Objective Build a predictive model integrating radiomics features with clinical characteristics for the prognosis prediction of acute anterior circulation large vessel occlusion(LVO)stroke patients after mechanical thrombectomy(MT),and explore its predictive value.Methods Patients with acute ischemic stroke who underwent endovascular treatment for LVO of the anterior circulation were enrolled consecutively from the endovascular treatment registry database for acute anterior circulation ischemic stroke(ACTUAL)and the Nanjing stroke registry system from January 2014 to January 2025 retrospectively.Baseline,clinical and imaging data were collected from enrolled patients,including gender,age,medical history(atrial fibrillation,hypertension,diabetes),smoke history,admission blood pressure,blood glucose,National Institutes of Health stroke scale(NIHSS)score,Alberta stroke program early CT score(ASPECTS),occluded blood vessels(internal carotid artery,middle cerebral artery),trial of Org 10172 in acute stroke treatment(TOAST)classification(atherosclerotic,cardiogenic embolism,others),collateral status(American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology[ASITN/SIR]classification),the onset-to-door time,the time from onset to puncture,the operation time,the time from onset to recanalization,recanalization status(modified thrombolysis in cerebral infarction[mTICI]score),symptomatic intracerebral hemorrhage(sICH)within 72 hours after MT and functional outcome at 90 days post-MT(modified Rankin scale[mRS]score).Divide all patients into a training set and a validation set in a ratio of 7∶3.The training set is used to build the predictive model,and the validation set is used to verify the predictive model.In the training set,patients were divided into a good prognosis group(mRS score 0-2)and a poor prognosis group(mRS score 3-6),the variables with P＜0.05 from the univariate Logistic regression analysis were enrolled into the multivariate Logistic regression analysis to screen the clinical risk factors affecting prognosis.The preoperative head MR axial diffusion weighted imaging sequence images of patients in the training set were selected.The Pyradiomics toolkit of the Python 3.6 platform was used to implement radiomics feature extraction.After conducting consistency analysis on the extracted features,standardization processing was performed.In the training set,feature dimension reduction is carried out on the radiomics feature values obtained after extraction and processing.The least absolute shrinkage and selection operator(LASSO)model was used to screen the features.The support vector machine(SVM),k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm are used to respectively construct models based on the screened radiomics features,use grid search with cross validation(GridSearchCV)to gain specific parameters in each model.The receiver operating characteristic(ROC)curve was used to analyze and compare the area under the curve(AUC)of each radiomics model,screen the most suitable radiomics model,and verify it in the validation set.The predicted probability value of prognosis calculated by this model is taken as the radiomics score.In the training set,the radiomics scores and the screened clinical risk factors were taken as independent variables,and a multivariate Logistic regression analysis was conducted.A nomogram was used to construct a comprehensive prediction model of radiomics plus clinical factors for predicting the prognosis of MT in acute stroke patients of LVO.The AUC of the clinical factor prediction model,the radiomics prediction model,and the radiomics plus clinical factor comprehensive prediction model were compared in the training set and the validation set,respectively.Results A total of 107 acute anterior LVO patients who underwent MT were included,comprising 72 males and 35 females,aged 27 to 87 years,with a median age of 64(56,71)years.There were 74 cases in the training set,among which 48 cases had a good prognosis and 26 cases had a poor prognosis.There were 33 cases in the validation set,among which 24 cases had a good prognosis and 9 cases had a poor prognosis.The NIHSS score of patients in the training set was lower than that of patients in the validation set(12[8,19]points vs.15[11,21]points,P=0.03),while there were no statistically significant differences in the remaining baseline,clinical and imaging data compared with the validation set(all P＞0.05).(1)Included the variables with P＜0.05 from the univariate Logistic regression analysis into the multivariate Logistic regression analysis.The results showed that age(OR,1.066,95％CI 1.003-1.133,P=0.039)and admission NIHSS score(OR,1.126,95％CI 1.028-1.233,P=0.011)were independent risk factors for poor prognosis of MT in patients with acute anterior circulation LVO stroke.(2)A total of 725 radiomics features were extracted.The results of intra-observer consistency analysis showed that the median intraclass correlation coefficient(ICC)of radiomics features was 0.75(0.56,0.87),and there were 424 features with ICC＞0.7 and 127 features with ICC＞0.9.The results of the inter-observer consistency analysis showed that the median ICC of radiomics features was 0.73(0.53,0.86).After dimensionality reduction using the LASSO,12 most relevant features were selected and incorporated into the radiomics-based prognostic model.The AUCs of the radiomics prediction models constructed by applying SVM,k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm were 0.803,0.890,0.969,1.000 and 1.000,respectively.The AUCs in the validation set were 0.769,0.743,0.817,0.792 and 0.799,respectively.SVM was selected as the final algorithm for the construction of the radiomics model.The radiomics data were input into SVM to obtain the radiomics score of each patient.(3)A comprehensive predictive nomogram model combining radiomics and clinical factors was constructed based on radiomics score,age,and the NIHSS score at admission.In the validation group,the integrated model demonstrated a significantly higher AUC-ROC(0.918,95％CI 0.831-0.969)compared to the radiomics model(AUC 0.803,95％CI0.694-0.886,P=0.026)and the clinical-feature model(AUC 0.784,95％CI0.674-0.872,P=0.009).In the validation set,there were no statistically significant difference among the integrated model(AUC 0.935,95％CI 0.792-0.991),radiomics model(AUC 0.769,95％CI 0.589-0.897,P=0.111)and the clinical-feature model(AUC 0.894,95％CI 0.737-0.974,P=0.602).The integrated model exhibited good calibration in both the training set and the validation set(Hosmer-Lemeshow test,P values were respectively 0.350,0.580).Conclusion The integrated radiomics-clinical model can provide effective prediction of MT on outcomes in acute anterior circulation LVO stroke patients,and it may offer an objective basis for clinical decision-making.

Objective Build a predictive model integrating radiomics features with clinical characteristics for the prognosis prediction of acute anterior circulation large vessel occlusion(LVO)stroke patients after mechanical thrombectomy(MT),and explore its predictive value.Methods Patients with acute ischemic stroke who underwent endovascular treatment for LVO of the anterior circulation were enrolled consecutively from the endovascular treatment registry database for acute anterior circulation ischemic stroke(ACTUAL)and the Nanjing stroke registry system from January 2014 to January 2025 retrospectively.Baseline,clinical and imaging data were collected from enrolled patients,including gender,age,medical history(atrial fibrillation,hypertension,diabetes),smoke history,admission blood pressure,blood glucose,National Institutes of Health stroke scale(NIHSS)score,Alberta stroke program early CT score(ASPECTS),occluded blood vessels(internal carotid artery,middle cerebral artery),trial of Org 10172 in acute stroke treatment(TOAST)classification(atherosclerotic,cardiogenic embolism,others),collateral status(American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology[ASITN/SIR]classification),the onset-to-door time,the time from onset to puncture,the operation time,the time from onset to recanalization,recanalization status(modified thrombolysis in cerebral infarction[mTICI]score),symptomatic intracerebral hemorrhage(sICH)within 72 hours after MT and functional outcome at 90 days post-MT(modified Rankin scale[mRS]score).Divide all patients into a training set and a validation set in a ratio of 7∶3.The training set is used to build the predictive model,and the validation set is used to verify the predictive model.In the training set,patients were divided into a good prognosis group(mRS score 0-2)and a poor prognosis group(mRS score 3-6),the variables with P＜0.05 from the univariate Logistic regression analysis were enrolled into the multivariate Logistic regression analysis to screen the clinical risk factors affecting prognosis.The preoperative head MR axial diffusion weighted imaging sequence images of patients in the training set were selected.The Pyradiomics toolkit of the Python 3.6 platform was used to implement radiomics feature extraction.After conducting consistency analysis on the extracted features,standardization processing was performed.In the training set,feature dimension reduction is carried out on the radiomics feature values obtained after extraction and processing.The least absolute shrinkage and selection operator(LASSO)model was used to screen the features.The support vector machine(SVM),k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm are used to respectively construct models based on the screened radiomics features,use grid search with cross validation(GridSearchCV)to gain specific parameters in each model.The receiver operating characteristic(ROC)curve was used to analyze and compare the area under the curve(AUC)of each radiomics model,screen the most suitable radiomics model,and verify it in the validation set.The predicted probability value of prognosis calculated by this model is taken as the radiomics score.In the training set,the radiomics scores and the screened clinical risk factors were taken as independent variables,and a multivariate Logistic regression analysis was conducted.A nomogram was used to construct a comprehensive prediction model of radiomics plus clinical factors for predicting the prognosis of MT in acute stroke patients of LVO.The AUC of the clinical factor prediction model,the radiomics prediction model,and the radiomics plus clinical factor comprehensive prediction model were compared in the training set and the validation set,respectively.Results A total of 107 acute anterior LVO patients who underwent MT were included,comprising 72 males and 35 females,aged 27 to 87 years,with a median age of 64(56,71)years.There were 74 cases in the training set,among which 48 cases had a good prognosis and 26 cases had a poor prognosis.There were 33 cases in the validation set,among which 24 cases had a good prognosis and 9 cases had a poor prognosis.The NIHSS score of patients in the training set was lower than that of patients in the validation set(12[8,19]points vs.15[11,21]points,P=0.03),while there were no statistically significant differences in the remaining baseline,clinical and imaging data compared with the validation set(all P＞0.05).(1)Included the variables with P＜0.05 from the univariate Logistic regression analysis into the multivariate Logistic regression analysis.The results showed that age(OR,1.066,95％CI 1.003-1.133,P=0.039)and admission NIHSS score(OR,1.126,95％CI 1.028-1.233,P=0.011)were independent risk factors for poor prognosis of MT in patients with acute anterior circulation LVO stroke.(2)A total of 725 radiomics features were extracted.The results of intra-observer consistency analysis showed that the median intraclass correlation coefficient(ICC)of radiomics features was 0.75(0.56,0.87),and there were 424 features with ICC＞0.7 and 127 features with ICC＞0.9.The results of the inter-observer consistency analysis showed that the median ICC of radiomics features was 0.73(0.53,0.86).After dimensionality reduction using the LASSO,12 most relevant features were selected and incorporated into the radiomics-based prognostic model.The AUCs of the radiomics prediction models constructed by applying SVM,k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm were 0.803,0.890,0.969,1.000 and 1.000,respectively.The AUCs in the validation set were 0.769,0.743,0.817,0.792 and 0.799,respectively.SVM was selected as the final algorithm for the construction of the radiomics model.The radiomics data were input into SVM to obtain the radiomics score of each patient.(3)A comprehensive predictive nomogram model combining radiomics and clinical factors was constructed based on radiomics score,age,and the NIHSS score at admission.In the validation group,the integrated model demonstrated a significantly higher AUC-ROC(0.918,95％CI 0.831-0.969)compared to the radiomics model(AUC 0.803,95％CI0.694-0.886,P=0.026)and the clinical-feature model(AUC 0.784,95％CI0.674-0.872,P=0.009).In the validation set,there were no statistically significant difference among the integrated model(AUC 0.935,95％CI 0.792-0.991),radiomics model(AUC 0.769,95％CI 0.589-0.897,P=0.111)and the clinical-feature model(AUC 0.894,95％CI 0.737-0.974,P=0.602).The integrated model exhibited good calibration in both the training set and the validation set(Hosmer-Lemeshow test,P values were respectively 0.350,0.580).Conclusion The integrated radiomics-clinical model can provide effective prediction of MT on outcomes in acute anterior circulation LVO stroke patients,and it may offer an objective basis for clinical decision-making.

Objective:To explore the prognostic prediction value of quantitative digital subtraction angiography (DSA) parameters in patients with acute anterior circulation ischemic stroke undergoing mechanical thrombectomy, and whether the clinical values vary by stroke etiology.Methods:This study was a post hoc analysis of the Multicenter Prospective Captor Trial. Patients with acute anterior circulation large-vessel occlusion and successful recanalization from April 2018 to July 2019 were screened. Post-processing analysis was performed on the DSA imaging sequence after recanalization, and 4 regions of interest (ROI) were selected in the target vessel: ROI1 (the proximal of the internal carotid artery-C2 segment), ROI2 (the starting point of the internal carotid artery-C7 segment), ROI3 (the end of the middle cerebral artery-M1 segment), and ROI4 (the end of the middle cerebral artery-M2 segment). Time to peak (TTP) was defined as the time at contrast concentration of selected ROI reached its maximum. Relative TTP (rTTP) was calculated by subtracting the TTP of ROI1 from the TTP of distalis ROIs. Successful recanalization was defined as modified Thrombolysis In Cerebral Infarction (mTICI) grade≥2b. Favorable outcomes at 3 months were defined as the modified Rankin Scale score≤2. According to the modified Rankin Scale score, the patients were divided into good prognosis group and poor prognosis group. The differences in clinical characteristics, postoperative hemodynamic parameters, and other data were compared between patients with good and poor prognoses. Univariate and multivariate Logistic regression was used to analyze factors related to a good prognosis. Finally, the prognostic prediction value of hemodynamic parameters was analyzed in patients with different Trial of Org10172 in Acute Stroke Treatment etiological classifications.Results:A total of 245 patients were collected, of which 161 patients [age 69 (60, 76) years, 92 (57.1%) male] were finally included in the analysis, including 36 cases of large artery atherosclerosis (LAA) stroke, 76 cases of cardiogenic embolism (CE), and 49 cases of other causes of stroke. Seventy-one (44.1%) patients had favorable outcomes at 3 months. The post-operative hemodynamic analysis indicated that patients with favorable outcomes ( n=71) had a higher proportion of mTICI grade 3 [54/71 (76.1%) vs 41/90 (45.6%),χ 2=15.26, P<0.001] and lower rTTP 31 [means TTP ROI3-TTP ROI1;0.33 (0.23, 0.54) s vs 0.47 (0.31, 0.65) s, Z=-2.71, P=0.007] than patients with unfavorable outcomes ( n=90). The mTICI score and rTTP 31 were respectively included in multivariate Logistic regression models. It was shown that mTICI grade 3 (adjusted OR=5.97, 95% CI 2.49-14.27, P<0.001) and rTTP 31 (adjusted OR=0.24, 95% CI 0.06-0.99, P=0.048) were significantly associated with favorable outcomes, and the area under the receiver operating characteristic curve of the models had no statistically significant difference ( P=0.170). Subgroup analysis showed that rTTP 31 was significantly associated with the prognosis of patients with LAA stroke ( OR=0, 95% CI 0-0.25, P=0.014), while mTICI grade was associated with the prognosis of patients with CE ( OR=3.91, 95% CI 1.40-10.91, P=0.009) and other etiologies ( OR=7.35, 95% CI 1.92-28.14, P=0.004). Conclusions:In patients with acute anterior circulation ischemic stroke and successful recanalization, both mTICI score and rTTP 31 had significant predictive value for favorable outcomes at 3 months. Moreover, rTTP 31 was significantly associated with the prognosis of patients with LAA stroke, while mTICI score was significantly related to the prognosis of patients with CE and other causes of stroke.

Objective:To analyze the epidemiological characteristics of COVID-19 caused by 2019-nCoV Delta variant (B.1.617.2) in Gansu province, and provide evidence for the prevention and control of COVID-19.Methods:The information of COVID-19 cases, including demographic characteristics, epidemiological history, onset date, diagnosis date, exposure place, detection way and infection source, in Gansu from 17 October to 25 November, 2021 were collected. Software Excel 2016,SPSS 22 and ArcGIS 10.7 were used for data process and analysis.Results:As of November 25, 2021, a total of 146 COVID-19 cases had been reported in Gansu and the epidemic affected 10 counties (districts) in 5 cities. The epidemic of COVID-19 in Gansu had three stages: imported case stage,imported-local case stage and local case stage. The age of cases ranged from 1 to 87 years,and the cases in age group 18-59 years accounted for 59.59% (87/146). The male to female ratio of the cases was 1∶1.12 (69∶77). The cases were mainly people engaged in business services (17.12%, 25/146),retirees (15.75%, 23/146),students (13.70%, 20/146),the jobless and unemployed (12.33%, 18/146). In 3 epidemic stages, the cases aged 18-59 years accounted for 44.44%,54.41% and 70.00% respectively,showing an upward trend,and there were differences among different populations (trend χ2=23.24, P<0.001). Also, the incubation period of the cases tended to decrease, and severe cases accounted for 33.33% (6/18), 19.12% (13/68) and 3.33% (2/60) respectively, showing a downward trend. Community screening (25.34%, 37/146) and close contact screening were the main ways to detect cases,the cases detected by close contact screening in 3 epidemic stages accounted for 50.00% (9/18), 66.18% (45/68) and 86.67% (52/60) respectively. The epidemic had obvious case clustering in confined places,and the main exposure modes were living together (24.66%), working/studying together (11.64%), taking same transportation (9.59%) and dining together (9.59%). Conclusions:The COVID-19 epidemic in Gansu was caused by 2019-nCoV Delta variant from imported cases. The virus was mainly transmitted through travel, sharing transportation, dining together and home contact. The characteristics of COVID-19 epidemic in Gansu changed with time, the case's clinical symptoms were not obvious and the incubation period became shorter. The infections mainly occurred in group aged 18 years and above.

Objective:To explore the application value in cerebral blood perfusion status of dynamic susceptibility contrast enhanced perfusion weighted imaging (DSC-PWI) in ischemic cerebrovascular disease (ICVD).Methods:Retrospective analysis of 31 cases ICVD head magnetic resonance imaging (MRI), diffusion weighted imaging (DWI), magnetic resonance angiography (MRA) and DSC-PWI image in clinical diagnosis, and selectively analyze parameters including relative cerebral blood flow (rCBF), relative cerebral blood volume (rCBV), mean transit time (MTT) and time to peak (TTP) for generalize its characteristics.Results:31 cases of ICVD, 16 cases hypoperfusion, rCBF decreased significantly, rCBV decreased, MTT and TTP prolonged, include 7 cases of large vessel disease and 5 cases of small vessel disease. The collateral circulation formed 9 cases, including 3 cases with good compensation, rCBF normal, rCBV normal, MTT and TTP prolonged, 6 cases with bad compensation, rCBF decreased, rCBV normal or increased, MTT and TTP prolonged. The blood reperfusion in 3 cases, rCBF normal or slightly increased, rCBV increased, MTT shortened or normal, and TTP shortened. The excessive perfusion in 3 cases, rCBF significantly increased and rCBV significantly increased, with MTT and TTP shortened.Conclusions:DSC-PWI can reliably reflect the perfusion state and collateral circulation compensation of ICVD, so as to guide the selection of clinical treatment program and significantly improve the prognosis of patients.

Objective:To understand the epidemiological characteristics of COVID-19 cases in different epidemic stages in Gansu province.Methods:Epidemiological investigation was conducted to collect the information of confirmed COVID-19 cases, including demographic, epidemiological and clinical information.Results:As of 25 February 2020, a total of 91 confirmed COVID-19 cases had been reported in Gansu. The epidemic of COVID-19 in Gansu can be divided as three different stages, i.e. imported case stage, imported-case plus indigenous case stage, and indigenous case stage. A total of 63 cases were clustered cases (69.23%), 3 cases were medical staff infected with non-occupational exposure.The initial symptoms included fever (54.95%, 50/91), cough (52.75%, 48/91), or fatigue (28.57%, 26/91), the proportion of each symptom showed a decreasing trend along with the three epidemic stages, but only the differences in proportions of fever (trend χ2=2.20, P<0.05) and fatigue (trend χ2=3.18, P<0.05) among the three epidemic stages were statistically significant. The cases with critical severe symptoms accounted for 42.85% (6/14), 23.73% (14/59) and 16.67% (3/18), respectively, in three epidemic stages, showed a decreasing trend ( H=6.45, P<0.05). Also, the incubation period prolonged along with the epidemic stage ( F=51.65, P<0.01), but the intervals between disease onset and hospital visit ( F=5.32, P<0.01), disease onset and diagnosis ( F=5.25, P<0.01) became shorter along with the epidemic stage. Additionally, the basic reproduction number ( R0) had decreased from 2.61 in imported case stage to 0.66 in indigenous case stage. Conclusions:The COVID-19 epidemic in Gansu was caused by the imported cases, and about 2/3 cases were clustered ones. No medical worker was observed to be infected by occupational exposure. With the progression of COVID-19 epidemic in Gansu, the change in initial symptom and incubation period suggests. the early screening cannot only depend on body temperature monitoring.

Objective: To analyze the spatial distribution and both hot and cold spots of incidence on Brucellosis in Gansu province from 2013 to 2018. Methods: Based on data from the Infectious Disease Reporting Information System in China, data related to space-time distribution and both hot and cold spots of Brucellosis in Gansu province from 2013 to 2018 were analyzed, by using the ArcGIS 10.5 software and GeoDa 1.6 software. Results: The trend surface analysis showed that the incidence of Brucellosis decreased gradually from the northern to southern parts with slightly higher in the west than in the east of Gansu. Global spatial autocorrelation analysis showed that the Moran’s I of Brucellosis in Gansu province from 2013 to 2018 were 0.242, 0.487, 0.424, 0.460, 0.427 and 0.468 (P<0.01), suggesting that the incidence of Brucellosis had spatial clustering features in Gansu province. Local global spatial autocorrelation analysis showed that the number of hot spots were declining in the eastern and north-central regions while the hot spots were increasing in the Hexi area (Wuwei, Zhangye, Jinchang and Jiuquan cities). The cold spots areas were mainly seen in the eastern, southeastern and south of central regions in Gansu province. Conclusions The incidence of Brucellosis showed obviously spatial clustering characteristics in Gansu province. The number of hot spots were decreasing in the eastern and north of central regions while the hot spots were increasing in the Hexi area, suggesting that the prevention and control measures for Brucellosis should be further strengthened in these areas.

Objective: To analyze the epidemiological and spatial-temporal distribution of Brucellosis, epidemic encephalitis B and hemorrhagic fever with renal syndrome (HFRS) in Gansu province during 2014-2018 so as to provide evidence for the prevention and control of those diseases. Methods: A database was established in Gansu province from 2014 to 2018, using the geographical information system. A spatial distribution map was drawn, with trend analysis and space-time clustering used to study the 3-dimention of the diseases, by using both ArcGIS 10.5 and SaTScan 9.6 softwares. Results: Results from the trend surface analysis showed that the incidence of Brucellosis decreased gradually from north to south parts while the U type curve could reflect the distribution from the east to the west areas. Incidence of epidemic encephalitis B decreased significantly from south to north areas in the province, with incidence higher in the eastern than in the mid-west region. Difference on the incidence of HFRS was not significantly visible in the eastern and western regions, while the incidence was slightly higher in the southern than the northern parts of the province. Spatial and space-time clustering did exist among the 3 diseases in Gansu from 2014 to 2018. The areas with clusters of Brucellosis appeared in the eastern parts during 2014-2015, including 19 counties. The areas with secondary clusters of Brucellosis were seen in the Hexi district, including 4 counties, during 2017-2018. The areas with high incidence of epidemic encephalitis B were clustered in the middle and southeast areas, including 32 counties, during 2017-2018. Areas with most clusters of HFRS appeared in Min county of Dingxi city in 2018, with the areas of secondary clusters in 8 counties of the eastern areas in 2018. Conclusions The overall incidence rates of the 3 natural focus diseases were in a upward trend and showing obvious characteristics on spatial clustering. According to the distributive characteristics, effective measures should be developed accordingly.

Objective To investigate the effect of human serum albumin (Alb) on the permeability of blood-brain barrier (BBB) after subarachnoid hemorrhage (SAH) and the pathways for Alb uptake in endothelial cells.Methods Mouse brain endothelial cells (bEnd.3) were cultured in the Transwell chamber was used to induce a BBB model.A SAH in vitro model was induced by adding 10 μmol/L oxyhemoglobin into the culture medium.The cells were divided into 3 groups:control group,SAH group,and Alb group (10 mg/ml).Transendothelial electric resistance (TEER) was used to detect the permeability of BBB.A confocal microscope was used to observe whether the fluorescent labeled Alb could be uptaken by bEnd.3cells.Immunoprecipitation was used to detect whether Alb could interact with the cells of caveolin 1 (Cav-1).According to the principle of siRNA,Cav-1 siRNA was transfected into bEnd.3 cells to inhibit the expression of Cav-1.Western blot analysis was used to detect whether bEnd.3 cells could uptake Alb.TEER was used to detect the permeability of BBB.Results Compared with the SAH group,the TEER value of the Alb group increased significantly (P =0.011).Alb was uptaken by bEnd.3 cells and interacted with Cav-1 in bEnd.3 cells.Cav-1 siRNA transfection could significantly inhibit the expression of Cav-1 in bEnd.3cells and reduce the uptake ability of Alb by cells (P=0.025),resulting in a significant decrease in the protective effect of Alb on BBB (P ＜ 0.001).Conclusion Cav-1 may be uptaken by endothelial cells under the participation of Cav-1 and improve the permeability of BBB after SAH.

Objective To investigate the expression level and clinical significance of growth differentiation factor-15 (GDF-15) in type 2 diabetic nephropathy.Methods Sixty-eight patients (DN) of type 2 diabetic nephropathy treated in our hospital from January 2012 to 2013 January were chosen,in the same period,66 cases with type 2 diabetes mellitus (DM) and 30 cases normal volunteers without family history of diabetes in (NC) were studied as control in this study.Their blood routine and renal functions were detected.GDF-15 level was examined by enzyme linked immunosorbent assay (ELISA),receiver operating characteristic curve (ROC) curve was used to analyze the diagnostic significance of GDF-15 level.Results The level of GDF-15 in blood of patients with type 2 diabetic nephropathy was significantly higher than that in patients with type 2 diabetes and normal control group.GDF-15 levels was negatively correlated with low density lipoprotein (LDL),fasting insulin (INS) and glomerular filtration rate (eGFR) (P ＜ 0.05),and positively correlated with cholesterol (TC),triglyceride (TG),high density lipoprotein (HDL),fasting blood glucose (FBG),glycosylated hemoglobin (HbA1c),high-sensitivity C-reactive protein (hsCRP),creatinine (SCR),urea nitrogen (BUN),24 h urinary albumin (mAlb),and urinary albumin excretion rate (URER) (P ＜0.05).GDF-15 level had no correlation with body mass index (BMI),systolic blood pressure (SBP),and diastolic blood pressure (DBP) (P ＞ 0.05).GDF-15 levels could be used to diagnose type 2 diabetic nephropathy with 82.2％ sensitivity and 70.2％ specificity.Conclusions GDF-15 level has important significance in the diagnosis and pathogenesis of type 2 diabetic nephropathy.