OBJECTIVETo investigate the relationship between angiotensin converting enzyme (ACE) gene and exercise-induced silent myocardial ischemia (SI) in patients with type 2 diabetes mellitus.

METHODSOne hundred and eight patients suffering from type 2 diabetes mellitus with normal rest electrocardiograph and 50 healthy individuals were selected randomly. SI was diagnosed with treadmill exercise test and ACE genotypes were detected with PCR.

RESULTS(1) The control group and type 2 diabetes mellitus group had similar distribution of ACE genotypes and alleles (P>0.05). Compared with the non-SI group, the SI group had significantly higher ACE D allele prevalence (Chi-square=4.501, P<0.05); however, the two groups had similar prevalence of ACE genotypes (P>0.05). (2) There were no significant differences in clinical characteristics and serum lipoproteins among the three ACE genotypes (II, DD,ID) of type 2 diabetes mellitus (P>0.05). (3) The prevalence of SI in DD group was found to be 68.2%, which was significantly higher than that in II genotype group (39.5%, Chi-square=4.593, P<0.05).

CONCLUSIONACE D allele increases the risk of SI in type 2 diabetes mellitus.

Adult ; Aged ; Diabetes Mellitus, Type 2 ; blood ; genetics ; Exercise ; physiology ; Female ; Gene Frequency ; Genetic Predisposition to Disease ; genetics ; Genotype ; Humans ; Lipoproteins ; blood ; Male ; Middle Aged ; Myocardial Ischemia ; diagnosis ; genetics ; physiopathology ; Peptidyl-Dipeptidase A ; genetics ; Polymerase Chain Reaction

In order to improve the accuracy and efficiency of automatic seizure detection, the paper proposes a method based on improved genetic algorithm optimization back propagation (IGA-BP) neural network for epilepsy diagnosis, and uses the method to achieve detection of clinical epilepsy rapidly and effectively. Firstly, the method extracted the linear and nonlinear features of the epileptic electroencephalogram (EEG) signals and used a Gaussian mixture model (GMM) to perform cluster analysis on EEG features. Next, expectation maximization (EM) algorithm was used to estimate GMM parameters to calculate the optimal parameters for the selection operator of genetic algorithm (GA). The initial weights and thresholds of the BP neural network were obtained through using the improved genetic algorithm. Finally, the optimized BP neural network is used for the classification of the epileptic EEG signals to detect the epileptic seizure automatically. Compared with the traditional genetic algorithm optimization back propagation (GA-BP), the IGA-BP neural network can improve the population convergence rate and reduce the classification error. In the process of automatic detection of epilepsy, the method improves the detection accuracy in the automatic detection of epilepsy disorders and reduced inspection time. It has important application value in the clinical diagnosis and treatment of epilepsy.