Influence of physical fitness on antioxidant activity and malondialdehyde level in healthy older adults
Bibliographic record
Abstract
The aim of this study was to investigate how physical fitness level could affect antioxidant activity and malondialdehyde (MDA) level at rest and in response to exhaustive exercise in healthy older adults. Fifty older adults (average age: 66.1 ± 3.8 years) were divided according to their physical fitness level into an unfit group (UG) (n = 15), a low fitness level group (LFG) (n = 18), and a high fitness level group (HFG) (n = 17). Fitness status was classified based on answers to a questionnaire about physical activity in the previous 12 months. Before and after an incremental cycle ergometer test to exhaustion, the following markers were assessed: superoxide dismutase (SOD), glutathione peroxidase (GPX), glutathione reductase, ascorbic acid, α-tocopherol, and MDA. At rest, SOD, GPX, and α-tocopherol activities were higher in the HFG (p < 0.05), whereas MDA level was lower in the LFG in comparison with the 2 other groups (p < 0.05). During the postexercise period, antioxidant activity increased only in the LFG and the HFG (GPX, SOD, and α-tocopherol). MDA level increased in all groups after the exercise (p < 0.05). In addition, MDA level was higher during the recovery period in the HFG as compared with the others groups. This study concluded that both low and high physical fitness levels help maintain better antioxidant defenses in older adults. However, a higher physical fitness level, rather than a lower physical fitness level, could increase lipid peroxidation.
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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 | 0.000 | 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".