The effects of mild‐exercise training cessation on physiological adaptations and gene expressions in human skeletal muscle
Bibliographic record
Abstract
Stoppage of endurance exercise training leads to complete loss of V̇O 2 max gain but not submaximal and maximal exercise blood lactate concentrations. However, the detailed mechanisms are still unknown because the transcriptome of skeletal muscle after detraining has never been characterized. Thus, we investigated the effects of exercise‐training cessation at lactate threshold (LT) intensity on physiological adaptations and skeletal muscle transcriptome. Twelve weeks of detraining abolished the effect of 12‐weeks LT training on V̇O 2 max . The detraining also reversed V̇O 2 at LT intensity, however, the value tended to be higher than the pre‐training level (p = 0.07). Moreover, the training cessation did not affect the number of capillaries around type I fiber which was increased by the LT training. The global gene expression profile measured by serial analysis of gene expression (SAGE) revealed that the training and detraining modulated 249 and 245 characterized transcripts, respectively. The majority of training‐responsive transcripts (86%) showed a significant reversible effect of detraining. However, the most‐induced transcripts by the training were still elevated after the same period of detraining. These genes are involved in oxygen transport, creatine and mitochondrial energy metabolisms, contractile apparatus, and protein synthesis. Incomplete reversal of these transcripts as well as sustained capillary distribution by the detraining may contribute to the partial reversal of V̇O 2 at LT intensity.
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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) | 0.002 | 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".