Brazilian Morus nigra

Morus nigra has been used popularly for several proposes, including diabetic. In an attempt to support medicinal value, the acute hypoglycemic, hypolipidemic, and antioxidant effects of the ethanolic extract of Morus nigra (EEMn 200 or 400 mg/kg b.w.) were evaluated in normal and alloxan-induced diabetic treated for 14 days. Serum biochemical and antioxidant analysis were performed at the end of experiment. Oral glucose tolerance test was performed at 10th and 15th days. Chromatographic analysis by HPLC-DAD of EEMn was performed. Insulin was used as positive control to glycemic metabolism as well as fenofibrate to lipid metabolism. EEMn (400 mg/kg/day) reduced fasting and postprandial glycaemia, improved oral glucose tolerance, and reduced lipolysis and proteolysis in diabetic rats. EEMn decreased the blood levels of total cholesterol and increased HDL level when compared to the diabetic control rats. At higher levels, EEMn reduced triglycerides and VLDL levels in diabetic rats. Also, EEMn reduced malondialdehyde and increased the reduced glutathione levels in liver of diabetic rats. Chromatographic analysis identified the presence of the flavonoids rutin, isoquercetin, and kaempferitrin. Acute EEMn treatment reduced hyperglycemia, improved oral glucose tolerance, and minimized dyslipidemia and oxidative stress leading to a reduction in atherogenic index in alloxan-induced diabetic rats

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