Protein synthesis, autophagy, and regeneration-related signaling in fast/slow muscles in two mammalian models of disuse
Bibliographic record
Abstract
Whether regulation of protein synthesis, autophagy, and regeneration are involved in slow/fast muscles in two disuse models (hindlimb unloading Sprague-Dawley rats ( Rattus norvegicus domestica (Berkenhout, 1769))) (HLU) and hibernating ground squirrels ( Spermophilus dauricus Brandt, 1843 (HIB)) is still unclear. Our results showed (1) fiber cross-sectional area was reduced in soleus (SOL) and extensor digitorum longus (EDL) of HLU whereas no change in HIB. The satellite cells/fiber was reduced and myonuclei/fiber was increased in SOL of HLU, while the percentage of satellite cells was significantly reduced in EDL of HIB. (2) Protein levels of phosphorylated- (P-)Akt, mTORC1, P-mTORC1, and P-S6K1 were reduced in SOL of HLU, whereas phosphorylated S6K1 was increased only in EDL of HIB. (3) Myostatin decreased in EDL of HLU but decreased in SOL of HIB. (4) Beclin1 increased in EDL of HLU and in SOL of HIB. (5) The activity of cathepsin L increased in SOL of HIB. (6) Collagen III increased in both SOL and EDL of HLU, but myogenin and collagen III increased in SOL, and collagen III was reduced in EDL of HIB. Taken together, Akt-mTORC1, Beclin1, and myogenin signaling showed muscle-specific responses in slow-twitch versus fast-twitch muscles, which may contribute to disuse atrophy in non-hibernators and anti-atrophy in hibernators.
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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 | 0.000 |
| 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".