Short-term changes in handgrip strength, body composition, and lymphedema induced by breast cancer surgery

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Sources and levels of selenium on breast meat quality of broilers

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Primary relapse site pattern in women with triple-negative breast cancer

Despite the remarkable improvements in breast cancer (BC) characterization, accurate prediction of BC clinical behavior is often still difficult to achieve. Some studies have investigated the association between the molecular subtype, namely the basal-like BC and the pattern of relapse, however only few investigated the association between relapse pattern and immunohistochemical defined triple-negative breast cancers (TNBCs). The aim of this study was to evaluate the pattern of relapse in patients with TNBC, namely the primary distant relapse site:One-hundred twenty nine (129) invasive breast carcinomas with follow-up information were classified according to the molecular subtype using immunohistochemistry for ER, PgR and Her2. The association between TNBC and distant relapse primary site was analyzed by logistic regression. Using multivariate logistic regression analysis patients with TNBC displayed only 0.09(95% CI: 0.00-0.74; p = 0.02) the odds of, the non-TNBC patients of developing bone primary relapse. Regarding visceral and lymph-node relapse, no differences between in this cohort were found.Though classically regarded as aggressive tumors, TNBCs rarely development primary relapse in bone when compared to non-TNBC, a clinical relevant fact when investigating a metastasis of an occult or non-sampled primary BC. (C) 2014 Elsevier GmbH. All rights reserved.

Optimum-Path Forest Applied for Breast Masses Classification

In Computer-Aided Diagnosis-based schemes in mammography analysis each module is interconnected, which directly affects the system operation as a whole. The identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest for further image segmentation. This study aims to evaluate the performance of three techniques in classifying regions of interest as containing masses or without masses (without clinical findings), as well as the main contribution of this work is to introduce the Optimum-Path Forest (OPF) classifier in this context, which has never been done so far. Thus, we have compared OPF against with two sorts of neural networks in a private dataset composed by 120 images: Radial Basis Function and Multilayer Perceptron (MLP). Texture features have been used for such purpose, and the experiments have demonstrated that MLP networks have been slightly better than OPF, but the former is much faster, which can be a suitable tool for real-time recognition systems.

Biotransformed soybean extract induces cell death of estrogen-dependent breast cancer cells by modulation of apoptotic proteins

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Effects of nemorosone, isolated from the plant Clusia rosea, on the cell cycle and gene expression in MCF-7 BUS breast cancer cell lines

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Metalloproteomic approach of mercury in breast milk samples of lactating women in the Amazon region, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Immunoexpression of ROCK-1 and MMP-9 as prognostic markers in breast cancer

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)