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ABSTRACTMouse orthotopic xenograft tumor models are commonly employed in studies investigating the mechanisms underlying the development and progression of tumors and their preclinical treatment. However, the unavailability of mature and visualized orthotopic xenograft models of nasopharyngeal carcinoma limits the development of treatment strategies for this cancer. The aim of this study was to provide a simple and reliable method for building an orthotopic xenograft model of nasopharyngeal carcinoma. Human nasopharyngeal carcinoma (C666–1-luc) cells, stably expressing the firefly luciferase gene, were injected subcutaneously into the right axilla of BALB/C nude mice. Four weeks later, the resulting subcutaneous tumors were cut into small blocks and grafted into the nasopharynx of immunodeficient BALB/C nude mice to induce tumor formation. Tumor growth was monitored by bioluminescence imaging and small animal magnetic resonance imaging (MRI). The expression of histological and immunological antigens associated with orthotopic xenograft nasopharyngeal carcinoma was analyzed by tissue section analysis and immunohistochemistry (IHC). A visualized orthotopic xenograft nasopharyngeal carcinoma model was successfully developed in this study. Luminescence signal detection, micro-MRI, and hematoxylin and eosin staining revealed the successful growth of tumors in the nasopharynx of the nude mice. Moreover, IHC analysis detected cytokeratin (CK), CK5/6, P40, and P63 expression in the orthotopic tumors, consistent with the reported expression of these antigens in human nasopharyngeal tumors. This study established a reproducible, visual, and less lethal orthotopic xenograft model of nasopharyngeal carcinoma, providing a platform for preclinical research.

Because steel strips are covered with scales and water during hot-rolling, it is difficult to recognize the defects from images of hot-rolled strips through conventional methods. Principles and characteristics of fractal dimensions were studied, and computation of the fractal dimensions of the defect images with Peleg Covered Carpet is presented. Fractal dimensions of piecewise linearly transformed and smoothed images were used as features for classification of defects. These features were inputted to train the AdaBoost classifier. Experiments with samples of pimples, shells and scales from a real surface inspection system of hot-rolled strips showed that it is effective to recognized scales from other defects, and the total classification rate of this method is higher than 90%.