Encapsulation of the HSP-90 Chaperone Inhibitor 17-AAG in Stable Liposome Allow Increasing the Therapeutic Index as Assessed, in vitro, on Leishmania (L) amazonensis Amastigotes-Hosted in Mouse CBA Macrophages

The current long-term treatment for leishmaniasis causes severe side effects and resistance in some cases. An evaluation of the anti-leishmanial potential of an HSP90-inhibitor, 17-allylamino-17-demethoxygeldanamycin (17-AAG), demonstrated its potent effect against Leishmania spp. in vitro and in vivo. We have previously shown that 17-AAG can kill L. (L) amazonensis promastigotes with an IC50 of 65 nM and intracellular amastigote at concentrations as low as 125 nM. As this compound presents low solubility and high toxicity in human clinical trials, we prepared an inclusion complex containing hydroxypropyl-β-cyclodextrin and 17-AAG (17-AAG:HPβCD) to improve its solubility. This complex was characterized by scanning electron microscopy, and X-ray diffraction. Liposomes-containing 17-AAG:HPβCD was prepared and evaluated for encapsulation efficiency (EE%), particle size, polydispersity index (PDI), pH, and zeta potential, before and after accelerated and long-term stability testing. An evaluation of leishmanicidal activity against promastigotes and intracellular amastigotes of L. (L) amazonensis was also performed. The characterization techniques utilized confirmed the formation of the inclusion complex, HPβCD:17-AAG, with a resulting 33-fold-enhancement in compound water solubility. Stability studies revealed that 17-AAG:HPβCD-loaded liposomes were smaller than 200 nm, with 99% EE. Stability testing detected no alterations in PDI that was 0.295, pH 7.63, and zeta potential +22.6, suggesting liposome stability, and suitability for evaluating leishmanicidal activity. Treatment of infected macrophages with 0.006 nM of 17-AAG:HPβCD or 17-AAG:HPβCD-loaded liposomes resulted in almost complete amastigote clearance inside macrophages after 48 h. This reduction is similar to the one observed in infected macrophages treated with 2 μM amphotericin B. Our results showed that nanotechnology and drug delivery systems could be used to increase the antileishmanial efficacy and potency of 17-AAG in vitro, while also resulting in reduced toxicity that indicates these formulations may represent a potential therapeutic strategy against leishmaniasis

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

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

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

Synthesis, mechanism of formation, and molecular orbital calculations of arylamidoximes

A simple and easy synthesis of ten arylamidoximes from arylnitriles and hydroxylamine is described. The formation of the arylamides has been observed to a much lesser extent in the present work. A new mechanism for the formation of arylamidoximes, as well as arylamides, from arylnitriles and hydroxylamine is suggested. Quantum mechanical calculations have been carried out to support this mechanism. The enthalpy of formation in conjunction with atomic charges of the reactants and intermediates helped to understand more about the generation of the products.Brazilian National Research Council (CNPq)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq