HEMATOMA ESPONTÂNEO DE RETO ABDOMINAL: RELATO DE CASO

Introdução: O Hematoma espontâneo do músculo reto abdominal é uma condição incomum, causada pela concentração de sangue em sua própria bainha muscular e sem associação com traumas, embora outras condições tivessem sido relacionadas à entidade. O quadro clínico geralmente é inespecífico e possui semelhança com outras condições abdominais agudas, o que requer, na maioria das vezes, confirmação diagnóstica através de exame de imagem, evitando desta forma intervenções cirúrgicas desnecessárias. Objetivo: Descrever um caso clínico desta entidade pouco comum na prática médica, ocorrido e conduzido no Hospital Escola Luiz Gioseffi Jannuzzi da Faculdade de Medicina de Valença/RJ. Relato de caso: Mulher, idosa, queixando-se de dor mesogástrica e em flancos há cerca de uma semana, sem fatores desencadeantes ou de melhora, associada ao surgimento de equimoses periumbilical e em flancos há dois dias. Ao exame, havia abaulamento assimétrico em mesogástrio mais pronunciado à esquerda, sinais de Laffond e Fothergill e massa palpável volumosa, dolorosa, fixa, bem delimitada e mantida com a manobra de Smith-Bates. TC de abdome sugeriu hematoma espontâneo de reto abdominal, entidade de conduta expectante. Após manejo das demais condições clínicas, recebeu alta hospitalar em cerca de duas semanas após internação. Conclusão: O Hematoma espontâneo do músculo reto abdominal ainda é uma entidade pouco enfatizada, devido ao pequeno número de casos descritos. É importante tê-la em mente para sua correta identificação e conduta, evitando assim possíveis iatrogenias

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