Carrier-Free Silibinin/Sorafenib Microparticles Alleviate Metabolic Dysfunction-Associated Steatotic Liver Disease by Modulating Fatty Acid Metabolism
Bibliographic record
Abstract
Feifei Han,1,* Haiping Wang,1,2,* Li Wang,1 Limei Fan,1 Sibei Peng,1 Xiaoying Hou,1,2 Xiji Shu,1,2 Binlian Sun,1,2 Yuchen Liu1,2 1Cancer Institute, School of Medicine, Jianghan University, Wuhan, Hubei, 430056, People’s Republic of China; 2Hubei Key Laboratory of Cognitive and Affective Disorders, Jianghan University, Wuhan, Hubei, 430056, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuchen Liu, Email yuchen.liu@jhun.edu.cnIntroduction: Metabolic dysfunction-associated steatotic liver disease (MASLD), characterized by excessive fat accumulation in the liver, is the most prevalent cause of chronic liver disease globally. The clinical use of pharmacological agents such as silibinin and sorafenib is limited due to poor water solubility, low bioavailability, and potential side effects, necessitating innovative therapeutic approaches.Methods: In this study, we developed self-assembled, carrier-free microparticles of silibinin and sorafenib (SIL-SOR-MPs) using magnetic stirring and evaluated their therapeutic effects on MASLD both in vitro and in vivo.Results: Compared to free SIL and free SOR, SIL-SOR-MPs significantly reduced lipid accumulation in HepG2 cells and effectively alleviated hepatic steatosis and liver damage in mice. Mechanistic investigations further showed that SIL-SOR-MPs more effectively down-regulated lipid synthesis genes and up-regulated genes involved in lipid oxidation.Discussion: In summary, our study highlights that carrier-free SIL-SOR-MPs demonstrate the ability to reverse the progression of MASLD and present a promising therapeutic strategy.Keywords: metabolic dysfunction-associated steatotic liver disease, silibinin, sorafenib, self-assembled carrier-free microparticles, fatty acid metabolism
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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 | 0.000 |
| 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".