Chenglong Zheng

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.

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.

1-Deoxynojirimycin: Occurrence, Extraction, Chemistry, Oral Pharmacokinetics, Biological Activities and In Silico Target Fishing.

1-Deoxynojirimycin (DNJ, C6H13NO4, 163.17 g/mol), an alkaloid azasugar or iminosugar, is a biologically active natural compound that exists in mulberry leaves and Commelina communis (dayflower) as well as from several bacterial strains such as Bacillus and Streptomyces species. Deoxynojirimycin possesses antihyperglycemic, anti-obesity, and antiviral features. Therefore, the aim of this detailed review article is to summarize the existing knowledge on occurrence, extraction, purification, determination, chemistry, and bioactivities of DNJ, so that researchers may use it to explore future perspectives of research on DNJ. Moreover, possible molecular targets of DNJ will also be investigated using suitable in silico approach.

Identifying Metabolite Biomarkers in Unstable Angina In-Patients by Feature Selection Based Data Mining Methods

Unstable angina (UA) is a most dangerous type of Coronary Heart Disease (CHD) that causing more and more mortality and morbidity world wide. Identification of biomarkers for UA in the level of metabolomics is a better avenue to understand the inner mechanism of it. We carried out clinical epidemiology to collect plasmas of UA in-patients and controls. Metabolomics data are obtained by gas chromatography techniques. We presented a novel computational strategy to select biomarkers as few as possible for UA in the data. We combined independent t test and classification based data mining methods as well as backward elimination technique to select as few as possible metabolite biomarkers with best classification performances. By the novel method, we select five metabolites for UA. The associated biomedical literatures support the finding. The novel method presented here provides a better insight into the pathology of a disease. Feature selection based data mining methods better suit to identifying biomarkers for UA

A New Biomarkers Feature Pattern Consisting of TNF-α, IL-10, and IL-8 for Blood Stasis Syndrome with Myocardial Ischemia

Objective. To explore new diagnostic patterns for syndromes to overcome the insufficiency of obtainable macrocharacteristics and specific biomarkers. Methods. Chinese miniswines were subjected to Ameroid constrictor, placed around the proximal left anterior descending branch. On the 4th week, macrocharacteristics, coronary angiography, echocardiography, and hemorheology indices were detected for diagnosis. IL-1, IL-6, IL-8, IL-10, TNF-α, and hsCRP in serum were detected, and Decision Tree was built. Results. According to current official-issued standard, model animals matched the diagnosis of blood stasis syndrome with myocardial ischemia based on findings, including >90% occlusion, attenuated left ventricular segmental motion, dark red or purple tongues, and higher blood viscosity. Significant decrease of IL-10 and increase of TNF-α were found in model animals. However, in the Decision Tree, besides IL-10 and TNF-α, IL-8 helped to increase the accuracy of classification to 86%. Conclusions. The Decision Tree building with TNF-α, IL-10, and IL-8 is helpful for the diagnosis of blood stasis syndrome in myocardial ischemia animals. What is more is that our data set up a new path to the differentiation of syndrome by feature patterns consisting of multiple biomarkers not only for animals but also for patients. We believe that it will contribute to the standardization and international application of syndromes.

Research on FTIR Fingerprints of Soil and Regional Selection of Chinese Herbal Medicine Cultivation

The geographical choice of Chinese herbal medicine is an important research issue in Traditional Chinese Medicine. By using high resolution FTIR technique, 53 soil samples were investigated in the comparative study. The results showed that soil samples at 3621, 3425, 1642, 1435, 1029, 878, 760 and 690cm-1 had community peaks. However, their relative intensities were variant, in addition, the peaks at 1796, 2982 and 2874cm-1 were not the same which can be objectively used to differentiate soil in distinct regions. Moreover, the experiments showed that the soil conditions in Tianshui and Dingxi of Gansu province in China were similar to the counterpart in Shanxi province in China. We conclude that FT-IR spectroscopy can not only provide rather valuable information for Chinese herbal medicine transplanting but for soil research.