Effect of fucoidan on ethanol-induced liver injury and steatosis in mice and the underlying mechanism
Bibliographic record
Abstract
Background: Alcoholic liver disease is caused as a result of chronic alcohol consumption. In this study, we used an alcoholic liver injury mouse model to investigate the effect of fucoidan on ethanol-induced liver injury and steatosis and the underlying mechanisms. Methods: All mice were randomly divided into four groups: 1) control group, 2) model group, 3) diammonium glycyrrhizinate treatment group (200 mg/kg body weight), and 4) fucoidan treatment group (300 mg/kg body weight). Administration of ethanol for 8 weeks induced liver injury and steatosis in mice. Results: Fucoidan treatment decreased serum alanine aminotransferase activity, serum total cholesterol levels, and hepatic triglyceride levels, and improved the morphology of hepatic cells. Fucoidan treatment upregulated the expression of AMPKα1, SIRT1, and PGC-1α and inhibited the expression of ChREBP and HNF-1α. The levels of hepatic IL-6 and IL-18 were significantly decreased in the fucoidan group. Further, the levels of cytochrome P450-2E1 (CYP2E1), glucose-regulated protein (GRP) 78, and 3-nitrotyrosine (3-NT) in hepatic tissues were reduced in the fucoidan group as compared to the model group. Fucoidan significantly reversed the reduction of ileac Farnesoid X receptor (FXR) and fibroblast growth factor 15 (FGF15) levels induced by alcohol- feeding and reduced CYP7A1 (cholesterol 7α-hydroxylase) expression and total bile acid levels in the liver tissue. In addition, fucoidan regulated the structure of gut flora, with increased abundance of Prevotella and decreased abundance of Paraprevotella and Romboutsia as detected by 16S rDNA high-throughput sequencing. Conclusion: Fucoidan inhibited alcohol-induced steatosis and disorders of bile acid metabolism in mice through the AMPKα1/SIRT1 pathway and the gut microbiota–bile acid–liver axis and protected against alcohol- induced liver injury in vivo.
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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) | 0.000 | 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) | 0.001 | 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".