Effect of Mulberry Leaf Extract on Hepatic Lipogenesis, Lipolysis, and Fibrosis in High Fat Diet-Induced Obese Mice
Bibliographic record
Abstract
The primary aim of this study was to investigate whether mulberry leaf extract (MLE) attenuates obesity-induced hepatic lipogenesis and fibrosis in high fat diet (HFD)-induced obese mice and to elucidate its underlying mechanism in which MLE regulates lipogenesis and fibrosis. HFD-induced obese mice treated with 133 mg/kg and 666 mg/kg MLE showed significantly improved plasma lipid profiles and atherogenic index. MLE treatment significantly reversed up-regulation of genes associated with LXRa-mediated lipogenesis (LPL, SREBP1c, FAS, C/EBPa, and aP2), and genes related to hepatic fibrosis (a-SMA and Type1 collagen) whereas MLE significantly stimulated expressions of lipolysis related genes (UCP2 and PPARa) in the HFD-fed obese mice. Moreover, MLE protected the anti-oxidant defense system from obesity-induced oxidative stress through Nrf2 activation in the HFD-induced obese mice. In conclusion, MLE might inhibit hepatic lipogenesis and fibrosis, and stimulate lipolysis by regulation of Nrf2 activation. Therefore, the present study suggests MLE might be a potential therapeutic substance for obesity-induced non-alcoholic fatty liver disease (NAFLD).
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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 | 0.000 |
| 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) | 0.002 | 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".