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4 result(s) for "Del Carro, Karina Bertazo"
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Maternal and Fetal Implications of Oropouche Fever, Espírito Santo State, Brazil, 2024
Reemergence of Oropouche fever in Brazil raises concerns about potential risks for infection in pregnancy. We describe a case series of Oropouche fever in pregnant women and their neonates in Espírito Santo State, Brazil, in 2024. Of 73 pregnancies, 15 pregnancies concluded by the end of the study period; of those, 14 resulted in live births and 1 in spontaneous abortion. Placental reverse transcription PCR tests were positive for Oropouche virus RNA in 5 infections in the third trimester. Two infections occurred in the first trimester, resulting in 1 spontaneous abortion and 1 live birth with corpus callosum dysgenesis. Of 13 infections that occurred in the third trimester, 1 showed possible intrapartum transmission with clinical manifestations in the neonate, whereas the others were asymptomatic. We found no anomalies in third-trimester infections. These findings suggest possible vertical transmission of Oropouche virus and a potential link with spontaneous abortion or malformation.
Assessing geographic and climatic variables to predict the potential distribution of the visceral leishmaniasis vector Lutzomyia longipalpis in the state of Espírito Santo, Brazil
Visceral leishmaniasis (VL) is an infectious disease caused by the protozoa Leishmania chagasi, whose main vector in South America is Lutzomyia longipalpis. The disease was diagnosed in the Brazilian state of Espírito Santo (ES) for the first time in 1968. Currently, this disease has been considered endemic in 10 municipalities. Furthermore, the presence of L. longipalpis has been detected in eight other municipalities where the transmission has not been reported thus far. In this study, we performed species distribution modeling (SDM) to identify new and most likely receptive areas for VL transmission in ES. The sandflies were both actively and passively collected in various rural area of ES between 1986 and 2017. The collection points were georeferenced using a global positioning system device. Climatic data were retrieved from the WorldClim database, whereas geographic data were obtained from the National Institute for Space Research and the Integrated System of Geospatial Bases of the State of Espírito Santo. The maximum entropy algorithm was used through the MIAmaxent R package to train and test the distribution models for L. longipalpis. The major contributor to model generation was rocky outcrops, followed by temperature seasonality. The SDM predicted the expansion of the L. longipalpis-prone area in the Doce River Valley and limited the probability of expanding outside its watershed. Once the areas predicted suitable for L. longipalpis occurrence are determined, we can avoid the inefficient use of public resources in conducting canine serological surveys where the vector insect does not occur.
Assessing geographic and climatic variables to predict the potential distribution of the visceral leishmaniasis vector Lutzomyia longipalpis in the state of Espírito Santo, Brazil
Visceral leishmaniasis (VL) is an infectious disease caused by the protozoa Leishmania chagasi, whose main vector in South America is Lutzomyia longipalpis. The disease was diagnosed in the Brazilian state of Espírito Santo (ES) for the first time in 1968. Currently, this disease has been considered endemic in 10 municipalities. Furthermore, the presence of L. longipalpis has been detected in eight other municipalities where the transmission has not been reported thus far. In this study, we performed species distribution modeling (SDM) to identify new and most likely receptive areas for VL transmission in ES. The sandflies were both actively and passively collected in various rural area of ES between 1986 and 2017. The collection points were georeferenced using a global positioning system device. Climatic data were retrieved from the WorldClim database, whereas geographic data were obtained from the National Institute for Space Research and the Integrated System of Geospatial Bases of the State of Espírito Santo. The maximum entropy algorithm was used through the MIAmaxent R package to train and test the distribution models for L. longipalpis. The major contributor to model generation was rocky outcrops, followed by temperature seasonality. The SDM predicted the expansion of the L. longipalpis-prone area in the Doce River Valley and limited the probability of expanding outside its watershed. Once the areas predicted suitable for L. longipalpis occurrence are determined, we can avoid the inefficient use of public resources in conducting canine serological surveys where the vector insect does not occur.