Muscle hypertrophy models: Applications for research on aging
Bibliographic record
Abstract
Muscle hypertrophy is an adaptive response to overload that requires increasing gene transcription and synthesis of muscle-specific proteins resulting in increased protein accumulation. Progressive resistance training (P RT ) is thought to be among the best means for achieving hypertrophy in humans. However, hypertrophy and functional adaptations to P RT in the muscles of humans are often difficult to evaluate because adaptations can take weeks, months, or even years before they become evident, and there is a large variability in response to P RT among humans. In contrast, various animal models have been developed which quickly result in extensive muscle hypertrophy. Several such models allow precise control of the loading parameters and records of muscle activation and performance throughout overload. Scientists using animal models of muscle hypertrophy should be familiar with the advantages and disadvantages of each and thereby choose the model that best addresses their research question. The purposes of this paper are to review animal models currently being used in basic research laboratories, discuss the hypertrophic and functional outcomes as well as applications of these models to aging, and highlight a few mechanisms involved in regulating hypertrophy as a result of applying these animal models to questions in research on aging. © 2005 Canadian Society for Exercise Physiology.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".