Association Between Interleukin 6 -174 G/c Promoter Gene Polymorphism And Runners’ Responses To The Dietary Ingestion Of Antioxidant Supplementation Based On Pequi (caryocar Brasiliense Camb.) Oil: A Before-after Study

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Exercise is a double-edged sword: when practiced in moderation, it increases the expression of antioxidant enzymes, but when practiced strenuously it causes oxidative stress and cell damage. In this context, polymorphisms in the interleukin (IL)-6 gene should be investigated better because they can influence performance, at least in exercise that generates oxidative stress and leads to muscular injuries with consequent inflammation. In this work, we investigated the influence of IL-6 –174 G/C polymorphism on tissue damage and inflammation markers, lipid peroxidation, hemogram and lipid profile of runners before and after ingestion of 400 mg of pequi oil in capsules supplied daily for 14 consecutive days. The IL-6 genotypes were associated with significant differences in lipid peroxidation, with the CC mutant having lower values. There were also significant differences among these genotypes in the response to supplementation with pequi oil, exercise-induced damage and C-reactive protein (CRP) levels. The best protection against damage was observed with the heterozygous genotype. Although the CC genotype showed an increase in CRP levels after supplementation, the lack of a positive correlation between triglycerides and high-sensitivity CRP in this mutant genotype after supplementation indicated a protective effect of pequi. These findings deserve further investigation, particularly with regard to the quantification of circulating IL-6 concentrations. © 2016, Sociedade Brasileira de Genética.394554566CNPq, Conselho Nacional de Desenvolvimento Científico e TecnológicoUnB, Universidade de BrasíliaConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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