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

Cortisol basal em asmáticos em uso de duas diferentes doses de propionato de fluticasona

OBJETIVOS: Avaliar os valores de cortisol basal em asmáticos persistentes em uso de propionato de fluticasona inalatório na dose de 200 ou 300 mcg/dia. MÉTODOS: O diagnóstico e a classificação da gravidade da asma basearam-se nas recomendações do Global Initiative for Asthma. Pacientes menores de 11 anos receberam fluticasona na dose de 200 mcg/dia, e aqueles com mais de 11 anos receberam 300 mcg/dia. Após 10 semanas de tratamento, a dosagem do cortisol foi realizada para avaliação da função adrenal. RESULTADOS: Foram avaliados 41 pacientes (65,9% do sexo masculino) entre 6 e 18 anos. Não houve diferença significativa entre as médias de cortisol basal nos pacientes que receberam 200 mcg/dia de propionato de fluticasona (n = 13) e naqueles que receberam 300 mcg/dia (n = 28). CONCLUSÕES: Os achados mostram que doses baixas a moderadas de propionato de fluticasona não causam supressão adrenal

Refluxo gastroesofágico e asma na infância: um estudo de sua relação através de monitoramento do pH esofágico Gastroesophageal reflux and asthma in childhood: a study on their relationship using esophageal PH monitoring

OBJETIVOS: Este trabalho tem como objetivo verificar a prevalência do refluxo gastroesofágico em crianças com asma e avaliar se o índice de refluxo tem uma boa sensibilidade e especificidade para o diagnóstico de refluxo gastroesofágico. MÉTODOS: Foram estudadas 69 crianças de 1 a 5 anos, com asma, através do exame de pHmetria de 24 horas. RESULTADOS: A idade das crianças variou de 12,4 a 63,1 meses, com uma média de 30,79, sendo que 62,3% eram do sexo masculino. O refluxo gastroesofágico foi observado em 68,1% das crianças. Quando separados os pacientes em duas categorias (asma moderada e grave), a associação foi de 58,5 e 82,1%, respectivamente. O refluxo gastroesofágico oculto ocorreu em 31,8% dos casos. O índice de refluxo mostrou uma sensibilidade de 89,4%, especificidade de 95,5%, valor preditivo positivo de 97,7% e valor preditivo negativo de 80,8%. CONCLUSÕES: Os resultados obtidos neste estudo indicam uma elevada associação entre o refluxo gastroesofágico e a asma e sugerem que o índice de refluxo, como parâmetro único, tem uma boa sensibilidade e especificidade para o diagnóstico da doença do refluxo gastroesofágico.<br>OBJECTIVES: This study aims at verifying the prevalence of gastroesophageal reflux in asthmatic children, and at determining the sensitivity and specificity of the reflux index for the diagnosis of gastroesophageal reflux disease. METHODS: Sixty-nine children, aged 1-5 years, with asthma, were studied by 24-hour pH monitoring. The patients were randomly selected. RESULTS: Ages varied from 12.4 to 63.1 months, mean age = 30.79 months, and 62.3% were males. Gastroesophageal reflux was observed in 68.1% of the children. The patients were divided into two groups, moderate and severe asthma, and gastroesophageal reflux was diagnosed in 58.5 and 82.1% of the cases, respectively. Occult gastroesophageal reflux occurred in 31.8% of the cases. The reflux index showed an sensitivity of 89.4%, specificity of 95.5%, positive predictive value of 97.7% and negative predictive value of 80.8%. CONCLUSIONS: The results of this study indicate a relationship between gastroesophageal reflux and asthma, and suggest that the reflux index as a single parameter of pH monitoring has good sensitivity and specificity for the diagnosis of gastroesophageal reflux disease