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.

Neurological diseases (NDs) are progressive disorders, the progression of which can be significantly affected by a range of common diseases that present as comorbidities. Clinical studies, including epidemiological and neuropathological analyses, indicate that patients with type 2 diabetes (T2D) have worse progression of NDs, suggesting pathogenic links between NDs and T2D. However, finding causal or predisposing factors that link T2D and NDs remains challenging. To address these problems, we developed a high-throughput network-based quantitative pipeline using agnostic approaches to identify genes expressed abnormally in both T2D and NDs, to identify some of the shared molecular pathways that may underpin T2D and ND interaction. We employed gene expression transcriptomic datasets from control and disease-affected individuals and identified differentially expressed genes (DEGs) in tissues of patients with T2D and ND when compared to unaffected control individuals. One hundred and ninety seven DEGs (99 up-regulated and 98 down-regulated in affected individuals) that were common to both the T2D and the ND datasets were identified. Functional annotation of these identified DEGs revealed the involvement of significant cell signaling associated molecular pathways. The overlapping DEGs (i.e., seen in both T2D and ND datasets) were then used to extract the most significant GO terms. We performed validation of these results with gold benchmark databases and literature searching, which identified which genes and pathways had been previously linked to NDs or T2D and which are novel. Hub proteins in the pathways were identified (including DNM2, DNM1, MYH14, PACSIN2, TFRC, PDE4D, ENTPD1, PLK4, CDC20B, and CDC14A) using protein-protein interaction analysis which have not previously been described as playing a role in these diseases. To reveal the transcriptional and post-transcriptional regulators of the DEGs we used transcription factor (TF) interactions analysis and DEG-microRNAs (miRNAs) interaction analysis, respectively. We thus identified the following TFs as important in driving expression of our T2D/ND common genes: FOXC1, GATA2, FOXL1, YY1, E2F1, NFIC, NFYA, USF2, HINFP, MEF2A, SRF, NFKB1, USF2, HINFP, MEF2A, SRF, NFKB1, PDE4D, CREB1, SP1, HOXA5, SREBF1, TFAP2A, STAT3, POU2F2, TP53, PPARG, and JUN. MicroRNAs that affect expression of these genes include mir-335-5p, mir-16-5p, mir-93-5p, mir-17-5p, mir-124-3p. Thus, our transcriptomic data analysis identifies novel potential links between NDs and T2D pathologies that may underlie comorbidity interactions, links that may include potential targets for therapeutic intervention. In sum, our neighborhood-based benchmarking and multilayer network topology methods identified novel putative biomarkers that indicate how type 2 diabetes (T2D) and these neurological diseases interact and pathways that, in the future, may be targeted for treatment.

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.

Discovering drug–target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug–target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.

Type 2 diabetes (T2D) is a chronic metabolic disorder characterised by high blood sugar and insulin insensitivity which greatly increases the risk of developing neurological diseases (NDs). The coexistence of T2D and comorbidities such as NDs can complicate or even cause the failure of standard treatments for those diseases. Comorbidities can be both causally linked and influence each other's development through genetic, molecular, environmental or lifestyle-based risk factors that they share. For T2D and NDs, such underlying common molecular mechanisms remain elusive but large amounts of molecular data accumulated on these diseases enable analytical approaches to identify their interconnected pathways. Here, we propose a framework to explore possible comorbidity interactions between a pair of diseases using a bioinformatic examination of the cellular pathways involved and explore this framework for T2D and NDs with analyses of a large number of publicly available gene expression datasets from tissues affected by these diseases. We designed a bioinformatics pipeline to analyse, utilize and combine gene expression, Gene Ontology (GO) and molecular pathway data by incorporating Gene Set Enrichment Analysis and Semantic Similarity. Our bioinformatics methodology was implemented in R, available at https://github.com/HabibUCAS/T2D_Comorbidity. We identified genes with altered expression shared by T2D and NDs as well as GOs and molecular pathways these diseases share. We also computed the proximity between T2D and neurological pathologies using these genes and GO term semantic similarity. Thus, our method has generated new insights into disease mechanisms important for both T2D and NDs that may mediate their interaction. Our bioinformatics pipeline could be applied to other co-morbidities to identify possible interactions and causal relationships between them.

Many popular applications, such as collaborative document editing, sentence translation, or citizen science resort to collaborative crowdsourcing, a special form of human-based computing, where, crowd workers with appropriate skills and expertise are required to form groups to solve complex tasks. While there has been extensive research on workers' task assignment for traditional microtask based crowdsourcing, they often ignore the critical aspect of collaboration. Central to any collaborative crowdsourcing process is the aspect of solving collaborative tasks that requires successful collaboration among the workers. Our formalism considers two main collaboration-related factors-affinity and upper critical mass-appropriately adapted from organizational science and social theories. Our contributions are three fold. First, we formalize the notion of collaboration among crowd workers and propose a comprehensive optimization model for task assignment in a collaborative crowdsourcing environment. Next, we study the hardness of the task assignment optimization problem and propose a series of efficient exact and ap

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan–Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.