Grape seed and skin extract mitigates garlic-induced oxidative stress in rat liver
Bibliographic record
Abstract
Garlic is a commonly used spice in folk medicine that can exert adverse health effects when given at a high dose. Grape seed and skin extract (GSSE) exhibits a variety of beneficial effects even at a high dose. In the present study we evaluated the toxicity of high-dose garlic treatment on liver and the protective effect of GSSE. Rats were intraperitoneally administered either with garlic extract (5 g·(kg body weight)(-1)) or GSSE (500 mg·(kg body weight)(-1)) or a combination of garlic and GSSE at the same doses daily for 1 month. Plasma and hepatic levels of cholesterol, triacylglycerol, and transaminases and liver antioxidant status were evaluated. Data showed that a high garlic dose induced liver toxicity and a pro-oxidative status characterized by increased malondialdehyde and decreased antioxidant enzyme activities as catalase, peroxidase, and superoxide dismutase. Garlic increased intracellular H(2)O(2) but decreased free iron and Ca(2+). GSSE alone or in co-treatment with garlic had the reverse effect and counteracted almost all garlic-induced deleterious impacts to near control levels. In conclusion, a high garlic dose induced a pro-oxidative state characterized by the Fenton reaction between H(2)O(2) and free iron, inducing Ca(2+) depletion, while GSSE exerted antioxidant properties and Ca(2+) repletion.
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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 | 0.000 | 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".