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Record W2105555168 · doi:10.1155/2010/937123

Regulation of PPAR<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>γ</mml:mi></mml:math>Coactivator-1<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math>Function and Expression in Muscle: Effect of Exercise

2010· article· en· W2105555168 on OpenAlexafffund
Giulia Uguccioni, Donna M. D’Souza, David A. Hood

Bibliographic record

VenuePPAR Research · 2010
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAlgorithmMitochondrial biogenesisCoactivatorMachine learningArtificial intelligenceBioinformaticsComputer scienceBiologyFunction (biology)Transcription factorGeneGenetics

Abstract

fetched live from OpenAlex

PPARγ coactivator-1α (PGC-1α) is considered to be a major regulator of mitochondrial biogenesis. Though first discovered in brown adipose tissue, this coactivator has emerged as a coordinator of mitochondrial biogenesis in skeletal muscle via enhanced transcription of many nuclear genes encoding mitochondrial proteins. Stimuli such as exercise provoke the activation of signalling cascades that lead to the induction of PGC-1α. Posttranslational modifications also regulate the function of PGC-1α, with a multitude of upstream molecules targeting the protein to modify its activity and/or expression. Previous research has established a positive correlation between resistance to fatigue and skeletal muscle mitochondrial content. Recently, studies have begun to elucidate the specific role of PGC-1α in exercise-related skeletal muscle adaptations, with several studies identifying it as a dominant regulator of organelle synthesis. This paper will highlight the function, regulation, and expression of PGC-1α, as well as the role of the coactivator during exercise adaptations.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.025
GPT teacher head0.290
Teacher spread0.265 · 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.

Study designTheoretical or conceptual
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

Citations28
Published2010
Admission routes2
Has abstractyes

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