Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM). Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC) were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA), while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA), the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM) is of great value, indicating the feasibility of digitalized tongue diagnosis.

The purpose of this study was to compare the predictive ability of five obesity indices, including body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), waist-to-hip ratio (WHpR) and body adiposity index (BAI), to predict multiple non-adipose metabolic risk factors, including elevated blood pressure (BP), elevated fasting plasma glucose (FPG), elevated triglyceride (TG), reduced high-density lipoprotein cholesterol (HDL-C), elevated serum uric acid (SUA) and non-alcoholic fatty liver disease (NAFLD), in an elderly Chinese population. A total of 5685 elderly Chinese subjects (≥60 years) were recruited into our community-based cross-sectional study. Receiver operating characteristic curve (ROC) analyses were used to compare the predictive ability as well as determine the optimal cut-off values of the obesity indices for multiple metabolic risk factors. According to the areas under the receiver operating characteristic curve (AUC), BMI, WC and WHtR were able to similarly predict high metabolic risk in males (0.698 vs. 0.691 vs. 0.688), while in females, BMI and WC were able to similarly predict high metabolic risk (0.676 vs. 0.669). The optimal cut-off values of BMI, WC and WHtR in males were, respectively, 24.12 kg/m2, 83.5 cm and 0.51, while in females, the values were 23.53 kg/m2 and 77.5 cm.

Alzheimer's disease (AD) is the most common form of dementia; its pathophysiological mechanism remains unclear. Long noncoding RNAs (lncRNAs) play key roles in AD. lncRNA EBF3-AS has been found dysregulated in AD, which is abundantly expressed in the brain. The aim of this study was to investigate the role of EBF3-AS in AD. Results showed that the expressions of lncRNA EBF3-AS and EBF3 (early B cell factor 3) were upregulated in hippocampus of APP/PS1 mice (AD model mice). EBF3-AS knockdown by siRNA inhibited the apoptosis induced by Aβ and okadaic acid (OA) in SH-SY5Y. The expression of EBF3 was downregulated in Aβ- and OA-treated SH-SY5Y, which was reversed by EBF3-AS knockdown. EBF3 knockdown can reverse the Aβ-induced apoptosis in SH-SY5Y. These results revealed that lncRNA EBF3-AS promoted neuron apoptosis in AD, and involved in regulating EBF3 expression. EBF3-AS may be a new therapeutic target for treatment of AD.

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