Mecanismos protetores induzidos pelo tratamento prévio com antígeno solúvel de taquizoítas (STAg) no intestino delgado de camundongos BALB/c e C57BL/6 infectados oralmente por Toxoplasma gondii

T. gondii can infect the gut mucosa by direct invasion of epithelial cells in the small intestine and these cells may respond directly to infection promoting a local immune response. C57BL/6 mice orally infected with a high parasitic load of T.gondii are highly susceptible, presenting a lethal ileitis. Recently, it was demonstrated that pretreatment with STAg protects C57BL/6 mice against intestinal pathology in oral T. gondii infection. To investigate the mechanisms induced by STAg in the small intestine in oral T.gondii infection, BALB/c and C57BL/6 mice were treated with STAg 48 hours before oral infection with 30 ME-49 cysts and sacrificed at 8 days of infection. Previous treatment with STAg were able of decrease parasitism and pathology in peripheral organs of BALB/c and C57BL/6 mice and induced a increase in amounts of goblet cells, IgA positive cells, Paneth cells and expression of cryptidin in the small intestine of both lineages of mice, moreover BALB/c mice presented higher amount of these cells comparing with C57BL/6 mice. The results suggests that STAg is able of promoting protective mechanisms in both lineages of mice, although these protection is more evidenced in BALB/c mice, and these mechanisms could be in part mediated by increase in goblet, Paneth and local secretion of IgA in the small intestine of mice orally infected with T.gondii.

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.