Sequestro hepático: descrição de um tipo de acometimento hepático na anemia falciforme

A anemia falciforme é uma doença genética autossômica recessiva muito prevalente em países com alta taxa de miscigenação, como o Brasil. Estima-se entre 25 mil e 30 mil brasileiros portadores da condição em homozigose e 7 milhões portadores do traço falciforme, portanto, heterozigotos. Indivíduos homozigotos ou portadores de hemoglobina S somada a outra hemoglobinopatia apresentam, ao longo da vida, complicações da doença. O envolvimento hepático está presente em 10 a 40% das complicações da anemia falciforme. Sequestro hepático agudo é uma das possíveis manifestações desse envolvimento, constituindo entidade clínica rara, de difícil reconhecimento. O quadro clínico mais frequente é dor no quadrante superior direito do abdome e hepatomegalia. Nos exames de laboratório, há evidência de anemia hemolítica por diminuição da hemoglobinemia, aumento da desidrogenase lática e da bilirrubinemia. O tratamento se baseia em analgesia e reposição volêmica que inclui transfusão de hemocomponentes, evitando hipoperfusão tecidual e coagulopatia. O objetivo do presente trabalho consiste em relatar o caso de uma paciente do sexo feminino de 23 anos de idade, apresentando-se com tosse seca e dor torácica com uma semana de evolução e palpação dolorosa de massa no quadrante inferior direito do abdome. Os exames complementares de sangue e de imagem corroboraram a hipótese diagnóstica, além de evidenciarem atrofia de baço. A paciente evoluiu com remissão dos sintomas e do quadro de hemólise ao longo dos 17 dias de internação, durante os quais foram transfundidos um total de 6 concentrados de hemácias fenotipados. Recebeu alta com previsão de acompanhamento ambulatorial com hematologista

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