Role of vitamin A oral supplementation on oxidative stress and inflammatory response in the liver of trained rats
Bibliographic record
Abstract
The use of dietary supplements to enhance the benefit of exercise training is a common practice. The liver is the organ where all substances are metabolized, and certain supplements have been associated with liver injury. Vitamin A (VA), a liposoluble vitamin stored in the liver, is commonly used as an antioxidant supplement. Here, we evaluated the effect of chronic VA supplementation on oxidative damage and stress parameters in trained rats. Animals were divided into the following groups: sedentary (SE), sedentary/VA (SE+VA), exercise training (ET), and exercise training/VA (ET+VA). During 8 weeks, animals were subjected to swimming (0%, 2%, 4%, 6% body weight) for 5 days/week and a VA daily intake of 450 retinol equivalents/day. Parameters were evaluated by enzymatic activity analysis, ELISA, and Western blotting. VA caused liver lipid peroxidation and protein damage in exercised rats and inhibited the increase in HSP70 expression acquired with exercise alone. The ET group showed higher levels of antioxidant enzyme activity, and VA inhibited this adaptation. Expression of the pro-inflammatory cytokines, interleukin (IL)-1β, and tumor necrosis factor-α was reduced in the ET+VA group, while the anti-inflammatory cytokine, IL-10, was increased. Western blotting showed that both exercised groups had lower levels of the receptor for advanced glycation end products, suggesting that VA did not affect this receptor. Our study demonstrated that, although VA caused oxidative damage, a controlled administration might exert anti-inflammatory effects. Further studies with higher VA doses and longer ET interventions would elucidate more the effects of the supplementation and exercise on liver parameters.
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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) | 0.001 | 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 | 0.001 | 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".