Protein carbonyl levels correlate with performance in elite field hockey players
Bibliographic record
Abstract
Excess and incorrectly selected exercise can degrade athletic performance from an imbalance in redox homeostasis and oxidative stress, but well-planned training and nutrition can improve antioxidant capacity. The aim of the study was to investigate how nutrient intake could influence oxidative stress and cell lesion biomarkers after 5 days of training followed by a game. Blood was collected from 10 athletes at the start of training (basal), after training (pre-game), and postgame. Their acceleration capacity also was measured pre- and postgame. Blood analysis showed an increase in lactate concentration postgame (13%) and total antioxidant capacity increased both pre-game (13.1%) and postgame (12.7%), all in comparison with basal levels. An oxidative stress marker, protein carbonyl (PC), increased 3-fold over the course of the game, which correlated with a decreased acceleration (r = 0.749). For biomarkers of tissue damage, creatine kinase and aspartate transaminase (AST) increased postgame by 150% and 75%, respectively. The AST variation had a high negative correlation with energy and carbohydrate consumption and a moderate correlation with lipid and vitamin C intake. Protein intake had a positive but moderate correlation with reduced glutathione. The observed correlations suggest that nutritional monitoring can improve exercise physiological homeostasis and that PC serves as a good biomarker for oxidative stress and performance loss.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".