AIM OF THE STUDY: The study is aimed to investigate the anti-depressant effect and the molecular mechanism of G. elata in vitro and in vivo using PC12 cells and zebrafish model, respectively.

MATERIAL AND METHODS: Network pharmacology was performed to explore the potential active ingredients and action targets of G. elata Blume extracts (GBE) against depression. The cell viability and proliferation were determined by MTT and EdU assay, respectively. TUNEL assay was used to examine the anti-apoptotic effect of GBE. Immunofluorescence and Western blot were used to detect the protein expression level. In addition, novel tank diving test was used to investigate the anti-depressant effect in zebrafish depression model. RT-PCR was used to analyze the mRNA expression levels of genes.

RESULTS: G. elata against depression on the reticulon 4 receptors (RTN4R) and apoptosis-related targets, which were predicted by network pharmacology. Furthermore, GBE enhanced cell viability and inhibited the apoptosis in PC12 cells against CORT treatment. GBE relieved depression-like symptoms in adult zebrafish, included increase of exploratory behavior and regulation of depression related genes. Mechanism studies showed that the GBE inhibited the expression of RTN4R-related and apoptosis-related genes.

METHOD: Literature search was performed. The clinical features and molecular characteristics of Chinese patients with sitosterolemia were analysed. Four children with sitosterolemia and the treatment experience were described.

RESULTS: Fifty-five patients with sitosterolemia have been reported in China. These patients were aged from 3 months to 67 years at diagnosis, and the median was 8 years of age. Several complications, such as xanthomas in 47 patients (85%), thrombocytopenia in 17 patients (31%), anemia in 14 patients (25%), and cardiovascular damage in 12 patients (22%), were observed. Thirty-nine patients (71%) exhibited mutations in the ABCG5 gene, 15 patients (27%) showed mutations in ABCG8, and variations in both genes occurred in one patient (2%). A patient with two clinically rare diseases, namely, sitosterolemia and glycogen storage disease type VI (GSD VI)), is reported here for the first time. The four reported patients were treated with low cholesterol and phytosterol-limited diet alone or combined with cholestyramine. Even though decreases were observed for total plasma cholesterol (TC) and low-density-lipoprotein cholesterol (LDL-C), and these levels were as low as normal in some patients, the levels of plant sterols remained above the normal range. However, TC, LDL-C and plant sterol levels remained at high levels in patients treated with a control diet control only.

RESULTS: In the present study, bioinformatics and cell biology were used to investigate the functions and signal pathway enrichments of differentially expressed genes. The bioinformatics analysis of three original microarray datasets (GSE73661, GSE75214 and GSE126124) in the NCBI-Gene Expression Omnibus database showed 17 down-regulated genes (logFC  0) existed in the enteritis tissue. Meanwhile, pathway enrichment and protein-protein interaction network analysis suggested that IBD is relevant to cytotoxicity, inflammation and apoptosis. Furthermore, Caco-2 cells were treated with the main oxidation products of deep-frying oil-total polar compounds (TPC) and its components (polymerized triglyceride, oxidized triglycerides and triglyceride degradation products) isolated from deep-frying oil. The flow cytometry experiment revealed that TPC and its components could induce apoptosis, especially for oxidized triglyceride. A quantitative polymerase chain reaction analysis demonstrated that TPC and its component could induce Caco-2 cell apoptosis through AQP8/CXCL1/TNIP3/IL-1.

METHOD: This study proposed a single-scale multi-input convolutional neural network (SSMICNN) method to classify ERP signals between aMCI patients with T2DM and the control group. Firstly, the 18-electrode ERP signal on alpha, beta, and theta frequency bands was extracted by using the fast Fourier transform, and then the mean, sum of squares, and absolute value feature of each frequency band were calculated. Finally, these three features are converted into multispectral images respectively and used as the input of the SSMICNN network to realize the classification task.

RESULTS: The results show that the SSMICNN can fuse MSI formed by different features, SSMICNN enriches the feature quantity of the neural network input layer and has excellent robustness, and the errors of SSMICNN can be simultaneously transmitted to the three convolution channels in the back-propagation phase. Comparison with Existing Method(s): SSMICNN could more effectively identify ERP signals from aMCI with T2DM from the control group compared to existing classification methods, including convolution neural network, support vector machine, and logistic regression.

OBJECTIVE: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.

METHODS: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.

RESULTS: The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.