Physiologic and molecular bases of muscle hypertrophy and atrophy: impact of resistance exercise on human skeletal muscle (protein and exercise dose effects)This paper is one of a selection of papers published in this Special Issue, entitled 14th International Biochemistry of Exercise Conference – Muscles as Molecular and Metabolic Machines, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Normally, skeletal muscle mass is unchanged, beyond periods of growth, but it begins to decline in the fourth or fifth decade of life. The mass of skeletal muscle is maintained by ingestion of protein-containing meals. With feeding, muscle protein synthesis (MPS) is stimulated and a small suppression of muscle protein breakdown (MPB) occurs, such that protein balance becomes positive (MPS>MPB). As the postprandial period subsides and a transition toward fasting occurs, the balance of muscle protein turnover becomes negative again (MPB>MPS). Thus, during maintenance of skeletal muscle mass, the long-term net result is that MPS is balanced by MPB. Acutely, however, it is of interest to determine what regulates feeding-induced increases in MPS, since it appears that, in a number of scenarios (for example aging, disuse, and wasting diseases), a suppression of MPS in response to feeding is a common finding. In fact, recent findings point to the fact that loss of skeletal muscle mass with disuse and aging is due not chronic changes in MPS or MPB, but to a blunted feeding-induced rise in MPS. Resistance exercise is a potent stimulator of MPS and appears to synergistically enhance the gains stimulated by feeding. As such, resistance exercise is an important countermeasure to disuse atrophy and to age-related declines in skeletal muscle mass. What is less well understood is how the intensity and volume of the resistance exercise stimulus is sufficient to result in rises in MPS. Recent advances in this area are discussed here, with a focus on human in vivo data.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".