Berberine Prevents Disease Progression of Nonalcoholic Steatohepatitis through Modulating Multiple Pathways
Bibliographic record
Abstract
Background and Aims: The disease progression of nonalcoholic fatty liver disease (NAFLD) from simple steatosis (NAFL) to nonalcoholic steatohepatitis (NASH) is driven by multiple factors. Berberine (BBR) is an ancient Chinese medicine and has various beneficial effects on metabolic diseases, including NAFLD/NASH. However, the underlying mechanisms remain incompletely understood due to the limitation of the NASH animal models used. Methods: A high-fat and high-fructose diet-induced mouse model of NAFLD, the best available preclinical NASH mouse model, was used. RNAseq, histological, and metabolic pathway analyses were used to identify the potential signaling pathways modulated by BBR. LC–MS was used to measure bile acid levels in the serum and liver. The real-time RT-PCR and Western blot analysis were used to validate the RNAseq data. Results: BBR not only significantly reduced hepatic lipid accumulation by modulating fatty acid synthesis and metabolism but also restored the bile acid homeostasis by targeting multiple pathways. In addition, BBR markedly inhibited inflammation by reducing immune cell infiltration and inhibition of neutrophil activation and inflammatory gene expression. Furthermore, BBR was able to inhibit hepatic fibrosis by modulating the expression of multiple genes involved in hepatic stellate cell activation and cholangiocyte proliferation. Consistent with our previous findings, BBR’s beneficial effects are linked with the downregulation of microRNA34a and long noncoding RNA H19, which are two important players in promoting NASH progression and liver fibrosis. Conclusion: BBR is a promising therapeutic agent for NASH by targeting multiple pathways. These results provide a strong foundation for a future clinical investigation.
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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 | 0.000 | 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) | 0.002 | 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".