Dietary <i>Salvia hispanica</i> L. reduces cardiac oxidative stress of dyslipidemic insulin-resistant rats
Bibliographic record
Abstract
Salvia hispanica L., commonly known as chia seed, has beneficial effects upon some signs of metabolic syndrome (MS), such as dyslipidemia and insulin resistance. However, its action on cardiac oxidative stress associated with MS remains unknown. The goal of this study was to analyze the possible beneficial effects of chia seed (variety Salba) upon the oxidative stress of left ventricle heart muscle (LV) of a well-established dyslipidemic insulin-resistant rat model induced by feeding them a sucrose-rich diet (SRD). Male Wistar rats received an SRD for 3 months. After that, for 3 additional months, half of the animals continued with the SRD, while the other half received the SRD containing chia as the source of dietary fat instead corn oil (SRD+chia). In the LV of SRD-fed rats, chia seed improved/reverted the depleted activity of antioxidant enzymes glutathione peroxidase, superoxide dismutase (SOD), and catalase, and ameliorated manganese superoxide dismutase messenger RNA (mRNA) levels increasing the expression of the nuclear factor erythroid 2-related factor 2 (Nrf2). Improved the glutathione redox estate, reactive oxygen species, and thiobarbituric acid reactive substances contents normalizing the p47NOX subunit mRNA level. Furthermore, chia normalized hypertension and plasma levels of pro-inflammatory cytokines and oxidative stress biomarkers. The findings show that chia seed intake impacts positively upon oxidative imbalance of LV of dyslipidemic insulin-resistant rats. Novelty Healthy effects of chia seed involve an improvement of cardiac antioxidant defenses through Nrf2 induction. Chia seed intake reduces cardiac oxidative stress markers of dyslipidemic insulin-resistant rats. Dietary chia seed restores cardiac unbalanced redox state of dyslipidemic insulin-resistant rats.
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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) | 0.000 | 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".