The protective effects of steamed ginger on adipogenesis in 3T3-L1 cells and adiposity in diet-induced obese mice
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: The steamed ginger has been shown to have antioxidative effects and a protective effect against obesity. In the present study, we investigated the effects of ethanolic extract of steamed ginger (SGE) on adipogenesis in 3T3-L1 preadipocytes and diet-induced obesity (DIO) mouse model. MATERIALS/METHODS: The protective effects of SGE on adipogenesis were examined in 3T3-L1 adipocytes by measuring lipid accumulations and genes involved in adipogenesis. Male C57BL/6J mice were fed a normal diet (ND, 10% fat w/w), a high-fat diet (HFD, 60% fat w/w), and HFD supplemented with either 40 mg/kg or 80 mg/kg of SGE for 12 weeks. Serum chemistry was measured, and the expression of genes involved in lipid metabolism was determined in the adipose tissue. Histological analysis and micro-computed tomography were performed to identify lipid accumulations in epididymal fat pads. RESULTS: In 3T3-L1 cells, SGE significantly decreased lipid accumulation, with concomitant decreases in the expression of adipogenesis-related genes. SGE significantly attenuated the increase in body, liver, and epididymal adipose tissue weights by HFD. Serum total cholesterol and triglyceride levels were significantly lower in SGE fed groups compared to HFD. In adipose tissue, SGE significantly decreased adipocyte size than that of HFD and altered adipogenesis-related genes. CONCLUSIONS: In conclusion, steamed ginger exerted anti-obesity effects by regulating genes involved in adipogenesis and lipogenesis in 3T3-L1 cell and epididymal adipose tissue of DIO mice.
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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".