Resistance training minimizes catabolic effects induced by sleep deprivation in rats
Bibliographic record
Abstract
Sleep deprivation (SD) can induce muscle atrophy. We aimed to investigate the changes underpinning SD-induced muscle atrophy and the impact of this condition on rats that were previously submitted to resistance training (RT). Adult male Wistar EPM-1 rats were randomly allocated into 1 of 5 groups: control, sham, SD (for 96 h), RT, and RT+SD. The major outcomes of this study were muscle fiber cross-sectional area (CSA), anabolic and catabolic hormone profiles, and the abundance of select proteins involved in muscle protein synthesis and degradation pathways. SD resulted in muscle atrophy; however, when SD was combined with RT, the reduction in muscle fiber CSA was attenuated. The levels of IGF-1 and testosterone were reduced in SD animals, and the RT+SD group had higher levels of these hormones than the SD group. Corticosterone was increased in the SD group compared with the control group, and this increase was minimized in the RT+SD group. The increases in corticosterone concentrations paralleled changes in the abundance of ubiquitinated proteins and the autophagic proteins LC3 and p62/SQSTM1, suggesting that corticosterone may trigger these changes. SD induced weight loss, but this loss was minimized in the RT+SD group. We conclude that SD induced muscle atrophy, probably because of the increased corticosterone and catabolic signal. High-intensity RT performed before SD was beneficial in containing muscle loss induced by SD. It also minimized the catabolic signal and increased synthetic activity, thereby minimizing the body's weight loss.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".