Remoção da citotoxicidade no ensaio de atividade estrogênica (YES) para amostras de sedimento lagunar: Métodos de extração e efeito matriz

A poluição de sistemas aquáticos com contaminantes emergentes é uma crescente preocupação. Dentre estes, os desreguladores endócrinos (DE) são substâncias que podem alterar o sistema endócrino de seres vivos até em baixas concentrações. Suas características físico-químicas indicam afinidade com matéria orgânica, sendo relevante o estudo de sedimentos. A atividade estrogênica pode ser avaliada pelo ensaio in vitro YES, porém matrizes ambientais complexas podem apresentar citotoxicidade e interferir no resultado do ensaio. Este estudo objetivou avaliar métodos de preparo de amostras de sedimento utilizando extração em fase sólida para remoção de compostos citotóxicos no ensaio in vitro YES. O uso isolado de EDTA para remoção de metais não foi eficiente para reduzir a citotoxicidade, enquanto a remoção foi completa com o cartucho SAX. Conclui-se que o uso combinado de cartuchos foi a técnica mais viável para a avaliação da atividade estrogênica de amostras de sedimento com ensaio YES

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

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

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

Rare earth elements as sediment contamination tracers in a coastal lagoon in the state of Rio de Janeiro, Brazil

Background: This study aimed to evaluate the environmental quality of Padre Lagoon, an urban coastal lagoon in Southeastern Rio de Janeiro, Brazil, employing rare earth elements (REEs) as tracers. Aim: The present study aimed to quantify REEs contents from Padre Lagoon to better ascertain the antropogenic environmental impact Methods: Surface sediments and one sediment core were investigated using a mass spectrometer. 14C radiocarbon dating was performed in the sediment core. Particle size analysis were made in the sediment core and the surface sediments. The Igeo index was used to isolates anthropogenic pollution. All statistical analyses were performed using the R program. Results: Sediments varied between coarse sand at different amounts. All analyzed samples contained detectable REEs, with a higher accumulation observed at the sediment core from 60 cm depth below the surface (roughly 320 cal yr BP) and, more significantly, in the upper 10 cm depth. The geoaccumulation index determined in both in the sediment core and in the surface samples indicated the presence of anthropogenic activity in the studied area. The statistical analysis showed a integration between rare earth elements and sandy mud lithology. Conclusion: The determined elements presented Igeo values indicative of slight environmental contamination. No industrial development complex is present in the study area, indicating that the main REE source to this environment may be industries located in the city of Rio de Janeiro through intensified atmospheric transport, carrying particles over long distances, potentially leading to significant biota and human health impacts in coastal environments

Factors associated to mortality in children with critical COVID-19 and multisystem inflammatory syndrome in a resource-poor setting

Abstract SARS-CoV-2 infection in children is usually asymptomatic/mild. However, some patients may develop critical forms. We aimed to describe characteristics and evaluate the factors associated to in-hospital mortality of patients with critical COVID-19/MIS-C in the Amazonian region. This multicenter prospective cohort included critically ill children (1 mo–18 years old), with confirmed COVID-19/MIS-C admitted to 3 tertiary Pediatric Intensive Care Units (PICU) in the Brazilian Amazon, between April/2020 and May/2023. The main outcome was in-hospital mortality and were evaluated using a multivariable Cox proportional regression. We adjusted the model for pediatric risk of mortality score version IV (PRISMIV) score and age/comorbidity. 266 patients were assessed with 187 in the severe COVID-19 group, 79 included in the MIS-C group. In the severe COVID-19 group 108 (57.8%) were male, median age was 23 months, 95 (50.8%) were up to 2 years of age. Forty-two (22.5%) patients in this group died during follow-up in a median time of 11 days (IQR, 2–28). In the MIS-C group, 56 (70.9%) were male, median age was 23 months and median follow-up was 162 days (range, 3–202). Death occurred in 17 (21.5%) patients with a median death time of 7 (IQR, 4–13) days. The mortality was associated with higher levels of Vasoactive Inotropic-Score (VIS), presence of acute respiratory distress syndrome (ARDS), higher levels of Erythrocyte Sedimentation Rate, (ESR) and thrombocytopenia. Critically ill patients with severe COVID-19 and MIS-C from the Brazilian Amazon showed a high mortality rate, within 12 days of hospitalization