Protective effect of rimonabant, a canabinoid receptor 1 antagonist, on nonalcoholic fatty liver disease in a rat model through modulation of the hepatic expression of activin A and follistatin
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a major cause of liver morbidity and mortality, and there is still no proven effective therapy. The endocannabinoid system plays an important role in various liver diseases. Activin A is a member of the transforming growth factor beta (TGF-β) superfamily and inhibits hepatocyte growth. Follistatin antagonizes the biological actions of activin A. This study was designed to investigate the effect of rimonabant (a potent cannabinoid receptor1 (CB1) antagonist) on NAFLD induced with a choline-deficient (CD) diet in rats, as well as to detect whether it can alter the hepatic expression of activin A and follistatin. Forty rats were distributed among 4 groups: the control group, the rimonabant treatment group (normal rats that received rimonabant); the CD diet group (NAFLD induced with a CD diet); and the CD diet + rimonabant group (NAFLD treated with rimonabant). It was found that the CD diet caused significant increase in liver index, serum levels of liver enzymes, malondialdehyde (MDA), TGF-β1, activin A, and CB1 expression in liver tissue, with a significant decrease in glutathione peroxidase (GSH-Px) and follistatin mRNA expression in liver tissues. The administration of rimonabant significantly improved all of the studied parameters compared with the group fed the CD diet alone. Histopathological examination supported these results. We concluded that rimonabant significantly counteracted NAFLD induced with the CD diet by decreasing oxidative stress and hepatic expression of TGF-β1, and modulating the hepatic expression of activin A and follistatin.
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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 | 0.001 | 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".