With the growth of people's living standard,the prevalence of childhood obesity is increasing year by year.Meanwhile,the adverse effect caused by obesity occurred more frequently as well.Therefore,obesity in childhood has become a critical issue in public health area.Complications of obesity may lead to enormous implications on children,including their endocrine,cardiovascular,digestive system,etc.,which will have an impact on the body growth in the adolescence.Obesity not only has a huge threat to children's physical and psychological health,but also has a long-term effect on body health condition in the future.Consequently,it is significant to diagnose the occurrences of complications of children,and implement the guiding and curing at an early stage.

Objective　To investigate the predictive value of TOAST（Trial of Org 10172 in Acute Stroke Treatment） and lipoprotein-associated phospholipase A2（Lp-PLA2） for early recurrence of ischemic cerebrovascular disease after transient ischemic attack（TIA）and minor ischemic stroke of anterior circulation. Methods　A total of 190 patients with TIA and minor ischemic stroke of anterior circulation were selected and the general information and supplementary examinations were recorded. The patients were classified etiologically into large-artery atherosclerotic stroke（LAA） group and non-LAA group according to TOAST. According to the recurrence of ischemic cerebrovascular disease within 30 days，the patients were divided into positive event group and negative event group，and the differences between the two groups were compared. ROC curve was used to determine the best cutoff value of Lp-PLA2 for predicting positive event. Multivariate analysis was performed to identify potential predictors of recurrence. Finally，ROC curve was used to analyze the predictive value of Lp-PLA2 and LAA alone or in combination for recurrent ischemic cerebrovascular disease in the early stage of TIA and minor ischemic stroke of anterior circulation. Results　For positive event group，the age，proportion of TIA or cerebral infarction history，proportion of LAA and the level of Lp-PLA2 were higher than those of negative event group（P<0.05）.The best cutoff value of Lp-PLA2 was 304.50 ng/ml（sensitivity and specificity were 0.645 and 0.711 respectively）. LAA and Lp-PLA2 ≥ 304.50 ng/ml were independent risk factors for recurrent ischemic cerebrovascular disease within 30 days after TIA and minor ischemic stroke of anterior circulation. The area under ROC curve of LAA，Lp-PLA2≥ 304.50 ng/ml and combination of both were 0.671，0.678 and 0.729 respectively. The area under ROC curve of combined prediction of LAA and Lp-PLA2 was the largest. Conclusion　LAA combined with Lp-PLA2≥304.50 ng/ml could predict the risk of early recurrence of ischemic cerebrovascular disease in TIA and minor ischemic stroke of anterior circulation.

Objective To synthesize the natural cyclopeptide auyuittuqamide A by Fmoc-based solid phase linear synthesis and liquid phase cyclization. Methods Using 2-chlorotriphenylmethyl chloride (CTC) resin as the solid support, 1,3-diisopropylcarbodiimide (DIC) and 1-hydroxybenzotriazole (HOBt) as the condensing agents, 9-fluorenylmethoxycarbonyl (Fmoc) to protect amino acids were assembled in sequence, and then the linear peptide bearing the protected groups was obtained in presence of trifluoroethanol (TFE) cutting reagent. The protected linear peptide was cyclized with the aid of benzotriazole hexafluorophosphate (PyBOP) and 1-hydroxybenzotriazole (HOBt) in dichloromethane (DCM) solution, followed by trifluoroacetic acid (TFA) deprotection to obtain the cyclic peptide, auyuittuqamide A that was purified by preparative HPLC and characterized by HR-MS and 500MHz ¹H-NMR. Results The purity of auyuittuqamide A was more than 95% and the total yield was 5.48%. Conclusion This method has simple synthesis steps and high yield. It is the first to establish a fully synthesis method for the natural cyclic peptide auyuittuqamide A, which lays the foundation for further research of auyuittuqamide A.

The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.
Humans ; Adult ; Imagination ; Neural Networks, Computer ; Imagery, Psychotherapy/methods* ; Electroencephalography/methods* ; Algorithms ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted