Independent and Combined Effects of All-Out Sprint and Low-Intensity Continuous Exercise on Plasma Oxidative Stress Biomarkers in Trained Judokas
Bibliographic record
Abstract
The purpose of this study was to assess oxidative stress biomarkers prior to and following different forms of exercise. Ten elite male judokas (age: 18.1±1.7 years, athletic experience: 6 years with a training frequency of 6 Judo-sessions/week) performed three cycle ergometry sessions comprising a 30s Wingate test (MAX), 30 min at 60% maximal-aerobic-power-output (LOW) or these two exercise protocols combined (COMBINED) in a repeated-measures design. Venous blood-samples were collected before, and 0(P0), 5(P5), 10(P10) and 20(P20) min after each exercise protocol and assessed for malondialdehyde concentration ([MDA]), glutathione peroxidase (GPX), superoxide dismutase (SOD) and glutathione reductase (GR) content, and total-antioxidant-status (TAS). Plasma [MDA] was found to be increased above baseline at P0 and P5 in the MAX, LOW and COMBINED conditions (p0.05). Twenty min post-exercise, plasma GPX, SOD, GR content and TAS were lower in the MAX compared to the LOW and COMBINED conditions (p<0.05). In conclusion, the findings from this study reveal that redox-related biomarkers exhibited divergent response dynamics following different forms of exercise, which might have implications for understanding the mechanisms of exercise-induced skeletal muscle fatigue and adaptive remodeling.
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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 | 0.000 |
| 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".