Increased caspase-3 immunoexpression and morphology alterations in oenocytes and trophocytes of Apis mellifera larvae induced by toxic secretion of Epormenis cestri

Toxic honeydew produced by Flatidae Epormenis cestri in Uruguay has been shown to cause among honeybees (Apis mellifera) colonies a massive larva death called “River disease”, but the intrinsic mechanisms are still unknown. Because fat body cells, oenocytes and trophocytes, are known to regulated larvae metabolism, and to be affected by xenobiotics, we tested whether apoptosis of these cells can be an underlying cause of larvae death. Ten colonies were divided into two groups and fed with common honey or toxic honeydew obtained from colonies affected by “River disease”. Five-day-old larvae were collected and processed for histology and immunohistochemistry for caspase-3. The area, diameter, and immunostaining area in oenocytes and trophocytes were measured. The oenocyte and trophocyte cellular area decreased in the treated group (p=0.002; p<0.001 respectively) compared to the control group. The diameter of oenocytes (p=0.0002) and trophocytes (p<0.0001) decreased in the treated group. Caspase-3 was detected in cytoplasm in the control group but in the cytoplasm and nucleus in the treated group. The caspase-3 immunostaining area increased in oenocytes (p<0.002) and trophocytes (p<0.0001) of the treated group. The ingestion of toxic honeydew altered the morphology, localization and immunoexpression of caspase-3 in fat body cells, which suggests that the deregulation of the apoptotic mechanism affected the normal development in A. mellifera larvae

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts