Manifestação clínica-epidemiológica do Schwannoma vestibular: uma revisão de literatura

O Schwannoma vestibular (SV) responde por 8% dos tumores intracranianos e por 90% dos tumores do ângulo ponto-cerebelar. Entretanto, ainda existem poucos trabalhos que abordem o quadro clínico desencadeado por esse tumor. O objetivo desse trabalho é descrever as manifestações clínicas e epidemiológicas do SV. Trata-se de uma revisão de literatura. Foram pesquisados artigos publicados entre 2012-2017 na base de dados Pubmed Central®. Os termos utilizadosna pesquisa desses artigos foram: 1) “schwannoma/vestibular”; 2) “acoustic/neuroma”. Foram identificados 1.497 e 1.016 artigos a partir dos termos 1 e 2, respectivamente. Entretanto, foram analisados apenas 60 artigos a partir de cada termo, totalizando um levantamento de 120 artigos. Com a exclusão das nove repetições, restaram 111 artigos. Esses artigos foram selecionados mediante a leitura do título e do resumo. Foram excluídos os artigos com desenho do tipo relato de caso. Desse modo, foram selecionados 14 artigos. Os dados analisados nesses artigos foram: 1) descrição conceitual do SV; 2) epidemiologia (porcentagem por sexo e faixa etária) do SV; 3) expressão percentual da prevalência dos sintomas do SV. Foram utilizados oito, sete e cinco artigos nas análises 1, 2 e 3, respectivamente. Esse trabalho observou que as mulheres são mais acometidas do queos homens, que a idade média mais afetada está entre a quinta e a sexta década de vida. Quanto aos sintomas, baixa auditiva e zumbidos são os mais frequentes, estando comumente associados. O conhecimento dessa epidemiologia é relevante para o aprimoramento do manejo clínico dos pacientes com SV.estibular Schwannoma (VS) accounts for 8% of intracranial tumors and 90% of cerebellar-angle tumors. However, there are still few studies that address the clinical manifestations triggered by this tumor. The objective of this work is to describe the clinical manifestations and epidemiological of VS. This is a literature review. We searched articles published between 2012-2017 in the Pubmed Central® database. The terms usedin the research of these articles were: 1) “schwannoma / vestibular”; 2) “acoustic / neuroma”. We identified 1,497 and 1,016 articles from terms 1 and 2, respectively. However, only 60 articles were analyzed from each term, totalizing a survey of 120 articles. With the exclusion of the nine replicates, 111 articles remained. These articles were selected by reading the title and abstract. Articles with a case report design were excluded. Thus, 14 articles were selected. The data analyzed in these articles were: 1) conceptual description of VS; 2) epidemiology (percentage by sex and age group) of VS; 3) percentage expression of the prevalence of VS symptoms. Eight, seven and five articles were used in analyzes 1, 2 and 3, respectively. This study observed that women are more affected than men, that the average age most affected is between the fifth and sixth decade of life. As for the symptoms, low hearing and tinnitus are the most frequent, being commonly associated. The knowledge of this epidemiology is relevant for the improvement of the clinical management of patients with VS

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

Seminário de Dissertação (2024)

Página da disciplina de Seminário de Dissertação (MPPP, UFPE, 2022) Lista de participantes == https://docs.google.com/spreadsheets/d/1mrULe1y04yPxHUBaF50jhaM1OY8QYJ3zva4N4yvm198/edit#gid=