Aging is Associated with Reductions in Fascicle Length, Sarcomere Length and Serial Sarcomere Loss
Bibliographic record
Abstract
Introduction: Aging is associated with decreased active force production leading to muscle weakness and subsequently decreased muscle performance. Aging also affects the muscle’s passive force properties; whereby in old age passive muscle force has been shown to be elevated above that of young, which may be related to increased muscle stiffness with age. The purpose of this study was to investigate potential structural property changes that occur in aged muscle that may contribute to increased passive force. Methods : The muscle length where peak force occurred (i.e. plateau of the force-length relationship (FL); L 0 ) was determined for the medial gastrocnemius muscle (MG) of young ( n = 9) and old rats ( n = 8) rats. Muscles were fixed at L 0 in 10% formalin , fascicle length, sarcomere number and the sarcomere length were compared at L 0. Results: Muscle from old rats showed a reduction of ~14% in fascicle length, ~4% in sarcomere length and ~10% in sarcomere number, ( P < 0.001). Discussion: Shorter fascicles and reduced sarcomeres in series in muscle from old rats may explain increased passive forces in older individuals. Reduced sarcomere number in series would lead to overstretched sarcomeres, leading to increased tension on sarcomere passive force structures and sarcomeres operating on the descending limb of FL relationship.
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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) | 0.002 | 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".