Browsing by Subject "Hepatic tumor"

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    Comparative analysis of the expression profiles of genes related to the Gadd45α signaling pathway in four kinds of liver diseases
    (Universidad de Murcia, Departamento de Biologia Celular e Histiologia, 2020) Yang, Xianguang; Chen, Xuelin; Xia, Cong; Li, Shuaihong; Zhu, Lin; Xu, Cunshuan
    Gadd45α (growth arrest and DNA damage inducible alpha) is a member of a group of genes whose transcript levels are increased following stressful conditions that lead to growth arrest and treatment with agents that lead to DNA damage. Gadd45α is upregulated in liver cirrhosis (LC), hepatic cancer (HC), acute liver failure (AHF) and non-alcoholic fatty liver disease(NAFLD). Here, we investigated the essential differences in the Gadd45α signaling pathway in these diseases at the transcriptional level. The results showed that 44, 46, 71 and 27 genes significant changes in these diseases, and the H-cluster showed that the expression of the Gadd45α signaling-related genes was significantly different in the four liver diseases. DAVID functional analysis showed that the Gadd45α signaling pathway- related genes were mainly involved in cell adhesion and migration, cell proliferation, apoptosis, stress and inflammatory responses, etc. Ingenuity pathway analysis (IPA) software was used to predict the functions of the Gadd45α signaling-related genes, and the results indicated that there were significant changes in cell differentiation, DNA damage repair, autophagy, apoptosis and necrosis. Gadd45α signaling pathway is involved in four kinds of liver disease and regulates a variety of activities via P38 MAPK, NF-κB, mTOR/STAT3, P21, PCNA, PI3K/Akt and other signaling pathways. Modulation of Gadd45α may be exploited to prevent the progression of liver disease, and to identify specific treatments for different stages of liver disease. In summary, the Gadd45α signaling pathway is involved in four kinds of liver disease and regulates a variety of physiological activities through various signaling pathways.
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    Nuclei detection in hepatocellular carcinoma and dysplastic liver nodules in histopathology images using bootstrap regression
    (Universidad de Murcia, Departamento de Biologia Celular e Histiologia, 2020) Kalinathan, Lekshmi; Kathavarayan, Ruba Soundar; Kanmani, Madheswari; Dinakaran, Nagendram
    Hepatocellular carcinoma (HCC) is the most common primary malignant neoplasm of the liver representing the fifth most common malignancy worldwide. This tumor is more common in men than women, with a ratio of 2.7:1. Unlike HCC, Dysplasia is the precancerous nature of liver nodules and is characterized by cellular and nuclear enlargement, nuclear pleomorphism, and multinucleation. Area based Adaptive Expectation Maximization (EM) uses texture, layout, and context features of cells, and grows clusters to obtain texton maps of nucleus. A discriminative model of nucleus and cytoplastic changes of tumor is built by incorporating texture, layout, and context information efficiently. A bootsrap regression model of nuclei and cytoplastic changes are built by incorporating the aforementioned features efficiently. Mean squared error, Peak Signal to Noise ratio and Dice similarity values are used to evaluate the method's classification performance. The proposed method provides high classification and segmentation accuracy of nucleus and extra nuclear content in HCC and dysplasia, which are exceedingly textured in histopathology images, when compared to Adaptive K means, EM method and the state-of-the-art method, Convolutional Neural Networks (CNN). As texton detection reduces the cluttered background of nuclei, the proposed method would be a convenient mechanism for the classification of nuclei and non- nuclear features. In conclusion, this system can detect more eligible cells of precancerous nature as well as malignant cells even in a cluttered background of nuclei