The effects of antioxidant vitamin supplementation on resistance exercise induced lipid peroxidation in trained and untrained participants.
Bibliographic record
Abstract
BACKGROUND: The theoretical benefits of using antioxidant vitamin supplements to quench oxygen free radicals appear large. High intensity aerobic-type exercise produces oxygen free radicals that can cause damage to lipid membranes (lipid peroxidation) that may lead to many problems such as the inactivation of cell membrane enzymes, the progression of degenerative diseases (cardiovascular disease and cancer) and lessening of the effectiveness of the immune system. The major function of vitamin E is to work as a chain-breaking antioxidant in a fat soluble environment. Little research has examined lipid peroxidation associated with high intensity resistance exercise or possible protective effects of antioxidant supplementation or the effects of training state. RESULTS: There were no significant group (trained vs untrained) or treatment (vitamin E vs placebo) effects found between the 4 groups assessed. There was only one significant difference found and that was in the main effect for time (F = 22.41, p < 0.01). CONCLUSIONS: The Resistance Exercise Test caused a significant increase in malondialdehyde in all 4 groups at 6 hours post exercise. There was no evidence that vitamin E supplementation was effective in reducing oxidative damage in comparison to the placebo group. As well, there was no difference between the trained and untrained groups with respect to their impact on lipid peroxidation measures.
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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 | 0.001 |
| 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 | 0.001 | 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".