Analysis of bioactive components and synergistic action mechanism of ShuGan-QieZhi Capsule for treating non-alcoholic fatty liver disease.

BACKGROUND: ShuGan-QieZhi capsule (SGQZC) is a traditional Chinese preparation used to treat hyperlipidemia and obesity, even non-alcoholic fatty liver disease (NAFLD). However, its therapeutic effects, main bioactive ingredients, as well as potential mechanisms for NAFLD are still unclear. PURPOSE: To investigate the pharmacological effect, main active ingredients, and mechanisms of SGQZC against high-fat diet (HFD)-induced NAFLD in mice. METHODS: NAFLD models were established by feeding C57BL/6 J mice an HFD for 24 weeks. From the 12th week, HFD-fed mice received daily gavage of either SGQZC or silibinin for 12 weeks. Hepatic hypertrophy parameters, along with hepatic and systemic lipid metabolism changes in NAFLD mice, were assessed. Oil red O and histopathological staining techniques determined lipid accumulation and liver injury severity. qRT-PCR analysis measured the expression of genes tied to liver lipid metabolism and inflammation. HPLC-MS/MS identified the primary components of SGQZC in the serum. Human normal hepatocytes (LO2) and hepatic stellate cells (LX-2) were used to screen SGQZC's bioactive ingredients. Network pharmacological analysis, transcriptomics, and western blotting delved into SGQZC's synergistic mechanisms against NAFLD. RESULTS: SGQZC ameliorated abnormal lipid metabolism and liver hypertrophy in mice with HFD-induced NAFLD, consequently reducing hepatic lipid accumulation, inflammatory cell infiltration, and liver impairment. Eight crucial components of SGQZC were detected in serum using HPLC-MS/MS and were found to effectively attenuate lipid accumulation and inflammation in liver cells. Further investigation indicated that SGQZC modulates MAPK pathway and AKT/NF-κB pathway, subsequently improving lipid metabolism and inflammation. CONCLUSION: SGQZC alleviates NAFLD by synergistically modulating the MAPK-mediated lipid metabolism and inhibiting AKT/NF-κB pathways-mediated inflammation. Our findings reveal the enormous potential of SGQZC for the treatment of NAFLD, providing a possible new clinical therapeutic strategy.

Decoupling of nutrient stoichiometry in Suaeda glauca (Bunge) senesced leaves under salt treatment.

The stoichiometry of senesced leaves is pivotal in nutrient cycling and can be significantly influenced by soil salinization, a rising global issue threatening the functionality of ecosystems. However, the impacts of soil salinization on senesced leaf stoichiometry are not fully understood. In this study, we conducted a pot experiment with varying soil salt concentrations to examine their influence on the concentrations and stoichiometric ratios of nitrogen (N), phosphorus (P), sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), and zinc (Zn) in the senesced leaves of Suaeda glauca (Bunge). Compared to the control group, salt treatments significantly enhanced Na concentration while diminishing the concentrations of K, Ca, Mg, Zn, N, and P. Interestingly, as salinity levels escalated, N concentration maintained stability, whereas P concentration exhibited an increasing trend. Moreover, K, Ca, and Mg significantly declined as salt levels rose. Salt treatments brought about significant changes in stoichiometric ratios, with the N:P, K:Na, N:Na, N:Mg, and Ca : Mg ratios dropping and the N:Ca and N:K ratios rising, illustrating the varying nutrient coupling cycles under different salt conditions. These findings shed light on the plasticity of stoichiometric traits in S. glauca senesced leaves in response to soil salinization shifts, which could potentially offer insights into nutrient cycling reactions to soil salinization.

An ensemble learning based framework to estimate warfarin maintenance dose with cross-over variables exploration on incomplete data set.

MOTIVATION: Warfarin is a widely used oral anticoagulant, but it is challenging to select the optimal maintenance dose due to its narrow therapeutic window and complex individual factor relationships. In recent years, machine learning techniques have been widely applied for warfarin dose prediction. However, the model performance always meets the upper limit due to the ignoration of exploring the variable interactions sufficiently. More importantly, there is no efficient way to resolve missing values when predicting the optimal warfarin maintenance dose. METHODS: Using an observational cohort from the Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine, we propose a novel method for warfarin maintenance dose prediction, which is capable of assessing variable interactions and dealing with missing values naturally. Specifically, we examine single variables by univariate analysis initially, and only statistically significant variables are included. We then propose a novel feature engineering method on them to generate the cross-over variables automatically. Their impacts are evaluated by stepwise regression, and only the significant ones are selected. Lastly, we implement an ensemble learning based approach, LightGBM, to learn from incomplete data directly on the selected single and cross-over variables for dosing prediction. RESULTS: 377 unique patients with eligible and time-independent 1173 warfarin order events are included in this study. Through the comprehensive experimental results in 5-fold cross-validation, our proposed method demonstrates the efficiency of exploring the variable interactions and modeling on incomplete data. The R2 can achieve 75.0% on average. Moreover, the subgroup analysis results reveal that our method performs much better than other baseline methods, especially in the medium-dose and high-dose subgroups. Lastly, the IWPC dosing prediction model is used for further comparison, and our approach outperforms it by a significant margin. CONCLUSION: In summary, our proposed method is capable of exploring the variable interactions and learning from incomplete data directly for warfarin maintenance dose prediction, which has a great premise and is worthy of further research.