Efeitos do exercício de tronco na recuperação funcional da marcha em pacientes pós acidente vascular cerebral: uma revisão sistemática

Introdução: o acidente vascular cerebral (AVC), acarreta deficiências que variam em função da topografia, tipo e extensão da lesão, mas na maioria das vezes apresentam alterações cognitivas, motoras, sensoriais e autonômicas variadas. Entre essas alterações, a força de tronco reduzida após o AVC aumenta o trabalho mecânico para a marcha, repercutindo na capacidade funcional do paciente. Objetivo: investigar quais as técnicas mais utilizadas para o fortalecimento de tronco. Método: foram realizadas buscas nas bases de dados: Pubmed, Bireme, Scielo e PEDro. Foram incluídos estudos clínicos randomizados e controlados que compararam os efeitos do fortalecimento de tronco na marcha em pacientes acometidos pelo AVC. Resultados: dos 109 estudos encontrados, 10 foram selecionados e avaliados pela escala PEDro, e apresentaram boa qualidade metodológica. A intervenção mais encontrada foram os exercícios de estabilidade dos musculos mais profundas do tronco e da pelve. Conclusão: os exercícios de tronco influenciam de forma positiva aspectos importantes da marcha

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

Núcleos de Ensino da Unesp: artigos 2008

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq