Efeitos do fumo materno durante a gestação e complicações perinatais.

Objetivos: avaliar o perfil de puérperas do Hospital de Clínicas de Porto Alegre (HCPA), a prevalência do tabagismo entre elas e consequências deste hábito no binômio mãe-bebê.Métodos: estudo prospectivo, transversal. Incluídas pacientes hígidas, com gestação a termo, excluídas pacientes com recém-nascidos (RNs) com crescimento intrauterino restrito, gestações múltiplas, bebês com anormalidades cromossômicas, malformações, infecção intrauterina e dados incompletos. Variáveis contínuas descritas por medidas de tendência central e dispersão (média e DP padrão ou mediana e amplitudes interquartis); variáveis categóricas como freqüências absolutas e relativas. Projeto aprovado pelo GPPG do HCPA. Todas as pacientes assinaram termo de consentimento informado. Resultados: 718 puérperas, 23% eram tabagistas na gestação. Não houve diferença estatisticamente significativa com relação à idade materna, número de cesarianas ou abortos e idade gestacional no momento do parto. Maior número de gestações prévias, ser solteira/separada, não branca, menor escolaridade e não realizar pré-natal foram fatores de risco para o tabagismo na gravidez. O peso dos RNs foi estatisticamente menor no grupo das gestantes tabagistas, com variação média de 143g a menos nesse grupo. O número de bebês pequenos para idade gestacional foi significativamente maior no grupo de gestantes fumantes. Evolução do bebê, peso da placenta e índice de Apgar não houve diferença estatística entre os grupos.Conclusão: o estudo foi relevante para o conhecimento do perfil das puérperas fumantes do HCPA e apontou a importância da realização de pré-natal e busca de estratégias de tratamento para estas pacientes com prevenção de complicações gestacionais e perinatais

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