Unravelling the mechanisms regulating muscle mitochondrial biogenesis
Bibliographic record
Abstract
Skeletal muscle is a tissue with a low mitochondrial content under basal conditions, but it is responsive to acute increases in contractile activity patterns (i.e. exercise) which initiate the signalling of a compensatory response, leading to the biogenesis of mitochondria and improved organelle function. Exercise also promotes the degradation of poorly functioning mitochondria (i.e. mitophagy), thereby accelerating mitochondrial turnover, and preserving a pool of healthy organelles. In contrast, muscle disuse, as well as the aging process, are associated with reduced mitochondrial quality and quantity in muscle. This has strong negative implications for whole-body metabolic health and the preservation of muscle mass. A number of traditional, as well as novel regulatory pathways exist in muscle that control both biogenesis and mitophagy. Interestingly, although the ablation of single regulatory transcription factors within these pathways often leads to a reduction in the basal mitochondrial content of muscle, this can invariably be overcome with exercise, signifying that exercise activates a multitude of pathways which can respond to restore mitochondrial health. This knowledge, along with growing realization that pharmacological agents can also promote mitochondrial health independently of exercise, leads to an optimistic outlook in which the maintenance of mitochondrial and whole-body metabolic health can be achieved by taking advantage of the broad benefits of exercise, along with the potential specificity of drug action.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".