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

Association between temperament dimensions and genetic polymorphisms in depressed patients

Introdução: Formas de categorizar a personalidade foram propostas ao longo da história e sua associação com transtornos psiquiátricos tem sido extensivamente estudada. Algumas teorias propõem que as vias serotoninérgicas desempenhariam um papel no desenvolvimento da personalidade e nos transtornos de humor. Estudos tentaram encontrar associações entre polimorfismos genéticos e traços de personalidade, com resultados conflitantes e grandes diferenças metodológicas. Objetivo: Investigar a associação entre polimorfismos específicos das vias serotoninérgicas e diferentes domínios de temperamentos do inventário de temperamento e caráter de Cloninger (ITC). Métodos Foram selecionados 179 indivíduos do banco de dados do estudo elect-Tdcs que preencheram o ITC e colheram material genético. Regressões lineares múltiplas e de Kernel foram realizadas entre os scores de temperamento do ITC e os polimorfismos 5-HTLPPR, rs6313, rs7997012, rs1800532, rs6295 e rs878567. Resultados: Foi encontrada uma associação entre o polimorfismo rs1800532 e o score de Auto-transcendência e entre o rs7997012 com o domínio de Persistência. Para os demais domínios personalidade não foi possível encontrar nenhuma associação entre os polimorfismos estudados. Conclusão: Este estudo contribui para uma maior compreensão sobre possíveis associações entre alguns polimorfismos das vias serotoninérgicas e escores do ITC em uma população de deprimidos no Brasil. Porém, mais estudos são necessários para melhor compreensão de tais achadosIntroduction: Ways to categorize personality and association with psychiatric disorders have been extensively studied. Some theories propose that serotonergic pathways could be involved in personality development and mood disorders. Previous studies tried to find associations between genetic polymorphisms and personality traits, with conflicting results and major methodological differences. Objective: To investigate an association between specific polymorphisms of serotonergic pathways and temperament scores from Temperament and Character Inventory (TCI). Methods: 179 subjects were selected from the ELECT-tDCS study database. Temperament dimensions were assessed by TCI and genetic material collected. Multiple linear regressions and Kernel regressions were performed between TCI temperament scores and the polymorphisms 5-HTLPPR, rs6313, rs7997012, rs1800532, rs6295 and rs878567. Results: An association was found between rs1800532 polymorphism and Self Transcendence and between rs7997012 and Persistence. For other personality traits it was not possible to find any relationship between the polymorphisms studied. Conclusions: This study contributes to a better understanding of possible associations between certain polymorphisms in the serotonergic pathways and ITC scores in a population of depressed individuals in Brazil. However, further studies are needed to gain a better understanding of these finding

The Escitalopram versus Electric Current Therapy for Treating Depression Clinical Study (ELECT-TDCS): rationale and study design of a non-inferiority, triple-arm, placebo-controlled clinical trial

CONTEXT AND OBJECTIVE: Major depressive disorder (MDD) is a common psychiatric condition, mostly treated with antidepressant drugs, which are limited due to refractoriness and adverse effects. We describe the study rationale and design of ELECT-TDCS (Escitalopram versus Electric Current Therapy for Treating Depression Clinical Study), which is investigating a non-pharmacological treatment known as transcranial direct current stimulation (tDCS).DESIGN AND SETTING: Phase-III, randomized, non-inferiority, triple-arm, placebo-controlled study, ongoing in São Paulo, Brazil.METHODS: ELECT-TDCS compares the efficacy of active tDCS/placebo pill, sham tDCS/escitalopram 20 mg/day and sham tDCS/placebo pill, for ten weeks, randomizing 240 patients in a 3:3:2 ratio, respectively. Our primary aim is to show that tDCS is not inferior to escitalopram with a non-inferiority margin of at least 50% of the escitalopram effect, in relation to placebo. As secondary aims, we investigate several biomarkers such as genetic polymorphisms, neurotrophin serum markers, motor cortical excitability, heart rate variability and neuroimaging.RESULTS: Proving that tDCS is similarly effective to antidepressants would have a tremendous impact on clinical psychiatry, since tDCS is virtually devoid of adverse effects. Its ease of use, portability and low price are further compelling characteristics for its use in primary and secondary healthcare. Multimodal investigation of biomarkers will also contribute towards understanding the antidepressant mechanisms of action of tDCS.CONCLUSION: Our results have the potential to introduce a novel technique to the therapeutic arsenal of treatments for depression