How can infectious medical waste be forecasted and transported during the COVID-19 pandemic? A hybrid two-stage method.

The COVID-19 pandemic has caused an unforeseen collapse of infectious medical waste (IMW) and an abrupt smite of the conveying chain. Hospitals and related treatment centers face great challenges during the pandemic because mismanagement may lead to more severe life threats and enlarge environmental pollution. Opportune forecasting and transportation route optimization, therefore, are crucial to coping with social stress meritoriously. All related hospitals and medical waste treatment centers (MWTCs) should make decisions in perspective to reduce the economic pressure and infection risk immensely. This study proposes a hybrid dynamic method, as follows: first to forecast confirmed cases via infectious disease modeling and analyze the association between IMW outflows and cases; next to construct a model through time-varying factors and the lagging factor to predict the waste quantity; and then to optimize the transportation network route from hospitals to MWTCs. For demonstration intentions, the established methodology is employed to an illustrative example. Based on the obtained results, in finding the process of decision making, cost becomes the common concern of decision-makers. Actually, the infection risk among publics has to be considered simultaneously. Therefore, realizing early warning and safe waste management has an immensely positive effect on epidemic stabilization and lifetime health.

The genetic characteristics of human coronavirus OC43 in cases of severe acute respiratory infection

Human coronavirus (HCoV) is one of the important pathogens of human respiratory tract infection. in order to clarify the genetic characteristics of HCoV-0C43 in severe acute respiratory infection (SARI) cases at the molecular level, a total of 374 samples obtained from SARI cases in Henan Province, China, in 2019 were screened for the nucleic acids of HCoV -0C43 by real - time polymerase chain reaction (PCR). Reverse transcription-PCR amplification and sequencing of spike (5) RNA-dependent RNA polymerase (12dRp) and nucleocapsid (N) was carried out in samples with positive detection of the nucleic acids of FICoV-0C43. Upon. combination Of 42 representative sequences obtained from the GenBank database, phylogenetic trees were constructed based on three full-length sequences of S, RdRp and N genes. The FICoV -0C43 strains obtained from SARI cases were genotyped and the genetic characteristics of three target genes were analyzed. Variations in the amino acids of S protein (an important antigen of HCoV-0C43) were also analyzed. Results showed that 15 (4.01%) out of 374 samples from SARI cases were positive for FICoV-0C43, and the full-length sequences of S, RdRp and N genes were obtained from 4 out of 15 samples. Based on the phylogenetic trees of these three target genes, three strains belonged to the U genotype and one strain belonged to the H genotype. Analysis of the amino - acid variations of S protein indicated that there were three special sites of amino - acid variation (L272P, P5165 and 5902A) among the G genotype strains in 2019, including the three strains in our study and USA /MN306041/SC0810/2019. Another special variation in amino acids (N484D) was found among the II genotype strains in 2019, including one strain in our study and USA/MN306043/SC0841/2019. Based on the genotype identification and genetic characteristics of HCoV-0C43 strains from SARI cases in Henan Province in 2019, baseline data for the study of molecular epidemiology of HCoV 0C43 in China have been provided.

Identification of transcriptomics biomarker for the early prediction of critically ill COVID-19 patients (preprint)

Objective Identifying the biological subsets of severe COVID-19 could provide a basis for finding biomarkers for the early prediction of the prognosis of severe COVID-19 and poor prognosis, and may facilitate specific treatment for COVID-19.Methods In this study we downloaded microarray dataset GSE172114 from the Gene Expression Omnibus (GEO) database in NCBI, and screened differentially-expressed genes (DEGs) by using the limma package in R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted, and the results were presented by volcano, Venn, heat, and enrichment pathway bubble maps in the R language package. Gene set enrichment analysis (GSEA) was used to explore and demonstrate the signal pathways related to severe COVID-19. Protein-Protein Interaction (PPI) Network analysis and visualization were performed by using STRING and Cytoscape. Seven key protein expression molecules were screened by the MOCDE plug-in. Then, the cytoHubba plug-in was used to screen 10 candidate genes with maximal clique centrality (MCC) algorithm as the standard, and the intersection with the Venn diagram was used to obtain seven Hub genes. Receiver operating characteristic (ROC) curves were drawn to determine the area under the curve (AUC), and the predictive value of the key genes was evaluated.Results A total of 210 DEGs were identified, including 186 upregulated genes as well as downregulated ones. GO enrichment and KEGG pathway analysis were used, and the results were presented by volcano, Venn, heat, and enrichment pathway bubble maps in the R language package. Gene set enrichment analysis (GSEA) was used to explore and demonstrate the signal pathways related to severe COVID-19. Protein interaction network (PPI) analysis and visualization were performed by using STRING and Cytoscape. Seven key protein expression molecules were screened by the MOCDE plug-in. Then, the cytoHubba plug-in was used to screen 10 candidate genes with maximal clique centrality (MCC) algorithm as the standard, and the intersection with the Venn diagram was used to obtain seven Hub genes. Receiver operating characteristic (ROC) curves were drawn to determine the area under the curve (AUC), and the predictive value of the key genes was evaluated. The AUC of the PLSCR1 gene was 0.879, which was the most significantly upregulated key gene in critically ill COVID-19 patients.Conclusions Based on bioinformatics analysis, we found that the screened candidate gene, PLSCR1, may be closely related to the occurrence of severe COVID-19, and can thus be used for the early prediction of patients with severe COVID-19, and may provide meaningful research direction for their treatment.