ABSTRACT
Faced with the impact of the coronavirus disease (COVID-19) pandemic, governments must protect the well-being of the population. Aside from considerations, such as keeping the virus from spreading and treating patients, the government should also be concerned about the mental health of its citizens during the epidemic. This study aimed to help users who develop depression due to COVID-19 on social media, reduce the cost of counselling, and reduce the need for users to visit the hospital for counselling. This study investigated the opportunities for blockchain technology to provide psychological help to social media users suffering from depression caused by the pandemic. Blockchain-based technology has been used to develop a new model that enables a user autonomy system to allow users to control their own data fully. The model utilizes a delegated proof of stake consensus blockchain to manage depression data to enable low cost and information security while discussing aspects related to trust, privacy, interoperability, and integration with other information communication technologies. The blockchain framework has been proven to provide secure and reliable information management that meets the user requirements for information autonomy. The frame-work can be combined with various other information technologies to extend its functionality further. Author
ABSTRACT
Objective To analyze and predict hematopoietic injury caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and potential therapeutic drugs, and to provide theoretical basis for clinical treatment of the hematopoietic injury. Methods The gene expression omnibus (GEO) database was used to screen the whole genome expression data related to SARS-CoV-2 infection. The R language package was used for differential expression analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis. The core genes were screened by protein-protein interaction (PPI) network analysis using STRING online analysis website. Then the self-developed apparent precision therapy prediction platform (EpiMed) was used to analyze diseases, drugs and related target genes. Results A total of 222 differential genes were screened, including 172 up-regulated and 50 down-regulated. GO enrichment analysis suggested that gene is mainly related to type I interferon response, cell cycle regulation, inflammatory cell migration, innate immune response, secretion of blood particles and vesicles, chemokines and their receptors. KEGG enrichment analysis suggested that gene is mainly related to viral infection, myocardial injury, complement and coagulation cascade, cell chemotaxis, platelet activation, acute inflammation, immune response, cellular signal transduction and so on. Ten core genes such as STAT1, IL-6, IRF7, TNF, MX1, ISG15, IFIH1, IRF9, DDX58 and GBP1were screened by PPI network analysis. EpiMed screened 10 drugs with potential intervention effects, including Rabdosia rubescens, sirolimus, glucocorticoid, Houttuynia cordata, Polygonum multiflorum, Red peony, tretinoin, Glycyrrhiza, cyclosporine A, fluvastatin and so on. Conclusions SARS-CoV-2 infection can damage the hematopoietic system by changing the expression of a series of genes. The potential intervention drugs screened from this have certain reference significance for the basic and clinical research of coronavirus disease 2019 (COVID-19). © 2020 People's Military Medical Press. All rights reserved.