SARS-CoV-2 spike host cell surface exposure promoted by a COPI sorting inhibitor.

Via an insufficient coat protein complex I (COPI) retrieval signal, the majority of SARS-CoV-2 spike (S) is resident in host early secretory organelles and a tiny amount is leaked out in cell surface. Only surface-exposed S can be recognized by B cell receptor (BCR) or anti-S therapeutic monoclonal antibodies (mAbs) that is the trigger step for B cell activation after S mRNA vaccination or infected cell clearance by S mAbs. Now, a drug strategy to promote S host surface exposure is absent. Here, we first combined structural and biochemical analysis to characterize S COPI sorting signals. A potent S COPI sorting inhibitor was then invented, evidently capable of promoting S surface exposure and facilitating infected cell clearance by S antibody-dependent cellular cytotoxicity (ADCC). Importantly, with the inhibitor as a probe, we revealed Omicron BA.1 S is less cell surface exposed than prototypes because of a constellation of S folding mutations, possibly corresponding to its ER chaperone association. Our findings not only suggest COPI is a druggable target against COVID-19, but also highlight SARS-CoV-2 evolution mechanism driven by S folding and trafficking mutations.

An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19.

A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients. The model was developed using a cohort of 3028 patients diagnosed with COVID-19 and exhibiting common clinical symptoms that were internally verified (AUC 0.8517, 95% CI 0.8433, 0.8601). A total of 15 high risk factors for deterioration and their approximate warning ranges were identified. This included prothrombin time (PT), prothrombin activity, lactate dehydrogenase, international normalized ratio, heart rate, body-mass index (BMI), D-dimer, creatine kinase, hematocrit, urine specific gravity, magnesium, globulin, activated partial thromboplastin time, lymphocyte count (L%), and platelet count. Four of these indicators (PT, heart rate, BMI, HCT) and comorbidities were selected for a streamlined combination of indicators to produce faster results. The resulting model showed good predictive performance (AUC 0.7941 95% CI 0.7926, 0.8151). A website for quick pre-screening online was also developed as part of the study.

Real-Time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic.

Coronavirus Disease 2019 (COVID-19) is a highly infectious virus that has created a health crisis for people all over the world. Social distancing has proved to be an effective non-pharmaceutical measure to slow down the spread of COVID-19. As unmanned aerial vehicle (UAV) is a flexible mobile platform, it is a promising option to use UAV for social distance monitoring. Therefore, we propose a lightweight pedestrian detection network to accurately detect pedestrians by human head detection in real-time and then calculate the social distancing between pedestrians on UAV images. In particular, our network follows the PeleeNet as backbone and further incorporates the multi-scale features and spatial attention to enhance the features of small objects, like human heads. The experimental results on Merge-Head dataset show that our method achieves 92.22% AP (average precision) and 76 FPS (frames per second), outperforming YOLOv3 models and SSD models and enabling real-time detection in actual applications. The ablation experiments also indicate that multi-scale feature and spatial attention significantly contribute the performance of pedestrian detection. The test results on UAV-Head dataset show that our method can also achieve high precision pedestrian detection on UAV images with 88.5% AP and 75 FPS. In addition, we have conducted a precision calibration test to obtain the transformation matrix from images (vertical images and tilted images) to real-world coordinate. Based on the accurate pedestrian detection and the transformation matrix, the social distancing monitoring between individuals is reliably achieved.

Surgical Management Strategy for Breast Cancer Patients During the COVID-19 Outbreak.

In December 2019, a new coronavirus pneumonia began to break out globally. COVID-19 pandemic challenges the health systems worldwide and influences the treatments for other diseases. The incidence rate of breast cancer ranks first among all malignant tumors among women. During the pandemic, medical workers should strictly monitor the condition of patients and strengthen the management and prevention measures to make sure patients can be operated safely. This article will discuss the arrangements and management of surgical treatment for patients with breast cancer.