Colombia is an endemic country for dengue fever where the four serotypes of virus dengue (DENV1–4) circulate simultaneously, and all types are responsible for dengue cases in the country. The control strategies are guided by entomological surveillance. However, heterogeneity in aedic indices is not well correlated with the incidence of the disease in cities such as Riohacha, Bello and Villavicencio. As an alternative, molecular detection of dengue virus in mosquitoes has been proposed as a useful tool for epidemiological surveillance and identification of serotypes circulating in field. We conducted a spatiotemporal fieldwork in these cities to capture adult mosquitoes to assess vector infection and explain the differences between Breteau indices and disease incidence. DENV infection in females and DENV serotype identification were evaluated and infection rates (IR) were estimated. The relationship between density, dengue cases and vector index was also estimated with logistic regression modeling and Pearson’s correlation coefficient. The lack of association between aedic indices and dengue incidence is in agreement with the weak associations between the density of the mosquitoes and their infection with DENV in the three cities. However, association was evident between the IR and dengue cases in Villavicencio. Furthermore, we found important negative associations between temperature and lag time from two to six weeks in Riohacha. We conclude that density of mosquitoes is not a good predictor of dengue cases. Instead, IR and temperature might explain better such heterogeneity.
Climatic variables related to temperature affect dengue epidemiology in different way. According to the temperature of each city, transmission might be positively or negatively affected.
The establishment of Leishmania infection in mammalian hosts and the subsequent manifestation of clinical symptoms require internalization into macrophages, immune evasion and parasite survival and replication. Although many of the genes involved in these processes have been described, the genetic and genomic variability associated to differences in virulence is largely unknown. Here we present the genomic variation of four Leishmania (Viannia) panamensis strains exhibiting different levels of virulence in BALB/c mice and its application to predict novel genes related to virulence. De novo DNA sequencing and assembly of the most virulent strain allowed comparative genomics analysis with sequenced L. (Viannia) panamensis and L. (Viannia) braziliensis strains, and showed important variations at intra and interspecific levels. Moreover, the mutation detection and a CNV search revealed both base and structural genomic variation within the species. Interestingly, we found differences in the copy number and protein diversity of some genes previously related to virulence. Several machine-learning approaches were applied to combine previous knowledge with features derived from genomic variation and predict a curated set of 66 novel genes related to virulence. These genes can be prioritized for validation experiments and could potentially become promising drug and immune targets for the development of novel prophylactic and therapeutic interventions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.