Qi Shi

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.

Clinical Data Mining of Phenotypic Network in Angina Pectoris of Coronary Heart Disease

Coronary heart disease (CHD) is the leading causes of morbidity and mortality in China. The diagnosis of CHD in Traditional Chinese Medicine (TCM) was mainly based on experience in the past. In this paper, we proposed four MI-based association algorithms to analyze phenotype networks of CHD, and established scale of syndromes to automatically generate the diagnosis of patients based on their phenotypes. We also compared the change of core syndromes that CHD were combined with other diseases, and presented the different phenotype spectra.

Identification of metabolic biomarkers in patients with type 2 diabetic coronary heart diseases based on metabolomic approach

Type 2 diabetic coronary heart disease (T2DM-CHD) is a kind of serious and complex disease. Great attention has been paid to exploring its mechanism; however, the detailed understanding of T2DM-CHD is still limited. Plasma samples from 15 healthy controls, 13 coronary heart disease (CHD) patients, 15 type 2 diabetes mellitus (T2DM) patients and 28 T2DM-CHD patients were analyzed in this research. The potential biomarkers of CHD and T2DM were detected and screened out by 1H NMR-based plasma metabolic profiling and multivariate data analysis. About 11 and 12 representative metabolites of CHD and T2DM were identified respectively, mainly including alanine, arginine, proline, glutamine, creatinine and acetate. Then the diagnostic model was further constructed based on the previous metabolites of CHD and T2DM to detect T2DM-CHD with satisfying sensitivity of 92.9%, specificity of 93.3% and accuracy of 93.2%, validating the robustness of 1H NMR-based plasma metabolic profiling to diagnostic strategy. The results demonstrated that the NMR-based metabolomics approach processed good performance to identify diagnostic plasma biomarkers and most identified metabolites related to T2DM and CHD could be considered as predictors of T2DM-CHD as well as the therapeutic targets for prevention, which provided new insight into diagnosing and forecasting of complex diseases.

Study on Qi Deficiency Syndrome Identification Modes of Coronary Heart Disease Based on Metabolomic Biomarkers

Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and mortality. With the era of systems biology bursting into reality, the analysis of the whole biological systems whether they are cells, tissues, organs, or the whole organisms has now become the norm of biological researches. Metabolomics is the branch of science concerned with the quantitative understandings of the metabolite complement of integrated living systems and their dynamic responses to the changes of both endogenous and exogenous factors. The aim of this study is to discuss the characteristics of plasma metabolites in CHD patients and CHD Qi deficiency syndrome patients and explore the composition and concentration changes of the plasma metabolomic biomarkers. The results show that 25 characteristic metabolites related to the CHD patients comparing with the healthy people, and 4 identifiable variables had significant differences between Qi deficiency and non-Qi deficiency patients. On the basis of identifying the different plasma endogenous metabolites between CHD patients and healthy people, we further prompted the metabolic rules, pathogenesis, and biological essence in Qi deficiency syndrome patients.

In Silico Syndrome Prediction for Coronary Artery Disease in Traditional Chinese Medicine

Coronary artery disease (CAD) is the leading causes of deaths in the world. The differentiation of syndrome (ZHENG) is the criterion of diagnosis and therapeutic in TCM. Therefore, syndrome prediction in silico can be improving the performance of treatment. In this paper, we present a Bayesian network framework to construct a high-confidence syndrome predictor based on the optimum subset, that is, collected by Support Vector Machine (SVM) feature selection. Syndrome of CAD can be divided into asthenia and sthenia syndromes. According to the hierarchical characteristics of syndrome, we firstly label every case three types of syndrome (asthenia, sthenia, or both) to solve several syndromes with some patients. On basis of the three syndromes classes, we design SVM feature selection to achieve the optimum symptom subset and compare this subset with Markov blanket feature select using ROC. Using this subset, the six predictors of CADs syndrome are constructed by the Bayesian network technique. We also design Naïve Bayes, C4.5 Logistic, Radial basis function (RBF) network compared with Bayesian network. In a conclusion, the Bayesian network method based on the optimum symptoms shows a practical method to predict six syndromes of CAD in TCM.

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 Students 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.

Characteristic Pattern Study of Coronary Heart Disease with Blood Stasis Syndrome Based on Decision Tree

Coronary heart disease (CHD) remains the single leading cause of death of adults worldwide, but the traditional related factors can not explain the whole situations. Unstable angina (UA) is a type of CHD. The aim of this study was to establish clinical diagnose pattern for UA with blood stasis syndrome. Twenty-two biological parameters were detected on seven hundreds and seventy-six unstable angina with or without blood stasis syndrome patients. Using decision tree, we gain a pattern made by four biological parameters which could distinguish unstable angina with blood stasis syndrome patients from the none-blood stasis syndrome patients. The diagnosis accuracy could reach 82%. The obtained patterns are validated by 3-fold cross validation. Though the diagnosis accuracy is not very high, the pattern may be useful in the syndrome clinical diagnosis in the future.

Pharmacokinetics of oral administration of 2, 3, 5, 4'- tetrahydroxystilbene-2-O-β-d-glucoside from Polygonum multiflorum in beagle dogs

Pharmacokinetics studies of traditional Chinese medicine are increasingly showing its importance and necessity. In this study, we aim to determine the pharmacokinetics of oral administration of 2,3,5,4-tetrahydroxystilbene-2-O-β-d-glucoside (SBGC) fromxa0Polygonum multiflorumxa0orxa0P. multiflorumxa0extracting in Beagle dogs. Beagle dogs were fed withxa0SBGC (2.0, 1.5 and 1.0 g/kg body weight) orxa0P.xa0multiflorumxa0extractsxa0(2.5 g/kg body weight) by oral gavage. We then examined pharmacokinetic parameters, including the area under the plasma concentration-time curve, maximum plasma concentration, time to maximum plasma concentration, half-life of absorption, half-life of distribution, half-life of elimination, and average drug retention time in the blood. The basic pharmacokinetic characteristics were similar between these two situations. When Beagle dogs were fed withxa0P. multiflorumxa0extracts, SBGC was absorbed and distributed faster, but the elimination speed remained at the same level.xa0The current findings provide a clear guidance for the clinical application ofxa0P. multiflorum. n n xa0 n n Key words:xa0Pharmacokinetics, beagle dogs,xa0Polygonum multiflorum, 2,3,5,4-tetrahydroxystilbene-2-O-β-d-glucoside.

Exploration of the biological basis of coronary heart disease angina pectoris with Qi deficiency and Qi stagnation based on GenCLiP gene mining software

The aim of this study is taking coronary heart disease (CHD) angina pectoris (AP) as an example to approach the manipulation and application of GenCLip, gene mining software in searching diseasesyndrome related genes. According to results from CIPHER, a prediction software, 100 genes were predicted based on the similarity of the characterization of CHD. GenCLiP gene mining software was applied to find the disease-syndrome related genes of CHD AP with qi deficiency and qi stagnation. There are 9 genes, ANG, APOA1, MEF2A, PPP1R12C, SREBF1, TCAP, TNNI3, TNNT2, and TPM1 related to both qi deficiency and qi stagnation syndromes of CHD angina pectoris. 7 genes (AST, ELN, FCN1, MYLK, MYLK2, MYOG, and PRTN3) are specifically related to qi deficiency syndrome, while 4 (IL32, PAM, TNFSF8, and TNNC1) are specifically related to qi stagnation syndrome. The study concluded that application of GenCLiP gene-mining software to explore the disease-syndrome related genes is an effective, rapid and feasible method, which has certain reference value in the study of the biological basis of traditional Chinese medicine (TCM) syndrome and can be extended to the identification of the syndrome related genes in other diseases.

Phenomics Research on Coronary Heart Disease Based on Human Phenotype Ontology

The characteristics of holistic, dynamics, complexity, and spatial and temporal features enable “Omics” and theories of TCM to interlink with each other. HPO, namely, “characterization,” can be understood as a sorting and generalization of the manifestations shown by people with diseases on the basis of the phenomics. Syndrome is the overall “manifestation” of human body pathological and physiological changes expressed by four diagnostic methods information. The four diagnostic methods data could be the most objective and direct manifestations of human body under morbid conditions. In this aspect, it is consistent with the connation of “characterization.” Meanwhile, the four diagnostic methods data also equip us with features of characterization in HPO. In our study, we compared 107 pieces of four diagnostic methods information with the “characterization database” to further analyze data of four diagnostic methods characterization in accordance with the common characteristics of four diagnostic methods information and characterization and integrated 107 pieces of four diagnostic methods data to relevant items in HPO and finished the expansion of characterization information in HPO.