Metabolic Profiling Study of Yang Deficiency Syndrome in Hepatocellular Carcinoma by H

This study proposes a 1H NMR-based metabonomic approach to explore the biochemical characteristics of Yang deficiency syndrome in hepatocellular carcinoma (HCC) based on serum metabolic profiling. Serum samples from 21 cases of Yang deficiency syndrome HCC patients (YDS-HCC) and 21 cases of non-Yang deficiency syndrome HCC patients (NYDS-HCC) were analyzed using 1H NMR spectroscopy and partial least squares discriminant analysis (PLS-DA) was applied to visualize the variation patterns in metabolic profiling of sera from different groups. The differential metabolites were identified and the biochemical characteristics were analyzed. We found that the intensities of six metabolites (LDL/VLDL, isoleucine, lactate, lipids, choline, and glucose/sugars) in serum of Yang deficiency syndrome patients were lower than those of non-Yang deficiency syndrome patients. It implies that multiple metabolisms, mainly including lipid, amino acid, and energy metabolisms, are unbalanced or weakened in Yang deficiency syndrome patients with HCC. The decreased intensities of metabolites including LDL/VLDL, isoleucine, lactate, lipids, choline, and glucose/sugars in serum may be the distinctive metabolic variations of Yang deficiency syndrome patients with HCC. And these metabolites may be potential biomarkers for diagnosis of Yang deficiency syndrome in HCC

Highly Effective Configurational Assignment Using Bisthioureas as Chiral Solvating Agents in the Presence of DABCO

A highly effective H-1 NMR method for determining the absolute configurations of various chiral a-hydroxyl acids and their derivatives has been developed with the use of bisthioureas (R)-CSA 1 and (S)-CSA 1 as chiral solvating agents in the presence of DABCO, giving distinguishable proton signals with up to 0.66 ppm chemical shift nonequivalence. Computational modeling studies were performed with Gaussian09 to reveal the chiral recognition mechanism

Protective Effects of Dexrazoxane against Doxorubicin-Induced Cardiotoxicity: A Metabolomic Study

<div><p>Cardioprotection of dexrazoxane (DZR) against doxorubicin (DOX)-induced cardiotoxicity is contentious and the indicator is controversial. A pairwise comparative metabolomics approach was used to delineate the potential metabolic processes in the present study. Ninety-six BALB/c mice were randomly divided into two supergroups: tumor and control groups. Each supergroup was divided into control, DOX, DZR, and DOX plus DZR treatment groups. DOX treatment resulted in a steady increase in 5-hydroxylysine, 2-hydroxybutyrate, 2-oxoglutarate, 3-hydroxybutyrate, and decrease in glucose, glutamate, cysteine, acetone, methionine, asparate, isoleucine, and glycylproline.DZR treatment led to increase in lactate, 3-hydroxybutyrate, glutamate, alanine, and decrease in glucose, trimethylamine N-oxide and carnosine levels. These metabolites represent potential biomarkers for early prediction of cardiotoxicity of DOX and the cardioprotective evaluation of DZR.</p></div

DZR protected against DOX-induced myocardial damage.

<p>Histopathology of HE staining of left ventricular walls indicated myocardial damage and cardioprotective effects of DZR(<b>A</b>). Magnification200x. DZR abrogated DOX-induced increase serum creatine kinase (<b>B</b>),CK-MB(<b>C</b>), cardiac LDH(D), cTNT(E), as well as decrease cardiac total glutathione (F) and GSH/GSSH ratio (G).</p

GOPS: A general optimal control problem solver for autonomous driving and industrial control applications

Solving optimal control problems serves as the basic demand of industrial control tasks. Existing methods like model predictive control often suffer from heavy online computational burdens. Reinforcement learning has shown promise in computer and board games but has yet to be widely adopted in industrial applications due to a lack of accessible, high-accuracy solvers. Current Reinforcement learning (RL) solvers are often developed for academic research and require a significant amount of theoretical knowledge and programming skills. Besides, many of them only support Python-based environments and limit to model-free algorithms. To address this gap, this paper develops General Optimal control Problems Solver (GOPS), an easy-to-use RL solver package that aims to build real-time and high-performance controllers in industrial fields. GOPS is built with a highly modular structure that retains a flexible framework for secondary development. Considering the diversity of industrial control tasks, GOPS also includes a conversion tool that allows for the use of Matlab/Simulink to support environment construction, controller design, and performance validation. To handle large-scale problems, GOPS can automatically create various serial and parallel trainers by flexibly combining embedded buffers and samplers. It offers a variety of common approximate functions for policy and value functions, including polynomial, multilayer perceptron, convolutional neural network, etc. Additionally, constrained and robust algorithms for special industrial control systems with state constraints and model uncertainties are also integrated into GOPS. Several examples, including linear quadratic control, inverted double pendulum, vehicle tracking, humanoid robot, obstacle avoidance, and active suspension control, are tested to verify the performances of GOPS

The typical 1H-NMR spectra of the serum from the different treatment models.

<p>The metabolites are assigned and marked. The overlapping peaks were identified by adding a reference substance to the sample.</p

Two-paired PLS-DA score plot and S-plot revealed DOX-induced metabolic perturbations.

<p>The comparative analysis of DOX_C and cancer control groups reveal altered metabolite levels following DOX treatment of tumor-bearing mice (<b>A</b>), while the comparative analysis of DOX_N and normal control groups reveal distinct metabolic effects of DOX in normal animals (<b>B</b>).</p

Overall profiling of the eight groups and abnormal metabolism in cancer.

<p>Score plot of the eight groups (A) shows treatment differences. The summary of pairwise metabolomics analysis of the model represents the cumulative R2X, R2Y and Q2 levels (<b>B</b>). The score plot and S-plot of the pairwise analysis of cancer and control groups reveal altered metabolites including creatine, UDP-glucose, VLDL/LDL, glycerol, TMAO, taurine, carnosine, lactate, acetone, glutamate, and aspartate (<b>C</b>).</p

Effect of DOX and DZR treatment on body and organ weights.

<p>Effect of DOX and DZR treatment on body and organ weights.</p

Cardioprotective effects of DZR on DOX-induced cardiotoxicity involving reprogrammed metabolic pathway.

<p>Cardioprotective effects of DZR on DOX-induced cardiotoxicity involving reprogrammed metabolic pathway.</p