Effects of mulberry leaf extracts on activity and mRNA expression of five cytochrome P450 enzymes in rat

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450)inhibition is an important consideration in drug discovery. Mulberry leaves are of broad popular use for food or remedy purposes, which is believed to contain substances that are beneficial for preventing and alleviating diabetes. However, there is a paucity of information about the effect of mulberry leaves on rat CYP450 enzymes activities and the mRNA expression levels in vivo. The present study aimed to investigate the effect of mulberry leaves on activities of rat CYP450 enzymes (CYP3A4, CYP2C8, CYP2C19, CYP2D6, and CYP1A2) through both probe-drug cocktail approach and real-time polymerase chain reaction (RT-qPCR). The pharmacokinetic results indicated that the aqueous extract of mulberry leaves (AML) exhibited induction effects on CYP3A4 activities, and AML exhibited inhibitory effects on CYP1A2, CYP2D6, and CYP2C8, while no obvious effect was observed on CYP2C19 activity. Additionally, the ethanol extract of mulberry leaves (EML) could induce the activities of CYP3A4. In addition, EML exhibited inhibitory effects on CYP1A2, CYP2D6, and CYP2C19, while no significant change in CYP2C8 activity was observed. Accordingly, the level of mRNA expression of five CYP enzymes were consistent with the result of pharmacokinetic. The results of our study may form a practical strategy for assessing CYP-mediated HDI

Model Predictive Optimization Control Strategy for Three-level AFE Converter

In view of the fact of traditional MPC prediction for three-level AFE converter with numerous switching vectors, time-cost computation and complex control, a simplified model predictive control algorithm is proposed in this paper. The multiple current prediction is transformed into a single virtual reference voltage vector prediction according to the inverse procedure of the model current prediction, and vector distribution method is adopted which can screens out the optimal vector. In the process of rolling optimization, multi-objective control is carried out by adding neutral point potential balance and reducing switching losses and other constraints to the cost function. Also the control delay of the algorithm is compensated. Finally, simulation experiments of three-level AFE converter under steady-state and dynamic conditions are provided. The results have verified correctness and practicability of the strategy

A Novel Aqueous Asymmetric Supercapacitor based on Pyrene-4,5,9,10-Tetraone Functionalized Graphene as the Cathode and Annealed Ti3C2Tx MXene as the Anode

Asymmetric supercapacitors (ASCs), employing two dissimilar electrode materials with a large redox peak position difference as cathode and anode, have been designed to further broaden the voltage window and improve the energy density of supercapacitors. Organic molecule based electrodes can be constructed by combining redox-active organic molecules with conductive carbon-based materials such as graphene. Herein, pyrene-4,5,9,10-tetraone (PYT), a redox-active molecule with four carbonyl groups, exhibits a four-electron transfer process and can potentially deliver a high capacity. PYT is noncovalently combined with two different kinds of graphene (Graphenea [GN] and LayerOne [LO]) at different mass ratios. The PYT-functionalized GN electrode (PYT/GN 4–5) possesses a high capacity of 711 F g−1\ua0at 1 A g−1\ua0in 1\ua0M H2SO4. To match with the PYT/GN 4–5 cathode, an annealed-Ti3C2Tx\ua0(A-Ti3C2Tx) MXene anode with a pseudocapacitive character is prepared by pyrolysis of pure Ti3C2Tx. The assembled PYT/GN 4–5//A-Ti3C2Tx\ua0ASC delivers an outstanding energy density of 18.4\ua0Wh kg−1\ua0at a power density of 700\ua0W kg−1. The PYT-functionalized graphene holds great potential for high-performance energy storage devices

Diagnostic Measures for Missing Covariate Data and Semiparametric Models for Neuroimaging

This dissertation is composed of two major topics: a) diagnostic measures for generalized linear models (GLMs) with missing covariate data, and b) semiparametric models for neuroimaging data. The first topic, diagnostic measures for GLMs with missing covariate data, is covered in two thesis papers. In the first paper, we carry out an in-depth investigation for assessing the influence of observations and model misspecification in the presence of missing covariate data in GLMs. Our diagnostic measures include case-deletion measures and conditional residuals. We use the conditional residuals to construct goodness of fit statistics for testing possible misspecifications in model assumptions. We develop specific strategies for incorporating missing data into goodness of fit statistics in order to increase the power of detecting model misspecification, and employ a resampling method to approximate the p-value of the goodness of fit statistics. In the second paper, we formally set up a general local influence method to carry out sensitivity analyses of minor perturbations to GLMs with missing covariate data. We examine two types of perturbation schemes (the single-case and global perturbation schemes) and show that the metric tensor of a perturbation manifold provides useful information for selecting an appropriate perturbation. We also develop several local influence measures to identify influential points and test model misspecification. The second topic, semiparametric models for neuroimaging data, also consists of two thesis papers. The main objective of the first paper is to develop an adjusted exponentially tilted empirical likelihood (ETEL) procedure for the analysis of neuroimaging data. We propose a likelihood ratio statistic to test hypotheses and construct goodness of fit statistics for testing possible model misspecifications and apply them to the classification of time-dependent covariates. Our semiparametric method avoids standard parametric assumptions and the adjustment to the ETEL method can dramatically improve its finite sample performance over the original ETEL. In the second paper, we develop a semiparametric framework for describing the variability of medial representation (m-rep) of subcortical subjects and its association with covariates in a Euclidean space. Because the elements of the m-rep do not form a vector space, applying classical multivariate regression techniques may be inadequate in establishing the association between an m-rep and covariates of interest. Our semiparametric model avoids specifying a probability distribution on a Riemannian manifold. We develop an estimation procedure based on the annealing evolutionary stochastic approximation Monte Carlo (AESAMC) algorithm to obtain parameter estimates and establish their limiting distributions. We use Wald statistics to carry out tests of hypotheses