Alcohol extract of North American ginseng (<i>Panax quinquefolius</i>) reduces fatty liver, dyslipidemia, and other complications of metabolic syndrome in a mouse model
Bibliographic record
Abstract
We investigated whether North American ginseng (Panax quinquefolius) could reduce development of the metabolic syndrome phenotype in a mouse model (ETKO) of the disease. Young ETKO mice have no disease but similar to humans start to develop the fatty liver, hypertriglyceridemia, obesity, and insulin resistance at 25-30 weeks of age, and the disease continues to progress with ageing. ETKO mice were orally given an ethanol extract of ginseng roots at 4 and 32 weeks of age. Treatments with ginseng eliminated the ETKO fatty liver, reduced hepatic and intestinal lipoprotein secretion, and reduced the level of circulating lipids. Improvements by ginseng treatments were manifested as a reduction in the expression of genes involved in the regulation of fatty acid and triglyceride (fat) synthesis and secretion by the lipoproteins on one hand, and the stimulation of fatty acid oxidation and triglyceride degradation by lipolysis on the other hand. These processes altogether improved glucose, fatty acid, and triglyceride metabolism, reduced liver fat load, and reversed the progression of metabolic syndrome. These data confirm that treatments with North American ginseng could alleviate metabolic syndrome through the maintenance of a better balance between glucose and fatty acid metabolism, lipoprotein secretion, and energy homeostasis in disease-prone states.
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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".