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      Medizintechnik Life Science Engineering : Interdisziplinarität · Biokompatibilität · Technologien · Implantate · Diagnostik · Werkstoffe · Business 

      Bildanalyse in Medizin und Biologie Beispiele und Anwendungen

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          Prevalence of CD44+/CD24-/low cells in breast cancer may not be associated with clinical outcome but may favor distant metastasis.

          Breast cancer is composed of phenotypically diverse populations of cancer cells. The ability to form breast tumors has been shown by in vitro/in vivo studies to be restricted to epithelial tumor cells with CD44(+)/CD24(-/low) characteristics. Validation of these findings with respect to detection in clinical samples, prognosis, and clinical relevance is in demand. We investigated breast cancer tissues for the prevalence of CD44(+)/CD24(-/low) tumor cells and their prognostic value. The study included paraffin-embedded tissues of 136 patients with and without recurrences. In addition, a breast cancer progression array with normal, carcinoma in situ, and carcinoma tissues was analyzed. We applied double-staining immunohistochemistry for the detection of CD44(+)/CD24(-/low) cells. Evaluation was by microscopic pathologic inspection and automated image analysis. CD44(+)/CD24(-/low) cells ranged from 0% to 40% in normal breast and from 0% to 80% in breast tumor tissues. The prevalence of CD44(+)/CD24(-/low) tumor cells in 122 tumors was 10% in the remainder. There was no significant correlation between CD44(+)/CD24(-/low) tumor cell prevalence and tumor progression. Although recurrences of tumors with high percentages of CD44(+)/CD24(-/low) tumor cells were mainly distant, preferably osseous metastasis, there was no correlation with the event-free and overall survival. There was no influence on the response to different treatment modalities. Our findings suggest that the prevalence of CD44(+)/CD24(-/low) tumor cells in breast cancer may not be associated with clinical outcome and survival but may favor distant metastasis.
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            Hexagonal packing of Drosophila wing epithelial cells by the planar cell polarity pathway.

            The mechanisms that order cellular packing geometry are critical for the functioning of many tissues, but they are poorly understood. Here, we investigate this problem in the developing wing of Drosophila. The surface of the wing is decorated by hexagonally packed hairs that are uniformly oriented by the planar cell polarity pathway. They are constructed by a hexagonal array of wing epithelial cells. Wing epithelial cells are irregularly arranged throughout most of development, but they become hexagonally packed shortly before hair formation. During the process, individual cell boundaries grow and shrink, resulting in local neighbor exchanges, and Cadherin is actively endocytosed and recycled through Rab11 endosomes. Hexagonal packing depends on the activity of the planar cell polarity proteins. We propose that these proteins polarize trafficking of Cadherin-containing exocyst vesicles during junction remodeling. This may be a common mechanism for the action of planar cell polarity proteins in diverse systems.
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              Automated segmentation of lateral ventricles from human and primate magnetic resonance images using cognition network technology.

              Automatic segmentation of different types of tissue from magnetic resonance images is of great importance for clinical and research applications, particularly large-scale and longitudinal studies of brain pathology. We developed a fully automated algorithm for the segmentation of lateral ventricles from cranial magnetic resonance images. This problem is of interest in the study of schizophrenia, dementia and other neuropsychiatric disorders. Our algorithm achieves comparable results to expert human raters. The theoretical approach, which is based on an emerging object-oriented technology that has been adapted and evaluated to process 3D data for the first time, may, in the future, be transferred to other important problems of magnetic resonance image analysis like gray/white matter segmentation.
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                Book Chapter
                : 983-1005
                10.1007/978-3-540-74925-7_43
                50855fdd-a648-410e-ab2a-65f0ea6f986c
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