MicroRNAs (miRNAs) are noncoding RNA molecules of 21-24 nt that regulate the expression of target genes in a post-transcriptional manner. Evidence indicates that miRNAs play essential roles in embryogenesis, cell differentiation and pathogenesis of human diseases. This study describes a comparison between the miRNA profile of the systemic lupus erythematosus (SLE) patients and the controls to develop further understanding of the pathogenesis of SLE. Peripheral blood mononuclear cells were isolated from blood samples of 23 SLE patients, 10 idiopathic thrombocytopenic purpura patients and 10 healthy controls. The miRNA microarray chip analysis identified 16 miRNAs differentially expressed in SLE. The chip results were confirmed by northern blot analysis. This work indicates that miRNAs are potential diagnosis biomarkers and probable factors involved in the pathogenesis of SLE.

We introduce the concept of natural connectivity as a robustness measure of complex networks. The natural connectivity has a clear physical meaning and a simple mathematical formulation. It characterizes the redundancy of alternative paths by quantifying the weighted number of closed walks of all lengths. We show that the natural connectivity can be derived mathematically from the graph spectrum as an average eigenvalue and that it increases strictly monotonically with the addition of edges. We test the natural connectivity and compare it with other robustness measures within a scenario of edge elimination. We demonstrate that the natural connectivity has an acute discrimination which agrees with our intuition.

The concept of natural connectivity is reported as a robustness measure of complex networks. The natural connectivity has a clear physical meaning and a simple mathematical formulation. It is shown that the natural connectivity can be derived mathematically from the graph spectrum as an average eigenvalue and that it changes strictly monotonically with the addition or deletion of edges. By comparing the natural connectivity with other typical robustness measures within a scenario of edge elimination, it is demonstrated that the natural connectivity has an acute discrimination which agrees with our intuition.

No abstract

The current pandemic of coronavirus disease 19 (COVID-19) has affected more than 160 million of individuals and caused millions of deaths worldwide at least in part due to the unclarified pathophysiology of this disease. Therefore, identifying the underlying molecular mechanisms of COVID-19 is critical to overcome this pandemic. Metabolites mirror the disease progression of an individual by acquiring extensive insights into the pathophysiological significance during disease progression. We provide a comprehensive view of metabolic characterization of sera from COVID-19 patients at all stages using untargeted and targeted metabolomic analysis. As compared with the healthy controls, we observed different alteration patterns of circulating metabolites from the mild, severe and recovery stages, in both discovery cohort and validation cohort, which suggest that metabolic reprogramming of glucose metabolism and urea cycle are potential pathological mechanisms for COVID-19 progression. Our findings suggest that targeting glucose metabolism and urea cycle may be a viable approach to fight against COVID-19 at various stages along the disease course.