Ideação suicida, ansiedade e depressão em pacientes com esclerose múltipla

Transtornos psiquiátricos frequentemente ocorrem em pacientes com esclerose múltipla (EM). No entanto, os artigos sobre estas comorbidades são limitados. Pretendemos investigar as relações entre EM, ansiedade, depressão e ideação suicida. Métodos: Cento e trinta e dois pacientes com EM remitente-recorrente foram avaliados usando a Escala de Estado de Incapacidade Expandida, Inventário de Depressão de Beck-II (IDB-II), Escala de Beck para Ideação de Suicídio (BSI) e Escala de Ansiedade e Depressão. Resultados: Uma análise de regressão hierárquica foi realizada para avaliar as variáveis. A equação de regressão previu significativamente o escore BSI (R2 = 0,306; R2 ajustado = 0,273; F (9,125) = 9,18; p < 0,0005) e o escore no IDB-II foi a única variável que contribuiu significativamente para este modelo (p < 0,0005). Conclusões: Uma alta prevalência de depressão e ansiedade e uma maior taxa de ideação suicida foram identificadas em pacientes com EM em comparação com a população em geral. A presença de sintomas depressivos pareceu ter uma influência direta no risco de suicídio.Psychiatric disorders frequently occur in patients with multiple sclerosis (MS); however, limited reports are available on these comorbidities. We aimed to investigate the relationships among MS, anxiety, depression, and suicidal ideation. Methods: One hundred and thirty two patients with relapsing-remitting MS were evaluated using the Expanded Disability Status Scale, Beck Depression Inventory-II (BDI-II), Beck Scale for Suicide Ideation (BSI), and Hospital Anxiety and Depression Scale. Results: A hierarchical regression analysis was performed to evaluate the variables. The regression equation significantly predicted the BSI score (R2 = 0.306; adjusted R2 = 0.273; F (9, 125) = 9.18; p < 0.0005), and the BDI-II score was the only variable that contributed significantly to this model (p < 0.0005). Conclusions: A high prevalence of depression and anxiety, and a higher rate of suicidal ideation were identified in MS patients compared to the general population. The presence of depressive symptoms appeared to have a direct influence on the risk of suicide

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

Suicidal ideation, anxiety, and depression in patients with multiple sclerosis

ABSTRACT Psychiatric disorders frequently occur in patients with multiple sclerosis (MS); however, limited reports are available on these comorbidities. We aimed to investigate the relationships among MS, anxiety, depression, and suicidal ideation. Methods: One hundred and thirty two patients with relapsing-remitting MS were evaluated using the Expanded Disability Status Scale, Beck Depression Inventory-II (BDI-II), Beck Scale for Suicide Ideation (BSI), and Hospital Anxiety and Depression Scale. Results: A hierarchical regression analysis was performed to evaluate the variables. The regression equation significantly predicted the BSI score (R2 = 0.306; adjusted R2 = 0.273; F (9, 125) = 9.18; p < 0.0005), and the BDI-II score was the only variable that contributed significantly to this model (p < 0.0005). Conclusions: A high prevalence of depression and anxiety, and a higher rate of suicidal ideation were identified in MS patients compared to the general population. The presence of depressive symptoms appeared to have a direct influence on the risk of suicide

Suicidal ideation, anxiety, and depression in patients with multiple sclerosis

<div><p>ABSTRACT Psychiatric disorders frequently occur in patients with multiple sclerosis (MS); however, limited reports are available on these comorbidities. We aimed to investigate the relationships among MS, anxiety, depression, and suicidal ideation. Methods: One hundred and thirty two patients with relapsing-remitting MS were evaluated using the Expanded Disability Status Scale, Beck Depression Inventory-II (BDI-II), Beck Scale for Suicide Ideation (BSI), and Hospital Anxiety and Depression Scale. Results: A hierarchical regression analysis was performed to evaluate the variables. The regression equation significantly predicted the BSI score (R2 = 0.306; adjusted R2 = 0.273; F (9, 125) = 9.18; p < 0.0005), and the BDI-II score was the only variable that contributed significantly to this model (p < 0.0005). Conclusions: A high prevalence of depression and anxiety, and a higher rate of suicidal ideation were identified in MS patients compared to the general population. The presence of depressive symptoms appeared to have a direct influence on the risk of suicide.</p></div