The emerging roles of miRNA-mediated autophagy in ovarian cancer.

Ovarian cancer is one of the common tumors of the female reproductive organs. It has a high mortality rate, is highly heterogeneous, and early detection and primary prevention are very complex. Autophagy is a cellular process in which cytoplasmic substrates are targeted for degradation in lysosomes through membrane structures called autophagosomes. The periodic elimination of damaged, aged, and redundant cellular molecules or organelles through the sequential translation between amino acids and proteins by two biological processes, protein synthesis, and autophagic protein degradation, helps maintain cellular homeostasis. A growing number of studies have found that autophagy plays a key regulatory role in ovarian cancer. Interestingly, microRNAs regulate gene expression at the posttranscriptional level and thus can regulate the development and progression of ovarian cancer through the regulation of autophagy in ovarian cancer. Certain miRNAs have recently emerged as important regulators of autophagy-related gene expression in cancer cells. Moreover, miRNA analysis studies have now identified a sea of aberrantly expressed miRNAs in ovarian cancer tissues that can affect autophagy in ovarian cancer cells. In addition, miRNAs in plasma and stromal cells in tumor patients can affect the expression of autophagy-related genes and can be used as biomarkers of ovarian cancer progression. This review focuses on the potential significance of miRNA-regulated autophagy in the diagnosis and treatment of ovarian cancer.

Stochastic analysis of COVID-19 by a SEIR model with Lévy noise.

We propose a Lévy noise-driven susceptible-exposed-infected-recovered model incorporating media coverage to analyze the outbreak of COVID-19. We conduct a theoretical analysis of the stochastic model by the suitable Lyapunov function, including the existence and uniqueness of the positive solution, the dynamic properties around the disease-free equilibrium and the endemic equilibrium; we deduce a stochastic basic reproduction number R0 s for the extinction of disease, that is, if R0 s≤1, the disease will go to extinction. Particularly, we fit the data from Brazil to predict the trend of the epidemic. Our main findings include the following: (i) stochastic perturbation may affect the dynamic behavior of the disease, and larger noise will be more beneficial to control its spread; (ii) strengthening social isolation, increasing the cure rate and media coverage can effectively control the spread of disease. Our results support the feasible ways of containing the outbreak of the epidemic.