Orientações para Realização de Exames de Ressonância Magnética Nuclear em Pacientes com Dispositivos Eletrônicos Cardíacos

Estima-se que até 75% dos pacientes com dispositivos cardíacos eletrônicos implantáveis (DCEIs) terão indicação de exame de ressonância nuclear magnética (RNM) ao longo da vida. Pelas características dos dispositivos, esses foram excluídos historicamente do rol de pacientes considerados elegíveis ao exame

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

Liver multi-parametric magnetic resonance in non-alcoholic fatty liver disease patients

Introdução: a esteato-hepatite não alcoólica (EHNA) é uma das principais causas de doença hepática crônica, intimamente relacionada à obesidade e à síndrome metabólica, representando um risco significativo de inflamação e fibrose deste órgão para os pacientes. Para melhorar o diagnóstico e monitoramento desses pacientes, as modalidades de imagem estão sendo utilizadas no intuito de desenvolver biomarcadores não invasivos dessas patologias. Neste estudo, foram avaliados pacientes com EHNA utilizando relaxometria de multicomponente (RMC) por ressonância magnética (RM) visando validar biomarcadores de imagem para fibrose, inflamação, esteatose e siderose. Resultados: pacientes com diagnóstico de EHNA por biópsia percutânea (n = 106) foram selecionados prospectivamente para um exame de ressonância magnética quantitativa, que dura menos de 10 minutos, juntamente com 16 voluntários saudáveis, selecionados após exclusão laboratorial de doença hepática destes últimos. O parâmetro de RMC mensurando a fração de água extracelular não vascular (FAECnv) foi correlacionado significativamente com o estadio de fibrose na histologia, obtendo coeficiente de correlação de Spearman (rs) de 0,73, sem sobreposição nos intervalos de confiança de 95% de cada estágio. A fibrose inicial poderia ser distinguida com uma área abaixo da curva (AUROC) de 0,95 (especificidade de 94%; sensibilidade de 87%). Balonização e inflamação lobular também foram significativamente correlacionados com a FAECnv (rs = 0,65 e 0,52, respectivamente). A esteatose foi significativamente correlacionada com a fração de gordura pela densidade de prótons (rs = 0,72), e a esteatose inicial pode ser distinguida de voluntários normais com uma AUROC de 0,98 (limiar de 5%; especificidade de 98%; sensibilidade de 88%). A siderose também foi significativamente correlacionada com R2* (rs= 0,52). Conclusões: Este estudo valida uma técnica de ressonância magnética multiparamétrica para discriminação precisa de fibrose inicial, inflamação, esteatose e siderose em pacientes com EHNABackground and Aims: non-alcoholic steatohepatitis (NASH) is one of the main causes of chronic liver disease, closely related to obesity and the metabolic syndrome, posing a significant risk to patients from inflammation and fibrosis. To better diagnose and monitor these patients, imaging modalities are being used to develop non-invasive biomarkers of these pathologies. In this study, we evaluate NASH patients using magnetic resonance imaging (MRI) multi-component relaxometry (MCR), with the goal to validate imaging biomarkers for fibrosis, inflammation, steatosis and siderosis. Results: patients with a NASH diagnosis by needle biopsy (n=106) were prospectively selected for a quantitative MRI exam taking less than 10 minutes, along with 16 healthy volunteers with laboratory exclusion of liver disease. The MCR parameter quantifying the extra-cellular nonvascular water fraction (ECNVwf) was significantly correlated with histology fibrosis staging, with a Spearmans correlation coefficient (rs) of 0.73, and no overlap in the 95% confidence intervals of each stage. Initial fibrosis could be distinguished with an AUROC of 0.95 (specificity of 94%; sensitivity of 87%). Ballooning and lobular inflammation were also significantly correlated with ECNVwf (rs = 0.65 and 0.52, respectively). Steatosis was significantly correlated with the proton density fat fraction (rs = 0.72), and initial steatosis could be distinguished with an AUROC of 0.98 (5% threshold; specificity of 98%; sensitivity of 88%). Siderosis was also significantly correlated with R2* (rs = 0.52). Conclusions: this study validates a multi-parametric MRI technique for accurate discrimination of initial fibrosis, inflammation, steatosis and siderosis in NASH patient