Muscle hypertrophy models: Applications for research on aging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 it