Combination of multiple omics and machine learning identifies diagnostic genes for ARDS and COVID-19 (preprint)

BACKGROUND Acute respiratory distress syndrome (ARDS) is a common acute clinical syndrome of the respiratory system with a high mortality rate and difficult prognosis.COVID-19 is a serious respiratory infectious disease caused by coronaviruses in a global pandemic. Some studies have suggested a possible association between COVID-19 and ARDS, but few studies have investigated the mechanism of interaction between them.METHODS Microarray data of ARDS (GSE32707 and GSE66890) and COVID-19 (GSE213313) were downloaded from the GEO database and searched for common differential genes for enrichment analysis.WGCNA was used to identify co-expression modules and genes associated with ARDS and COVID-19. RF and LASSO were performed for candidate gene identification. Machine learning XGBoost improved the diagnosis of hub genes in ARDS and COVID-19. The degree of immune cell infiltration in ARDS and COVID-19 samples was assessed using the CIBERSORT algorithm, and the relationship between hub genes and infiltrating immune cells was investigated. Changes in pathway activity per cell were visualized using Seurat standard flow down clustering (seurat) to visualize peripheral blood mononuclear cell (PBMC) single-cell RNA sequencing (scRNA-seq) data from patients with sepsis-combined ARDS and patients with sepsis alone.RESULTS Limma difference analysis identified 314 up-regulated genes and 241 down-regulated genes in ARDS and COVID-19.WGCNA identified the purple-red co-expression module as the core module of ARDS and COVID-19. Five candidate genes, namely HIST1H2BK, TCF4, OLFM4, KIF14 and HK1, were screened using two machine learning algorithms, RF and LASSO. XGBoost constructed diagnostic models to evaluate the hub genes with high diagnostic efficacy in ARDS and COVID-19. Single-cell sequencing revealed the presence of alterations in five immune subpopulations, including monocytes, B cells, T cells, NK cells and platelets, with high expression levels and cellular occupancy of TCF4 and HK1, which are involved in oxidative reactions.

Asymmetric impact of Sino-US interest rate differentials and economic policy uncertainty ratio on RMB exchange rate

By using the NARDL model, we investigate the asymmetric relationship between Sino-US interest rate differentials, economic policy uncertainty (EPU) ratio, and the RMB exchange rate both in the long and short run. We further explore the changes in the asymmetric relations in light of the shocks of the 2008 international financial crisis and the COVID-19 pandemic. Our empirical results show that the long-run asymmetric effects of Sino-US interest rate differentials and EPU ratio on the RMB exchange rate are significant. Specifically, the RMB exchange rate appreciation resulting from the widening Sino-US interest rate differentials is greater than its depreciation resulting from the narrowing interest rate differentials in the long run. And the RMB exchange rate responds more intensely to the increase of Sino-US EPU ratio in the long run. In addition, in the aftermath of the 2008 international financial crisis, the impact of Sino-US interest rate differentials on the RMB exchange rate is found to be weaker, while the impact of the Sino-US EPU ratio on the RMB exchange rate is reinforced. COVID-19, for its part, has simultaneously intensified the responses of the RMB exchange rate to Sino-US interest rate differentials and EPU ratio.