1. To investigate Genkwa Flos hepatotoxicity, a cell metabolomics strategy combined with serum pharmacology was performed on human HL-7702 liver cells in this study.
2. Firstly, cell viability and biochemical indicators were determined and the cell morphology was observed to confirm the cell injury and develop a cell hepatotoxicity model. Then, with the help of cell metabolomics based on UPLC-MS, the Genkwa Flos group samples were completely separated from the blank group samples in the score plots and seven upregulated as well as two down-regulated putative biomarkers in the loading plot were identified and confirmed. Besides, two signal molecules and four enzymes involved in biosynthesis pathway of lysophosphatidylcholine and the sphingosine kinase/sphingosine-1-phosphate pathway were determined to investigate the relationship between Genkwa Flos hepatotoxicity and these two classic pathways. Finally, the metabolic pathways related to specific biomarkers and two classic metabolic pathways were analyzed to explain the possible mechanism of Genkwa Flos hepatotoxicity.
3. Based on the results, lipid peroxidation and oxidative stress, phospholipase A2/lysophosphatidylcholine pathway, the disturbance of sphingosine-1-phosphate metabolic profile centered on sphingosine kinase/sphingosine-1-phosphate pathway and fatty acid metabolism might be critical participators in the progression of liver injury induced by Genkwa Flos. 相似文献
目的:探讨椎动脉型颈椎病、交感型颈椎病、神经根型颈椎病之间颈椎旋转、半失稳的关系。方法:本组112例,其中椎动脉型38型、交感型36例、神经根型38例,应用图像存档和传输系统(picture archiving and communication systems,PACS)在X线正位片上测量患者每个颈椎椎体的旋转度和在侧位片上测量椎体半失稳的位移距离。结果:在C6旋转度上,椎动脉型颈椎病和交感型颈椎病均与神经根型颈椎病有统计学差异(P<0·01),椎动脉型颈椎病和交感型颈椎病间的C2旋转度有统计学差异(P<0·05),椎动脉型颈椎病和神经根型颈椎病间的C4旋转度有统计学差异(P<0·05)。在椎体半失稳的位移距离和椎体半失稳率上,椎动脉型颈椎病和交感型颈椎病均与神经根型颈椎病有统计学差异(P<0·01)。结论:在椎动脉型颈椎病和交感型颈椎病中椎体半失稳和颈椎旋转是它们发病的一个重要因素,而在神经根型颈椎病中不是发病的重要因素。 相似文献
Summary Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross‐validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison. 相似文献