Grape seed and skin extract mitigates heart and liver oxidative damage induced by a high-fat diet in the rat: gender dependency
Bibliographic record
Abstract
Obesity is a public health problem contributing to morbidity and mortality from metabolic syndrome. It has long been recognized that there is a gender dependency in several obesity-related health risks. Using a high fat diet (HFD) to induce obesity in Wistar rats, we studied the gender dependency of fat-induced oxidative stress in the heart and liver, with a special emphasis on the distribution of transition metals, as well as the protective effects of grape seed and skin extract (GSSE). HFD induced obesity in both male and female rats, characterized by increased body weight as well as relative liver mass in both genders, and increased relative heart mass in the males only. HFD also provoked the accumulation of triglycerides and total cholesterol into the male hearts, and into the livers of both genders. HFD induced oxidative stress in the male hearts and also in the livers of both genders. Furthermore, HFD affected cardiac levels of copper in the males, and hepatic levels of copper and zinc in both genders, whereas HFD affected free iron in the male hearts and female livers, specifically. In conclusion, HFD treatment altered transition metal homeostasis more drastically in the male heart than in the female liver, and GSSE efficiently protected these organs against fat-induced disturbances, regardless of gender.
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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) | 0.000 | 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 | 0.001 |
| 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".