Chitosan-tripolyphosphate Nanoparticles As Arrabidaea Chica Standardized Extract Carrier: Synthesis, Characterization, Biocompatibility, And Antiulcerogenic Activity.

Natural products using plants have received considerable attention because of their potential to treat various diseases. Arrabidaea chica (Humb. & Bonpl.) B. Verlot is a native tropical American vine with healing properties employed in folk medicine for wound healing, inflammation, and gastrointestinal colic. Applying nanotechnology to plant extracts has revealed an advantageous strategy for herbal drugs considering the numerous features that nanostructured systems offer, including solubility, bioavailability, and pharmacological activity enhancement. The present study reports the preparation and characterization of chitosan-sodium tripolyphosphate nanoparticles (NPs) charged with A. chica standardized extract (AcE). Particle size and zeta potential were measured using a Zetasizer Nano ZS. The NP morphological characteristics were observed using scanning electron microscopy. Our studies indicated that the chitosan/sodium tripolyphosphate mass ratio of 5 and volume ratio of 10 were found to be the best condition to achieve the lowest NP sizes, with an average hydrodynamic diameter of 150±13 nm and a zeta potential of +45±2 mV. Particle size decreased with AcE addition (60±10.2 nm), suggesting an interaction between the extract's composition and polymers. The NP biocompatibility was evaluated using human skin fibroblasts. AcE-NP demonstrated capability of maintaining cell viability at the lowest concentrations tested, stimulating cell proliferation at higher concentrations. Antiulcerogenic activity of AcE-NP was also evaluated with an acute gastric ulcer experimental model induced by ethanol and indomethacin. NPs loaded with A. chica extract reduced the ulcerative lesion index using lower doses compared with the free extract, suggesting that extract encapsulation in chitosan NPs allowed for a dose reduction for a gastroprotective effect. The AcE encapsulation offers an approach for further application of the A. chica extract that could be considered a potential candidate for ulcer-healing pharmaceutical systems.103897-390

The role of spray-drying atmosphere on fridericia chica (bonpl.) L.G. Lohmann standardized extract production for wound healing activity

Fridericia chica (Bonpl.) L.G. Lohmann (synonym Arrabidaea chica Verlot) is widely used in Brazilian folk medicine. Considering overcoming pitfalls of scaling up production of plant extracts, herein the effects of N2 atmosphere for extract spray-drying process is reported. Samples were monitored by in vitro antioxidant activity and microbiological evaluation. The drying atmosphere influenced 3-deoxyanthocyanines content when using air as atomizing gas, decreasing carajurin (37.5%) content with concomitant increase in luteolin yield (24.1%). Both drying processes preserved the pharmacological activity. In the cell migration test with HaCaT cells, the extract dried under air flow (5 μg/mL) promoted wound closure by 78% (12 hours) whereas the extract dried using N2 flow promoted 49% (12 hours), with 98% closure (12 hours) for the positive control. The antimicrobial evaluation for Staphylococcus aureus did not differ within drying atmospheres, with MIC (minimum inhibitory concentration) at 0.39 mg/mL. Therefore, the drying process reported herein did not interfere with the biological activity’s outcome.The authors A.L.T.G.R.and M.A.F thank CNPq for research productivity fellowship. The authors also thank the Chemical, Biological, and Agricultural Pluridisciplinary Research Center (CPQBA/Unicamp) for the laboratory infrastructure. Sao Paulo Research Foundation (FAPESP). This work was supported by the Coordination for the Improvement of Higher Education Personnel (CAPES, Brazil, Financing Code 001) and the National Council for Scientific and Technological Development (CNPq, Brazil, grant numbers # 132448/2016-5, # 132207/2017-6, # 429463/2018-9).Peer reviewe

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