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

Escalas psicométricas como instrumentos de rastreamento para depressão em estudantes do ensino médio

CONTEXTO: A depressão é uma importante causa de suicídio em adolescentes. Portanto, são necessários instrumentos adequados para o rastreamento da depressão nessa população. OBJETIVO: Avaliar escalas de depressão como instrumentos de rastreamento para depressão em estudantes brasileiros do ensino médio. MÉTODOS: Estudo transversal. Três escalas (BDI, CES-D, e CRS) e um teste para avaliar sintomas psiquiátricos gerais (SRQ) foram aplicados individualmente a 503 estudantes do ensino médio com idades entre 15 e 17 anos. Os resultados foram comparados aos obtidos com os critérios de depressão maior do manual diagnóstico e estatístico de transtornos mentais (DSM-IV). RESULTADOS: A prevalência de depressão maior utilizando-se os critérios do DSM-IV foi de 10,9%. Adolescentes com depressão maior apresentaram escores significativamente mais altos (p = 0,001) no SRQ e nas três escalas avaliadas em comparação ao grupo sem depressão. A sensibilidade e a especificidade para identificar depressão pelo BDI, CES-D e CRS foram, respectivamente, 0,77 e 0,70, 0,75 e 0,73 e 0,82 e 0,71 (curva ROC). Os melhores pontos de corte foram 9 para o BDI, 10 para a CRS e 14 para a CES-D. A frequência de sintomas depressivos foi maior em meninas (aproximadamente 2:1). CONCLUSÃO: Esses achados indicam o uso do BDI, da CES-D e da CRS apenas para o rastreamento, ou como uma avaliação sintomática adicional, da depressão em estudantes do ensino médio. A diferença entre meninos e meninas com relação aos escores nas escalas alerta contra o uso dos mesmos valores de corte para ambos os sexos

Predictors of length of stay in an acute psychiatric inpatient facility in a general hospital: a prospective study

Objective: There have been significant reductions in numbers of psychiatric beds and length of stay (LOS) worldwide, making LOS in psychiatric beds an interesting outcome. The objective of this study was to find factors measurable on admission that would predict LOS in the acute psychiatric setting. Methods: This was a prospective, observational study. Results: Overall, 385 subjects were included. The median LOS was 25 days. In the final model, six variables explained 14.6% of the variation in LOS: not having own income, psychiatric admissions in the preceding 2 years, high Clinical Global Impression and Brief Psychiatric Rating Scale scores, diagnosis of schizophrenia, and history of attempted suicide. All variables were associated with longer LOS, apart from history of attempted suicide. Conclusions: Identifying patients who will need to stay longer in psychiatric beds remains a challenge. Improving knowledge about determinants of LOS could lead to improvements in the quality of care in hospital psychiatry