DeepTrace: Learning to Optimize Contact Tracing in Epidemic Networks with Graph Neural Networks

The goal of digital contact tracing is to diminish the spread of an epidemic or pandemic by detecting and mitigating public health emergencies using digital technologies. Since the start of the COVID-$19$ pandemic, a wide variety of mobile digital apps have been deployed to identify people exposed to the SARS-CoV-2 coronavirus and to stop onward transmission. Tracing sources of spreading (i.e., backward contact tracing), as has been used in Japan and Australia, has proven crucial as going backwards can pick up infections that might otherwise be missed at superspreading events. How should robust backward contact tracing automated by mobile computing and network analytics be designed? In this paper, we formulate the forward and backward contact tracing problem for epidemic source inference as maximum-likelihood (ML) estimation subject to subgraph sampling. Besides its restricted case (inspired by the seminal work of Zaman and Shah in 2011) when the full infection topology is known, the general problem is more challenging due to its sheer combinatorial complexity, problem scale and the fact that the full infection topology is rarely accurately known. We propose a Graph Neural Network (GNN) framework, named DeepTrace, to compute the ML estimator by leveraging the likelihood structure to configure the training set with topological features of smaller epidemic networks as training sets. We demonstrate that the performance of our GNN approach improves over prior heuristics in the literature and serves as a basis to design robust contact tracing analytics to combat pandemics

Identification of functional genes in liver fibrosis based on bioinformatics analysis of a lncRNA-mediated ceRNA network

Abstract Background Liver fibrosis is a major global healths problem; nevertheless, its molecular mechanism are not completely clear. This study aimed to build a lncRNA-miRNA-mRNA network, identify potentially related lncRNAs, and explore the pathogenesis of liver fibrosis. Materials and methods We used the Gene Expression Omnibus databases and bioinformatics analysis to identify differentially expressed genes (DEGs) between liver fibrosis and normal tissues. The ceRNA network was constructed according to the interactions between DElncRNA, miRNA, and DEmRNA. Then, these DEGs were identified using functional enrichment analysis, and a protein–protein interaction (PPI) network was established. The critical lncRNAs were verified using the quantitative real-time polymerase chain reaction (qRT-PCR). Results The ceRNA network was composed of three lncRNAs, five miRNAs, and 93 mRNAs. Gene Ontology functional enrichment analysis revealed significant enhancement in cell components, molecular function, and biological process. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed pathways associated with transcriptional misregulation in cancer, including the Rap1 signaling pathway, proteoglycans in cancer, mineral absorption, HTLV-l infection, and central carbon metabolism in cancer. According to the PPI network and the GSE84044 database, seven hub genes associated with liver fibrosis were identified. In addition, qRT-PCR revealed that lncRNA AC100861 (lncRNA TNFRSF10A-DT) was explicitly decreased in liver fibrosis tissues and activated hepatic stellate cells. Conclusions In summary, this study preliminarily found that lncRNA TNFRSF10A-DT may be a biomarker for the diagnosis and outcome of liver fibrosis. We uncovered a novel lncRNA-mediated ceRNA regulatory mechanism in the pathogenesis of liver fibrosis

Efficacy of fresh frozen plasma transfusion in decompensated cirrhosis patients with coagulopathy admitted to ICU: a retrospective cohort study from MIMIC-IV database

Abstract We aimed to explore the association between FFP transfusion and outcomes of DC patients with significant coagulopathy. A total of 693 DC patients with significant coagulopathy were analyzed with 233 patients per group after propensity score matching (PSM). Patients who received FFP transfusion were matched with those receiving conventional therapy via PSM. Regression analysis showed FFP transfusion had no benefit in 30-day (HR: 1.08, 95% CI 0.83–1.4), 90-day (HR: 1.03, 95% CI 0.80–1.31) and in-hospital(HR: 1.30, 95% CI 0.90–1.89) mortality, associated with increased risk of liver failure (OR: 3.00, 95% CI 1.78–5.07), kidney failure (OR: 1.90, 95% CI 1.13–3.18), coagulation failure (OR: 2.55, 95% CI 1.52–4.27), respiratory failure (OR: 1.76, 95% CI 1.15–2.69), and circulatory failure (OR: 2.15, 95% CI 1.27–3.64), and even associated with prolonged the LOS ICU (β: 2.61, 95% CI 1.59–3.62) and LOS hospital (β: 6.59, 95% CI 2.62–10.57). In sensitivity analysis, multivariate analysis (HR: 1.09, 95%CI 0.86, 1.38), IPTW (HR: 1.11, 95%CI 0.95–1.29) and CAPS (HR: 1.09, 95% CI 0.86–1.38) showed FFP transfusion had no beneficial effect on the 30-day mortality. Smooth curve fitting demonstrated the risk of liver failure, kidney failure and circulatory failure increased by 3%, 2% and 2% respectively, for each 1 ml/kg increase in FFP transfusion. We found there was no significant difference of CLIF-SOFA and MELD score between the two group on day 0, 3, 7, 14. Compared with the conventional group, INR, APTT, and TBIL in the FFP transfusion group significantly increased, while PaO2/FiO2 significantly decreased within 14 days. In conclusion, FFP transfusion had no beneficial effect on the 30-day, 90-day, in-hospital mortality, was associated with prolonged the LOS ICU and LOS hospital, and the increased risk of liver failure, kidney failure, coagulation failure, respiratory failure and circulatory failure events. However, large, multi-center, randomized controlled trials, prospective cohort studies and external validation are still needed to verify the efficacy of FFP transfusion in the future