Effect of oral CoQ<sub>10</sub>supplementation along with precooling strategy on cellular response to oxidative stress in elite swimmers
Bibliographic record
Abstract
High intensity and prolonged swimming trainings in a hot and humid environment lead to stimulated and increased production of reactive oxygen and nitrogen species (RONS). In this study, we examined the effects of 14-day coenzyme Q10 (CoQ10) supplementation and precooling strategy on the serum levels of NADPH-oxidase (NOX), hydrogen peroxide (H2O2), lactic acid (LA), creatine kinase (CK), 8-isoprostane (8-iso PGF2α), 8-hydroxy-2-deoxyguanosine (8-OHdG), aspartate aminotransferase (AST), protein carbonyls (PC), alanine aminotransferase (ALT), and gamma-glutamyl transferase (GGT) in adolescent elite swimmers. Thirty-six healthy boys (mean ± SD: age = 17 ± 1 years) were randomly assigned into 4 groups: supplementation, precooling, supplementation with precooling, and control. Blood sampling was carried out pre- and post- (two stages) administration of CoQ10 along with precooling with heavy trainings. ANCOVA and repeated measurement tests with the Bonferroni post-hoc test were used for statistical analysis of data (α = 0.05). No significant difference was found among the groups for serum levels of H2O2, NADPH-oxidase, CK, LA, 8-OHdG, 8-iso PGF2α, PC, AST, ALT, and GGT at pre-sampling (P > 0.05). The precooling group showed significant increase in index levels compared to the supplementation and supplementation with precooling groups in post sampling (stages 1 and 2), respectively (P < 0.05). Oral administration of CoQ10 inhibited adverse changes in oxidative stress and muscle and liver damage indices in the competition phase of swimming. No desired effect of the precooling strategy was found on the serum levels of NADPH-oxidase, CK, LA, 8-iso PGF2α, 8-OHdG, H2O2, AST, PC, ALT, and GGT.
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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) | 0.002 | 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".