DNA multigene characterization of Fasciola hepatica and Lymnaea neotropica and its fascioliasis transmission capacity in Uruguay, with historical correlation, human report review and infection risk analysis

Fascioliasis is a highly pathogenic zoonotic disease emerging in recent decades, in part due to the effects of climate and global changes. South America is the continent presenting more numerous human fascioliasis endemic areas and the highest Fasciola hepatica infection prevalences and intensities known in humans. These serious public health scenarios appear mainly linked to altitude areas in Andean countries, whereas lowland areas of non-Andean countries, such as Uruguay, only show sporadic human cases or outbreaks. To understand this difference, we characterized F. hepatica from cattle and horses and lymnaeids of Uruguay by sequencing of ribosomal DNA ITS-2 and ITS-1 spacers and mitochondrial DNA cox1, nad1 and 16S genes. Results indicate that vectors belong to Lymnaea neotropica instead of to Lymnaea viator, as always reported from Uruguay. Our correlation of fasciolid and lymnaeid haplotypes with historical data on the introduction and spread of livestock species into Uruguay allow to understand the molecular diversity detected. We study the life cycle and transmission features of F. hepatica by L. neotropica of Uruguay under standardized experimental conditions to enable a comparison with the transmission capacity of F. hepatica by Galba truncatula at very high altitude in Bolivia. Results demonstrate that although L. neotropica is a highly efficient vector in the lowlands, its transmission capacity is markedly lower than that of G. truncatula in the highlands. On this baseline, we review the human fascioliasis cases reported in Uruguay and analyze the present and future risk of human infection in front of future climate change estimations

A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil

Geographical information systems (GIS) are tools that have been recently tested for improving our understanding of the spatial distribution of disease. The objective of this paper was to further develop the GIS technology to model and control schistosomiasis using environmental, social, biological and remote-sensing variables. A final regression model (R² = 0.39) was established, after a variable selection phase, with a set of spatial variables including the presence or absence of Biomphalaria glabrata, winter enhanced vegetation index, summer minimum temperature and percentage of houses with water coming from a spring or well. A regional model was also developed by splitting the state of Minas Gerais (MG) into four regions and establishing a linear regression model for each of the four regions: 1 (R² = 0.97), 2 (R² = 0.60), 3 (R² = 0.63) and 4 (R² = 0.76). Based on these models, a schistosomiasis risk map was built for MG. In this paper, geostatistics was also used to make inferences about the presence of Biomphalaria spp. The result was a map of species and risk areas. The obtained risk map permits the association of uncertainties, which can be used to qualify the inferences and it can be thought of as an auxiliary tool for public health strategies