ELETROCONVULSOTERAPIA NA DEPRESSÃO ASSOCIADA À DOENÇA DE PARKINSON

Depression is a frequent comorbidity in Parkinson's disease (PD), worsening patients' quality of life due to the complex interaction between motor and non-motor symptoms. Electroconvulsive therapy (ECT) has emerged as a promising therapeutic option for treatment-resistant depression in PD, with evidence of significant improvement in depressive symptoms, especially in cases where other therapies have failed. This study reviews the current literature on ECT in PD, exploring neurobiological mechanisms, treatment protocols, and clinical and ethical challenges. The methodology included searches in databases such as PubMed, Scopus, and Web of Science, with inclusion criteria for randomized clinical trials, controlled trials, and systematic reviews. Qualitative data analysis indicated improvements in depressive symptoms and modest but significant impacts on motor symptoms. The findings underscore the need for controlled and long-term clinical trials to validate the safety and efficacy of ECT, proposing it as a viable approach to improving the mental health of patients with PD. However, it is crucial to evaluate the risks and benefits, especially regarding adverse cognitive and motor effects, and adopt a multidisciplinary and individualized approach to managing depression in PD.  A depressão é uma comorbidade frequente na doença de Parkinson (DP), agravando a qualidade de vida dos pacientes devido à interação complexa entre sintomas motores e não motores. A eletroconvulsoterapia (ECT) emergiu como uma opção terapêutica promissora para depressão resistente em DP, com evidências de melhoria significativa dos sintomas depressivos, especialmente em casos onde outras terapias falharam. Este estudo revisa a literatura atual sobre ECT na DP, explorando mecanismos neurobiológicos, protocolos de tratamento e desafios clínicos e éticos. A metodologia incluiu buscas em bases de dados como PubMed, Scopus e Web of Science, com critérios de inclusão para estudos clínicos randomizados, ensaios controlados e revisões sistemáticas. A análise qualitativa dos dados indicou melhorias nos sintomas depressivos e impactos modestos, porém significativos, nos sintomas motores. Os achados ressaltam a necessidade de ensaios clínicos controlados e de longo prazo para validar a segurança e eficácia da ECT, propondo-a como uma abordagem viável para melhorar a saúde mental dos pacientes com DP. Contudo, é crucial avaliar os riscos e benefícios, especialmente quanto aos efeitos cognitivos e motores adversos, e adotar uma abordagem multidisciplinar e individualizada no manejo da depressão em DP

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