Objective To study the changes of negative symptoms in PCP-induced schizophrenia rat model.Methods Thirty newborn female SD rats randomly divided into control group,PCP-week 6 group and PCP-week 10 group( n=10 in each group).Perinatal rat treated with PCP ( 10 mg/kg) on postnatal days 7,9 and 11(10 mg/kg,ip),and sucorse intalce test(SIT),forced swimming test(FST) and resident-intruder test(RIT) were used to test the emotional and negative symptoms.Results In the SIT,there was no difference between control and PCP groups (con:(28.24 ±0.86) ml/kg; week 6:(26.57 ± 1.01 ) ml/kg; week 10:(27.98 ±0.99) ml/kg,F =12.35,P ＞ 0.05 ).In the FST,PCP model rats showed longer still time ( con:(39.32 ± 1.98 ) s ; week 6:(52.39 ± 1.66)s,week 10:(55.56 ± 1.49)s,F=3.99,P＜ 0.05 ).In the RIT,PCP models rats showed less explore time ( (40.31 ± 13.56)s vs (63.90 ± 13.12)s,(43.65 ±12.86 )s vs (65.18 ± 15.12)s,P ＜ 0.05 ) and more escape time ((19.33±2.26) s vs (9.26 ± 1.32) s,(17.79 ±2.99) s vs (9.38 ± 1.36) s,P＜ 0.05).Conclusion Perinatal PCP injection can induce the long-lasting negative-symptoms changes.

Objective A BP neural network model for diagnosing type 2 diabetic nephropathy based on laboratory tests was developed and evaluated. Methods Patients with type 2 diabetic nephropathy from 5 hospitals of Chongqing,Guizhou and Sichuan Provinces from January 2016 to December 2016 were collected in the study. Totally 89 parameters were analyzed by univariate analysis to identify significant variables by SPSS 19. 0 and MATLAB 2014a. The diagnostic performance of the two methods were compared. Results A total of 477 patients with type 2 diabetic nephropathy and 449 patients of control group were included. Univariate analysis showed that 42 variables had significant difference. Logistic regression analysis showed that 12 variables were included in the optimal regression equation. This BP neural network had 42 input layer nodes,15 hidden layer nodes and 1 output layer nodes. The Youden index of logistic regression analysis and BP neural network(training set and test set) were 0.76,0.89 and 0.83. The accurately diagnosed were 88.12%,94.24%,and 91.34%,the AUC were 0.95,0.98,and 0.96. Conclusion A BP neural network model was developed,which has important accessory diagnostic value for diagnosis of type 2 diabetic nephropathy. But all these conclusions need further validation in clinic.