AEROBIC EXERCISE INDUCES DIFFERENTIAL MYOGENIC RESPONSES IN YOUNG VERSUS OLD SKELETAL MUSCLE
Bibliographic record
Abstract
The factors contributing to the myogenic processes in the period between exercise bouts have not be examined with respect to aging. PURPOSE This study was designed to determine if the responses of a number of factors thought to be involved in the regulation of skeletal muscle myogenesis were similarly affected by a prior bout of aerobic exercise. METHODS Young (4 mo) and old (24 mo) C57Bl6 female mice were randomly assigned to either control or exercise groups (2.5 hour duration treadmill run) and sacrificed 12 hours following the acute exercise bout. Plasma insulin-like growth factor-1 (IGF-1) levels and skeletal muscle levels of calcineurin, myostatin, IGF-1 and IGF binding protein-3 (IGFBP-3) were determined. RESULTS Exercise had no effect on circulating IGF-1 levels or tissue IGF-1 levels but tissue IGFBP-3 levels were significantly increased (p < 0.05) in the young exercised animals. Whereas myostatin levels decreased following the exercise in the young animals (p < 0.05), they were unchanged in the older animals. Calcineurin levels were increased with exercise in both the young and old animals (p < 0.05) with the increase being twice as large in the young animals. CONCLUSION These data suggest that these myogenic factors are differentially affected by acute aerobic exercise with aging. This study was funded by a grant from the MSFHR to WSP.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".