Post-stroke cognitive impairment (PSCI) directly affects the outcome of patients with stroke. Enlarged perivascular spaces (EPVS) suggest the impairment of brain clearance mechanism and may affect cognitive function. More and more studies have confirmed that the presence of EPVS will aggravate PSCI. This article reviews the relationship between EPVS and PSCI.

Objective To study the application of autoregressive integrated moving average(ARIMA)model to predict the monthly reported malaria cases in China,so as to provide a reference for prevention and control of malaria. Methods SPSS 24.0 software was used to construct the ARIMA models based on the monthly reported malaria cases of the time series of 2006-2015 and 2011-2015,respectively. The data of malaria cases from January to December,2016 were used as validation data to compare the accuracy of the two ARIMA models. Results The models of the monthly reported cases of malaria in China were ARIMA(2,1,1)(1,1,0)12 and ARIMA(1,0,0)(1,1,0)12 respectively. The comparison between the predictions of the two models and actual situation of malaria cases showed that the ARIMA model based on the data of 2011-2015 had a higher ac-curacy of forecasting than the model based on the data of 2006-2015 had. Conclusion The establishment and prediction of ARIMA model is a dynamic process,which needs to be adjusted unceasingly according to the accumulated data,and in addi-tion,the major changes of epidemic characteristics of infectious diseases must be considered.

Objective:To explore the correlation between enlarged perivascular spaces and other imaging markers of cerebrovascular disease in patients with ischemic stroke.Methods:Totally 287 patients with ischemic stroke hospitalized in neurology department from January 2018 to January 2019 were selected. According to the severity of EPVS in different parts of the brain, the correlations between the severity of EPVS in different parts of the brain and cerebral microbleeds (CMBs), white matter hyperintensity (WMH), lacunar infarcts (LIs) were analyzed. SPSS 22.0 software was used for analysis. Chi-square test, independent sample t-test, rank-sum test and non parametric Mann-Whitney U test were used for group comparison, and Logistic regression analysis was used for multivariate analysis. Results:EPVS was common and severe in patients with ischemic stroke. Periventricular white matter hyperintensity(PWMH)( β=1.604, P<0.001, OR=4.971, 95% CI=2.015-12.263), CMBs ( β=1.224, P=0.018, OR=3.339, 95% CI=1.232-9.383) and LIs ( β=0.626, P=0.047, OR=1.871, 95% CI=1.009-3.470) were independent risk factors for BG-EPVS. PWMH ( r=0.614), DWMH ( r=0.622), LIs ( r=0.532) were positively correlated with the severity of BG-EPVS (all P<0.01). Conclusion:The imaging makers of CSVD are related to BG-EPVS, which can affect the severity of brain BG-EPVS in patients with ischemic stroke.