Prolonged moderate-intensity aerobic exercise does not alter apoptotic signaling and DNA fragmentation in human skeletal muscle
Bibliographic record
Abstract
Apoptosis in skeletal muscle plays an important role in age- and disease-related tissue dysfunction. Physical activity can influence apoptotic signaling; however, this process has not been well studied in human skeletal muscle. The purpose of this study was to perform a comprehensive analysis of apoptosis-related proteins/enzymes, DNA fragmentation, and oxidative stress in skeletal muscle of humans during an acute bout of prolonged moderate-intensity exercise. Eight healthy, recreationally active individuals (age 20.8 +/- 0.5 yr, Vo(2peak) 51.2 +/- 0.9 ml . kg(-1) . min(-1), BMI 21.5 +/- 0.8 kg/m(2)) exercised on a cycle ergometer at approximately 60% Vo(2peak) for 2 h. Muscle biopsies were obtained at rest as well as at 60 and 120 min of exercise. Although exercise was associated with a significant whole body and muscle metabolic response, there were no significant changes in the content of antiapoptotic (ARC, Bcl-2, Hsp70, XIAP) and proapoptotic (AIF, Bax, Smac) proteins, activity of proteolytic enzymes (caspase-3, caspase-8, caspase-9), DNA fragmentation, or TUNEL-positive nuclei in skeletal muscle. Furthermore, the protein levels of several antioxidant enzymes (catalase, CuZnSOD, MnSOD), concentrations of GSH and GSSG, and degree of ROS generation in skeletal muscle were not altered by exercise. Fiber type-specific analysis also revealed that ARC (P < 0.001) and Hsp70 (P < 0.05) protein were significantly higher in type I compared with type IIA and type IIAX/X fibers; however, protein levels were not affected by exercise. These findings suggest that a single bout of prolonged moderate-intensity aerobic exercise is not sufficient to alter apoptotic signaling in skeletal muscle of healthy humans.
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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) | 0.001 | 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".