BackgroundGrowing evidence suggests that epicardial adipose tissue (EAT) may play a key role in the pathogenesis and development of coronary artery disease (CAD) by producing several inflammatory adipokines. Chemerin, a novel adipokine, has been reported to be involved in regulating immune responses and glucolipid metabolism. Given these properties, chemerin may provide an interesting link between obesity, inflammation and atherosclerosis. In this study, we sought to determine the relationship of chemerin expression in EAT and the severity of coronary atherosclerosis in Han Chinese patients.MethodsSerums and adipose tissue biopsies (epicardial and thoracic subcutaneous) were obtained from CAD (n = 37) and NCAD (n = 16) patients undergoing elective cardiac surgery. Gensini score was used to assess the severity of CAD. Serum levels of chemerin, adiponectin and insulin were measured by ELISA. Chemerin protein expression in adipose tissue was detected by immunohistochemistry. The mRNA levels of chemerin, chemR23, adiponectin and TNF-alpha in adipose tissue were detected by RT-PCR.ResultsWe found that EAT of CAD group showed significantly higher levels of chemerin and TNF-alpha mRNA, and significantly lower level of adiponectin mRNA than that of NCAD patients. In CAD group, significantly higher levels of chemerin mRNA and protein were observed in EAT than in paired subcutaneous adipose tissue (SAT), whereas such significant difference was not found in NCAD group. Chemerin mRNA expression in EAT was positively correlated with Gensini score (r = 0.365, P < 0.05), moreover, this correlation remained statistically significant (r = 0.357, P < 0.05) after adjusting for age, gender, BMI and waist circumference. Chemerin mRNA expression in EAT was also positively correlated with BMI (r = 0.305, P < 0.05), waist circumference (r = 0.384, P < 0.01), fasting blood glucose (r = 0.334, P < 0.05) and negatively correlated with adiponectin mRNA expression in EAT (r = -0.322, P < 0.05). However, there were no significant differences in the serum levels of chemerin or adiponectin between the two groups. Likewise, neither serum chemerin nor serum adiponectin was associated with Gensini score (P > 0.05).ConclusionsThe expressions of chemerin mRNA and protein are significantly higher in EAT from patients with CAD in Han Chinese patients. Furthermore, the severity of coronary atherosclerosis is positive correlated with the level of chemerin mRNA in EAT rather than its circulating level.

The English composition is an important indicator of English learners’ overall language skills and is asked in large-scale English examinations, both in China’s college entrance examinations and graduate examinations and in the TOEFL, GRE, and IELTS examinations in Europe and the United States. Some automatic scoring systems for English writing have been created in the United States and internationally, however the systems still have issues with generalization, accuracy, and error correction. In this paper, we present a method to improve the accuracy of existing automatic composition scoring systems through deep learning techniques in a wireless network environment. Experiments reveal that the method can accurately assess the quality of English learners’ writings, paving the way for the creation of an automated composition scoring system for large-scale machine testing and web-based self-learning platforms.

We show how to optimize English diagnostic Q matrix based on cognitive diagnostic model fitting method. Firstly, attribute annotation verifies the reliability of existing Q matrix and fitting analysis, as researchers found that they still have the original Q matrix optimization space; secondly, this paper proposes a classification algorithm based on organization evolution and the information entropy of English in the diagnosis of intelligent evaluation algorithm, the running mechanism of the existing evolutionary algorithm, and the evolution of its direct effects on operation data rather than the rule. After the end of evolution, rules can be extracted from each organization to avoid meaningless rules in the process of evolution. According to the characteristics of the classification problem, we put forward three kinds of evolutionary operators and a selection mechanism, which is presented based on the information office of the evolution of the way of attribute importance. Based on this definition, the organizational fitness function, and finally the algorithm used in six test data sets and compared with the existing two classification methods, the experimental results show that the method obtained the higher forecast accuracy, and smaller rule sets are produced. Finally, a matching combination and quantitative fitting screening based on G-DINA measurement model were decomposed and analyzed, and a better fitting model was optimized based on the original Q matrix model. The results show that, first, the optimized new model is better than the original model in relative data fitting value and interpretation and diagnosis of fractional variation; second, the new model has a higher correlation with the results of self-evaluation, indicating that the probability of the new model is closer to the results of self-evaluation.