Prolonged endurance exercise decreases fiber loss and fiber atrophy in aged male rats
Bibliographic record
Abstract
Sarcopenia is the age‐related loss of muscle mass and function. Effects of long‐term exercise training on sarcopenia have not been well defined. In this study we assessed skeletal muscle adaptations to prolonged exercise training in rats from late‐middle age to old age. Male rats, 24 mo of age, trained on a treadmill at three levels of intensity: high (HI: 13m/min, 5% incline for 30 min, 5 days per week), moderate (MI: 13m/min, 0% incline for 30 min, 5 days per week), minimally active (MA: 5m/min, for 5 min, 2 days per week) and a sedentary group (S). Rats ran for one year until the rats were age 36 months. The rectus femoris (RF) muscle from 24‐month Controls (C), and 36‐month HI, MI, MA, and S. The RF was weighed, bisected, frozen, sectioned and stained with hematoxylin and eosin. Digital images of RF cross‐sections were used to determine muscle area, fiber number and fiber diameter. Aging significantly reduced muscle mass, muscle area, fiber number and fiber diameter, (24‐month C compared to any of the 36‐month old S, MA, MI or HI). Among the 36‐mo. rats (HI, MI, MA, and S), no differences were observed in muscle mass or muscle area. However, there was a significant increase in fiber number and fiber diameter in all exercise groups (HI, MI, and MA) compared to S. These results indicate that any level of exercise, even minimal levels of activity is capable of decreasing the effects of sarcopenia in aging rats. Supported by NIH AG 030423.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".