Supplementation with vitamin A enhances oxidative stress in the lungs of rats submitted to aerobic exercise
Bibliographic record
Abstract
Exercise training induces reactive oxygen species production and low levels of oxidative damage, which are required for induction of antioxidant defenses and tissue adaptation. This process is physiological and essential to improve physical conditioning and performance. During exercise, endogenous antioxidants are recruited to prevent excessive oxidative stress, demanding appropriate intake of antioxidants from diet or supplements; in this context, the search for vitamin supplements that enhance the antioxidant defenses and improve exercise performance has been continuously increasing. On the other hand, excess of antioxidants may hinder the pro-oxidant signals necessary for this process of adaptation. The aim of this study was to investigate the effects of vitamin A supplementation (2000 IU/kg, oral) upon oxidative stress and parameters of pro-inflammatory signaling in lungs of rats submitted to aerobic exercise (swimming protocol). When combined with exercise, vitamin A inhibited biochemical parameters of adaptation/conditioning by attenuating exercise-induced antioxidant enzymes (superoxide dismutase and glutathione peroxidase) and decreasing the content of the receptor for advanced glycation end-products. Increased oxidative damage to proteins (carbonylation) and lipids (lipoperoxidation) was also observed in these animals. In sedentary animals, vitamin A decreased superoxide dismutase and increased lipoperoxidation. Vitamin A also enhanced the levels of tumor necrosis factor alpha and decreased interleukin-10, effects partially reversed by aerobic training. Taken together, the results presented herein point to negative effects associated with vitamin A supplementation at the specific dose here used upon oxidative stress and pro-inflammatory cytokines in lung tissues of rats submitted to aerobic exercise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".