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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".