Role of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) in denervation-induced atrophy in aged muscle: facts and hypotheses
Bibliographic record
Abstract
Aging-related loss of muscle mass, a biological process named sarcopenia, contributes to mobility impairment, falls, and physical frailty, resulting in an impaired quality of life in older people. In view of the aging of our society, understanding the underlying mechanisms of sarcopenia is a major health-care imperative. Evidence obtained from human and rodent studies demonstrates that skeletal muscle denervation/reinnervation cycles occur with aging, and that progressive failure of myofiber reinnervation is a major cause of the accelerating phase of sarcopenia in advanced age. However, the mechanisms responsible for the loss of myofiber innervation with aging remain unknown. The two major strategies that counteract sarcopenia, that is, caloric restriction and endurance training, are well known to protect neuromuscular junction (NMJ) integrity, albeit through undefined mechanisms. Interestingly, both of these interventions better preserve PGC-1α expression with aging, a transcriptional coactivator which has recently been shown to regulate key proteins involved in maintaining NMJ integrity. We therefore propose that the aging-related decline in PGC-1α may be a central mechanism promoting instability of the NMJ and consequently, aging-related alterations of myofiber innervation in sarcopenia. Similarly, the promotion of PGC-1α expression by both caloric restriction and exercise training may be fundamental to their protective benefits for aging muscle by better preserving NMJ integrity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".