30 research outputs found
Congenital Zika syndrome is associated with maternal protein malnutrition
Zika virus (ZIKV) infection during pregnancy is associated with a spectrum of developmental impairments known as congenital Zika syndrome (CZS). The prevalence of this syndrome varies across ZIKV endemic regions, suggesting that its occurrence could depend on cofactors. Here, we evaluate the relevance of protein malnutrition for the emergence of CZS. Epidemiological data from the ZIKV outbreak in the Americas suggest a relationship between undernutrition and cases of microcephaly. To experimentally examine this relationship, we use immunocompetent pregnant mice, which were subjected to protein malnutrition and infected with a Brazilian ZIKV strain. We found that the combination of protein restriction and ZIKV infection leads to severe alterations of placental structure and embryonic body growth, with offspring displaying a reduction in neurogenesis and postnatal brain size. RNA-seq analysis reveals gene expression deregulation required for brain development in infected low-protein progeny. These results suggest that maternal protein malnutrition increases susceptibility to CZS.Fil: Barbeito Andrés, Jimena. Universidade Federal do Rio de Janeiro; Brasil. Universidad Nacional Arturo Jauretche. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos; ArgentinaFil: Pezzuto, Paula. Universidade Federal do Rio de Janeiro; BrasilFil: Higa, Luiza. Universidade Federal do Rio de Janeiro; BrasilFil: Dias, André Alves. Universidade Federal do Rio de Janeiro; BrasilFil: Vasconcelos, Janaina. Universidade Federal do Pará; BrasilFil: Santos, T. M. P.. Universidade Federal do Rio de Janeiro; BrasilFil: Ferreira, Jéssica. Universidade Federal do Rio de Janeiro; BrasilFil: Ferreira, R. O.. Universidade Federal do Rio de Janeiro; BrasilFil: Dutra, F. F.. Universidade Federal do Rio de Janeiro; BrasilFil: Rossi, A. D.. Universidade Federal do Rio de Janeiro; BrasilFil: Barbosa, R. V.. Universidade Federal Do Rio de Janeiro. Centro Nacional de Biologia Estrutural E Bioimagem.; BrasilFil: Amorim, C. K. N.. Evandro Chagas Institute; BrasilFil: de Souza, M. P. C.. Evandro Chagas Institute; BrasilFil: Chimelli, L.. Instituto Estadual do Cérebro Paulo Niemeyer ; BrasilFil: Aguiar, R. S.. Universidade Federal do Rio de Janeiro; BrasilFil: Gonzalez, Paula Natalia. Universidad Nacional Arturo Jauretche. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos; ArgentinaFil: Lara, F. A.. Oswaldo Cruz Institute; BrasilFil: Castro, M.C.. Harvard University. Harvard School of Public Health; Estados UnidosFil: Molnár, Z.. University of Oxford; Reino UnidoFil: Lopes, R. T.. Universidade Federal do Rio de Janeiro; BrasilFil: Bozza, M. T.. Universidade Federal do Rio de Janeiro; BrasilFil: Vianez, J. L. S. G.. Evandro Chagas Institute; BrasilFil: Barbeito, Claudio Gustavo. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Cuervo, P.. Oswaldo Cruz Institute; BrasilFil: Bellio, M.. Universidade Federal do Rio de Janeiro; BrasilFil: Tanuri, A.. Universidade Federal do Rio de Janeiro; BrasilFil: Garcez, P. P.. Universidade Federal do Rio de Janeiro; Brasi
Call for emergency action to restore dietary diversity and protect global food systems in times of COVID-19 and beyond: Results from a cross-sectional study in 38 countries
Background: The COVID-19 pandemic has revealed the fragility of the global food system, sending shockwaves across countries' societies and economy. This has presented formidable challenges to sustaining a healthy and resilient lifestyle. The objective of this study is to examine the food consumption patterns and assess diet diversity indicators, primarily focusing on the food consumption score (FCS), among households in 38 countries both before and during the first wave of the COVID-19 pandemic. Methods: A cross-sectional study with 37 207 participants (mean age: 36.70 ± 14.79, with 77 % women) was conducted in 38 countries through an online survey administered between April and June 2020. The study utilized a pre-tested food frequency questionnaire to explore food consumption patterns both before and during the COVID-19 periods. Additionally, the study computed Food Consumption Score (FCS) as a proxy indicator for assessing the dietary diversity of households. Findings: This quantification of global, regional and national dietary diversity across 38 countries showed an increment in the consumption of all food groups but a drop in the intake of vegetables and in the dietary diversity. The household's food consumption scores indicating dietary diversity varied across regions. It decreased in the Middle East and North Africa (MENA) countries, including Lebanon (p < 0.001) and increased in the Gulf Cooperation Council countries including Bahrain (p = 0.003), Egypt (p < 0.001) and United Arab Emirates (p = 0.013). A decline in the household's dietary diversity was observed in Australia (p < 0.001), in South Africa including Uganda (p < 0.001), in Europe including Belgium (p < 0.001), Denmark (p = 0.002), Finland (p < 0.001) and Netherland (p = 0.027) and in South America including Ecuador (p < 0.001), Brazil (p < 0.001), Mexico (p < 0.0001) and Peru (p < 0.001). Middle and older ages [OR = 1.2; 95 % CI = [1.125–1.426] [OR = 2.5; 95 % CI = [1.951–3.064], being a woman [OR = 1.2; 95 % CI = [1.117–1.367], having a high education (p < 0.001), and showing amelioration in food-related behaviors [OR = 1.4; 95 % CI = [1.292–1.709] were all linked to having a higher dietary diversity. Conclusion: The minor to moderate changes in food consumption patterns observed across the 38 countries within relatively short time frames could become lasting, leading to a significant and prolonged reduction in dietary diversity, as demonstrated by our findings.Revisión por pare
Detecção por sorologia do Melon yellowing associated virus (MYaV) em áreas produtoras de melão no Nordeste brasileiro
Impacto das malformações congênitas na mortalidade perinatal e neonatal em uma maternidade-escola do Recife
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4
While the increasing availability of global databases on ecological communities has advanced our knowledge
of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In
the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of
Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus
crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced
environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian
Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by
2050. This means that unless we take immediate action, we will not be able to establish their current status,
much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio
Caesalpinioideae (leguminosae) lenhosas na Estação Ambiental de Volta Grande, Minas Gerais, Brasil
The TcI and TcII Trypanosoma cruzi experimental infections induce distinct immune responses and cardiac fibrosis in dogs
ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest
Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events. © 2019 The Authors. Ecology © 2019 The Ecological Society of Americ
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost