Mechanism of Ⅲ in the treatment of proteinuria in chronic kidney disease: a network pharmacology-based study / 南方医科大学学报

OBJECTIVE@#To identify the main active components in Ⅲ and their targets and explore the mechanism by which Ⅲ alleviates proteinuria in chronic kidney disease (CKD) based on network pharmacology.@*METHODS@#The active components of Ⅲ and their potential targets, along with the oral bioavailability and drug-like properties of each component were searched in the TCMSP database. The proteinuria-related targets were searched in the GeneCards database. The active component-target network was constructed using Cytoscape software, and the acquired information of the targets from ClueGO was used for enrichment analysis of the gene pathways.@*RESULTS@#A total of 102 active components were identified from Ⅲ. These active components acted on 126 targets, among which 69 were related to proteinuria. Enrichment analysis revealed fluid shear stress- and atherosclerosisrelated pathways as the highly significant pathways in proteinuria associated with CKD.@*CONCLUSIONS@#We preliminarily validated the prescription of Ⅲ and obtained scientific evidence that supported its use for treatment of proteinuria in CKD. The findings in this study provide a theoretical basis for further study of the mechanism of Ⅲ in the treatment of proteinuria in CKD.

Brain function network analysis and recognition for psychogenic non-epileptic seizures based on resting state electroencephalogram / 生物医学工程学杂志

Studies have shown that the clinical manifestation of patients with neuropsychiatric disorders might be related to the abnormal connectivity of brain functions. Psychogenic non-epileptic seizures (PNES) are different from the conventional epileptic seizures due to the lack of the expected electroencephalographically epileptic changes in central nervous system, but are related to the presence of significant psychological factors. Diagnosis of PNES remains challenging. We found in the present work that the connectivity between the frontal and parieto-occipital in PNES was weaker than that of the controls by using network analysis based on electroencephalogram (EEG) signals. In addition, PNES were recognized by using the network properties as linear discriminant nalysis (LDA) input and classification accuracy was 85%. This study may provide a feasible tool for clinical diagnosis of PNES.