Effects of Cnidoscolus Quercifolius Pohl leaves extracts on glucemia reduction in diabetic mice / Efeitos das folhas de Cnidoscolus Quercifolius Pohl sobre a redução da glucemia em ratos diabéticos

Obesity, metabolic syndrome and diabetes are epidemic chronic situations in industrialized countries that are associated with the reduction of life quality and increase of patients’ mortality. Before the serious epidemiological picture and the impact that the diabetes causes in the society, the use of different therapeutic interventions is priority in the scientific community. Thus, the goal of this work valued the hypoglycemic effect of the aqueous and methanolic extracts of the leaves of Cnidoscolus quercifolius Pohl (faveleira). The phytochemical analysis demonstrated the carbolic acids presence, flavonols, xanthone, catechin, triterpenoids, tannin and coumarins in both extracts and the liquid chromatography of high efficiency revealed the presence of the gallic acid; a powerful metabolite antioxidant. The Diabetes was induced in mice Swiss with alloxan that they did not present mortality when treated with 100, 200 mg / kg of methanolic extract and 100, 200 and 400 mg / kg of aqueous extract for 30 days. Histopathological analysis of the animal’s organs (kidney, pancreas, liver) did not reveal architectural alteration. All the diabetic animals submmited to the extracts presented a higher reduction on the blood sugar level percentage than the ones which were undergone to the standard drug. It is important to highlight that the blood sugar level - of the diabetic animals undergone to 400 mg / kg of weight of the aqueous extract - presented glycemic reduction of 39,81 % after 30 days of treatment. These results are very promising because they show great potential for the use of this typical Brazilian Caatinga plant as an alternative therapeutic option to slow down or reduce the risk of hyperglycemia and oxidative stress in diabetic patients

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

Characterization of Cnidoscolus quercifolius Pohl bark root extract and evaluation of cytotoxic effect on human tumor cell lines

Objective: To evaluate the chemical components of active extract from Cnidoscolus quercifolius root bark and its cytotoxic potential against several tumor strains. Methods: The high-performance liquid chromatography with diode-array detection and 1H and 13C nuclear magnetic resonance spectroscopy of the extract were used to distinguish the existence of possible functional groups in the root bark extract. The in vitro cytotoxic activity of methanol extract on human colon cancer cell lines was evaluated using OVCAR-8, SF-295, HCT-116, HL-60 strains and the samples were assessed by 3-(4,5-dimethylthiazol2-yl)-2,5-diphenyltetrazolium bromide method. Results: The analysis of nuclear magnetic spectra of the active chloroform fraction revealed the presence of absorptions bands correspondent to a mixture of favelines such as neofavelanone, deoxofaveline or methyl-faveline, which structures were confirmed by ultraviolet spectra upon high-performance liquid chromatography with diode-array detection analysis. The active fraction showed cytotoxic effects in the tested strains, HCT-116, SF-295, OVCAR-8 and HL-60 cells with IC50 of 72 hours ranging from 4.95 to 15.23 μg/mL. Conclusions: The results suggest that the substances present in faveleira (Cnidoscolus quercifolius) root bark extract have a cytotoxic potential against several tumor lines, showing a broader antitumour potential, and in addition no adverse to healthy cells. Therefore, the root bark extract of Cnidoscolus quercifolius has a possibility of use for anticarcinogenic therapies

FORAGE SORGHUM CULTIVARS PERFORMANCE IN DIFERENT SOIL AND CLIMATE ENVIRONMENTS IN PERNAMBUCO AND ALAGOAS, NORTHEASTERN BRAZIL

The main objective of this work was to evaluate 20 forage sorghum varietiesin different environments, being five in Pernambuco and two in Alagoas, to recommendation.The cultivars were evaluated through the plant height, dry matter yield and water use efficiencyparameters. The experimental design was randomized blocks. The variance analyses werecarried out individually for environments, and grouping analyses for Pernambuco and Alagoas separately. The main conclusions were that the dry matter production and water use efficiencyparameters, when associated to each other, seemed to be appropriate for the forage sorghumvarieties selection under semi-arid conditions; the CSF11 and CSF12 varieties presented highperformance in all of Pernambuco and Alagoas environments, when compared to the others