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Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2) is an enveloped single-stranded RNA virus that can lead to respiratory symptoms and damage many organs such as heart, kidney, intestine, brain and liver. It has not been clearly documented whether myocardial injury is caused by direct infection of cardiomyocytes, lung injury, or other unknown mechanisms. The gene expression profile of GSE150392 was obtained from the Gene Expression Omnibus (GEO) database. The processing of high-throughput sequencing data and the screening of differentially expressed genes (DEGs) were implemented by R software. The R software was employed to analyze the Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The protein-protein interaction (PPI) network of the DEGs was constructed by the STRING website. The Cytoscape software was applied for the visualization of PPI network and the identification of hub genes. The statistical analysis was performed by the GraphPad Prism software to verify the hub genes. A total of 516 up-regulated genes and 191 down-regulated genes were screened out. The top 1 enrichment items of GO in biological process (BP), Cellular Component (CC), and Molecular Function (MF) were type I interferon signaling pathway, sarcomere, and receptor ligand activity, respectively. The top 10 enrichment pathways, including TNF signaling pathway, were identified by KEGG enrichment analysis. A PPI network was established, consisting of 613 nodes and 3,993 edges. The 12 hub genes were confirmed as statistically significant, which was verified by GSE151879 dataset. In conclusion, the hub genes of human iPSC-cardiomyocytes infected with SARS-CoV-2 were identified through bioinformatics analysis, which may be used as biomarkers for further research.

Purpose Acute respiratory viral infections pose significant morbidity and mortality, making it essential to diagnose respiratory viral infections rapidly. In this study, the diagnostic efficacy of the Luminex xTAG Respiratory Virus Panel (RVP) FAST v2 test was evaluated on respiratory viral infections. Materials and Methods Information was retrieved from electronic databases, including Embase, Web of Science, PubMed, and Cochrane Library, for systematic review. Studies that fulfilled predefined inclusion criteria were included. After the extraction of information, statistical software was utilized for quality evaluation, data analysis, and assessment of publication bias. Results Eighty groups in fourfold tables from nine articles were included to perform statistical analyses. Therein, the mean specificity and mean sensitivity of Luminex xTAG RVP FAST v2 test for the detection of respiratory viral infections were 0.99 (0.98–0.99) and 0.88 (0.87–0.90), respectively. Additionally, the negative and positive likelihood ratios were 0.14 (0.11–0.19) and 87.42 (61.88–123.50), respectively. Moreover, the diagnostic odds ratio and area under the curve of summary receiver operating characteristic were 714.80 and 0.9886, respectively. Conclusion The Luminex xTAG RVP FAST v2 test could be a reliable and rapid diagnostic method for multiple respiratory viral infections.

The topic tree-based topic structure model is proposed using five-tuple and probability theory of ontology. Vocabularies in the glossary are presented with leaf nodes of the topic tree. The results of simulation experiment on real news corpus eventually show that the topic similarity of sym-KL divergence could construct a topic tree more accurately and dig the potential semantic topic characteristics of time and space more deeply in the text stream compared with other flat topic structure models.

Currently, most of the existing social networks on Internet are distributed, decentralized systems, and they are particularly vulnerable to Sybil attack in which a single malicious user introduces multiple bogus identities and pretends to be multiple and real users in the network. With these controlled identities, the malicious user can create a Byzantine failure in collaborative tasks by ‘out vote’ the real identities. This paper conducts a survey on the network security of social networks to provide the overview of the current online security of the social networks and the corresponding defend methods. Based on the survey, this paper proposes Sybil-Resist, a Random Walk-based Sybil attack defense protocol devoting to identifying the Sybil nodes and the Sybil region efficiently. The simulation results obtained by a more realistic simulation topology show that the proposed scheme outperforms existing solutions in terms of detection accuracy and running time.