Form III-like conformation and Form I-like packing in a chloroform channel solvate of the diuretic drug chlortalidone

Chlortalidone (CTD) is an antihypertensive drug for which only two solid state phases have been structurally elucidated thus far. Here, we have prepared a chloroform solvate thereof, namely, CTD Form IV, and its structure was compared to those of Form I and Form III. Its two conformers exhibit a dual structural feature in relation to the antecedent polymorphs. Both CTD molecules of Form IV adopt a Form III-like conformation, which is featured, if the conformation of CTD Form I is used as a reference, by a rotation of about 90 degrees on the axis of the C-C bond bridging the substituted benzene and isoindolinyl rings. However, CTD Form IV assembles as in the Form I crystal packing despite the different stacking fashion of their centrosymmetric dimers. In contrast to Form I, there is no offset stacking in Form IV, which forces a bend of ca. 24 degrees between the planes passing through the isoindolinyl moieties of two [100]-stacked dimers. Chloroform molecules at a maximum stoichiometry of 0.25 mol per mol of the drug play a stabilizing role in the assembly of Form IV by filling the channels formed on the crystals.FAPEMIG [APQ-02685-09, APQ-01093-10]FAPEMIGFINEPFINEP [134/08]CAPESCAPES [AUXPE-PNPD 1865/2008]CNPqCNPq [472623/2011-7, 476870/2011-9

Diretriz sobre Diagnóstico e Tratamento da Cardiomiopatia Hipertrófica – 2024

Hypertrophic cardiomyopathy (HCM) is a form of genetically caused heart muscle disease, characterized by the thickening of the ventricular walls. Diagnosis requires detection through imaging methods (Echocardiogram or Cardiac Magnetic Resonance) showing any segment of the left ventricular wall with a thickness > 15 mm, without any other probable cause. Genetic analysis allows the identification of mutations in genes encoding different structures of the sarcomere responsible for the development of HCM in about 60% of cases, enabling screening of family members and genetic counseling, as an important part of patient and family management. Several concepts about HCM have recently been reviewed, including its prevalence of 1 in 250 individuals, hence not a rare but rather underdiagnosed disease. The vast majority of patients are asymptomatic. In symptomatic cases, obstruction of the left ventricular outflow tract (LVOT) is the primary disorder responsible for symptoms, and its presence should be investigated in all cases. In those where resting echocardiogram or Valsalva maneuver does not detect significant intraventricular gradient (> 30 mmHg), they should undergo stress echocardiography to detect LVOT obstruction. Patients with limiting symptoms and severe LVOT obstruction, refractory to beta-blockers and verapamil, should receive septal reduction therapies or use new drugs inhibiting cardiac myosin. Finally, appropriately identified patients at increased risk of sudden death may receive prophylactic measure with implantable cardioverter-defibrillator (ICD) implantation.La miocardiopatía hipertrófica (MCH) es una forma de enfermedad cardíaca de origen genético, caracterizada por el engrosamiento de las paredes ventriculares. El diagnóstico requiere la detección mediante métodos de imagen (Ecocardiograma o Resonancia Magnética Cardíaca) que muestren algún segmento de la pared ventricular izquierda con un grosor > 15 mm, sin otra causa probable. El análisis genético permite identificar mutaciones en genes que codifican diferentes estructuras del sarcómero responsables del desarrollo de la MCH en aproximadamente el 60% de los casos, lo que permite el tamizaje de familiares y el asesoramiento genético, como parte importante del manejo de pacientes y familiares. Varios conceptos sobre la MCH han sido revisados recientemente, incluida su prevalencia de 1 entre 250 individuos, por lo tanto, no es una enfermedad rara, sino subdiagnosticada. La gran mayoría de los pacientes son asintomáticos. En los casos sintomáticos, la obstrucción del tracto de salida ventricular izquierdo (TSVI) es el trastorno principal responsable de los síntomas, y su presencia debe investigarse en todos los casos. En aquellos en los que el ecocardiograma en reposo o la maniobra de Valsalva no detecta un gradiente intraventricular significativo (> 30 mmHg), deben someterse a ecocardiografía de esfuerzo para detectar la obstrucción del TSVI. Los pacientes con síntomas limitantes y obstrucción grave del TSVI, refractarios al uso de betabloqueantes y verapamilo, deben recibir terapias de reducción septal o usar nuevos medicamentos inhibidores de la miosina cardíaca. Finalmente, los pacientes adecuadamente identificados con un riesgo aumentado de muerte súbita pueden recibir medidas profilácticas con el implante de un cardioversor-desfibrilador implantable (CDI).A cardiomiopatia hipertrófica (CMH) é uma forma de doença do músculo cardíaco de causa genética, caracterizada pela hipertrofia das paredes ventriculares. O diagnóstico requer detecção por métodos de imagem (Ecocardiograma ou Ressonância Magnética Cardíaca) de qualquer segmento da parede do ventrículo esquerdo com espessura > 15 mm, sem outra causa provável. A análise genética permite identificar mutações de genes codificantes de diferentes estruturas do sarcômero responsáveis pelo desenvolvimento da CMH em cerca de 60% dos casos, permitindo o rastreio de familiares e aconselhamento genético, como parte importante do manejo dos pacientes e familiares. Vários conceitos sobre a CMH foram recentemente revistos, incluindo sua prevalência de 1 em 250 indivíduos, não sendo, portanto, uma doença rara, mas subdiagnosticada. A vasta maioria dos pacientes é assintomática. Naqueles sintomáticos, a obstrução do trato de saída do ventrículo esquerdo (OTSVE) é o principal distúrbio responsável pelos sintomas, devendo-se investigar a sua presença em todos os casos. Naqueles em que o ecocardiograma em repouso ou com Manobra de Valsalva não detecta gradiente intraventricular significativo (> 30 mmHg), devem ser submetidos à ecocardiografia com esforço físico para detecção da OTSVE.   Pacientes com sintomas limitantes e grave OTSVE, refratários ao uso de betabloqueadores e verapamil, devem receber terapias de redução septal ou uso de novas drogas inibidoras da miosina cardíaca. Por fim, os pacientes adequadamente identificados com risco aumentado de morta súbita podem receber medida profilática com implante de cardiodesfibrilador implantável (CDI)

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

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 CARNIVORES: a data set on carnivore distribution in the Neotropics

Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non-detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non-governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer-reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non-detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio-temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large-scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use 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

Proceedings Of The 23Rd Paediatric Rheumatology European Society Congress: Part Two

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