Nuciferine Prevents Hepatic Steatosis and Injury Induced by a High-Fat Diet in Hamsters
Bibliographic record
Abstract
BACKGROUND: Nuciferine is a major active aporphine alkaloid from the leaves of N. nucifera Gaertn that possesses anti-hyperlipidemia, anti-hypotensive, anti-arrhythmic, and insulin secretagogue activities. However, it is currently unknown whether nuciferine can benefit hepatic lipid metabolism. METHODOLOGY/PRINCIPAL FINDINGS: In the current study, male golden hamsters were randomly divided into four groups fed a normal diet, a high-fat diet (HFD), or a HFD supplemented with nuciferine (10 and 15 mg/kg·BW/day). After 8 weeks of intervention, HFD-induced increases in liver and visceral adipose tissue weight, dyslipidemia, liver steatosis, and mild necroinflammation in hamsters were analyzed. Nuciferine supplementation protected against HFD-induced changes, alleviated necroinflammation, and reversed serum markers of metabolic syndrome in hamsters fed a HFD. RT-PCR and western blot analyses revealed that hamsters fed a HFD had up-regulated levels of genes related to lipogenesis, increased free fatty acid infiltration, and down-regulated genes involved in lipolysis and very low density lipoprotein secretion. In addition, gene expression of cytochrome P4502E1 and tumor necrosis factor-α were also increased in the HFD group. Nuciferine supplementation clearly suppressed HFD-induced alterations in the expression of genes involved in lipid metabolism. CONCLUSIONS/SIGNIFICANCE: Nuciferine supplementation ameliorated HFD-induced dyslipidemia as well as liver steatosis and injury. The beneficial effects of nuciferine were associated with altered expression of hepatic genes involved in lipid metabolism.
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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 | 0.000 |
| 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".