Supplementation with high-content docosahexaenoic acid triglyceride in attention-deficit hyperactivity disorder: a randomized double-blind placebo-controlled trial.

Background: Attention-deficit hyperactivity disorder (ADHD) is a complex disorder in terms of etiology, clinical presentation, and treatment outcome. Pharmacological and psychological interventions are recommended as primary treatments in ADHD; however, other nonpharmacological intervention such as a dietary supplementation with omega-3 polyunsaturated fatty acids (ω-3 PUFAs) has emerged as an attractive option. Purpose: The objective of the present study was to assess whether dietary supplementation with highly concentrated ω-3 docosahexaenoic acid (DHA) triglyceride may improve symptoms in ADHD. Method: A 6-month prospective double-blind placebo-controlled randomized clinical trial was designed in 66 patients with ADHD, aged between 6 and 18 years. Participants in the experimental group received a combination of ω-3 fatty acids (DHA 1,000 mg, eicosapentaenoic acid 90 mg, and docosapentaenoic acid 150 mg). Instruments included d2-test, AULA Nesplora, EDAH scales, and abbreviated Conner's Rating Scale. Results: In the cognitive test, between-group differences were not found, but within-group differences were of a greater magnitude in the DHA group. Between-group differences in favor of the DHA arm were observed in behavioral measures, which were already detected after 3 months of treatment. Results were not changed when adjusted by ADHD medication. Conclusions: This study provides further evidence of the beneficial effect of supplementation with ω-3 DHA in the management of ADHD

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