A Brief Review of Critical Processes in Exercise-Induced Muscular Hypertrophy
Bibliographic record
Abstract
With regular practice, resistance exercise can lead to gains in skeletal muscle mass by means of hypertrophy. The process of skeletal muscle fiber hypertrophy comes about as a result of the confluence of positive muscle protein balance and satellite cell addition to muscle fibers. Positive muscle protein balance is achieved when the rate of new muscle protein synthesis (MPS) exceeds that of muscle protein breakdown (MPB). While resistance exercise and postprandial hyperaminoacidemia both stimulate MPS, it is through the synergistic effects of these two stimuli that a net gain in muscle proteins occurs and muscle fiber hypertrophy takes place. Current evidence favors the post-exercise period as a time when rapid hyperaminoacidemia promotes a marked rise in the rate of MPS. Dietary proteins with a full complement of essential amino acids and high leucine contents that are rapidly digested are more likely to be efficacious in this regard. Various other compounds have been added to complete proteins, including carbohydrate, arginine and glutamine, in an attempt to augment the effectiveness of the protein in stimulating MPS (or suppressing MPB), but none has proved particularly effective. Evidence points to a higher protein intake in combination with resistance exercise as being efficacious in allowing preservation, and on occasion increases, in skeletal muscle mass with dietary energy restriction aimed at the promotion of weight loss. The goal of this review is to examine practices of protein ingestion in combination with resistance exercise that have some evidence for efficacy and to highlight future areas for investigation.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".