An antibody cocktail with broadened mutational resistance and effective protection against SARS-CoV-2.

Neutralizing antibodies have been proven to be highly effective in treating mild and moderate COVID-19 patients, but continuous emergence of SARS-CoV-2 variants poses significant challenges. Antibody cocktail treatments reduce the risk of escape mutants and resistance. In this study, a new cocktail composed of two highly potent neutralizing antibodies (HB27 and H89Y) was developed, whose binding epitope is different from those cocktails that received emergency use authorization. This cocktail showed more potent and balanced neutralizing activities (IC50 0.9-11.3 ng mL-1) against a broad spectrum of SARS-CoV-2 variants over individual HB27 or H89Y antibodies. Furthermore, the cocktail conferred more effective protection against the SARS-CoV-2 Beta variant in an aged murine model than monotherapy. It was shown to prevent SARS-CoV-2 mutational escape in vitro and effectively neutralize 61 types of pseudoviruses harbouring single amino acid mutation originated from variants and escape strains of Bamlanivimab, Casirivimab and Imdevimab with IC50 of 0.6-65 ng mL-1. Despite its breadth of variant neutralization, the HB27+H89Y combo and EUA cocktails lost their potencies against Omicron variant. Our results provide important insights that new antibody cocktails covering different epitopes are valuable tools to counter virus mutation and escape, highlighting the need to search for more conserved epitopes to combat Omicron.

Primary case of COVID-19 and its occurrence and development evidence

Primary case, commonly known as "No. 0 patient", refers to the first person who is infected by a virus or bacterial disease when the epidemic situation spreads. "No. 0 patient" often causes a large-scale outbreak of infectious diseases. Tracing the epidemiology of "No. 0 patient" and the origin of the occurrence and development are helpful to make clear and understand the pathogen of infection. It is of great significance to control the source of infection and prevent the disease. In this study, from the confirmation of the first diagnosis case of COVID-19, the analysis of the earliest outbreak time point and the carding of the occurrence and development time line of COVID-19, the process from discovery to outbreak of COVID-19 is preliminarily restored, which has important reference value for further understanding of SARS-CoV-2 and COVID-19.

A Cascade-SEME network for COVID-19 detection in chest x-ray images.

PURPOSE: The worldwide spread of the SARS-CoV-2 virus poses unprecedented challenges to medical resources and infection prevention and control measures around the world. In this case, a rapid and effective detection method for COVID-19 can not only relieve the pressure of the medical system but find and isolate patients in time, to a certain extent, slow down the development of the epidemic. In this paper, we propose a method that can quickly and accurately diagnose whether pneumonia is viral pneumonia, and classify viral pneumonia in a fine-grained way to diagnose COVID-19. METHODS: We proposed a Cascade Squeeze-Excitation and Moment Exchange (Cascade-SEME) framework that can effectively detect COVID-19 cases by evaluating the chest x-ray images, where SE is the structure we designed in the network which has attention mechanism, and ME is a method for image enhancement from feature dimension. The framework integrates a model for a coarse level detection of virus cases among other forms of lung infection, and a model for fine-grained categorisation of pneumonia types identifying COVID-19 cases. In addition, a Regional Learning approach is proposed to mitigate the impact of non-lesion features on network training. The network output is also visualised, highlighting the likely areas of lesion, to assist experts' assessment and diagnosis of COVID-19. RESULTS: Three datasets were used: a set of Chest x-ray Images for Classification with bacterial pneumonia, viral pneumonia and normal chest x-rays, a COVID chest x-ray dataset with COVID-19, and a Lung Segmentation dataset containing 1000 chest x-rays with masks in the lung region. We evaluated all the models on the test set. The results shows the proposed SEME structure significantly improves the performance of the models: in the task of pneumonia infection type diagnosis, the sensitivity, specificity, accuracy and F1 score of ResNet50 with SEME structure are significantly improved in each category, and the accuracy and AUC of the whole test set are also enhanced; in the detection task of COVID-19, the evaluation results shows that when SEME structure was added to the task, the sensitivities, specificities, accuracy and F1 scores of ResNet50 and DenseNet169 are improved. Although the sensitivities and specificities are not significantly promoted, SEME well balanced these two significant indicators. Regional learning also plays an important role. Experiments show that Regional Learning can effectively correct the impact of non-lesion features on the network, which can be seen in the Grad-CAM method. CONCLUSIONS: Experiments show that after the application of SEME structure in the network, the performance of SEME-ResNet50 and SEME-DenseNet169 in both two datasets show a clear enhancement. And the proposed regional learning method effectively directs the network's attention to focus on relevant pathological regions in the lung radiograph, ensuring the performance of the proposed framework even when a small training set is used. The visual interpretation step using Grad-CAM finds that the region of attention on radiographs of different types of pneumonia are located in different regions of the lungs.

Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images.

Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. To address these challenges, Multiscale Attention Guided deep network with Soft Distance regularization (MAG-SD) is proposed to automatically classify COVID-19 from pneumonia CXR images. In MAG-SD, MA-Net is used to produce prediction vector and attention from multiscale feature maps. To improve the robustness of trained model and relieve the shortage of training data, attention guided augmentations along with a soft distance regularization are posed, which aims at generating meaningful augmentations and reduce noise. Our multiscale attention model achieves better classification performance on our pneumonia CXR image dataset. Plentiful experiments are proposed for MAG-SD which demonstrates its unique advantage in pneumonia classification over cutting-edge models. The code is available at https://github.com/JasonLeeGHub/MAG-SD.