Efeitos da sibutramina sobre o sistema reprodutor feminino de ratas Wistar

 A obesidade é fator de risco para diversas outras doenças tornando a pessoa obesa mais propensa a desenvolver problemas como hipertensão arterial, doenças cardiovasculares, diabetes tipo 2, entre outras. Dos fármacos antiobesidade, destaca-se a Sibutramina, utilizada como antidepressivo e após algum tempo constatou-se o seu potencial efeito emagrecedor. Esta investigação teve como objetivo avaliar os efeitos da sibutramina sobre o sistema reprodutor feminino de ratas Wistar. Os animais foram distribuídos em 2 grupos com 10 animais cada grupo: o grupo 1 (controle) recebeu 0,3mL de soro fisiológico ( SF) a 0,9%, o grupo 2 recebeu 15mg/Kg de peso diluído em 0,3mL de água destilada de sibutramina, por gavagem, diariamente, durante 24 dias. Após esse período os animais foram previamente anestesiados para coleta de sangue por punção cardíaca em seguida os animais foram eutanasiados de acordo com as normas do Comitê de ética e pesquisa animal (CEPan). Os ovários e o útero foram fixados em formol 10% e corados com Hematoxilina /Eosina (H.E.) O peso corpóreo das ratas foi influenciado significativamente (p<0,001) pelo tratamento com sibutramina, em comparação ao grupo controle, assim como o consumo alimentar e hídrico, o grupo que recebeu tratamento com sibutramina ingeriu menos ração e água comparados ao grupo controle. Na histopatologia encontrou-se no ovário folículos atrésicos e intensa vascularização no estroma ovariano e no útero acentuadas projeções papilares e intensa proliferação epitelial. Conclui-se que a administração da sibutramina, na dosagem utilizada nesta pesquisa, reduziu o ganho de peso nas ratas e alterou o parênquima normal dos tecidos reprodutivos de ratas Wistar

Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Evaluation of the Biological Behavior of Mucograft ® in Human Gingival Fibroblasts: An In Vitro Study

Mucograft® is a resorbing porcine matrix composed of type I and type III collagen, used for soft tissue augmentation in guided tissue bony regeneration procedures. This in vitro study aimed to evaluate the biological behavior of Mucograft® in human gingival fibroblasts, as well as the ability of the matrix to induce production of extracellular matrix. Six resorbing Mucograft® matrices (MCG) were cut into 3 x 2 mm rectangles and 5 x 5 mm squares and were placed in 96-and 24-well plates, respectively. The control group (CTRL) consisted of cells plated on polystyrene without the MCG. After one, two, three and seven days, cell proliferation and viability were assessed using the Trypan exclusion method and MTT test, respectively. Type III collagen (COL 3A1) and vimentin (VIM) expression were also evaluated at 10 and 14 days, using Western blotting. Statistical analysis, using ANOVA with post hoc Bonferroni test, revealed that human gingival fibroblasts from MCG showed similar results (p>0.05) for proliferation and viability as the cells cultured on CTRL. After 14 days, a significant decrease in COL 3A1 expression (p<0.05) was observed when cultured with the MCG. VIM expression showed no significant difference at any time period (p>0.05). Although no increase in extracellular matrix secretion was observed in this in vitro study, Mucograft® presented cellular compatibility, being an option for a scaffold whenever it is required

Neotropical xenarthrans: a dataset of occurrence of xenarthran species in the Neotropics.

International audienceXenarthrans—anteaters, sloths, and armadillos—have essential functions forecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosys-tem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts withdomestic dogs, these species have been threatened locally, regionally, or even across their fulldistribution ranges. The Neotropics harbor 21 species of armadillos, 10 anteaters, and 6 sloths.Our data set includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae(3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data onDasypus pilo-sus(Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized,but new genetic studies have revealed that the group is represented by seven species. In thisdata paper, we compiled a total of 42,528 records of 31 species, represented by occurrence andquantitative data, totaling 24,847 unique georeferenced records. The geographic range is fromthe southern United States, Mexico, and Caribbean countries at the northern portion of theNeotropics, to the austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regardinganteaters,Myrmecophaga tridactylahas the most records (n=5,941), andCyclopessp. havethe fewest (n=240). The armadillo species with the most data isDasypus novemcinctus(n=11,588), and the fewest data are recorded forCalyptophractus retusus(n=33). Withregard to sloth species,Bradypus variegatushas the most records (n=962), andBradypus pyg-maeushas the fewest (n=12). Our main objective with Neotropical Xenarthrans is to makeoccurrence and quantitative data available to facilitate more ecological research, particularly ifwe integrate the xenarthran data with other data sets of Neotropical Series that will become available very soon (i.e., Neotropical Carnivores, Neotropical Invasive Mammals, andNeotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure,habitat loss, fragmentation effects, species invasion, and climate change effects will be possiblewith the Neotropical Xenarthrans data set. Please cite this data paper when using its data inpublications. We also request that researchers and teachers inform us of how they are usingthese data

Field and classroom initiatives for portable sequence-based monitoring of dengue virus in Brazil

This work was supported by Decit, SCTIE, Brazilian Ministry of Health, Conselho Nacional de Desenvolvimento Científico - CNPq (440685/ 2016-8, 440856/2016-7 and 421598/2018-2), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES - (88887.130716/2016-00), European Union’s Horizon 2020 Research and Innovation Programme under ZIKAlliance Grant Agreement (734548), STARBIOS (709517), Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro – FAPERJ (E-26/2002.930/2016), International Development Research Centre (IDRC) Canada (108411-001), European Union’s Horizon 2020 under grant agreements ZIKACTION (734857) and ZIKAPLAN (734548).Fundação Ezequiel Dias. Laboratório Central de Saúde Pública do Estado de Minas Gerais. Belo Horizonte, MG, Brazil / Latin American Genomic Surveillance Arboviral Network.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil / Latin American Genomic Surveillance Arboviral Network.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil Latin American Genomic Surveillance Arboviral Network.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Laboratório de Ecologia de Doenças Transmissíveis na Amazônia. Manaus, AM, Brazil.Secretaria de Saúde do Estado de Mato Grosso do Sul. Laboratório Central de Saúde Pública. Campo Grande, MS, Brazil.Fundação Ezequiel Dias. Laboratório Central de Saúde Pública do Estado de Minas Gerais. Belo Horizonte, MG, Brazil.Laboratório Central de Saúde Pública Dr. Giovanni Cysneiros. Goiânia, GO, Brazil.Laboratório Central de Saúde Pública Professor Gonçalo Moniz. Salvador, BA, Brazil.Secretaria de Saúde do Estado da Bahia. Salvador, BA, Brazil.Laboratório Central de Saúde Pública Dr. Milton Bezerra Sobral. Recife, PE, Brazil.Laboratório Central de Saúde Pública do Estado de Mato Grosso. Cuiabá, MT, Brazil.Laboratório Central de Saúde Pública do Distrito Federal. Brasília, DF, Brazil.Fundação Ezequiel Dias. Laboratório Central de Saúde Pública do Estado de Minas Gerais. Belo Horizonte, MG, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Coordenação Geral dos Laboratórios de Saúde Pública. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Coordenação Geral dos Laboratórios de Saúde Pública. Brasília, DF, Brazil.Organização Pan-Americana da Saúde / Organização Mundial da Saúde. Brasília, DF, Brazil.Organização Pan-Americana da Saúde / Organização Mundial da Saúde. Brasília, DF, Brazil.Organização Pan-Americana da Saúde / Organização Mundial da Saúde. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde Coordenação Geral das Arboviroses. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde Coordenação Geral das Arboviroses. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde Coordenação Geral das Arboviroses. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde Coordenação Geral das Arboviroses. Brasília, DF, Brazil.Fundação Hemocentro de Ribeirão Preto. Ribeirão Preto, SP, Brazil.Gorgas Memorial Institute for Health Studies. Panama, Panama.Universidade Federal da Bahia. Vitória da Conquista, BA, Brazil.Laboratorio Central de Salud Pública. Asunción, Paraguay.Fundação Oswaldo Cruz. Bio-Manguinhos. Rio de Janeiro, RJ, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Coordenação Geral dos Laboratórios de Saúde Pública. Brasília, DF, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, BrazilFundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, BrazilMinistério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Laboratório Central de Saúde Pública do Estado de Mato Grosso do Sul. Campo Grande, MS, Brazil.Laboratório Central de Saúde Pública do Estado de Mato Grosso do Sul. Campo Grande, MS, Brazil.Instituto de Investigaciones en Ciencias de la Salud. San Lorenzo, Paraguay.Secretaria de Estado de Saúde de Mato Grosso do Sul. Campo Grande, MS, Brazil.Fundação Oswaldo Cruz. Campo Grande, MS, Brazil.Fundação Hemocentro de Ribeirão Preto. Ribeirão Preto, SP, Brazil.Laboratório Central de Saúde Pública Dr. Giovanni Cysneiros. Goiânia, GO, Brazil.Laboratório Central de Saúde Pública Dr. Giovanni Cysneiros. Goiânia, GO, Brazil.Laboratório Central de Saúde Pública Professor Gonçalo Moniz. Salvador, BA, Brazil.Laboratório Central de Saúde Pública Dr. Milton Bezerra Sobral. Recife, PE, Brazil.Laboratório Central de Saúde Pública do Distrito Federal. Brasília, DF, Brazil.Secretaria de Saúde de Feira de Santana. Feira de Santana, Ba, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Secretaria de Saúde do Estado de Minas Gerais. Belo Horizonte, MG, Brazil.Hospital das Forças Armadas. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Universidade Nova de Lisboa. Instituto de Higiene e Medicina Tropical. Lisboa, Portugal.University of Sydney. School of Life and Environmental Sciences and School of Medical Sciences. Marie Bashir Institute for Infectious Diseases and Biosecurity. Sydney, NSW, Australia.University of KwaZulu-Natal. College of Health Sciences. KwaZulu-Natal Research Innovation and Sequencing Platform. Durban, South Africa.University of Oxford. Peter Medawar Building. Department of Zoology. Oxford, UK.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Universidade Estadual de Feira de Santana. Salvador, BA, Brazil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brazil.Universidade de Brasília. Brasília, DF, Brazil.Universidade Salvador. Salvador, BA, Brazil.Fundação Ezequiel Dias. Belo Horizonte, MG, Brazil.Fundação Ezequiel Dias. Belo Horizonte, MG, Brazil.Fundação Ezequiel Dias. Belo Horizonte, MG, Brazil.Fundação Ezequiel Dias. Belo Horizonte, MG, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Hantaviroses e Rickettsioses. Rio de Janeiro, RJ, Brazil.Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Laboratório de Ecologia de Doenças Transmissíveis na Amazônia. Manaus, AM, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Faculdade de Medicina Veterinária. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Faculdade de Medicina Veterinária. Belo Horizonte, MG, Brazil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brazil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brazil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brazil.Laboratório Central de Saúde Pública do Estado do Paraná. Curitiba, PR, Brazil.Laboratório Central de Saúde Pública do Estado de Rondônia. Porto Velho, RO, Brazil.Laboratório Central de Saúde Pública do Estado do Amazonas. Manaus, AM, Brazil.Laboratório Central de Saúde Pública do Estado do Rio Grande do Norte. Natal, RN, Brazil.Laboratório Central de Saúde Pública do Estado de Mato Grosso. Cuiabá, MT, Brazil.Laboratório Central de Saúde Pública Professor Gonçalo Moniz. Salvador, BA, Brazil.Laboratório Central de Saúde Pública Professor Gonçalo Moniz. Salvador, BA, Brazil.Laboratório Central de Saúde Pública Noel Nutels. Rio de Janeiro, RJ, Brazil.Instituto Adolfo Lutz. São Paulo, SP, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Universidade de São Paulo. Instituto de Medicina Tropical. São Paulo, SP, Brazil.Universidade de São Paulo. Instituto de Medicina Tropical. São Paulo, SP, Brazil.Universidade de São Paulo. Instituto de Medicina Tropical. São Paulo, SP, Brazil.University of Oxford. Peter Medawar Building. Department of Zoology. Oxford, UK.Instituto Nacional de Enfermedades Virales Humanas Dr. Julio Maiztegui. Pergamino, Argentina.Gorgas Memorial Institute for Health Studies. Panama, Panama.Gorgas Memorial Institute for Health Studies. Panama, Panama.Gorgas Memorial Institute for Health Studies. Panama, Panama.Instituto de Salud Pública de Chile. Santiago, Chile.Instituto de Diagnóstico y Referencia Epidemiológicos Dr. Manuel Martínez Báez. Ciudad de México, México.Instituto Nacional de Enfermedades Infecciosas Dr Carlos G Malbrán. Buenos Aires, Argentina.Ministerio de Salud Pública de Uruguay. Montevideo, Uruguay.Instituto Costarricense de Investigación y Enseñanza em Nutrición y Salud. Tres Ríos, Costa Rica.Instituto Nacional de Investigacion en Salud Publica Dr Leopoldo Izquieta Pérez. Guayaquil, Ecuador.Instituto Nacional de Investigacion en Salud Publica Dr Leopoldo Izquieta Pérez. Guayaquil, Ecuador.Universidade Federal de Pernambuco. Recife, PE, Brazil.Secretaria de Saúde do Estado de Minas Gerais. Belo Horizonte. MG, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Universidade Federal do Rio de Janeiro. Rio de Janeiro, RJ, Brazil.Universidade Federal do Rio de Janeiro. Rio de Janeiro, RJ, Brazil.Universidade Federal do Rio de Janeiro. Rio de Janeiro, RJ, Brazil.Universidade Federal do Rio de Janeiro. Rio de Janeiro, RJ, Brazil.Universidade Federal de Ouro Preto. Ouro Preto, MG, Brazil.Universidade Federal de Ouro Preto. Ouro Preto, MG, Brazil.Universidade Federal de Ouro Preto. Ouro Preto, MG, Brazil.Universidade Federal de Ouro Preto. Ouro Preto, MG, Brazil.Fundação Hemocentro de Ribeirão Preto. Ribeirão Preto, SP, Brazil.Secretaria de Saúde de Feira de Santana. Feira de Santana, BA, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Belo Horizonte, MG, Brazil.Brazil experienced a large dengue virus (DENV) epidemic in 2019, highlighting a continuous struggle with effective control and public health preparedness. Using Oxford Nanopore sequencing, we led field and classroom initiatives for the monitoring of DENV in Brazil, generating 227 novel genome sequences of DENV1-2 from 85 municipalities (2015–2019). This equated to an over 50% increase in the number of DENV genomes from Brazil available in public databases. Using both phylogenetic and epidemiological models we retrospectively reconstructed the recent transmission history of DENV1-2. Phylogenetic analysis revealed complex patterns of transmission, with both lineage co-circulation and replacement. We identified two lineages within the DENV2 BR-4 clade, for which we estimated the effective reproduction number and pattern of seasonality. Overall, the surveillance outputs and training initiative described here serve as a proof-of-concept for the utility of real-time portable sequencing for research and local capacity building in the genomic surveillance of emerging viruses

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data

NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics

Xenarthrans—anteaters, sloths, and armadillos—have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, 10 anteaters, and 6 sloths. Our data set includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the southern United States, Mexico, and Caribbean countries at the northern portion of the Neotropics, to the austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n = 5,941), and Cyclopes sp. have the fewest (n = 240). The armadillo species with the most data is Dasypus novemcinctus (n = 11,588), and the fewest data are recorded for Calyptophractus retusus (n = 33). With regard to sloth species, Bradypus variegatus has the most records (n = 962), and Bradypus pygmaeus has the fewest (n = 12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other data sets of Neotropical Series that will become available very soon (i.e., Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans data set. Please cite this data paper when using its data in publications. We also request that researchers and teachers inform us of how they are using these data