No abstract

Background Because of the increasing dysplasia rate in the lifelong course of inflammatory bowel disease (IBD) patients, it is imperative to characterize the crosstalk between IBD and colorectal cancer (CRC). However, there have been no reports revealing the occurrence of the ceRNA network in IBD-related CRC. Methods In this study, we conducted gene expression profile studies of databases and performed an integrated analysis to detect the potential of lncRNA-miRNA-mRNA ceRNA in regulating disease transformation. R packages were used to screen differentially expressed mRNA, lncRNA and miRNA among CRC, IBD and normal tissue. The lncRNA-miRNA-mRNA network was constructed based on predicted miRNA-targeted lncRNAs and miRNA-targeted mRNAs. Functional analyses were then conducted to identify genes involved in the ceRNA network, and key lncRNAs were evaluated based on several clinical outcomes. Results A total of three lncRNAs, 15 miRNAs, and 138 mRNAs were identified as potential mediators in the pathophysiological processes of IBD-related CRC. Gene Ontology annotation enrichment analysis confirmed that the dysplasia process was strongly associated with immune response, response to lipopolysaccharide, and inflammatory response. Survival analysis showed that LINC01106 (HR = 1.7; p < 0.05) were strongly associated with overall survival of colorectal cancer patients. The current study identified a series of IBD-related mRNAs, miRNA, and lncRNAs, and highlighted the important role of ceRNAs in the pathogenesis of IBD-related CRC. Among them, the LINC01106-miRNA-mRNA axis was identified as vital targets for further research.

Riok3 inhibits the antiviral immune response by facilitating TRIM40-mediated RIG-I and MDA5 degradation Graphical abstract Highlights d Riok3 negatively and selectively inhibits RNA viruses both in vivo and in vitro d Riok3 directly interacts with TRIM40, RIG-I, and MDA5 to form a protein complex d Riok3 promotes TRIM40-mediated K27-and K48-linked ubiquitination of RIG-I and MDA5 d TRIM40 induces the proteasomal degradation of both RIG-I and MDA5

In this work we propose a deep adaptive sampling (DAS) method for solving partial differential equations (PDEs), where deep neural networks are utilized to approximate the solutions of PDEs and deep generative models are employed to generate new collocation points that refine the training set. The overall procedure of DAS consists of two components: solving the PDEs by minimizing the residual loss on the collocation points in the training set and generating a new training set to further improve the accuracy of current approximate solution. In particular, we treat the residual as a probability density function and approximate it with a deep generative model, called KRnet. The new samples from KRnet are consistent with the distribution induced by the residual, i.e., more samples are located in the region of large residual and less samples are located in the region of small residual. Analogous to classical adaptive methods such as the adaptive finite element, KRnet acts as an error indicator that guides the refinement of the training set. Compared to the neural network approximation obtained with uniformly distributed collocation points, the developed algorithms can significantly improve the accuracy, especially for low regularity and high-dimensional problems. We present a theoretical analysis to show that the proposed DAS method can reduce the error bound and demonstrate its effectiveness with numerical experiments.