The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein–protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors–genes interaction, protein–drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.

Introduction The pathophysiology for Coronavirus Disease 2019 (COVID-19) infection is characterized by cytokine oxidative stress and endothelial dysfunction. Intravenous (IV) vitamin C has been utilized as adjuvant therapy in critically ill patients with sepsis for its protective effects against reactive oxygen species and immunomodulatory effects. The primary objective of this study was to evaluate the effects of IV vitamin C in critically ill patients with COVID-19 infection. Methods Retrospective observational cohort study with propensity score matching of intensive care unit (ICU) patients who received 1.5 grams IV vitamin C every 6 hours for up to 4 days for COVID-19 infection. The primary study outcome was in-hospital mortality. Secondary outcomes included vasopressor requirements in norepinephrine equivalents, ICU length of stay, and change in Sequential Organ Failure Assessment (SOFA) score. Results Eight patients received IV vitamin C and were matched to 24 patients. Patients in the IV vitamin C group had higher rates of hospital mortality [7 (88%) vs. 19 (79%), P = 0.049]. There was no difference in the daily vasopressor requirement in the treatment group or between the 2 groups. The mean SOFA scores post-treatment was higher in the IV vitamin C group (12.4 ± 2.8 vs. 8.1 ± 3.5, P < 0.005). There was no difference in ICU length of stay between the treatment and control groups. Conclusion Adjunctive IV vitamin C for the management of COVID-19 infection in critically ill patients may not decrease the incidence of mortality, vasopressor requirements, SOFA scores, or ventilator settings.

A number of emerging applications, such as, collaborative document editing, sentence translation, and citizen journalism require workers with complementary skills and expertise to form groups and collaborate on complex tasks. While existing research has investigated task assignment for knowledge intensive crowdsourcing, they often ignore the aspect of collaboration among workers, that is central to the success of such tasks. Research in behavioral psychology has indicated that large groups hinder successful collaboration. Taking that into consideration, our work is one of the first to investigate and formalize the notion of collaboration among workers and present theoretical analyses to understand the hardness of optimizing task assignment. We propose efficient approximation algorithms with provable theoretical guarantees and demonstrate the superiority of our algorithms through a comprehensive set of experiments using real-world and synthetic datasets. Finally, we conduct a real world collaborative sentence translation application using Amazon Mechanical Turk that we hope provides a template for evaluating collaborative crowdsourcing tasks in micro-task based crowdsourcing platforms.