Study on TCM Syndrome Identification Modes of Coronary Heart Disease Based on Data Mining

Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and high mortality. TCM has played an important role in the treatment of CHD. Syndrome differentiation based on information from traditional four diagnostic methods has met challenges and questions with the rapid development and wide application of system biology. In this paper, methods of complex network and CHAID decision tree were applied to identify the TCM core syndromes of patients with CHD, and to establish TCM syndrome identification modes of CHD based on biological parameters. At the same time, external validation modes were also constructed to confirm the identification modes

The Effects of Jiang-Zhi-Ning and Its Main Components on Cholesterol Metabolism

To examine how Jiang-Zhi-Ning (JZN) regulates cholesterol metabolism and compare the role of its four main components. We established a beagle model of hyperlipidemia, fed with JZN extract and collected JZN-containing serum 0, 1, 2, 4, and 6 h later. Human liver cells Bel-7402 were stimulated with 10% JZN-containing serum as well as the four main components of JZN and Atorvastatin. The mRNA expression of LDL receptor (LDL-R), 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMG-CoAR), cytochrome P450 7A1 (CYP7A1), and acetyl-Coenzyme A acetyltransferase 2 (ACAT2) was measured by real-time PCR. LDL-R surface expression and LDL-binding and internalization were examined by flow cytometry. The results showed that JZN-containing serum significantly increased the mRNA expression of LDL-R, HMG-CoAR, and CYP7A1 in Bel-7402 cells. All the four components significantly increased the mRNA and protein expression of LDL-R and HMG-CoAR and decreased the mRNA and protein expression of ACAT2 in Bel-7402 cells. Hyperinand chrysophanol also markedly increased the mRNA expression of CYP7A1. Stimulation with stilbene glycosidesignificantly increased the surface expression of LDL-R and the binding and internalization of LDL. In conclusion, JZN and its four components have close relationship with the process of cholesterol metabolism, emphasizing their promising application as new drug candidates in the treatment of hyperlipidemia

Experimental and Numerical Evaluation of the Ablation Process of Carbon/Carbon Composites Using High Velocity Oxygen Fuel System

The ablation process of carbon/carbon (C/C) composites was tested under hypersonic flowing propane flame. The microstructures of C/C composites were characterized and the numerical analysis was performed. Two typical ablation morphologies of the carbon fibers, which are drum-like and needle-like shapes, were observed depending on the alignments of fibers to the flame directions. Temperature fields in the composites were analyzed using finite element method, and the mechanisms that govern the formation of different ablation behaviors were elucidated. For paralleled fiber bundles, the highest temperature situates in the middle parts underlying the ablation pits, where the drum-like shape is formed. For perpendicular fiber bundles, the highest temperature appears at the turning point between the transverse section and the surface of fiber, which leads to the gradual ablation from the fiber surface toward the axis, and eventually the formation of the needle-like shape

Metabolomics-Based Study of Clinical and Animal Plasma Samples in Coronary Heart Disease with Blood Stasis Syndrome

The aim of this study is to explore a bridge connecting the mechanism basis and macro syndromes of coronary heart disease with experimental animal models. GC-MS technique was used to detect the metabolites of plasma samples in mini swine models with myocardial infarction (MI) and patients with unstable angina (UA). 30 metabolites were detected in the plasma samples of more than 50 percent of model group and control group in swine, while 37 metabolites were found in the plasma samples of UA patients and healthy control group. 21 metabolites in the plasma samples of swine model and 20 metabolites in patients with UA were found of significant value. Among which, 8 shared metabolites were found of low level expression in both swine model and UA patients. Independent Student's t-test, principal component analysis (PCA), and hierarchicalcluster analysis (HCA) were orderly applied to comprehend inner rules of variables in the data. The 8 shared metabolites could take place of the 21 or 20 metabolites in classification of swine model with MI and UA patients, which could be considered as a bridge connecting the mechanism basis and macrosyndromes of swine model with MI and UA patients

The integrated microbial genomes (IMG) system

The integrated microbial genomes (IMG) system is a new data management and analysis platform for microbial genomes provided by the Joint Genome Institute (JGI). IMG contains both draft and complete JGI genomes integrated with other publicly available microbial genomes of all three domains of life. IMG provides tools and viewers for analyzing genomes, genes and functions, individually or in a comparative context. IMG allows users to focus their analysis on subsets of genes and genomes of interest and to save the results of their analysis. IMG is available at

Identification of ferroptosis-related molecular clusters and genes for diabetic osteoporosis based on the machine learning

BackgroundDiabetic osteoporosis exhibits heterogeneity at the molecular level. Ferroptosis, a controlled form of cell death brought on by a buildup of lipid peroxidation, contributes to the onset and development of several illnesses. The aim was to explore the molecular subtypes associated with ferroptosis in diabetic osteoporosis at the molecular level and to further elucidate the potential molecular mechanisms.MethodsIntegrating the CTD, GeneCards, FerrDb databases, and the microarray data of GSE35958, we identified ferroptosis-related genes (FRGs) associated with diabetic osteoporosis. We applied unsupervised cluster analysis to divide the 42 osteoporosis samples from the GSE56814 microarray data into different subclusters based on FRGs. Subsequently, FRGs associated with two ferroptosis subclusters were obtained by combining database genes, module-related genes of WGCNA, and differentially expressed genes (DEGs). Eventually, the key genes from FRGs associated with diabetic osteoporosis were identified using the least absolute shrinkage and selection operator (LASSO), Boruta, support vector machine recursive feature elimination (SVM ­ RFE), and extreme gradient boosting (XGBoost) machine learning algorithms. Based on ROC curves of external datasets (GSE56815), the model’s efficiency was examined.ResultsWe identified 15 differentially expressed FRGs associated with diabetic osteoporosis. In osteoporosis, two distinct molecular clusters related to ferroptosis were found. The expression results and GSVA analysis indicated that 15 FRGs exhibited significantly different biological functions and pathway activities in the two ferroptosis subclusters. Therefore, we further identified 17 FRGs associated with diabetic osteoporosis between the two subclusters. The results of the comprehensive analysis of 17 FRGs demonstrated that these genes were heterogeneous and had a specific interaction between the two subclusters. Ultimately, the prediction model had a strong foundation and excellent AUC values (0.84 for LASSO, 0.84 for SVM ­ RFE, 0.82 for Boruta, and 0.81 for XGBoost). IDH1 is a common gene to all four algorithms thus being identified as a key gene with a high AUC value (AUC = 0.698).ConclusionsAs a ferroptosis regulator, IDH1 is able to distinguish between distinct molecular subtypes of diabetic osteoporosis, which may offer fresh perspectives on the pathogenesis of the disease’s clinical symptoms and prognostic heterogeneity

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels

Biological, clinical and population relevance of 95 loci for blood lipids.

Plasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P < 5 x 10(-8)), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes-GALNT2, PPP1R3B and TTC39B-with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD