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

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

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

Epidemiology of hydrocephalus in Brazil

Objective: Describe the epidemiological profile and social-economic burden that hydrocephalus patients represent to the national public health system, using data available at the online database of the Brazilian Health Ministry (DataSUS). Methods: This is a populational study based on descriptive statistics of all clinical and surgical appointments included in the DataSUS database. Data included herein were collected between 2015 and 2021 and subdivided into three main groups, related to hydrocephalus incidence and mortality, hospitalizations, and financial costs. Results: In the study period, 3993 new cases of congenital hydrocephalus were diagnosed, with 6051 deaths overall. The mortality rate in the country was 1.5/100000 live births and the prevalence was 0.374/100000 inhabitants. The number of hospitalizations resulting from treatment procedures and complications of hydrocephalus was 137,880 and there was a reduction of up to 27.2% during the SARS-CoV-2 pandemics concerning previous years. Total costs for hydrocephalus management in the country amounted to 140,610,585.51 dollars. Conclusions: Hydrocephalus has a significant impact on public health budgets and pediatric mortality rates; however, it is probably underestimated, due to the paucity of demographic data and epidemiological studies in Latin America and, specifically, in Brazil. The dataSUS also has several limitations in accessing certain data related to hydrocephalus, making it difficult to have a more assertive understanding of the disease in Brazil. The results of this study provide important guidance for future research projects in clinical and experimental hydrocephalus and also the creation of public policies for better governance and care of hydrocephalus patients