Análise da expressão dos receptores HER-2 e sua correlação clínica em pacientes com neoplasia gástrica do setor de Oncologia do Hospital de Clínicas da Universidade Federal de Uberlândia-UFU

Introduction: Gastric cancer is currently the fourth higher cancer mortality rate among men in the world and the fifth among women, despite the progressive advances in oncology. The identification of tumor receptors and the development of target-drugs to block them has contributed to increased survival and quality of life of patients, but it becomes important to know the tumor profile of the population being treated, avoiding burdening treatment with examinations and treatments that are not cost-effective. Objective: To evaluate the profile of the population with gastric cancer treated in five years at the Clinical Hospital of the Federal University of Uberlândia and verify the correlation between overexpression of HER-2 receptor with an unfavorable prognosis. Methods: 203 records with gastric cancer were selected through the system database, attending a five-year period, of which 117 paraffin blocks were available for immunohistochemical assessment of HER2 receptor. Results: 2.6% of tumors showed overexpression of HER2, considering for this study two crosses as positive. There was no statistically significant difference in correlation between expression of the HER2 receptor with age, gender, tumor grade, local involvement, Lauren classification, Borrmann classification or staging. Conclusion: For this studied population, we can conclude that there is no need to employ HER2 blockers with high cost as a target-therapy in patients with gastric cancer, since no clinical benefit probably will be obtained due to a low percentage of these patients that demonstrated superexpression of this receptor or even there is no patients with gastric cancer with superexpression of HER2 with more than three crosses of positivity in immunochemistry

ToPI - uma abordagem online para identificar locais de interesse utilizando fotografias geo-referenciadas

Due to the growing use of social networks people no longer just consume data, they also produce and share it. Geo-tagged information, i.e., data with geographical location, have been used in many attempts to identify popular places and help tourists that will visit unfamiliar cities. This Master Thesis presents an online strategy that uses geo-tagged photos and their metadata in order to identify places of interest inside a given geographical area and retrieve relevant related information. The whole process runs automatically in real time, returning updated information about places. The proposed strategy takes into account the inherent dynamism of social media, and thus is robust under inconsistencies and/or outdated information, a common issue in solutions that rely on previously stored data. The analysis of the results showed that our approach is very promising, returning places that present high agreement with those from a popular travel website.

Uma abordagem baseada em filtragem colaborativa integrada a mapas de saliência para a recomendação de imagens

Nowadays, the amount of customers using sites for shopping is greatly increasing, mainly due to the easiness and rapidity of this way of consumption. The sites, differently from physical stores, can make anything available to customers. In this context, Recommender Systems (RS) have become indispensable to help consumers to find products that may possibly pleasant or be useful to them. These systems often use techniques of Collaborating Filtering (CF), whose main underlying idea is that products are recommended to a given user based on purchase information and evaluations of past, by a group of users similar to the user who is requesting recommendation. One of the main challenges faced by such a technique is the need of the user to provide some information about her preferences on products in order to get further recommendations from the system. When there are items that do not have ratings or that possess quite few ratings available, the recommender system performs poorly. This problem is known as new item cold-start. In this paper, we propose to investigate in what extent information on visual attention can help to produce more accurate recommendation models. We present a new CF strategy, called IKB-MS, that uses visual attention to characterize images and alleviate the new item cold-start problem. In order to validate this strategy, we created a clothing image database and we use three algorithms well known for the extraction of visual attention these images. An extensive set of experiments shows that our approach is efficient and outperforms state-of-the-art CF RS.