Síndromes hipertensivas específicas da gestação em adolescentes e suas repercussões maternas e perinatais: uma revisão integrativa de literatura / Hypertensive syndromes specific to pregnancy in adolescents and their maternal and perinatal repercussions: an integrative literature review

A vida sexual atualmente está iniciando de forma mais precoce, em torno dos 10 aos 14 anos e nem sempre os adolescentes avaliam os riscos sobre a prática. As síndromes hipertensivas específicas gestacionais (SHEG) são a segunda causa de mortalidade materna em todo o mundo, superadas apenas pelas hemorragias. Somado a isso, podem culminar em encefalopatia, comprometimento cardíaco e renal, e coagulopatias. No Brasil, as SHEG são consideradas a primeira causa de mortalidade materna, acarretando cerca de 5 a 17% das gestantes. Sendo assim, as pacientes jovens possuem um duplo fator de risco para o desenvolvimento da doença as quais são a primiparidade e a gravidez precoce. Este trabalho teve como objetivos identificar as repercussões maternas e perinatais ocasionadas pelas síndromes hipertensivas específicas da gravidez em adolescentes, caracterizar os fatores de risco associados às SHEG nesse perfil de gestante e analisar os desfechos materno-fetais. O presente trabalho trata-se de uma revisão integrativa de literatura cuja bibliografia levantada ocorreu por meio da busca nas bases de dados Google Acadêmico, Scientific Electronic Library Online- SCIELO e PubMed utilizando publicações entre 2013 a 2020 e com os seguintes descritores: “síndromes hipertensivas”, “hipertensão gestacional”, “gravidez na adolescência”, “gestational hypertension”, “hypertensive disease” e “eclampsia”. A gestante adolescente já é caracterizada como gestação de alto risco a qual é imprescindível uma assistência médica de qualidade para que as complicações originadas pelas SHEG sejam minimizadas ou evitadas

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, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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