The importance of serial sarcomere addition for muscle function and the impact of aging
Bibliographic record
Abstract
During natural aging, skeletal muscle experiences impairments in mechanical performance due, in part, to changes in muscle architecture and size, notably with a loss of muscle cross-sectional area (CSA). Another important factor that has received less attention is the shortening of fascicle length (FL), potentially reflective of a decrease in serial sarcomere number (SSN). Interventions that promote the growth of new serial sarcomeres, such as chronic stretching and eccentric-biased resistance training, have been suggested as potential ways to mitigate age-related impairments in muscle function. Although current research suggests it is possible to stimulate serial sarcomerogenesis in muscle in old age, the magnitude of sarcomerogenesis may be less than in young muscle. This blunted effect may be partly due to age-related impairments in the pathways regulating mechanotransduction, muscle gene expression, and protein synthesis, as some have been implicated in SSN adaptation. The purpose of this review was to investigate the impact of aging on the ability for serial sarcomerogenesis and elucidate the molecular pathways that may limit serial sarcomerogenesis in old age. Age-related changes in mechanistic target of rapamycin (mTOR), insulin-like growth factor 1 (IGF-1), myostatin, and serum response factor signaling, muscle ring finger protein (MuRFs), and satellite cells may hinder serial sarcomerogenesis. In addition, our current understanding of SSN in older humans is limited by assumptions based on ultrasound-derived fascicle length. Future research should explore the effects of age-related changes in the identified pathways on the ability to stimulate serial sarcomerogenesis, and better estimate SSN adaptations to gain a deeper understanding of the adaptability of muscle in old age.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".