Preventive Effects of Selenium Yeast, Chromium Picolinate, Zinc Sulfate and their Combination on Oxidative Stress, Inflammation, Impaired Angiogenesis and Atherogenesis in Myocardial Infarction in Rats
Bibliographic record
Abstract
PURPOSE: Accumulating evidences suggest a critical role of trace metal dyshemostasis in oxidative stress and cardiac dysfunction after myocardial infarction (MI). This study investigated the cardioprotective effects of selenium yeast (Se), chromium picolinate Cr(pic)3, zinc sulfate (Zn) and their combination on isoproterenol (ISO)-induced MI. METHODS: Rats were divided into six groups: normal control, ISO control, Se-pretreated (0.1 mg/kg), Cr(pic)3-pretreated (400 µg/kg), Zn-pretreated (30 mg/kg) and metal combination-pretreated groups. All metals were administered for 28 days and at the 27th day, MI was induced by subcutaneous injection of ISO (85 mg/kg) once for two consecutive days. RESULTS: ISO control group showed hyperlipidemia, elevation of cardiac biomarkers and lipid peroxidation and increased immunostaining of p47 phox NADPH oxidase subunit in addition to decreased levels of glutathione (GSH), superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx). Cardiac levels of tumor necrosis factor-α (TNF-α) were increased, while vascular endothelial growth factor (VEGF, the major angiogenic factor) was decreased. Pretreatment with Se normalized the cardiac enzymes, lipid peroxidation, GSH, SOD, CAT, GPx, TNF-α and VEGF (P<0.001) and reduced the immunostaining of p47 phox subunit. However, Se failed to correct the dyslipidemia. Cr(pic)3 significantly improved lipid profile (P<0.001) and all other biochemical deviations except for VEGF. Zn, but to lesser extent, reduced the oxidative damage and TNF-α levels and improved both dyslipidemia and angiogenesis. Combination therapy exhibited less prominent protection compared to individual metals. CONCLUSION: Daily supplementation with trace metals is promising for improving myocardial performance via preventing oxidative damage, induction of angiogenesis, anti-inflammatory and/or anti-hyperlipidemic mechanisms.
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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) | 0.001 | 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 | 0.000 | 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".