Skeletal Muscle Regeneration, Repair and Remodelling in Aging: The Importance of Muscle Stem Cells and Vascularization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it