0.05).The relative bioavailablity of diclofenac potassium granule was(99.47?9.79)%.CONCLUSION:The diclofenac potassium granule and diclofenac potassium tablet are bioequivalent.

The aim of this study is to establish an HPLC method for simultaneous determinations of mifepristone and its metabolites, mono-demethylated mifepristone, di-demethylated mifepristone and C-hydroxylated mifepristone in plasma and to evaluate the pharmacokinetic characteristics of mifepristone tablet. Twenty healthy female Chinese subjects were recruited and a series of blood samples were collected before and after 0.25, 0.5, 1.0, 1.5, 2.0, 4.0, 8.0, 12.0, 24.0, 48.0, 72.0 and 96.0 hours administration by a single oral dose of 75 mg mifepristone tablet. Mifepristone and its three metabolites were extracted from plasma using ethyl acetate and determined by high performance liquid chromatography. The main pharmacokinetic parameters of mifepristone and its metabolites, including Cmax, tmax, MRT, t(1/2), V, CL, AUC(0-96 h) and AUC(0-infinity), were calculated by Drug and Statistical Software Version 2.0. The simple, accurate and stable method allows the sensitive determinations ofmifepristone and its metabolites in human plasma up to 4 days after oral administration of 75 mg mifepristone tablet and the clinical applications of their pharmacokinetic studies.

Objective To mine valid information in public databases through bioinformatics analysis and machine learning models and to identify candidate genes related to preeclampsia,so as to improve the accuracy of early diagnosis and provide targets for pathogenesis,diagnosis and treatment research.Methods The RNA-seq datasets of placental tissue samples of preeclampsia patients and healthy pregnant women were retrieved from the Gene Expression Omnibus,and the gene expression matrix was obtained after data download,quality control,comparison and quantification through bioinformation analysis.The differentially expressed genes were screened by DESeq2 1.38.3,the enrichment pathway was determined using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes,the co-expression network was constructed using weighted gene co-expression network analysis(WGCNA),and the machine learning prediction model was established by random forest algorithm.Results A total of 49 common differentially expressed genes were screened from placental tissue samples of 156 pregnant women(70 preeclampsia patients and 86 healthy pregnant women)in 4 datasets and they were significantly enriched in extracellular regions,positive regulation pathway of follicle-stimulating hormone secretion,hormone activity pathway,and cytokine-cytokine receptor interaction pathway,etc.The 49 differentially expressed genes were categorized into 7 co-expression modules by WGCNA,and key modules highly related to preeclampsia were identified.Six candidate key genes(fms related receptor tyrosine kinase 1[FLT1],pappalysin 2[PAPPA2],protein phosphatase 1 regulatory inhibitor subunit 1C[PPP1R1C],myosin ⅦB[MYO7B],long intergenic non-protein coding RNA 2009[LINC02009],and inhibin subunit α[INHA])were screened.The random forest model based on these 6 key genes had good predictive value for preeclampsia(area under curve was 0.978).Conclusion Preeclampsia may be associated with genes for hormone secretion,immune response,angiogenic factors,pregnancy-associated plasma proteins,and inhibin,and these genes may be candidate diagnostic markers of preeclampsia.