Comparative effect of vitamin D3 and carbenoxolone treatments in metabolic syndrome rats
Bibliographic record
Abstract
Metabolic syndrome (MetS) is a cluster of cardiovascular risk factors including central obesity, hypertension, insulin resistance, dyslipidemia, and hyperglyemia. MetS is found to be a positive predictor of cardiovascular morbidity and mortality. The present study was planned to test the efficacy of vitamin D3 supplementation as compared with cortisol inhibition on MetS parameters. Wistar rats were allocated into four groups: control, untreated MetS, and MetS treated with either vitamin D3 (10 µg/kg) or carbenoxolone (50 mg/kg). MetS was induced by combination of high-fat diet and oral fructose. After the induction period (8 weeks), MetS was confirmed, and treatment modalities started for a further 4 weeks. Compared with untreated MetS, vitamin D3- and carbenoxolone-treated rats showed significant reduction in blood pressure, body mass index, Lee index, waist circumference, retroperitoneal fat, and improvement of dyslipidemia. Meanwhile, treatment with carbenoxolone significantly lowered the elevated liver enzymes, and vitamin D3 resulted in improved insulin sensitivity, enhanced glucose uptake by muscles, and replenished glycogen content in the liver and muscles near control levels. In conclusion, although treatment with vitamin D3 or carbenoxolone reduced the risk factors associated with MetS, vitamin D3 was effective in ameliorating insulin resistance which is the hallmark of MetS.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".