Validation of ±-tocopherol and 4-nerolidylcathecol quantitative assessment methodologies

O objetivo do nosso trabalho foi desenvolver um método para avaliação da concentração do ±-tocoferol, considerado o antioxidante lipofílico de maior importância, e do 4-nerolidilcatecol (4-NC), uma substância natural com comprovada ação antioxidante in vitro e in vivo, em matriz biológica (homogeneizado de pele). Utilizamos a cromatografia de alta eficiência acoplada a um detector eletroquímico, sendo que o método apresentou linearidade para as concentrações de 0,025 µg/mL a 0,1 µg/mL para o ±-T (tempo de retenção 3, 4 min) e de 0,15 µg/mL a 2,5 µg/mL para o 4-NC (tempo de retenção 2,06 min), dissolvidos em etanol e etanol:água (1:1). A taxa de recuperação do ±-T adicionado nas concentrações de 0,5; 0,1 e 0,025 µg/mL aos homogeneizados de pele foi de 94,03; 111,2 e 80,7%, respectivamente. A taxa de recuperação de 4-NC adicionado nas concentrações de 2,5; 0,625 e 0,156 µg/mL foi de 103,7; 91,7 e 91,7%. Este método analítico foi e está sendo empregado, com sucesso devido à sua precisão e rapidez, em diversas análises do laboratório.Topical administration of antioxidants, such as ±-tocopherol (±-T), provides an efficient manner of enriching the endogenous cutaneous protection system, and it constitutes a successful strategy for diminishing the ultraviolet radiation-mediated oxidative damage. Besides ±-tocopherol the use of other natural occurring compounds with antioxidant activity has been proposed for the same purpose. The aim of this study was to develop a validated analytical method for the determination of a-tocopherol and 4-nerolidylcathecol (4-NC) concentrations in skin homogenates in a pharmaceutical formulations. We employed liquid chromatography with electrochemical detection. Chromatography was performed on a Supelcosil LC-8, 3 mm, 75x4.6 mm column (Supelco, Bellefonte, PA, USA) with a mobile phase of methanol:water (9:1) for 4-NC and (95:5) for a-T, both containing 20 mM LiClO4 and 2 mM KCl. The flow rate was set at 1.0 ml/min. We established validation parameters including sensitivity, precision, accuracy, stability and found a linear relationship between the concentrations ranges of 0.025 µg/mL to 0.1 µg/mL of ±-T and 0.15 mg/mL to 2.5 mg/mL of 4-NC. The recovery of ±-T from skin homogenates was 94.03, 111.2 and 80.7% for the concentrations of 0.5, 0.1 and 0.025 µg/mL respectively. The recovery for the following concentrations of 4-NC: 2.5, 0.625 and 0.156 µg/mL was 103.7, 91.7 and 91.7%. This analytical procedure has been successfully employed in cutaneous permeation studies, antioxidant activity studies and determinations of 4-NC in Pothomorphe umbellata root extracts

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