Resistance exercise and appropriate nutrition to counteract muscle wasting and promote muscle hypertrophy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Loss of skeletal muscle mass is a common feature of a number of clinical scenarios including limb casting, bed rest, and various disorders such as HIV-AIDS, sepsis, cancer cachexia, heart failure, and uremia. Commonly, muscle disuse (hypodynamia) is the sole reason, or a large part, of why muscle mass is lost. The reduction in strength, or dynapenia, that accompanies these conditions is also a function of the degree of hypodynamia and is related to muscle loss. RECENT FINDINGS: The major and consistent finding in a number of human-based models of muscle wasting is a decline in the synthesis of new muscle proteins both in the postabsorptive and fed states. Thus, countermeasures are best suited to those that augment muscle protein synthesis and not those that attempt to counteract proteolysis. Our main thesis is that retention of muscle mass in wasting conditions will be achieved to the greatest extent by focussing on increased muscle use with moderate-to-high resistance loads as the primary countermeasure with a secondary countermeasure being to provide adequate nutritional support. Either intervention alone will alleviate some part of hypodynamia-induced muscle mass loss and dynapenia; however, together nutrition and muscular contraction will result in greater mitigation of muscle loss. SUMMARY: Advances in our understanding of hypodynamia-induced muscle loss, a condition common to almost all syndromes of muscle wasting, has led to a focus on reduced basal and feeding-induced elevations in protein synthesis. Countermeasures for wasting should focus on stimulating anabolism rather than alleviating catabolism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".