Avaliação da superexpressão da proteína p53 e das mutações no éxon 8 do gene TP53 em carcinomas mamários caninos e glândulas normais

Este estudo foi realizado com o objetivo de avaliar a expressão da proteína p53, pela técnica de imuno-histoquímica, em neoplasmas mamários malignos em cadelas, além de investigar mutações no éxon 8 do gene supressor Tp53 por meio do padrão de bandas obtidas por PCR-RFLP. Dezenove mamas de cadelas saudáveis foram usadas como controle (Grupo 1). Amostras de 18 casos de tumores malignos (Grupo 2) e suas glândulas mamárias contralaterais (Grupo 3) foram obtidas na rotina do Hospital Veterinário da UFRPE. Os tumores foram identificados histologicamente e classificados em graus de malignidade. O método da estreptoavidina-biotina peroxidase foi utilizado para a análise da expressão de p53 por imuno-histoquímica, de acordo com a localização e intensidade da coloração. A expressão da proteína p53 não foi observada nas amostras do Grupo 1, mas foi encontrada em todas as amostras de tumores malignos (Grupo 2) seja só no núcleo, ou também no citoplasma. No Grupo 3, a expressão foi observada em quatro amostras normais e em duas que apresentavam tumor. Para a análise molecular, o DNA genômico foi extraído e submetido à PCR-RFLP com as seguintes endonucleases: AluI, BsoBI, DdeI e SmaI. O padrão de bandas foi polimórfico entre os grupos, mas não entre as variantes tumorais. Esse polimorfismo detectou mutações no fragmento estudado - éxon 8 do gene Tp53 - que podem resultar em alterações nos nucleotídeos, localizados nos sítios de restrição das enzimas. Esses achados levam a conclusão de que a imunoexpressão da p53 não tem relação com o subtipo histológico ou grau de malignidade do tumor, mas sim com a presença dos tumores no tecido mamário de cadelas. A PCR-RFLP pode ser usada como importante ferramenta para o estudo da carcinogênese mamária na cadela, possibilitando gerar diagnósticos precoces através do polimorfismo obtido com endonucleases de restrição pré-selecionadas

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