Hepatocyte paraffin 1 (Hep Par 1), a murine monoclonal antibody, is widely used in surgical pathology practice to determine the hepatocellular origin of neoplasms. However, identity of the antigen for Hep Par 1 is unknown. The aim of this study was to characterize the Hep Par 1 antigen. To identify the antigen, immunoprecipitation was used to isolate the protein from human liver tissue, and a distinct protein band was detected at approximately 165 kDa. The protein band was also present in small intestinal tissue, but was not present in several other non-liver tissues nor in three human hepatocellular carcinoma cell lines, Huh-7, HepG2, and LH86. The protein was purified and analyzed by mass spectrometry. It was identified as carbamoyl phosphate synthetase 1 (CPS1). CPS1 is a rate-limiting enzyme in urea cycle and is located in mitochondria. We demonstrated that hepatoid tumors (gastric and yolk sac) were immunoreactive with both Hep Par 1 antibody and anti-CPS1 antibody, further confirming the results of mass spectrometric analysis. We found that the three human hepatocellular carcinoma cell lines do not express either CPS1 RNA or protein. We confirmed that the gene was present in these cell lines, suggesting that suppression of CPS1 expression occurs at the transcriptional level. This finding may have relevance to liver carcinogenesis, since poorly differentiated hepatocellular carcinomas exhibit poor to absent immunoreactivity to Hep Par 1. In conclusion, we have identified the antigen for Hep Par 1 antibody as a urea cycle enzyme CPS1. Our results should encourage further investigation of potential role that CPS1 expression plays in liver pathobiology and carcinogenesis. The histological distinction between hepatocellular carcinomas (HCC) and metastatic adenocarcinoma to the liver can sometimes be a challenging dilemma for surgical pathologists, particularly given the histological variants of HCC that can occur. In addition, tumors in other sites can display hepatoid morphologic features, adding to the diagnostic challenge when considering their metastasis to the liver. In the end, a wide panel of immunohistochemical markers is often used for the differential diagnosis of HCC, cholangiocarcinoma and metastatic adenocarcinoma. These markers include alpha-fetoprotein (AFP), polyclonal carcinoembryonic antigen (pCEA), and alpha-1-antitrypsin.

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While content-based landmark image search has recently received a lot of attention and became a very active domain, it still remains a challenging problem. Among the various reasons, high diverse visual content is the most significant one. It is common that for the same landmark, images with a wide range of visual appearances can be found from different sources and different landmarks may share very similar sets of images. As a consequence, it is very hard to accurately estimate the similarities between the landmarks purely based on single type of visual feature. Moreover, the relationships between landmark images can be very complex and how to develop an effective modeling scheme to characterize the associations still remains an open question. Motivated by these concerns, we propose multimodal hypergraph (MMHG) to characterize the complex associations between landmark images. In MMHG, images are modeled as independent vertices and hyperedges contain several vertices corresponding to particular views. Multiple hypergraphs are firstly constructed independently based on different visual modalities to describe the hidden high-order relations from different aspects. Then, they are integrated together to involve discriminative information from heterogeneous sources. We also propose a novel content-based visual landmark search system based on MMHG to facilitate effective search. Distinguished from the existing approaches, we design a unified computational module to support query-specific combination weight learning. An extensive experiment study on a large-scale test collection demonstrates the effectiveness of our scheme over state-of-the-art approaches.

PurposeNowadays, more and more Chinese consumers purchase luxury goods on live streaming platforms. However, the existing literature rarely focuses on this emerging phenomenon. This article attempts to construct a theoretical model based on the perceived value theory to explain this phenomenon.Design/methodology/approachIn total, 354 online questionnaires are collected, and the partial least square structural equation model is used to analyze the model empirically.FindingsThe results show that consumers' perceived luxury values (financial value, functional value, individual value and social value) have a significant and positive effect on customer engagement, which further leads to purchase intention.Originality/valueIn view of fact that there is a big difference between luxury goods and nonluxury goods, yet the existing literature rarely distinguishes between luxury goods and nonluxury goods in the context of live streaming shopping, this article attempts to use perceived value theory to examine consumers' luxury purchase intentions in live streaming shopping and explores whether customer engagement is a mediating mechanism of perceived luxury values that influences purchase intention in live streaming.

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