The benefit of a supplement with the antioxidant melatonin on redox status and muscle damage in resistance-trained athletes
Bibliographic record
Abstract
Previous data showed that the administration of high doses of melatonin improved the circadian system in athletes. Here, we investigated in the same experimental paradigm whether the antioxidant properties of melatonin has also beneficial effects against exercise-induced oxidative stress and muscle damage in athletes. Twenty-four athletes were treated with 100 mg·day−1 of melatonin or placebo 30 min before bedtime during 4 weeks in a randomized double-blind scheme. Exercise intensity was higher during the study that before starting it. Blood samples were collected before and after treatment, and plasma was used for oxygen radical absorption capacity (ORAC), lipid peroxidation (LPO), nitrite plus nitrate (NOx), and advanced oxidation protein products (AOPP) determinations. Glutathione (GSH), glutathione disulphide (GSSG) levels, and glutathione peroxidase (GPx) and reductase (GRd) activities, were measured in erythrocytes. Melatonin intake increased ORAC, reduced LPO and NOx levels, and prevented the increase of AOPP, compared to placebo group. Melatonin was also more efficient than placebo in reducing GSSG·GSH−1 and GPx·GRd−1 ratios. Melatonin, but not placebo, reduced creatine kinase, lactate dehydrogenase, creatinine, and total cholesterol levels. Overall, the data reflect a beneficial effect of melatonin treatment in resistance-training athletes, preventing extra- and intracellular oxidative stress induced by exercise, and yielding further skeletal muscle protection against exercise-induced oxidative damage.
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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) | 0.001 | 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".