Objective : To explore the causal relationship between 46 phenotypes ( including 15 seropositive case- control phenotypes and 31 quantitative antibody-measurement phenotypes) and ulcerative colitis( UC) using two- sample bidirectional Mendelian randomization( TSMR) . Methods: Single nucleotide polymorphisms ( SNPs) sig- nificantly associated with the relative abundance of the 46 antibody sera were extracted as instrumental variables ac- cording to preset thresholds . Summary statistics for UC were obtained from the OPEN GWAS database ( n = 47 745) . MR-Egger regression , weighted median method ( WME) , inverse variance weighting ( IVW) , the simple mode method (SM) , and weighted multitude method (WM) were used to estimate the causal relationship between antibody levels and UC , primarily using the IVW method . The results were assessed according to the effect indica- tor dominance ratios (OR) and 95% confidence intervals (CI) . Sensitivity analysis , heterogeneity test , gene plei- otropy test , and outlier test (MR-PRESSO) were combined to verify the stability and reliability of the results , and the causal association study was performed again using reverse Mendelian randomization(MR) . Results : IVW re- sults showed that Epstein-Barr( EB) virus EA-D antibody levels ( OR = 0. 806 , 95% CI = 0. 693 - 0. 939 , P < 0. 01) , Epstein-Barr virus EBNA-1 antibody levels ( OR = 1 . 870% , 95% CI = 1 . 480 - 2. 360 , P < 0. 000 1) , Anti-polyomavirus 2 IgG seropositivity (OR = 0. 570 , 95% CI = 0. 435 - 0. 746 , P < 0. 000 1) were associated with UC . The inverse MR analysis revealed a causal effect on anti-polyomavirus 2 IgG seropositivity , and none of the a- bove revealed genetic pleiotropy or significant heterogeneity of IVs . Conclusion EB virus EBNA-1 antibody levels are positively associated with the risk of UC , while EB virus EA-D antibody levels and anti-polyomavirus 2 IgG se- ropositivity are negatively associated with the risk of UC , indicating that they are protective factors for UC .

Objective:To obtain the parameters associated with hematoma morpholoy by finite element analysis(FEA) and investigated their performance on predicting and diagnosis hematoma expansion(HE) in patients with spontaneous intracrebral hemorrhage(SICH).Methods:Patients with SICH who met research criteria were retrospective enrolled between June 2015 and December 2017. Clinical parameters on admission were collected, Perform 2 independent methodology on same patient to analysis the hematoma shape base on computed tomography(CT): Clinical routine method that performed by clinical investigator to identified margin irregularity of hematoma by CT ,and calculated the volume of hematoma by simplify Tada formula(ABC/2);The FEA method performed by FEA investigator and gain the hematoma 3 dimensional morphology and variables, include Volume, Surface area, and The quantity of triangles per square milimet surface(TQOT/mm 2). The HE was defined as volume enlargement of >33% compared with that on addmission. All patients were divided into HE and none HE group ,respectively, ABC/2 and FEA generated thire own HE and none HE group as different volume calcuation. The HE risk factors of ABC/2 and FEA were assessed in univariate and multivariable Logistic regression models. and the risk fators diagnosis value for HE were determined by the receiver operating characteristic(ROC) curves. Results:Total of 127 patients were enrolled, The mean time of symptom onset to hospital admitted was 3.08±1.34 h. There were 34(26.77%) cases HE identifed by ABC/2 and 31(24.41%)by FEA. Althought there are significant different (pearson χ2=53.66, P<0.01) of HE identification between ABC/2 and FEA, the 2 methods has moderate consistency (Kappa=0.65). All patients’ hematoma 3D reconstruction were performed by FEA and general observation show that TQOT/mm 2 most likely correlate to irregularity of hematoma 3D shape. Multivariable Logistic regression models indicated that ICH score( OR=1.79, 95% CI:1.19~2.68)was independent HE risk factor for ABC/2, respectively, TQOT/mm 2≥1.95/mm 2 ( OR=16.99,95% CI:5.98~48.33)and Ultraearly Hematoma Growth,(uHG) ( OR=1.05, 95% CI:1.01~1.09)were independent HE risk factor for FEA. With ROC analysis, both the ICH score of ABC/2 and uHG of FEA have low HE predictive and diagnosis value ,the area under the curve (AUC) were 0.64 and 0.67 respectively. However, TQOT/mm 2 was found to have excellent diagnosis value (AUC:0.9), sensitivity and specificity were 77% and 83% when the cut-off value was 1.95. Panel parameter model (TQOT/mm 2+uHG) was not be found to have a significant higher AUC than single parameter on FEA and the clinical routine parameters panel model (ICH +SB P>180 mmHg on addmission) have a unacceptable AUC(<0.7) as well as single parameters. Conclusions:Hematoma shape could be reconstructed and analysis by FEA and TQOT/mm 2 was likely relevance to hematoma morphology. TQOT/mm 2≥1.95 was indicate to have a better HE predicting and diagnosis value than any other risk factors and clinical parameters panel models in our reaserch.