen-related receptor alpha (ESRRA) is an orphan nuclear receptor (NR) that significantly influences cellular metabolism. ESRRA is predominantly expressed in metabolically-active tissues and regulates the transcription of metabolic genes, including those involved in mitochondrial turnover and autophagy. Although ESRRA activity is well-characterized in several types of cancer, recent reports suggest that it also has an important role in metabolic diseases. This minireview focuses on the regulation of cellular metabolism and function by ESRRA and its potential as a target for the treatment of metabolic disorders.

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 score0.006
Threshold uncertainty score0.019

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 integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.003

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 head0.019
GPT teacher head0.331
Teacher spread0.311 · 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

Citations99
Published2020
Admission routes1
Has abstractyes

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