Purpose. To investigate the reproducibility of aortic distensibility (D) measurement using CT and assess its clinical relevance in patients with infrarenal abdominal aortic aneurysm (AAA). Methods. 54 patients with infrarenal abdominal aortic aneurysm were studied to determine their distensibility by using 64-MDCT. Aortic cross-sectional area changes were determined at two positions of the aorta, immediately below the lowest renal artery (level 1.) and at the level of its maximal diameter (level 2.) by semiautomatic segmentation. Measurement reproducibility was assessed using intraclass correlation coefficient (ICC) and Bland-Altman analyses. Stepwise multiple regression analysis was performed to assess linear associations between aortic D and anthropometric and biochemical parameters. Results. A mean distensibility of Dlevel  1. = (1.05 ± 0.22) × 10−5  Pa−1 and Dlevel  2. = (0.49 ± 0.18) × 10−5  Pa−1 was found. ICC proved excellent consistency between readers over two locations: 0.92 for intraobserver and 0.89 for interobserver difference in level 1. and 0.85 and 0.79 in level 2. Multivariate analysis of all these variables showed sac distensibility to be independently related (R2 = 0.68) to BMI, diastolic blood pressure, and AAA diameter. Conclusions. Aortic distensibility measurement in patients with AAA demonstrated high inter- and intraobserver agreement and may be valuable when choosing the optimal dimensions graft for AAA before endovascular aneurysm repair.

Objectives To develop and validate a radiomics nomogram for timely predicting severe COVID-19 pneumonia. Materials and methods Three hundred and sixteen COVID-19 patients (246 non-severe and 70 severe) were retrospectively collected from two institutions and allocated to training, validation, and testing cohorts. Radiomics features were extracted from chest CT images. Radiomics signature was constructed based on reproducible features using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm with 5-fold cross-validation. Logistic regression modeling was employed to build different models based on quantitative CT features, radiomics signature, clinical factors, and/or the former combined features. Nomogram performance for severe COVID-19 prediction was assessed with respect to calibration, discrimination, and clinical usefulness. Results Sixteen selected features were used to build the radiomics signature. The CT-based radiomics model showed good calibration and discrimination in the training cohort (AUC, 0.9; 95% CI, 0.843–0.942), the validation cohort (AUC, 0.878; 95% CI, 0.796–0.958), and the testing cohort (AUC, 0.842; 95% CI, 0.761–0.922). The CT-based radiomics model showed better discrimination capability (all p < 0.05) compared with the clinical factors joint quantitative CT model (AUC, 0.781; 95% CI, 0.708–0.843) in the training cohort, the validation cohort (AUC, 0.814; 95% CI, 0.703–0.897), and the testing cohort (AUC, 0.696; 95% CI, 0.581–0.796). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics model outperformed the clinical factors model and quantitative CT model alone. Conclusions The CT-based radiomics signature shows favorable predictive efficacy for severe COVID-19, which might assist clinicians in tailoring precise therapy. Key Points • Radiomics can be applied in CT images of COVID-19 and radiomics signature was an independent predictor of severe COVID-19. • CT-based radiomics model can predict severe COVID-19 with satisfactory accuracy compared with subjective CT findings and clinical factors. • Radiomics nomogram integrated with the radiomics signature, subjective CT findings, and clinical factors can achieve better severity prediction with improved diagnostic performance.

This study examined the effect of Notch-1 signaling on malignant behaviors of breast cancer cells by regulating breast cancer stem cells (BCSCs). BCSCs were enriched by using serum-free medium and knocked out of Notch-1 by using a lentiviral vector. Real-time polymerase chain reaction (RT-PCR) and Western blotting were used to detect the Notch-1 expression levels in breast cancer cell lines and BCSCs, and flow cytometry to detect the proportion of BCSCs in BCSC spheres. The BCSC self-renewal, migration, invasion, and tumorigenicity were examined by the tumor microsphere-forming assay and transwell assay and after xenotransplantation. The results showed that the Notch-1 silencing reduced the number of BCSC spheres, the proportion of BCSCs, and the number of cells penetrating through the transwell membrane. It also decreased the size of tumors that were implanted in the nude mice. These results suggest that Notch-1 signaling is intimately linked to the behaviors of BCSCs. Blocking Notch-1 signaling can inhibit the malignant behaviors of BCSCs, which may provide a promising therapeutical approach for breast cancer.