Effects of docosahexaenoic acid diet supplementation, training, and acute exercise on oxidative balance in neutrophils
Bibliographic record
Abstract
Diet supplementation with omega-3 fatty acids could influence the oxidative equilibrium, enhancing a pro-oxidant status. The aim was to determine the effects of diet supplementation with docosahexaenoic acid (DHA), training, and acute exercise on oxidative balance in neutrophils. Fifteen volunteer male soccer players were randomly assigned to a placebo or experimental group. The placebo group was supplemented with an almond-based beverage whereas the experimental group was supplemented with the same beverage enriched with DHA, in addition to their Mediterranean-type diet. Three blood samples were taken: in basal conditions at the beginning of the nutritional intervention and after 8 weeks of training season in basal and postexercise conditions. The training season significantly increased the antioxidant defenses of neutrophils, such as catalase, glutathione peroxidase and glutathione reductase enzyme activities; and decreased oxidative damage markers such as malondialdehyde, carbonyl and nitrotyrosine indexes. Oxidative damage markers decreased in neutrophils after acute exercise, which primed neutrophils to produce reactive oxygen and nitrogen species (RONS) after immune stimulation with zymosan or phorbol myristate acetate in trained footballers. DHA supplementation resulted in no significant effects on oxidative stress balance in neutrophils. In conclusion, DHA supplementation did not modify the adaptive response of the antioxidant system of neutrophils to training or the production of RONS induced by immune stimulation after acute exercise.
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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".