RESUMO
The online version of the original article can be found at https://doi.org/10.1631/jzus.B1900468 The original version of this article (Liu et al., 2020) unfortunately contained some mistakes. 1. Figs. 7c and 7d in p.229 were incorrect. The upper left and bottom left pictures in Fig. 7c were accidentally duplicated with the pictures at the same position of Fig. 1a. The upper right and bottom right pictures were mistakenly placed in Fig. 7c. Therefore, the calculation results in Fig. 7d were also mistaken. The correct versions should be as follows: 2. Because of the wrong pictures of Fig. 7c, the calculated results of "42.5%" in Abstract, Sections 3.9 and 5 are also mistaken. The correct result should be "45.2%." (1) Lines 10-12 of Abstract in p.218: "CSO-ss-SA/siRNA could effectively transmit siRNA into tumor cells, reducing the expression of RAC1 protein by 38.2% and decreasing the number of tumor-induced invasion cells by 42.5%." was incorrect. The correct version should be "CSO-ss-SA/siRNA could effectively transmit siRNA into tumor cells, reducing the expression of RAC1 protein by 38.2% and decreasing the number of tumor-induced invasion cells by 45.2%." (2) Lines 23-26 of Section 3.9 in p.227: "It was shown that the number of invasive tumor cells induced by DOX was reduced by 42.5% since CSO-ss-SA/siRNA downregulated the expression of RAC1 protein." was incorrect. The correct version should be "It was shown that the number of invasive tumor cells induced by DOX was reduced by 45.2% since CSO-ss-SA/siRNA downregulated the expression of RAC1 protein." (3) Lines 4-8 of Section 5 in p.231: "CSO-ss-SA, as an efficient redox-sensitive carrier for delivering siRNA silencing RAC1 into tumor cells, reduced the expression of RAC1 by 38.2% and decreased DOX-induced tumor invasion cells by 42.5% in vitro." was incorrect. The correct version should be "CSO-ss-SA, as an efficient redox-sensitive carrier for delivering siRNA silencing RAC1 into tumor cells, reduced the expression of RAC1 by 38.2% and decreased DOX-induced tumor invasion cells by 45.2% in vitro."
RESUMO
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.
RESUMO
Objective To analyze the risk factors of congenital heart disease(CHD) in China by using the meta‐analysis method to provide reference for etiology study and prevention of CHD .Methods The Chinese literature database such as CBM , VIP ,CNKI ,Wan Fang were retrieved from 2005 to 2015 for collecting the related literatures ,then the collected literatures were screened ,performed the information extraction ,quality evaluation and merged analysis by using the Stata12 .0 software .Results In this study ,37 articles were included ,including 20 provinces (municipalities and autonomous regions) ,8 588 cases in the case group and 12 479 cases in the control group;a total of 21 risk factors(5 pre‐pregnant factors and 16 gestational factors) were included . Conclusion All 21 risk factors include 4 pre‐pregnancy factors ,15 gestational factors and 2 factors which not be verified .The top 3 risk factors and their OR values with 95% CI were gestational diabetes 5 .80(2 .72-12 .37) ,contacting the occupational risk factors 5 .14(3 .30-8 .00) ,advanced age before pregnancy 4 .96(1 .45-16 .97) .Not considering to be as the risk factors and their OR value with 95% CI are high body mass index before pregnancy 1 .32(0 .99 -1 .75) ,living near street during pregnancy 1 .36(0 .50-3 .71 ) .
RESUMO
Described in this paper are the characteristics of medical big data such as multi-dimensionalities, non-integrities, time sequencing, redundancies, their sources such as health service,medical insurance,health adminis-trations, producers of drugs and medical equipments, Internet, life science, their applications in R & D of drugs, diagnosis and treatment of diseases,medical insurance,intelligent decision-making,individual health management, and challenges they are faced such as data integration,data storage,data sharing, shortage of trained professionals, and privacy protection.
RESUMO
Objective To explore the clinical accuracy of urine cellular components of RBC,WBC,etc.detected by the Urit-1500 urine analyzer.Methods 1 085 urine samples in this hospital from June to October 2013 were selected and detected by the Urit-1500 urine analyzer and the microscope detection respectively.The results of urine cellular components detected by the urine analy-zer were analyzed.Results In 1 085 urine samples,the coincidence rate of two detection methods in detecting RBC and WBC was 93.8%(1 018/1 085)and 94.0%(1 020/1 085)respectively.In 295 RBC positive urine samples tested by the urine analyzer,the number of negative results tested by microscopy was 49,accounting for 16.6%.In 790 RBC negative urine samples tested by the u-rine analyzer,the number of negative results tested by microscopy was 18,accounting for 2.3%.In 197 WBC positive urine samples tested by urine analyzer,the number of negative results tested by microscopy was 28,accounting for 14.2%.In 888 WBC negative urine samples by urine analyzer,the number of negative results tested by microscopy was 37,accounting for 4.2%.Conclusion De-tecting urine RBC and WBC by using the Urit-1500 urine analyzer should combined with the microscopy and other test indexes and even clinical data to conduct the comprehensive analysis for reducing the misdiagnosis and missed diagnosis as far as possible.