Mapa conceitual e desafios da promoção à saúde na pandemia / Conceptual map and challenges of health promotion in pandemia

Introdução: A pandemia do covid-19 surgiu como uma emergência pública mundial trazendo para a promoção à saúde novos desafios na construção da integralidade no atendimento dos indivíduos e dos serviços de saúde. Objetivo: Traçar as perspectivas diante dos desafios da pandemia e sua contenção, em um mapa conceitual da promoção à saúde na Atenção Primária. Metodologia: Foi estruturada em um artigo de opnião com reflexões por meio de experiências obtidas durante a pandemia do COVID-19 na atenção primária e os seus desafios no fortalecimento dos eixos para a promoção da saúde. Resultados: Foram evidentes os desafios relacionados as perdas multifatoriais e multidimensionais de natureza social, física, psicológica e econômica adquiridos na pandemia. Conclusão: Para lidar com as consequências negativas desta pandemia, é imperioso um planejamento de saúde estratégico e multidisciplinar que deve estar sempre articulado e pautado de forma permanente para visar uma promoção efetiva da saúde

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

Amplificação e seqüenciamento de regiões de repetições em seqüências de DNA codificadoras de diferentes cepas de Mycoplasma hyopneumoniae

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Amplificação e seqüenciamento de regiões de repetições em seqüências de DNA codificadoras de diferentes cepas de Mycoplasma hyopneumoniae

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