Molecular factors involved in the hypolipidemic- and insulin-sensitizing effects of a ginger (<i>Zingiber officinale</i> Roscoe) extract in rats fed a high-fat diet
Bibliographic record
Abstract
Hypolipidemic and hypoglycemic properties of ginger in animal models have been reported. However, information related to the mechanisms and factors involved in the metabolic effects of ginger at a hepatic level are limited. The aim of the present study was to investigate molecular factors involved in the hypoglycemic and hypolipidemic effects of a hydroethanolic ginger extract (GE) in the liver of rats fed a high-fat diet (HFD). The study was conducted in male Wistar rats divided into the following 3 groups: (i) Rats fed a standard diet (3.5% fat), the control group; (ii) rats fed an HFD (33.5% fat); and (iii) rats fed an HFD treated with GE (250 mg·kg−1·day−1) for 5 weeks (HFD+GE). Plasma levels of glucose, insulin, lipid profile, leptin, and adiponectin were measured. Liver expression of glycerol phosphate acyltransferase (GPAT), cholesterol 7 alpha-hydroxylase, peroxisome proliferator-activated receptors (PPAR), PPARα and PPARγ, glucose transporter 2 (GLUT-2), liver X receptor, sterol regulatory element-binding protein (SREBP1c), connective tissue growth factor (CTGF), and collagen I was measured. Data were analyzed using a 1-way ANOVA, followed by a Newman−Keuls test if differences were noted. The study showed that GE improved lipid profile and attenuated the increase of plasma levels of glucose, insulin, and leptin in HFD rats. This effect was associated with a higher liver expression of PPARα, PPARγ, and GLUT-2 and an enhancement of plasma adiponectin levels. Furthermore, GE reduced liver expression of GPAT, SREBP1c, CTGF, and collagen I. The results suggest that GE might be considered as an alternative therapeutic strategy in the management of overweight and hepatic and metabolic−related alterations.
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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".