Distribution and infection of triatomines (Hemiptera: Reduviidae) by Trypanosoma cruzi in the state of Michoacán, Mexico

An entomological study of triatomine species was carried out to assess their prevalence in 10 localities of the state of Michoacán, Mexico. Entomological indices were calculated to estimate the risk for vector-borne transmission of Trypanosoma cruzi to the human population in this area. Four triatomine species ( Triatoma barberi , Triatoma dimidiata , Meccus pallidipennis and Meccus longipennis ) were collected from the study area. This is the first report of M. longipennis and T. dimidiata in Michoacán. M. pallidipennis was significantly (p < 0.05) more abundant than any of the other species collected in the study area. Infection indices were greater than 50% for each of the four collected triatomine species. Significantly more triatomines were collected from intradomiciliary areas than from peridomiciliary or sylvatic areas. Infestation, crowding and density indices were low, whereas colonisation indices were high in five localities. The current vectorial conditions in the study area require continuous entomological and serological surveillance to diminish the risk of T. cruzi transmission to human populations

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

Libro de Proyectos Finales 2021 primer semestre

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