Effect of garlic on lipid profile and expression of LXR alpha in intestine and liver of hypercholesterolemic mice
Bibliographic record
Abstract
BACKGROUND: Garlic is one of the medicinal plants which has showed beneficial effects on atherosclerosis risk factors. The liver X receptor α (LXRα) is an important regulator of cholesterol, triglyceride and glucose homeostasis that belongs to the nuclear receptor superfamily. In this study we investigated the effect of garlic on lipid profile, glucose as well as LXRα expression in intestine and liver of mice. METHODS: Forty male N-Mary mice were randomly divided into 3 groups (n = 8): group1 received chow + 2% cholesterol + 0.5% cholic acid, group 2: chow + 4% (w/w) garlic extract + 2% cholesterol + 0.5% cholic acid, and group 3: chow only. After one month of treatment, mice were anesthetized, blood was collected from their heart, and the first 10 cm of the small intestine and liver were removed. Glucose was measured by a glucometer; other biochemical factors were measured by enzymatic methods. LXR expression was checked by RT-PCR and western blotting. RESULTS: Compared with hypercholesterolemic mice, treatment with garlic extract significantly decreased total cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides, very low density lipoprotein-cholesterol (VLDL-C), atherogenic index, alanine aminotranferease (ALT) and aspartate aminotransferase (AST) (all of them P < 0.05). Change in HDL-C levels was not significant in garlic-extract treated animals compared with hypercholesterolemic group. LXR protein and mRNA in the intestine were increased in garlic-extract treated group compared with chow group (P < 0.05), while in the liver, only mRNA of LXR was increased in hypercholesterolemic control mice (P < 0.05). CONCLUSIONS: The present study demonstrated that garlic extract reduced LXRα expression in the liver and increased its expression in the intestine. These effects probably have an important role in reducing serum triglyceride and cholesterol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".