Global emergencies caused by the severe acute respiratory syndrome coronavirus (SARS-CoV), Middle-East respiratory syndrome coronavirus (MERS-CoV) and SARS-CoV-2 significantly endanger human health. The spike (S) glycoprotein is the key antigen and its conserved S2 subunit contributes to viral entry by mediating host-viral membrane fusion. However, structural information of the post-fusion S2 from these highly pathogenic humaninfecting coronaviruses is still lacking. We used single-particle cryo-electron microscopy to show that the post-fusion SARS-CoV S2 forms a further rotated HR1-HR2 six-helix bundle and a tightly bound linker region upstream of the HR2 motif. The structures of pre-and postfusion SARS-CoV S glycoprotein dramatically differ, resembling that of the Mouse hepatitis virus (MHV) and other class I viral fusion proteins. This structure suggests potential targets for the development of vaccines and therapies against a wide range of SARS-like coronaviruses.

No abstract

OBJECTIVE: To compare 2D and 3D radiomics features prognostic performance differences in CT images of non-small cell lung cancer (NSCLC). METHOD: We enrolled 588 NSCLC patients from three independent cohorts. Two sets of 463 patients from two different institutes were used as the training cohort. The remaining cohort with 125 patients was set as the validation cohort. A total of 1014 radiomics features (507 2D features and 507 3D features correspondingly) were assessed. Based on the dichotomized survival data, 2D and 3D radiomics indicators were calculated for each patient by trained classifiers. We used the area under the receiver operating characteristic curve (AUC) to assess the prediction performance of trained classifiers (the support vector machine and logistic regression). Kaplan–Meier and Cox hazard survival analyses were also employed. Harrell's concordance index (C-Index) and Akaike's information criteria (AIC) were applied to assess the trained models. RESULTS: Radiomics indicators were built and compared by AUCs. In the training cohort, 2D_AUC = 0.653, 3D_AUC = 0.671. In the validation cohort, 2D_AUC = 0.755, 3D_AUC = 0.663. Both 2D and 3D trained indicators achieved significant results (P < .05) in the Kaplan-Meier analysis and Cox regression. In the validation cohort, 2D Cox model had a C-Index = 0.683 and AIC = 789.047; 3D Cox model obtained a C-Index = 0.632 and AIC = 799.409. CONCLUSION: Both 2D and 3D CT radiomics features have a certain prognostic ability in NSCLC, but 2D features showed better performance in our tests. Considering the cost of the radiomics features calculation, 2D features are more recommended for use in the current study.