Avaliação epidemiológica de tumores hematológicos de pacientes internados em hospital terciário em Goiânia (GO) no período de agosto de 2022 a agosto de 2023

As doenças neoplásicas originam-se do acúmulo de mutações e crescimento celular desordenado, que se manifesta na forma de neoplasia ou tumor. Dentre as neoplasias, destacam-se as hematológicas - classificadas em mieloides, linfoides e subgrupos mais raros -, caracterizadas por terem prognóstico ruim, gravidade imediata e alta morbimortalidade, além de complexidade diagnóstica. Nesse viés, o objetivo do presente estudo é avaliar o perfil epidemiológico dos cânceres hematológicos de pacientes internados em hospital terciário onco-hematológico em Goiânia - GO no período de internação de agosto de 2022 a agosto de 2023. O trabalho configura-se como um estudo analítico, observacional e retrospectivo, de caráter quantitativo, que será realizado a partir da coleta de dados de prontuários eletrônicos para posterior análise, estratificação e correlação. Os dados coletados serão: subtipo de neoplasia; faixa etária; sexo; e desfecho prognóstico (tempo de internação, alta hospitalar ou óbito). Destarte, espera-se conhecer e descrever o perfil epidemiológico das neoplasias malignas hematológicas, a partir disso, será necessário relacionar a frequência dos subtipos com a idade e o sexo, além de associar os dados aos desfechos clínicos das neoplasias durante a internação dos pacientes. Assim, ao entender o comportamento das neoplasias hematológicas no âmbito da internação do grupo amostral, será possível identificar medidas direcionadas para a melhora de métodos de diagnósticos e de tratamento precoce

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

Fatores relacionados a vulnerabilidade no contexto da pandemia de COVID-19: uma revisão integrativa

A pandemia da COVID-19 afetou tanto epidemiologicamente quanto socialmente as camadas mais vulneráveis da sociedade mundial. Nesse cenário, a atual revisão integrativa, teve como objetivo determinar como a COVID-19 atinge as zonas vulneráveis da população. Para a seleção dos artigos científicos utilizou-se como banco de dados National Library of Medicine and National Institutes of Health (PUBMED), Biblioteca Virtual em Saúde (BVS) e Literatura Latino-Americana e do Caribe em Ciências da Saúde (LILACS) e o exemplar dessa revisão institui-se de 18 artigos originais. Consecutivo a análise dos artigos contidos nesse estudo, os resultados dos estudos salientam que os prejuízos da pandemia envolvem vários fatores sociais, econômicos e epidemiológicos, além de um alarmante dano em regiões que predomina indivíduos marginalizados. Em face ao exposto, conclui-se que há uma relação entre a COVID-19 e as regiões vulnerabilizadas. Dessa forma, os elevados níveis de mortalidade, juntamente com estragos econômicos, sociais e políticos, provocados pela rápida proliferação do SARS-CoV-2, estabelecem a atual realidade mundial