FABIO JOSE AYRES
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Artigo Científico The Lysyl Oxidase Inhibitor, b-Aminopropionitrile, Diminishes the Metastatic Colonization Potential of Circulating Breast Cancer Cells(2009) Bondareva, Alla; Downey, Charlene M.; FABIO JOSE AYRES; Liu, Wei; Boyd, Steven K.; Hallgrimsson, Benedikt; Jirik, Frank R.Lysyl oxidase (LOX), an extracellular matrix remodeling enzyme, appears to have a role in promoting breast cancer cell motility and invasiveness. In addition, increased LOX expression has been correlated with decreases in both metastases-free, and overall survival in breast cancer patients. With this background, we studied the ability of b-aminopropionitrile (BAPN), an irreversible inhibitor of LOX, to regulate the metastatic colonization potential of the human breast cancer cell line, MDA MB-231. BAPN was administered daily to mice starting either 1 day prior, on the same day as, or 7 days after intracardiac injection of luciferase expressing MDA-MB-231-Luc2 cells. Development of metastases was monitored by in vivo bioluminescence imaging, and tumor-induced osteolysis was assessed by micro-computed tomography (mCT). We found that BAPN administration was able to reduce the frequency of metastases. Thus, when BAPN treatment was initiated the day before, or on the same day as the intra-cardiac injection of tumor cells, the number of metastases was decreased by 44%, and 27%, and whole-body photon emission rates (reflective of total tumor burden) were diminished by 78%, and 45%, respectively. In contrast, BAPN had no effect on the growth of established metastases. Our findings suggest that LOX activity is required during extravasation and/or initial tissue colonization by circulating MDA-MB-231 cells, lending support to the idea that LOX inhibition might be useful in metastasis prevention.Artigo Científico Effect of Pixel Resolution on Texture Features of Breast Masses in Mammograms(2010) Rangayyan, Rangaraj M.; Nguyen, Thanh M.; FABIO JOSE AYRES; Nandi, Asoke K.The effect of pixel resolution on texture features computed using the gray-level co-occurrence matrix (GLCM) was analyzed in the task of discriminating mammographic breast lesions as benign masses or malignant tumors. Regions in mammograms related to 111 breast masses, including 65 benign masses and 46 malignant tumors, were analyzed at pixel sizes of 50, 100, 200, 400, 600, 800, and 1,000 μm. Classification experiments using each texture feature individually provided accuracy, in terms of the area under the receiver operating characteristics curve (AUC), of up to 0.72. Using the Bayesian classifier and the leave-one-out method, the AUC obtained was in the range 0.73 to 0.75 for the pixel resolutions of 200 to 800 μm, with 14 GLCM-based texture features using adaptive ribbons of pixels around the boundaries of the masses. Texture features computed using the ribbons resulted in higher classification accuracy than the same features computed using the corresponding regions within the mass boundaries. The t test was applied to AUC values obtained using 100 repetitions of random splitting of the texture features from the ribbons of masses into the training and testing sets. The texture features computed with the pixel size of 200 μm provided the highest average AUC with statistically highly significant differences as compared to all of the other pixel sizes tested, except 100 μm.