Acidic preconditioning reduces lipopolysaccharide-induced acute lung injury by upregulating the expression of angiotensin-converting enzyme 2.

Acid preconditioning (APC) through carbon dioxide inhalation can exert protective effects during acute lung injury (ALI) triggered by ischemia-reperfusion. Angiotensin-converting enzyme 2 (ACE2) has been identified as a receptor for severe acute respiratory syndrome coronavirus and the novel coronavirus disease-19. Downregulation of ACE2 plays an important role in the pathogenesis of severe lung failure after viral or bacterial infections. The aim of the present study was to examine the anti-inflammatory mechanism through which APC alleviates lipopolysaccharide (LPS)-induced ALI in vivo and in vitro. The present results demonstrated that LPS significantly downregulated the expression of ACE2, while APC significantly reduced LPS-induced ALI and provided beneficial effects. In addition, bioinformatics analysis indicated that microRNA (miR)-200c-3p directly targeted the 3'untranslated region of ACE2 and regulated the expression of ACE2 protein. LPS exposure inhibited the expression of ACE2 protein in A549 cells by upregulating the levels of miR-200c-3p. However, APC inhibited the upregulation of miR-200c-3p induced by LPS, as well as the downregulation of ACE2 protein, through the NF-κB pathway. In conclusion, although LPS can inhibit the expression of ACE2 by upregulating the levels of miR-200c-3p through the NF-κB pathway, APC inhibited this effect, thus reducing inflammation during LPS-induced ALI.

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.