Nutrient interaction for optimal protein anabolism in resistance exercise
Bibliographic record
Abstract
PURPOSE OF REVIEW: The rapid muscle loss that accompanies varying diseased states (cachexia) is due to an imbalance between muscle protein synthesis (MPS) and muscle protein breakdown In the current review, we will discuss and summarize recent evidence in order to provide practical recommendations on exercise and nutrient interventions for cachectic populations. RECENT FINDINGS: Resistance exercise is a potent stimulus for MPS, but cachexia patients may not be best placed to lift the heavy loads that, it was previously assumed, were a prerequisite for muscle hypertrophy. However, recent evidence from our lab shows that lower loads can effectively stimulate MPS and lead to hypertrophy. Protein ingestion potentiates resistance exercise-induced rates of MPS. The source and dose of the ingested protein are important to consider when attempting to maximize postresistance exercise MPS. Specifically, rapidly digested, leucine-rich protein sources may stimulate greater postexercise rates of MPS than other protein sources, as leucine acts as a key anabolic signal for mRNA translation. Furthermore, individuals undergoing relatively slow muscle atrophy (i.e., in sarcopenic elderly) respond positively to larger doses (40 g) of amino acids following exercise, whereas the response appears to plateau after moderate doses (20 g) in healthy, young adults. SUMMARY: Emerging evidence shows that manipulating traditional exercise loading and nutrient strategies may ameliorate cachexia.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 0.003 |
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".