Alternative extraction of alkaloid anticarcinogens from Brazilian "vinca rosea" using Ion exchange chromatography

Extracts in ethanol and ethanol-ammonia of dried leaves from Catharanthus roseus, gathered at Rio de Janeiro state, were adsorbed in a strongly acidic cation exchange resin with sulfonic acid group, using the finite bath method, resulting in an alkaloid retained fraction and an acidic and neutral unretained fraction. High Performance Liquid Chromatography showed the isolation of the alkaloid fraction to be highly selective and with good performance, with an absence of alkaloids in the unretained fraction, while the retained fraction presented 1,54-6,35 mg/g of vindoline and 0,12-0,91 mg/g of vinblastine, common for an alkaloid-rich concentrate, usually obtained by classic extraction with several steps using solvents

Desafios de um Periódico Científico Jovem de Instituição Pública rumo à Internacionalização: o caso da Revista Fitos Eletrônica

Relata o estudo de caso sobre a Revista Fitos Eletrônica, quanto ao processo de internacionalização, revelou o caminho e os desafios pelos quais um periódico jovem de instituição pública enfrenta. Com base na pesquisa de Borini e Ferreira (2015), o estudo apontou que tanto as pressões institucionais quanto a participação em redes de relacionamento internas e externas são os fatores proeminentes que promovem a internacionalização

Anti-inflammatory effect of Schinus terebinthifolius Raddi hydroalcoholic extract on neutrophil migration in zymosan-induced arthritis

AbstractEthnopharmacological relevanceSchinus terebinthifolius is a species of plant from the Anacardiaceae family, which can be found in different regions of Brazil. Schinus is popularly known as aroeirinha, aroeira-vermelha, or Brazilian pepper. In folk medicine, S. terebinthifolius is used for several disorders, including inflammatory conditions, skin wounds, mucosal membrane ulcers, respiratory problems, gout, tumors, diarrhea and arthritis. According to chemical analyses, gallic acid, methyl gallate and pentagalloylglucose are the main components of hydroalcoholic extracts from S. terebinthifolius leaves. In the present study, we demonstrated the ability of a hydroalcoholic extract to inhibit cell migration in arthritis and investigated the mechanisms underlying this phenomenon.Materials and methodsThe anti-inflammatory effect of S. terebinthifolius hydroalcoholic leaf extract (ST-70) was investigated in a zymosan-induced experimental model of inflammation. Male Swiss and C57Bl/6 mice received zymosan (100µg/cavity) via intra-thoracic (i.t.) or intra-articular (i.a.) injection after oral pre-treatment with ST-70. The direct action of ST-70 on neutrophils was evaluated via chemotaxis.ResultsST-70 exhibited a dose-dependent effect in the pleurisy model. The median effective dose (ED50) was 100mg/kg, which inhibited 70% of neutrophil accumulation when compared with the control group. ST-70 reduced joint diameter and neutrophil influx for synovial tissues at 6h and 24h in zymosan-induced arthritis. Additionally, ST-70 inhibited synovial interleukin (IL)-6, IL-1β, keratinocyte-derived chemokine (CXCL1/KC) and Tumor Necrosis Factor (TNF)-α production at 6h and CXCL1/KC and IL-1β production at 24h. The direct activity of ST-70 on neutrophils was observed via the impairment of CXCL1/KC-induced chemotaxis in neutrophils. Oral administration of ST-70 did not induce gastric damage. Daily administration for twenty days did not kill any animals. In contrast, similar administrations of diclofenac induced gastric damage and killed all animals by the fifth day.ConclusionsOur results demonstrated significant anti-inflammatory effects of ST-70, suggesting a putative use of this herb for the development of phytomedicines to treat inflammatory diseases, such as joint inflammation

Desenvolvimento e validação intralaboratorial de metodologia analítica para determinação do teor de álcool etílico nas formulações antissépticas líquidas e em gel por cromatografia líquida de alta eficiência com detecção por índice de refração (clae-ir) / Development and intra-laboratory validation of analytical methodology for the determination of ethyl alcohol content in liquid and gel antiseptic formulations by high performance liquid chromatography with refractive index detection (clae-ir)

Neste trabalho foi desenvolvido e validado uma metodologia analítica para determinação de álcool etílico na forma de gel em formulações de produtos de diversas categorias, tais como: medicamentos, cosméticos e saneantes. O objetivo desse trabalho foi obter uma metodologia capaz de executar a vigilância sanitária dos produtos comercializados na forma de “álcool em gel” no Brasil durante o período da pandemia da Covid-19. O método é baseado na separação do analito dos demais componentes da matriz que apresentam o grupamento hidroxila na formulação, utilizando-se inicialmente a separação através de uma coluna de fase reversa, onde a separação é promovida pela diferença de polaridade entre as moléculas e afinidade com a fase estacionária. Após passagem pela coluna de fase reversa, a separação será realizada por uma coluna de troca iônica, a qual se dá pela interação eletrostática entre a resina contendo grupos funcionais carregados e íons de cargas opostas. Os resultados mostraram que o método apresentou linearidade de 500 a 1500 mg/L. Os parâmetros de: seletividade, precisão e exatidão foram confirmados por ensaios de recuperação utilizando-se Material de Referência Certificado (MRC), na amostra (95 - 105%). Por esse método foram analisadas 85 amostras comercializadas e os resultados mostraram que 54 estavam satisfatórias e 31 insatisfatórias. Conclusões: Todos os parâmetros avaliados ficaram de acordo com resolução específica da ANVISA e todos os resultados mostraram que a metodologia pode ser reproduzida com segurança e confiabilidade, podendo ser empregada em programas de monitoramento com as vigilâncias municipais, estaduais e Anvisa

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