Objective To observe blood pressure variability (BPV) in elderly patients with primary hypertension and carotid artery intima-media thickness (IMT),and analyze the correlation between BPV and atherosclerosis.Methods One hundred and nine patients with primary hypertension were divided into non-carotid atherosclerosis group (IMT ＜ 1.0 mm,49 cases) and carotid atherosclerosis group (IMT ≥ 1.0mm,60 cases).24 h ambulatory blood pressure monitoring (ABPM) was used to measure the 24-hour mean systolic blood pressure (24 h SBP),24-hour mean diastolic pressure (24 h DBP) ; SBP of daytime (dSBP)and nighttime (nSBP) ; DBP of daytime (dDBP) and nighttime (nDBP).And their standard deviation and coetficient of variation (CV) was calculated.Results dSBP,24 h SBPCV,24 h DBPCV,dSBPCV,nSBPCV in carotid atherosclerosis group was higher than that in non-carotid atherosclerosis group [(132.3 ± 12.1)mm Hg(1 mm Hg =0.133 kPa) vs.(122.7 ± 10.8) mm Hg,0.118±0.011 vs.0.107 ± 0.023,0.142 ± 0.058vs.0.116 ±0.028,0.129 ±0.039 vs.0.105 ±0.017,0.119 ±0.060 vs.0.109 ±0.037],and there was significant difference (P ＜ 0.01 or ＜ 0.05).There was no significant difference in 24 h SBP,24 h DBP,dDBP,nSBP,nDBP,dSBPCV,nSBPCV between two groups (P ＞0.05).Conclusions BPV of carotid atherosclerosis is higher than that of non-carotid atherosclerosis.BPV and carotid artery IMT has a certain relevance.

Objective To investigate the effect of hepatitis C virus (HCV) core protein and nonstructural protein 4B(NS4B) on the proliferation of HepG2 cells and its possible mechanism.Methods The two recombinant plasmid pcDNA3.1 (-)Core and pcDNA3.1 (-) NS4B were transiently transfected respectively and co-transfected into HepG2 cells by lipofectamine, simultaneously HepG2 cells transfected with pcDNA3.1 (-) and untransfected HepG2 cells were used as control.The expression of mRNA and protein of HCV Core,NS4B,Wnt1,β-catenin,c-myc and CyclinDl in the cells of each group were detected by RT-PCR and Western blot respectively.Cell proliferation changes were measured by MTT assay and plate colony formation assay.The cell cycle distribution was tested by flow cytometry.Results ①HCV Core or/and NS4B mRNA and protein were expressed successfully in the HepG2 cells transfected with pcDNA3.1 (-)Core,pcDNA3.1 (-)NS4B alone or in combination.② The relative expression levels of mRNA and protein of Wnt1,β-catenin,c-myc and CyclinD1 were higher in the cells transfected with pcDNA3.1 (-) Core,pcDNA3.1 (-) NS4B alone or in combination than those in the cells transfected with pcDNA3.1 (-) and untransfected HepG2 cells(P ＜0.01).③Compared with the HepG2 cells transfected with pcDNA3.1 (-)and untransfected,cell viability,cloning efficiency and the percentage of cells at S and G2/M phases were significantly increased in the cells transfected with pcDNA3.1 (-) Core,pcDNA3.1 (-) NS4B alone or in combination (P ＜ 0.01).Conclusion HCV core protein and NS4B can accelerate cell cycle progression and promote cell proliferation of HepG2 cells probably through enhancement of Wntl,β-catenin,c-myc and CyclinD1 expression.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.