The effects of retinol oral supplementation in 6-hydroxydopamine dopaminergic denervation model in Wistar rats

Vitamin A (retinol) is involved in signaling pathways regulating gene expression and was postulated to be a major antioxidant and anti-inflammatory compound of the diet. Parkinson's disease (PD) is a progressive neurodegenerative disorder, characterized by loss of nigral dopaminergic neurons, involving oxidative stress and pro-inflammatory activation. The aim of the present study was to evaluate the neuroprotective effects of retinol oral supplementation against 6-hydroxydopamine (6-OHDA, 12 μg per rat) nigrostriatal dopaminergic denervation in Wistar rats. Animals supplemented with retinol (retinyl palmitate, 3000 IU/kg/day) during 28 days exhibited increased retinol content in liver, although circulating retinol levels (serum) were unaltered. Retinol supplementation did not protect against the loss of dopaminergic neurons (assessed through tyrosine hydroxylase immunofluorescence and Western blot). Retinol supplementation prevented the effect of 6-OHDA on Iba-1 levels but had no effect on 6-OHDA-induced GFAP increase. Moreover, GFAP levels were increased by retinol supplementation alone. Rats pre-treated with retinol did not present oxidative damage or thiol redox modifications in liver, and the circulating levels of TNF-α, IL-1β, IL-6 and IL-10 were unaltered by retinol supplementation, demonstrating that the protocol used here did not cause systemic toxicity to animals. Our results indicate that oral retinol supplementation is not able to protect against 6-OHDA-induced dopaminergic denervation, and it may actually stimulate astrocyte reactivity without altering parameters of systemic toxicity

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