An inducible hACE2 transgenic mouse model recapitulates SARS-CoV-2 infection and pathogenesis in vivo.

The classical manifestation of COVID-19 is pulmonary infection. After host cell entry via human angiotensin-converting enzyme II (hACE2), the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus can infect pulmonary epithelial cells, especially the AT2 (alveolar type II) cells that are crucial for maintaining normal lung function. However, previous hACE2 transgenic models have failed to specifically and efficiently target the cell types that express hACE2 in humans, especially AT2 cells. In this study, we report an inducible, transgenic hACE2 mouse line and showcase three examples for specifically expressing hACE2 in three different lung epithelial cells, including AT2 cells, club cells, and ciliated cells. Moreover, all these mice models develop severe pneumonia after SARS-CoV-2 infection. This study demonstrates that the hACE2 model can be used to precisely study any cell type of interest with regard to COVID-19-related pathologies.

Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography.

Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography.

Depression among schoolchildren and adolescents aged 9-17 years during the outbreak of COVID­19 in Beijing: a cross-sectional online survey.

The COVID-19 outbreak and related confinement have highly impacted psychological health among children and adolescents. This study aimed to explore the potential risk factors for depression among primary and middle school students and provide advices for psychological interventions during the outbreaks. An online cross-sectional survey was conducted among 18 primary and middle school students via quota sampling in Beijing during March 2020. The Center for Epidemiological Studies Depression Scale (CES-D) was used to assess depression. Differences between characteristics and depression were examined by chi-square tests. Multivariate logistic regression was used to reveal the potential risk factors for depression. A total of 7377 participants were included. The proportion of depression was 29.7%. Students in rural areas, with higher school categories, in graduating grades, with poor or excessive sleep duration, and without daily exercise were associated with a higher proportion of depression. Furthermore, students with a higher knowledge performance of COVID-19 showed a lower proportion of depression (odds ratio [OR] = 0.900, 95% confidence intervals [95% CI]: 0.888-0.913). Students who worried about academic performance (OR = 1.919, 95% CI: 1.718-2.144) or COVID-19 infection (OR = 1.450, 95% CI: 1.268-1.658) exhibited a high proportion of depression. The proportion of depression among primary and middle school students was negatively associated with the knowledge score and positively associated with their worry. Our findings suggest that psychological intervention might be more necessary for students with specific characteristics.