Aged skeletal muscle retains the ability to fully regenerate functional architecture
Bibliographic record
Abstract
While the general understanding of muscle regenerative capacity is that it declines with increasing age due to impairments in the number of muscle progenitor cells and interaction with their niche, studies vary in their model of choice, indices of myogenic repair, muscle of interest and duration of studies. We focused on the net outcome of regeneration, functional architecture, compared across three models of acute muscle injury to test the hypothesis that satellite cells maintain their capacity for effective myogenic regeneration with age. Muscle regeneration in extensor digitorum longus muscle (EDL) of young (3 mo-old), old (22 mo-old) and senescent female mice (28 mo-old) was evaluated for architectural features, fiber number and central nucleation, weight, collagen and fat deposition. The 3 injury paradigms were: a myotoxin (notexin) which leaves the blood vessels and nerves intact, freezing (FI) that damages local muscle, nerve and blood vessels and denervation-devascularization (DD) which dissociates the nerves and blood vessels from the whole muscle. Histological analyses revealed successful architectural regeneration following notexin injury with negligible fibrosis and fully restored function, regardless of age. In comparison, the regenerative response to injuries that damaged the neurovascular supply (FI and DD) was less effective, but similar across the ages. The focus on net regenerative outcome demonstrated that old and senescent muscle has a robust capacity to regenerate functional architecture.
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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) | 0.001 | 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".