New media platform's understanding of Chinese social workers' anti-epidemic actions: an analysis of network public opinion based on COVID-19.

Research on social public opinion of new media is currently an important interdisciplinary topic in the international academic community. Under the background of COVID-19, the major public health event of in China, this research took social workers as the research object who worked during the period of epidemic prevention and control. It referred to the international research on public opinion and selected 63 related hotly discussed articles and public comments on the WeChat public platform, the new Chinese Internet media. Moreover, the research conducted text mining on related public opinion with the 5 W communication model from public opinion evolution, text content, communication media, audiences, and public opinion influence, and used grounded theory building a development model of the generation of network public opinion. It also put forward the development needs of social work in the aspects of community resilience, social work practice, lack of public health social workers, and big data warning, etc., and pointed out that social work lacks its proper structural status in China's public health system and emergency management system.

A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study.

This study describes two complementary methods that use network-based and sequence similarity tools to identify drug repurposing opportunities predicted to modulate viral proteins. This approach could be rapidly adapted to new and emerging viruses. The first method built and studied a virus-host-physical interaction network; a three-layer multimodal network of drug target proteins, human protein-protein interactions, and viral-host protein-protein interactions. The second method evaluated sequence similarity between viral proteins and other proteins, visualized by constructing a virus-host-similarity interaction network. Methods were validated on the human immunodeficiency virus, hepatitis B, hepatitis C, and human papillomavirus, then deployed on SARS-CoV-2. Comparison of virus-host-physical interaction predictions to known antiviral drugs had AUCs of 0.69, 0.59, 0.78, and 0.67, respectively, reflecting that the scores are predictive of effective drugs. For SARS-CoV-2, 569 candidate drugs were predicted, of which 37 had been included in clinical trials for SARS-CoV-2 (AUC = 0.75, P-value 3.21 × 10-3). As further validation, top-ranked candidate antiviral drugs were analyzed for binding to protein targets in silico; binding scores generated by BindScope indicated a 70% success rate.