Resveratrol improves high-fat diet induced fatty liver and insulin resistance by concomitantly inhibiting proteolytic cleavage of sterol regulatory element-binding proteins, free fatty acid oxidation, and intestinal triglyceride absorption
Bibliographic record
Abstract
Resveratrol (RES) has the ability to ameliorate nonalcoholic fatty liver disease (NAFLD) and the mechanism remains unclear. Hence, using high-fat diet (HFD) obese rat model, we investigated the effect of a low dose of RES (20 mg/kg) on the hepatic sterol regulatory element-binding protein (SREBPs) - lipogenesis pathway, enzymes involved in β-oxidation and activity of pancreatic lipase. Four groups of rats (n = 8) of control (12% of calories as fat) and HFD (40% of calories as fat) were administered orally with either normal saline as a vehicle or RES as a concomitant treatment for 8 weeks on a daily basis. Then, various biochemical, histological, and molecular experiments were carried out. RES prevented the development and progression of NAFLD and significantly improved insulin sensitivity through (1) inhibiting the proteolytic cleavage of SREBPs-1 and SREBPs-2 without affecting their precursor mRNA or protein levels, (2) inhibiting free fatty acid β-oxidation and generation of reactive oxygen species through significant inhibition of CPT-1 and UCP-2, and (3) decreasing activity of pancreatic lipase in vivo and in vitro. In conclusion, our findings are the first in the literature to show new mechanisms of the hepatoprotective effect of RES against HFD induced NAFLD in 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) | 0.000 | 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".