4,007 research outputs found

    The increased expression of fatty acid-binding protein 9 in prostate cancer and its prognostic significance

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    In contrast to numerous studies conducted to investigate the crucial role of fatty acid binding protein 5 (FABP5) in prostate cancer, investigations on the possible involvement of other FABPs are rare. Here we first measured the mRNA levels of 10 FABPs in benign and malignant prostate cell lines and identified the differentially expressed FABP6 and FABP9 mRNAs whose levels in all malignant cell lines were higher than those in the benign cells. Thereafter we assessed the expression status of FABP6 and FABP9 in both prostate cell lines and in human tissues. FABP6 protein was overexpressed only in 1 of the 5 malignant cell lines and its immunostaining intensities were not significantly different between benign and malignant prostate tissues. In contrast, FABP9 protein was highly expressed in highly malignant cell lines PC-3 and PC3-M, but its level in the benign PNT-2 and other malignant cell lines was not detectable. When analysed in an archival set of human prostate tissues, immunohistochemical staining intensity for FABP9 was significantly higher in carcinomas than in benign cases and the increase in FABP9 was significantly correlated with reduced patient survival times. Moreover, the increased level of staining for FABP9 was significantly associated with the increased joint Gleason scores (GS) and androgen receptor index (AR). Suppression of FABP9 expression in highly malignant PC3-M cells inhibited their invasive potential. Our results suggest that FABP9 is a valuable prognostic marker to predict the outcomes of prostate cancer patients, perhaps by playing an important role in prostate cancer cell invasion

    An improved model for joint segmentation and registration based on linear curvature smoother

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    Image segmentation and registration are two of the most challenging tasks in medical imaging. They are closely related because both tasks are often required simultaneously. In this article, we present an improved variational model for a joint segmentation and registration based on active contour without edges and the linear curvature model. The proposed model allows large deformation to occur by solving in this way the difficulties other jointly performed segmentation and registration models have in case of encountering multiple objects into an image or their highly dependence on the initialisation or the need for a pre-registration step, which has an impact on the segmentation results. Through different numerical results, we show that the proposed model gives correct registration results when there are different features inside the object to be segmented or features that have clear boundaries but without fine details in which the old model would not be able to cope. </jats:p
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