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

AVALIAÇÃO DO RISCO DE DOENÇA RENAL EM PACIENTES COM HIV EM TERAPIA ANTIRRETROVIRAL: IMPORTANTE IMPACTO DA BETA2MICROGLOBULINA NA DETECÇÃO PRECOCE

Introdução/Objetivo: O objetivo deste estudo retrospectivo foi avaliar o risco de doença renal em pacientes vivendo com HIV e em uso de terapia antirretroviral, por meio da mensuração do biomarcador beta2microglobulina. Métodos: Foi realizado um estudo retrospectivo não randomizado com amostra por conveniência, incluindo pacientes em acompanhamento em um ambulatório especializado que realizaram dosagem de beta2microglobulina como parte de seu exame de rotina. Resultados: Foram avaliados 160 pacientes, sendo 66% do sexo masculino, com média de idade de 50,4 anos. A média de contagem de células CD4 foi de 634 cels/mm3, e 87,5% dos pacientes apresentaram carga viral abaixo do limite mínimo de detecção. A média de clearance de creatinina (ClCr) foi de 82, e a média de beta2microglobulina (b2m) foi de 2,5, sendo que 59% dos pacientes apresentaram valores acima do limite de referência (>2). Uso de terapia dupla 3TC/DTG, DTG/DRVr e ETV/DRVr as médias de ClCr 58,7 e b2m 2,9, sendo 86% >2. Diferença entre pacientes sem comorbidades (médias b2m 2,2 sendo 46,7% >2 e ClCr 87,3) e portando diabetes mellitus (DM) e/ou hipertensão arterial (HAS) (médias b2m 2,5 sendo 72% >2 e ClCr 79,5). PVH com Doença renal crônica (médias b2m 3,5 sendo 90% >2 e ClCr 52,7). Esquemas com AZT/ABV (médias b2m 2,6 sendo 100% >2 e ClCr 89) e TDF (médias b2m 2,2 sendo 52% >2 e ClCr 86,5). No que diz respeito aos esquemas de terapia contendo DTG, EFZ/VNP, DRVr e ATVr, observou-se em uso DTG (médias b2m 2,3 sendo 60% >2 e ClCr 76,3), EFZ/VNP (médias b2m 2,1 sendo 57% >2 e ClCr 90,4), DRVr (médias b2m 2,5 sendo 66% >2 e ClCr 85,8) e ATVr (médias b2m 2,8 sendo 52% >2 e ClCr 88,4). Os pacientes que apresentavam alteração de ClCr já estão em uso de terapias com menor toxicidade, mas é possível perceber elevação precoce em pacientes em uso de esquemas com nefrotoxicidade como TDF e ATVr, bem como em portadores de DM e HAS. Conclusão: Os resultados obtidos neste estudo demonstram a relevância da mensuração do biomarcador beta2microglobulina na avaliação do risco de doença renal em pacientes com HIV em uso de terapia antirretroviral, representando importante marcador na detecção precoce principalmente em pacientes com comorbidades e esquemas contendo medicações nefrotóxicas