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Liver fibrosis is a wound‐healing response represented by excessive extracellular matrix deposition. Activation of hepatic stellate cell (HSC) is the critical cellular basis for hepatic fibrogenesis, whereas hepatocyte undergoes epithelial‐mesenchymal transition (EMT) which is also involved in chronic liver injury. Long noncoding RNA H19 has been found to be associated with cholestatic liver fibrosis lately. However, the role of H19 in liver fibrosis remains largely to be elucidated. In this study, we found that the expression of H19 was significantly upregulated in the liver tissue of CCl4‐induced mice, a toxicant‐induced liver fibrogenesis model. Overexpression of H19 significantly aggravated activation of HSC and EMT of hepatocyte both by stimulating transforming growth factor‐β (TGF‐β) pathway. In terms of mechanism, H19 functioned as a competing endogenous RNA to sponge miR‐148a and subsequently sustained the level of ubiquitin‐specific protease 4 (USP4), which was an identified target of miR‐148a and was able to stabilize TGF‐β receptor I. In conclusion, our findings revealed a novel H19/miR‐148a/USP4 axis which promoted liver fibrosis via TGF‐β pathway in both HSC and hepatocyte, indicating that H19 could become a promising target for the treatment of liver fibrosis.

Overexpression of IDO significantly delays cardiac allograft acute rejection by shaping the types of T-cell responses elicited by alloantigen stimuli.

As a popular technique in recommender systems, Collaborative Filtering (CF) has received extensive attention in recent years. However, its privacy-related issues, especially for neighborhood-based CF methods, can not be overlooked. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Private Neighbor Collaborative Filtering (PNCF) algorithm. The algorithm includes two privacy-preserving operations: Private Neighbor Selection and Recommendation-Aware Sensitivity. Private Neighbor Selection is constructed on the basis of the notion of differential privacy to privately choose neighbors. Recommendation-Aware Sensitivity is introduced to enhance the performance of recommendations. Theoretical and experimental analysis are provided to show the proposed algorithm can preserve differential privacy while retaining the accuracy of recommendations.

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