Uso do carbureto: uma proposta de atividade experimental para a abordagem conjunta da ligação iônica e da ligação covalente

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β-cyclodextrin/isopentyl caffeate inclusion complex: synthesis, characterization and antileishmanial activity

Isopentyl caffeate (ICaf) is a bioactive ester widely distributed in nature. Our patented work has shown promising results of this molecule against Leishmania. However, ICaf shows poor solubility, which limits its usage in clinical settings. In this work, we have proposed the development of an inclusion complex of ICaf in -cyclodextrin (-CD), with the aim to improve the drug solubility, and thus, its bioavailability. The inclusion complex (ICaf:-CD) was developed applying three distinct methods, i.e., physical mixture (PM), kneading (KN) or co-evaporation (CO) in different molar proportions (0.25:1, 1:1 and 2:1). Characterization of the complexes was carried out by thermal analysis, Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM) and molecular docking. The ICaf:-CD complex in a molar ratio of 1:1 obtained by CO showed the best complexation and, therefore, was selected for further analysis. Solubility assay showed a marked improvement in the ICaf:-CD (CO, 1:1) solubility profile when compared to the pure ICaf compound. Cell proliferation assay using ICaf:-CD complex showed an IC50 of 3.8 and 2.7 µg/mL against L. amazonesis and L. chagasi promastigotes, respectively. These results demonstrate the great potential of the inclusion complex to improve the treatment options for visceral and cutaneous leishmaniases.This research was funded by Banco do Nordeste (grant FUNDECI/2016.0015), Coordenação Aperfeiçoamento de Pessoal de Nivel Superior (CAPES) and Fundação de Ámparo à Pesquisa do Estado de Sergipe (FAPITEC) (PROCESSO: 88887.159533/2017-00 extração, encapsulação e caracterização de bioativos para o interesse biotecnologico). Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq 301964/2019-0 Chamada 06/2019, and Chamada CNPq nº 01/2019) and from the Portuguese Science and Technology Foundation (FCT) project UIDB/04469/2020 (strategic fund).info:eu-repo/semantics/publishedVersio

Whole-genome sequencing of 1,171 elderly admixed individuals from Brazil

As whole-genome sequencing (WGS) becomes the gold standard tool for studying population genomics and medical applications, data on diverse non-European and admixed individuals are still scarce. Here, we present a high-coverage WGS dataset of 1,171 highly admixed elderly Brazilians from a census-based cohort, providing over 76 million variants, of which ~2 million are absent from large public databases. WGS enables identification of ~2,000 previously undescribed mobile element insertions without previous description, nearly 5 Mb of genomic segments absent from the human genome reference, and over 140 alleles from HLA genes absent from public resources. We reclassify and curate pathogenicity assertions for nearly four hundred variants in genes associated with dominantly-inherited Mendelian disorders and calculate the incidence for selected recessive disorders, demonstrating the clinical usefulness of the present study. Finally, we observe that whole-genome and HLA imputation could be significantly improved compared to available datasets since rare variation represents the largest proportion of input from WGS. These results demonstrate that even smaller sample sizes of underrepresented populations bring relevant data for genomic studies, especially when exploring analyses allowed only by WGS

TRANSTORNO OBSESSIVO-COMPULSIVO (TOC) E NEUROIMAGEM

Advanced neuroimaging techniques have played a key role in elucidating brain alterations in obsessive-compulsive disorder (OCD). These discoveries not only broaden our understanding of the pathophysiology of OCD, but also pave the way for the development of more effective and personalized therapeutic interventions. As neuroimaging skills are improved, an increasingly refined understanding of the neurobiological basis of psychiatric disorders such as OCD can be expected, offering hope and opportunities for a better quality of life for those suffering from these conditions. This study aimed to investigate the brain alterations associated with OCD using advanced neuroimaging techniques. A systematic literature review was carried out using the Scielo, Lilacs and Medline databases. After a qualitative analysis of the results, it was concluded that advanced neuroimaging techniques provide objective evidence of the brain alterations associated with Obsessive-Compulsive Disorder (OCD), contributing to a deeper understanding of the neural bases of the disorder and highlighting the crucial role of these techniques in research and the development of diagnostic and treatment strategies.As técnicas avançadas de neuroimagem têm desempenhado um papel fundamental na elucidação das alterações cerebrais no Transtorno obsessivo-compulsivo (TOC). Essas descobertas não só ampliam a compreensão da fisiopatologia do TOC, mas abrem caminho para o desenvolvimento de intervenções terapêuticas mais eficazes e personalizadas. À medida que são aprimoradas as habilidades em neuroimagem, pode-se esperar uma compreensão cada vez mais refinada das bases neurobiológicas dos transtornos psiquiátricos, como o TOC, oferecendo esperança e oportunidades para uma melhor qualidade de vida para aqueles que sofrem com essas condições. Este estudo teve como objetivo investigar as alterações cerebrais associadas ao TOC usando técnicas avançadas de neuroimagem. Diante disso, realizou-se uma revisão sistemática da literatura, utilizando as bases de dados Scielo, Lilacs e Medline. Após análise qualitativa dos resultados, concluiu-se que as técnicas avançadas de neuroimagem fornecem evidências objetivas das alterações cerebrais associadas ao Transtorno Obsessivo-Compulsivo (TOC), contribuindo para uma compreensão mais profunda das bases neurais do transtorno e destacando o papel crucial dessas técnicas na investigação e desenvolvimento de estratégias de diagnóstico e tratamento

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

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