Resistance Exercise, Aging, Disuse, and Muscle Protein Metabolism
Bibliographic record
Abstract
Skeletal muscle is the organ of locomotion, its optimal function is critical for athletic performance, and is also important for health due to its contribution to resting metabolic rate and as a site for glucose uptake and storage. Numerous endogenous and exogenous factors influence muscle mass. Much of what is currently known regarding muscle protein turnover is owed to the development and use of stable isotope tracers. Skeletal muscle mass is determined by the meal- and contraction-induced alterations of muscle protein synthesis and muscle protein breakdown. Increased loading as resistance training is the most potent nonpharmacological strategy by which skeletal muscle mass can be increased. Conversely, aging (sarcopenia) and muscle disuse lead to the development of anabolic resistance and contribute to the loss of skeletal muscle mass. Nascent omics-based technologies have significantly improved our understanding surrounding the regulation of skeletal muscle mass at the gene, transcript, and protein levels. Despite significant advances surrounding the mechanistic intricacies that underpin changes in skeletal muscle mass, these processes are complex, and more work is certainly needed. In this article, we provide an overview of the importance of skeletal muscle, describe the influence that resistance training, aging, and disuse exert on muscle protein turnover and the molecular regulatory processes that contribute to changes in muscle protein abundance. © 2021 American Physiological Society. Compr Physiol 11:2249-2278, 2021.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".