Aging‐related loss of skeletal muscle strength: the role of muscle quality (863.8)
Bibliographic record
Abstract
Objective: Skeletal muscle aging is associated with a progressive loss of mass and strength. In this process, the loss of muscle quality has been suggested to cause a disproportionate loss in muscle strength compared to the loss in mass. This study aimed at investigating the role of changes in muscle quality in the loss of muscle strength with aging. Methods: Ten young (24±3 yo) and 9 old (72±4 yo) active men were recruited and matched for physical activity levels. Body composition (DXA and MRI) and knee extension strength (KES) were assessed. Muscle quality indexes (quadriceps mass/KES, volume/KES and CSA/KES) were then calculated. Muscle biopsies were performed in the vastus Lateralis to assess fiber type proportion and size, and intracellular lipid content (Oil Red O). Results: KES, quadriceps mass, volume and CSA were lower in old vs. young adults (p<0.05). Fat mass percentage was higher in old vs. young adults (p<0.05), while total body weight was similar. No differences were observed between young and old men in muscle quality indexes, intracellular lipid content, fiber size and type proportion. However, type IIa fibers tended to be smaller in old than in young adults (p=0.052). Discussion: Our results suggest that, in active old men, the loss in muscle strength is mainly attributable to a decrease in muscle mass. Physical activity may contribute to the preservation of the quality of contraction per unit of muscle.
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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 | 0.000 |
| Open science | 0.000 | 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 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".