Spleen tyrosine kinase inhibition restores myeloid homeostasis in COVID-19.

Spleen tyrosine kinase (SYK) is a previously unidentified therapeutic target that inhibits neutrophil and macrophage activation in coronavirus disease 2019 (COVID-19). Fostamatinib, a SYK inhibitor, was studied in a phase 2 placebo-controlled randomized clinical trial and was associated with improvements in many secondary end points related to efficacy. Here, we used a multiomic approach to evaluate cellular and soluble immune mediator responses of patients enrolled in this trial. We demonstrated that SYK inhibition was associated with reduced neutrophil activation, increased circulation of mature neutrophils (CD10+CD33-), and decreased circulation of low-density granulocytes and polymorphonuclear myeloid-derived suppressor cells (HLA-DR-CD33+CD11b-). SYK inhibition was also associated with normalization of transcriptional activity in circulating monocytes relative to healthy controls, an increase in frequency of circulating nonclassical and HLA-DRhi classical monocyte populations, and restoration of interferon responses. Together, these data suggest that SYK inhibition may mitigate proinflammatory myeloid cellular and soluble mediator responses thought to contribute to immunopathogenesis of severe COVID-19.

UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients.

BACKGROUND: With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning. METHODS: This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality. RESULTS: The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3-10% higher than that of the existing models. CONCLUSION: In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.

COVID-19 In Shang Hai: It is Worth Learning from the Successful Experience in Preventing and Controlling the Overseas Epidemic Situation (preprint)

COVID-19 first appeared in Wuhan, Hubei Province,China in late December 2019 and spread rapidly in China. Currently, the spread of local epidemics has been basically blocked. The import of overseas epidemics has become the main form of growth in China's new epidemic. As an important international transportation hub in China, Shanghai is one of the regions with the highest risk of imported cases abroad. Due to imported of overseas cases are affected by the international epidemic trend. The traditional infectious disease model is difficult to accurately predict the cumulative trend of cumulative cases in the Shanghai areas. It is also difficult to accurately evaluate the effectiveness of the international traffic blockade. In this situation, this study takes Shanghai as an example to propose a new type of infectious disease prediction model. The model first uses the sparse graph model to analyze the international epidemic spread network to find countries and regions related to Shanghai. Next, multiple regression models were used to fit the existing COV-19 growth data in Shanghai. Finally, the model can predict the growth curve of Shanghai's epidemic without blocking traffic. The results show that the control measures taken by Shanghai are very effective. At present, more and more countries and regions will face the current situation in Shanghai. We recommend that other countries and regions learn from Shanghai's successful experience in preventing overseas imports in order to fully prepare for epidemic prevention and control.

A Sparse Gaussian Network Model for Prediction the Growth Trend of COVID-19 Overseas Import Case: When can Hong Kong Lift the International Traffic Blockad? (preprint)

The COVID-19 virus was first discovered from China. It has been widely spread internationally. Currently, compare with the rising trend of the overall international epidemic situation, China's domestic epidemic situation has been contained and shows a steady and upward trend. In this situation, overseas imports have become the main channel for china to increase the number of infected people. Therefore, how to track the spread channel of international epidemics and predict the growth of overseas case imports is become an open research question. This study proposes a Gaussian sparse network model based on lasso and uses Hong Kong as an example. To explore the COVID-19 virus from a network perspective and analyzes 75 consecutive days of COV-19 data in 188 countries and regions around the world. This article establishes an epidemic spread relationship network between Hong Kong and various countries and regions around the world and build a regression model based on network information to fit Hong Kong's COV-19 epidemic growth data. The results show that the regression model based on the relationship network can better fit the existing cumulative number growth curve. After combining the SEIJR model, we predict the future development trend of cumulative cases in Hong Kong (without blocking international traffic). Based on the prediction results, we suggest that Hong Kong can lift the international traffic blockade from early to mid-June