Skeletal Muscle Regeneration, Repair and Remodelling in Aging: The Importance of Muscle Stem Cells and Vascularization
Bibliographic record
Abstract
Sarcopenia is the age-related loss of skeletal muscle mass and strength. Ultimately, sarcopenia results in the loss of independence, which imposes a large financial burden on healthcare systems worldwide. A critical facet of sarcopenia is the diminished ability for aged muscle to regenerate, repair and remodel. Over the years, research has focused on elucidating underlying mechanisms of sarcopenia and the impaired ability of muscle to respond to stimuli with aging. Muscle-specific stem cells, termed satellite cells (SC), play an important role in maintaining muscle health throughout the lifespan. It is well established that SC are essential in skeletal muscle regeneration, and it has been hypothesized that a reduction and/or dysregulation of the SC pool, may contribute to accelerated loss of skeletal muscle mass that is observed with advancing age. The preservation of skeletal muscle tissue and its ability to respond to stimuli may be impacted by reduced SC content and impaired function observed with aging. Aging is also associated with a reduction in capillarization of skeletal muscle. We have recently demonstrated that the distance between type II fibre-associated SC and capillaries is greater in older compared to younger adults. The greater distance between SC and capillaries in older adults may contribute to the dysregulation in SC activation ultimately impairing muscle's ability to remodel and, in extreme circumstances, regenerate. This viewpoint will highlight the importance of optimal SC activation in addition to skeletal muscle capillarization to maximize the regenerative potential of skeletal muscle in older adults.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".