Assessing the regulation of skeletal muscle plasticity in response to protein ingestion and resistance exercise
Bibliographic record
Abstract
PURPOSE OF REVIEW: The main purpose of this review is to discuss novel methodological advances in the assessment of muscle protein synthesis (MPS) in response to protein feeding and resistance exercise. RECENT FINDINGS: In the past 20 years, there has been a shift from application of the nitrogen balance methods toward the infusion of stable isotopic tracers to assess rates of MPS in response to a range of perturbations. Although this approach has enabled MPS to be assessed with a greater temporal resolution and precision, the method limits the capture of MPS to relatively short-duration infusions of approximately 3-12 h. Recent refinement of analytical methods to assess long-term MPS responses have now provided a platform for studying the impact of exercise and nutrition on muscle anabolism with an extended temporal resolution from hours to days or even weeks. Finally, novel insights into cellular signaling processes may help delineate the molecular mechanisms that govern skeletal muscle plasticity in response to exercise and feeding. SUMMARY: Future work should focus on the impact of novel exercise and nutritional interventions on MPS in an extended postexercise adaptive period, that is, days. The findings of such investigations will help test the long-term efficacy of interventions to enhance skeletal muscle protein reconditioning and hypertrophy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".