Abordagens Diagnósticas e Terapêuticas nos Transtornos de Personalidade: Uma Revisão da Literatura

This article provides a literature review on Personality Disorders (PD), focusing on diagnostic and therapeutic approaches. The analysis spans studies from the last ten years, highlighting the transition to a dimensional approach in diagnostic criteria and the diversity of effective therapeutic strategies, including cognitive-behavioral and dialectical-behavioral therapies. The clinical complexity of PDs, the frequent presence of comorbidities, and therapeutic challenges are discussed. Future perspectives in PD research are explored, emphasizing the need for innovative therapeutic strategies and an enhanced understanding of neurobiological foundations. The review aims to contribute to more holistic clinical practices and inform future research in the field.O presente artigo oferece uma revisão da literatura sobre Transtornos de Personalidade (TP), enfocando abordagens diagnósticas e terapêuticas. A análise abrange estudos dos últimos dez anos, destacando a transição para uma abordagem dimensional nos critérios diagnósticos e a diversidade de estratégias terapêuticas efetivas, incluindo terapias cognitivo-comportamentais e dialecticocomportamentais. A complexidade clínica dos TP, a presença frequente de comorbidades e os desafios terapêuticos são discutidos. Perspectivas futuras na pesquisa sobre TP são exploradas, enfatizando a necessidade de estratégias terapêuticas inovadoras e uma compreensão aprimorada das bases neurobiológicas. A revisão busca contribuir para práticas clínicas mais holísticas e informar futuras investigações no campo

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