A practical guide for induction of type-2 diabetes in rat: Incorporating a high-fat diet and streptozotocin.

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has...

Potential value and impact of data mining and machine learning in clinical diagnostics.

A fuzzified decision tree (DT)-based computer vision system was developed in this study for classification of common Iranian teas. Images of different tea categories, including five green tea and five black tea classes were captured using a CCD camera. In total, 83 image-based information (including 18 colour, 13 texture, and 52 wavelet-based features) were extracted from the images and introduced into DT classifier to distinguish between different tea categories. Reduced Error Pruning (REP) and J48 DTs were applied as classifier and the overall accuracy of both evaluated DTs was equal to 94.79% for differentiating all tea categories. However, these DTs had almost complex structures which made them unsuitable for developing a Mamdani fuzzy system, based on their structure. So, in order to simplify the structures of the DTs to be more suitable for developing fuzzy sets, the DTs were examined for categorizing black and green teas separately. The overall accuracy of J48 DT was 92.917% and 95.000% for classification of black and green teas, respectively and for REP tree the accuracy was equal to 90.83% and 97.08%, respectively. The REP tree structure was used to set the membership functions and rules of the fuzzy system because of the simpler structure of REP tree, even though J48 and REP trees had almost equal performances. It was concluded that the DT-based fuzzy logic system can be used effectively to classify different quality categories of tea, based on image extracted features.

Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features

We developed a Cardiac ScoreCard based on a multivariate index assay.Using a single chip for multiple cardiac applications reduces cost and time.Lasso logistic regression performs shrinkage and selection in multimarker models.Biomarkers and traditional risk factors predict cardiac wellness and heart failure. Clinical decision support systems (CDSSs) have the potential to save lives and reduce unnecessary costs through early detection and frequent monitoring of both traditional risk factors and novel biomarkers for cardiovascular disease (CVD). However, the widespread adoption of CDSSs for the identification of heart diseases has been limited, likely due to the poor interpretability of clinically relevant results and the lack of seamless integration between measurements and disease predictions. In this paper we present the Cardiac ScoreCard-a multivariate index assay system with the potential to assist in the diagnosis and prognosis of a spectrum of CVD. The Cardiac ScoreCard system is based on lasso logistic regression techniques which utilize both patient demographics and novel biomarker data for the prediction of heart failure (HF) and cardiac wellness. Lasso logistic regression models were trained on a merged clinical dataset comprising 579 patients with 6 traditional risk factors and 14 biomarker measurements. The prediction performance of the Cardiac ScoreCard was assessed with 5-fold cross-validation and compared with reference methods. The experimental results reveal that the ScoreCard models improved performance in discriminating disease versus non-case (AUC=0.8403 and 0.9412 for cardiac wellness and HF, respectively), and the models exhibit good calibration. Clinical insights to the prediction of HF and cardiac wellness are provided in the form of logistic regression coefficients which suggest that augmenting the traditional risk factors with a multimarker panel spanning a diverse cardiovascular pathophysiology provides improved performance over reference methods. Additionally, a framework is provided for seamless integration with biomarker measurements from point-of-care medical microdevices, and a lasso-based feature selection process is described for the down-selection of biomarkers in multimarker panels.

Cardiac ScoreCard

Metabolomics in early detection and prognosis of acute coronary syndrome.

Background : Type II diabetes has become a serious threat to human health in recent years. Among adults above 20 years old in China, the prevalence rate of diabetes is 9.7%. Thus, it is imperative to study the mechanisms underlying type II diabetes to develop effective therapeutic treatments. Objective : To examine metabolic changes in a rat model of type II diabetes and explore mechanisms underlying the beneficial effects of traditional Chinese medicine (TCM) in this model. 120 rats were divided into four groups, including a control group, a high-fat diet group (high-fat diet and streptozotocin injection), a TCM group (high-fat diet, streptozotocin injection, followed by TCM administration), and a rosiglitazone maleate group (high-fat diet, streptozotocin injection, followed by rosiglitazone maleate administration). Metabolites in urine samples from 1-3 weeks (time point 1) and 4-6 weeks (time point 2) of drug administration were compared by gas chromatography-mass spectrometry (GC-MS). Results : Our results showed that in the high-fat diet group, at time point 2, the levels of dihydroxybenzoic acid, L-ascorbic acid, D-gluconic acid, octadecanoic acid, and glutaric acid in urine were significantly higher than at time point 1. In the TCM group, at time point 2, the urine levels of L-ascorbic acid were markedly lower than at time point 1. Conclusion : Our studies demonstrated that examining urine metabolic changes provided important insights into the mechanisms underlying type II diabetes as well as the therapeutic effects of TCM. Key words : Type II diabetes; Urine metabonomics; Traditional Chinese Medicine; Rat Model

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Effects of traditional Chinese medicine on rats with Type II diabetes induced by high-fat diet and streptozotocin: a urine metabonomic study

Identifying Metabolite and Protein Biomarkers in Unstable Angina In-patients by Feature Selection Based Data Mining Method

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.

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

The invention relates to new application of a traditional Chinese medicine composition. The traditional Chinese medicine composition is prepared from cortex lycii, the roots of kudzu vine, the roots of red-rooted salvia, hawthorn fruits and burdock. The traditional Chinese medicine composition has obvious functions of soothing the nerves, making people sleep peacefully (adjusting and smoothing emotions), improving cardiac functions and the periphery, achieving central analgesia and inducing diuresis.

New application of traditional Chinese medicine composition

Chronic heart failure (CHF) was characterized by the failure of enough blood supply from the heart to meet the body’s metabolic demands, and the prevalence of CHF continuously increases globally. The personalized diagnosis of Traditional Chinese Medicine (TCM) classifies CHF into several different syndrome types, and integrating Western and TCM to treat CHF has proved a validated therapeutic approach. Over the last few years, there has been a rapidly growing number of metabolomics applications aimed at finding biomarkers that could assist diagnosis, provide therapy guidance, and evaluate response to therapy for individualized intervention of CHF. Thus, in this review, particular attention will be paid to the past successes in applications of state-ofthe-art technology on metabolomics to contribute to biomarker discovery in CHF research.

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Application of Metabolomics for the Diagnosis and Traditional Chinese Medicine Syndrome Differentiation of Chronic Heart Failure

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