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Oxidative stress can adversely affect the health status of the body, more specifically by causing intestinal damage by disrupting the permeability of the intestinal barrier. This is closely related to intestinal epithelial cell apoptosis caused by the mass production of reactive oxygen species (ROS). Baicalin (Bai) is a major active ingredient in Chinese traditional herbal medicine that has antioxidant, anti-inflammatory, and anti-cancer properties. The purpose of this study was to explore the underlying mechanisms by which Bai protects against hydrogen peroxide (H2O2)-induced intestinal injury in vitro. Our results indicated that H2O2 treatment caused injury to IPEC-J2 cells, resulting in their apoptosis. However, Bai treatment attenuated H2O2-induced IPEC-J2 cell damage by up-regulating the mRNA and protein expression of ZO-1, Occludin, and Claudin1. Besides, Bai treatment prevented H2O2-induced ROS and MDA production and increased the activities of antioxidant enzymes (SOD, CAT, and GSH-PX). Moreover, Bai treatment also attenuated H2O2-induced apoptosis in IPEC-J2 cells by down-regulating the mRNA expression of Caspase-3 and Caspase-9 and up-regulating the mRNA expression of FAS and Bax, which are involved in the inhibition of mitochondrial pathways. The expression of Nrf2 increased after treatment with H2O2, and Bai can alleviate this phenomenon. Meanwhile, Bai down-regulated the ratio of phosphorylated AMPK to unphosphorylated AMPK, which is indicative of the mRNA abundance of antioxidant-related genes. In addition, knockdown of AMPK by short-hairpin RNA (shRNA) significantly reduced the protein levels of AMPK and Nrf2, increased the percentage of apoptotic cells, and abrogated Bai-mediated protection against oxidative stress. Collectively, our results indicated that Bai attenuated H2O2-induced cell injury and apoptosis in IPEC-J2 cells through improving the antioxidant capacity through the inhibition of the oxidative stress-mediated AMPK/Nrf2 signaling pathway. [ABSTRACT FROM AUTHOR]

Background: Hippocalcin‐like 1 (HPCAL1) is involved in the development of several cancer types. However, our understanding of the HPCAL1 activity in cholangiocarcinoma (CCA) remains limited. Methods: Two microarray datasets were used to screen for differentially expressed genes (DEGs) involved in the development of CCA. The Cancer Genome Atlas (TCGA)/Gene Expression Omnibus (GEO) database was integrated to determine the prognostic significance of DEGs in CCA. The association between clinical characteristics and HPCAL1 expression levels was initially explored to assess the clinical profile of CCA. The prognostic value of HPCAL1 overexpression in the validation cohort was analyzed, followed by Gene Ontology (GO) term analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of HPCAL1. Results: Three upregulated genes and 10 downregulated genes were detected from two microarray‐based screenings. High expression of HPCAL1 as a poor prognostic factor of CCA was validated using TCGA/GEO integrated database and our database. Univariate and multivariate analyses along with Kaplan–Meier survival analysis showed that high HPCAL1 expression was an independent factor affecting the overall survival and relapse‐free survival in patients with CCA. The high expression of HPCAL1 was significantly associated with cancer antigen 125 (CA‐125) levels, number of tumors, lymph node invasion, and TNM stage. Analysis of the enriched GO terms and KEGG pathways revealed that the high expression of HPCAL1 was involved in the critical biological processes and molecular pathways, including modulation by a host of symbiont processes, the clathrin coat, actinin binding, and Rap1 signaling pathways. Conclusion: HPCAL1 was enriched in CCA in our study and has the potential to be applied in the identification of patients with CCA with an unfavorable prognosis. [ABSTRACT FROM AUTHOR]

This work tackles the problem of estimating depth from a single RGB image of a scene. To model the complex relationship between depth and monocular images, we propose a fully convolutional neural network that incorporates skip connections along with encoding and decoding stages to output consistent and detailed depth maps. Additionally, our model is a single convolutional architecture that does not use post-processing strategies; thus, it equires relatively less computational power and time to train when compared to more complex works. Experimental analysis and evaluation with a variety of losses and accuracies show that the end-to-end training process results in a model that performs better than or similar to many past architectures trained on the same dataset. [ABSTRACT FROM AUTHOR]