Mechanical and chemical activation of muscle satellite cells is perturbed in normal aged mice
Bibliographic record
Abstract
Age‐related muscle atrophy and the importance of satellite cells in muscle maintenance, growth and repair led us to examine the effects of mechanical stretch, nitric oxide (NO), and age on satellite cell (SC) activation and gene expression in normal young and old mice. Baseline variables (body mass, muscle mass, fiber cross‐sectional area (CSA), muscle strength, SC population, stretch activation and gene expression) were obtained from normal C57/BL6 mice at 3, 6, 8, 12 and 18 months‐of‐age. Activation was assayed by 3H‐thymidine incorporation into extensor digitorum longus (EDL) muscles isolated for culture with (S) or without stretching (US). In a second experiment, muscle from 8‐ and 18‐month old mice was treated with one or more of: stretch, L‐Arginine (LA); NO‐donors (isosorbide dinitrate (ISDN) and MyoNovin (MN) a new formulation); Nω‐nitro‐L‐Arginine methyl ester (LN). EDL muscles from 6‐month‐old mice required a greater stretch stimulus (20% vs. 10% length increase) than EDL from younger mice to increase SC activation. Stretch did not increase SC activation in mice older than 6 months‐of‐age. NO from either endogenous (LA) or exogenous (ISDN, MN) NO donors increased SC activation by stretch in 8‐ but not 18‐mo‐old EDLs. Perturbed sensitivity to mechanical stimulation and NO in 18‐mo‐old mice may partly explain loss of muscle mass, fiber CSA, relative grip strength and SC pool size with age. Similar to dystrophic muscle, a disrupted dystrophin‐glycoprotein complex and subsequent alteration in NO availability may affect the ability of native satellite cells to maintain or effectively regenerate aged muscle. Grant Funding Source Canadian Space Agency Life Sciences
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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 | 0.001 | 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 | 0.001 |
| 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".