3 resultados para Utility of topology for education

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo


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Trichoepithelioma is a benign neoplasm that shares both clinical and histological features with basal cell carcinoma. It is important to distinguish these neoplasms because they require different clinical behavior and therapeutic planning. Many studies have addressed the use of immunohistochemistry to improve the differential diagnosis of these tumors. These studies present conflicting results when addressing the same markers, probably owing to the small number of basaloid tumors that comprised their studies, which generally did not exceed 50 cases. We built a tissue microarray with 162 trichoepithelioma and 328 basal cell carcinoma biopsies and tested a panel of immune markers composed of CD34, CD10, epithelial membrane antigen, Bcl-2, cytokeratins 15 and 20 and D2-40. The results were analyzed using multiple linear and logistic regression models. This analysis revealed a model that could differentiate trichoepithelioma from basal cell carcinoma in 36% of the cases. The panel of immunohistochemical markers required to differentiate between these tumors was composed of CD10, cytokeratin 15, cytokeratin 20 and D2-40. The results obtained in this work were generated from a large number of biopsies and resulted in the confirmation of overlapping epithelial and stromal immunohistochemical profiles from these basaloid tumors. The results also corroborate the point of view that trichoepithelioma and basal cell carcinoma tumors represent two different points in the differentiation of a single cell type. Despite the use of panels of immune markers, histopathological criteria associated with clinical data certainly remain the best guideline for the differential diagnosis of trichoepithelioma and basal cell carcinoma. Modern Pathology (2012) 25, 1345-1353; doi: 10.1038/modpathol.2012.96; published online 8 June 2012

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To perform a systematic review of the utility of the Beck Depression Inventory for detecting depression in medical settings, this article focuses on the revised version of the scale (Beck Depression Inventory-II), which was reformulated according to the DSM-IV criteria for major depression. We examined relevant investigations with the Beck Depression Inventory-II for measuring depression in medical settings to provide guidelines for practicing clinicians. Considering the inclusion and exclusion criteria seventy articles were retained. Validation studies of the Beck Depression Inventory-II, in both primary care and hospital settings, were found for clinics of cardiology, neurology, obstetrics, brain injury, nephrology, chronic pain, chronic fatigue, oncology, and infectious disease. The Beck Depression Inventory-II showed high reliability and good correlation with measures of depression and anxiety. Its threshold for detecting depression varied according to the type of patients, suggesting the need for adjusted cut-off points. The somatic and cognitive-affective dimension described the latent structure of the instrument. The Beck Depression Inventory-II can be easily adapted in most clinical conditions for detecting major depression and recommending an appropriate intervention. Although this scale represents a sound path for detecting depression in patients with medical conditions, the clinician should seek evidence for how to interpret the score before using the Beck Depression Inventory-II to make clinical decisions

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Atmospheric conditions at the site of a cosmic ray observatory must be known for reconstructing observed extensive air showers. The Global Data Assimilation System (GDAS) is a global atmospheric model predicated on meteorological measurements and numerical weather predictions. GDAS provides altitude-dependent profiles of the main state variables of the atmosphere like temperature, pressure, and humidity. The original data and their application to the air shower reconstruction of the Pierre Auger Observatory are described. By comparisons with radiosonde and weather station measurements obtained on-site in Malargue and averaged monthly models, the utility of the GDAS data is shown. (C) 2012 Elsevier B.V. All rights reserved.