Terapia endoscópica como procedimento diagnóstico e terapêutico no controle da hemorragia digestiva alta: Endoscopic therapy as a diagnostic and therapeutic procedure in the control of upper digestive hemorrhage

A hemorragia digestiva Alta (HDA), classificada como varicosa e não varicosa, trata-se de um sangramento originado do trato gastrointestinal próximo ao ângulo de Treitz, capaz de envolver o esôfago, estômago e duodeno. Mesmo com os avanços na ciência em relação a diagnósticos, a HDA está entre as emergências mais frequentes em serviços de saúde brasileiros, existindo a necessidade de atendimento mais ágil associado a um diagnóstico adequado, afim de evitar que o paciente venha a óbito. Dentre os principais fatores relacionados a HDA é possível citar a doença ulcerosa péptica e também as varizes esofágicas. Sabe-se que o sangramento de úlceras pépticas representa 25% dos casos de HDA e apresenta fatores de risco que contribuem para os sangramentos, quais sejam: infecção por Helicobacter pylori, utilização de anti-inflamatórios (AINEs), estresse e hipersecreção ácida. Por outro lado, as varizes esofágicas encontram-se presentes em 50% dos pacientes cirróticos, estando relacionada também a quadros de hipertensão portal. Por certo, o risco de sangramento de varizes esofágicas está intimamente relacionado ao tamanho, grau de disfunção hepática e também a presença de marcas vermelhas. Diante da grande variabilidade de achados clínicos, o diagnóstico etiológico é definido mediante a realização da endoscopia digestiva alta, devendo ser realizada de forma precoce, a partir da estabilização do paciente. É sabido que, no momento do diagnóstico de uma lesão sangrante a terapia endoscópica deverá ser imediatamente executada, a fim de promover a hemostasia e prevenir a recorrência do sangramento em grande parte dos pacientes

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