Relação entre terapia de reposição hormonal no climatério e o desenvolvimento de Neoplasias

O climatério é o período de transição entre a fase reprodutiva e não reprodutiva das mulheres, caracterizado por alterações hormonais que afetam o ciclo menstrual. A menopausa, definida como o último ciclo menstrual, marca o fim dessa fase. Durante o climatério, ocorrem diversos sintomas e a terapia de reposição hormonal (TRH) é uma opção de tratamento que consiste na reposição dos hormônios através de diferentes vias de administração. Estudos divergem quanto aos benefícios e riscos da TRH, especialmente em relação ao câncer de mama, mas enfatizam a importância do acompanhamento médico e reavaliação periódica dos benefícios e malefícios do tratamento. O presente artigo trata-se de uma revisão de literatura integrativa e tem como objetivo estabelecer uma relação entre a reposição de terapia hormonal no climatério e o desenvolvimento ou não de neoplasias. Utilizou-se para a pesquisa as bases de dados PubMed, SCIELO e LILACS, considerando artigos publicados nos últimos cinco anos (2018-2023). Os descritores "Climacteric", "Neoplasms" e "Hormone replacement therapy" foram combinados através do operador booleano "AND". Foram selecionados 27 artigos e após aplicação dos critérios de inclusão e exclusão, bem como a análise dos objetivos, 18 artigos foram selecionados. Com base na literatura pesquisada, verifica-se que o risco de câncer no ovário ou endométrio não está relacionado aos níveis metabólicos de estrogênio em mulheres que receberam terapia combinada de estrogênio/progesterona, mas o uso isolado de estrogênio pode aumentar os desfechos negativos, especialmente em mulheres obesas.  Tratando-se o câncer de mama, estudos apontam a relação entre a TRH com o surgimento do mesmo

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