Autophagy signaling following denervation‐induced muscle disuse in young and old animals
Bibliographic record
Abstract
Contractile activity is required to maintain both muscle mass and function. Decreases in muscle activity lead to atrophy, which is exacerbated by the loss of autophagy signaling. Consequently, autophagy appears to be activated to preserve muscle mass. We wished to establish whether autophagy was increased during disuse-induced muscle atrophy, and whether this was accelerated during aging-induced sarcopenia. Thus, muscle mass and autophagic proteins were compared in young (5mo) and old (35 mo) Fischer BN rats following 7 days of denervation. Muscle mass was decreased by 53% in old compared to young animals, indicating sarcopenia. However, denervation resulted in a 2-fold greater loss of muscle mass in young compared to old animals. Basal levels of autophagy regulators such as ULK1, LC3II, and ATG 7 were 1.5- to 3-fold greater in muscles of old animals. Denervation induced 4- to 5-fold increases in these proteins in young animals, but only 2-fold increases were evident in old animals. Interestingly, autophagy protein expression in both the young and old animals reached similar peak levels. These data suggest that: 1) the loss of contractile activity induces greater atrophy in young, compared to old animals, 2) autophagy signaling is elevated in muscle of old animals, and 3) denervation-induced disuse appears to provoke similar levels of autophagic signaling regardless of 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 | 0.000 | 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 | 0.000 |
| 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".