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2017-397 Rivers have been a major part in the development of human activities since the beginning of civilization. Globally, increased navigation in rivers and construction of oil storage infrastructure along its banks has increased the risk of spillage of these substances in freshwater bodies. Mitigation associated with such incidents impact depends largely on the formulation and implementation of adequate contingency plans. To make this possible the vulnerability assessment is a tool of primary information which integrates the identification of possible sources of hydrocarbon’s spills and the respective dispersion patterns (evaluation of susceptibility); as well as analysis of areas that could be more seriously affected by the presence of those spills (sensitivity testing). There are known methodologies and study cases for assessing vulnerability to oil spills in marine and coastal environments; however, for rivers there are not references of this type of work. This paper presents a methodological adaptation for assessing environmental vulnerability for oil spills in rivers, from the integration of known methodologies for evaluation of sensitivity and susceptibility in coastal marine and river environments. Given its standardization and wide use, the ESI (NOAA) method was selected for river sensitivity assessment. It was not considered necessary to have a standardized method for trajectory modeling and hydrocarbons spill degradation (susceptibility analysis), but it was established that in each case of study the selected tool must analyze the determinant processes as advection, adhesion to the edges, mechanical dispersion, evaporation, dissolution, and vertical mixing. Finally, an adaptation of the Index of Environmental Vulnerability to Oil (IEVO) was proposed. At the moment, the application of the methodology is being carried out in a river of Colombia, however the results still unfinished will not be part of the discussion of the work below.

Cutaneous leishmaniasis is a skin disease caused by flagellate protozoa of the genus Leishmania and transmitted by sandflies of the genus Lutzomyia. Around 1 million new cases occur in the world annually, with a total of 12 million people affected, mainly in rural areas with low access to health services and adequate treatments. In the area of the Americas, Colombia has one of the highest infection rates after Brazil. Topical treatments with pentamidine isethionate (PMD) present an attractive alternative due to their ease of application and low costs. However, cutaneous leishmaniasis lesions present nodules with seropurulent exudate that, when drying, form hyperkeratotic lesions, hindering the effective penetration of drugs for their treatment. The use of molecular histology techniques, such as MALDI-MSI, allow in situ evaluation of the penetration of the treatment to the sections of the dermis where the disease-causing parasite resides. However, the large volume of information generated makes it impossible to process it manually. Machine learning techniques allow the unsupervised processing of large amounts of information, generating prediction models for the classification of new information. This work proposes a low-cost method to generate cutaneous leishmaniasis detection and classification models using MALDI-MSI images taken from murine models. The proposed models allow a 95% efficiency when separating healthy samples from infected samples and an effectiveness of 67% when separating effectively treated samples from unsuccessfully treated samples.

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