Effects of Age and Exercise on Fiber Type and Total Fiber Number in Skeletal Muscle
Bibliographic record
Abstract
There have been varied reports of the effects of age and exercise on fiber type composition in skeletal muscle. To elucidate these discrepancies, we conducted a one year endurance exercise study in male FBN rats, starting with rats age 24 mo and continuing daily until age 36 mo. Rats were divided into 3 groups; high intensity (H), and moderate intensity (M) treadmill exercise, and sedentary (S). M and H animals trained at 13 m/min for 30 min/day, with the H group at 5% incline. At the end of the training protocol the plantaris muscle was removed, frozen and sectioned. Fiber type was determined by IHC using an anti‐type I MHC antibody (BA‐D5s). Total fiber number and number of type I v. type II fibers were determined from high resolution images of muscle cross sections. Total plantaris fiber number declined with age (decrease of 34% in 36 mo compared to 24 mo) but fiber number was not significantly different between the 36 mo exercise groups and the sedentary animals. The proportion of fibers that were type I fibers was 15% in 24 mo, and then 18%, 20% and 18% in S, M, and H respectively. None of these percentages were significantly different from one another, indicating no shift in fiber type with either aging or exercise. These results indicate that total fiber number decreases with age, but this fiber loss may not be preferential to specific fiber types, and also suggest that exercise is unable to minimize fiber loss. Supported by: NIH AG030423
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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 | 0.001 | 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 | 0.001 |
| 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".