Produção e caracterização de filtros hidrofóbicos de celulose vegetal / Production and characterization of hydrophobic filters made of vegetable cellulose

A celulose é um biopolímero abundante que pode ser obtido de fontes como plantas verdes, fungos, procariontes, entre outros. Fibras de coco foram utilizadas como matéria-prima para obtenção de celulose vegetal (CV), devido ao seu caráter renovável, biodegradabilidade e ser um resíduo agroindustrial. As fibras foram pré-tratadas com hidróxido de sódio 2% para remoção de impurezas, substâncias cerosas e extrativos hidrossolúveis. Para o processo de deslignificação da polpa de celulose foi utilizado hipoclorito de sódio 1,7%. A presença de poros na estrutura da celulose confere um alto grau de absorção e, sua capacidade hidrofílica, diminui a sua capacidade de sorver óleos e graxas. Porém, nanocristais de celulose vegetal (NCCV) puderam ser isolados de suas matrizes por um processo de hidrólise ácida com ácido sulfúrico 64%. Os nanocristais apresentam grupos hidroxila em sua estrutura, que possibilitam a modificação de superfície com substâncias com princípio ativo hidrofóbico, como os silanos. Após um eficiente processo de funcionalização dos nanocristais em meio aquoso na presença de metiltrietóxisilano (MTES) e posterior liofilização, obtiveram-se nanocristais de celulose vegetal silanizados (NCCVS), produto final que pode ser utilizado como componente de filtros para a retenção de óleos. O difratograma de raios-X (DRX) apontou o aumento da cristalinidade na amostra após a hidrólise ácida. A análise de Espectroscopia no Infravermelho com Transformada de Fourier (FTIR) indicou a presença de bandas características de silício e ligações do tipo O-Si-CH3 nos NCCVS. A Análise Termogravimétrica (TGA) evidenciou a presença do silano na amostra de NCCVS pela quantidade de resíduo em comparação à amostra de NCCV. Os nanocristais funcionalizados demonstraram propriedades hidrofóbicas e oleofílicas pela repulsão de uma gota de água e retenção de uma gota de xileno depositadas sobre a amostra, indicando que o material é uma alternativa para a remoção de óleos em superfícies hídricas

Leiomioma uterino - repercussões clínicas e manejo cirúrgico: Uterine leiomyoma - clinical repercussions and surgical management

O leiomioma uterino (LU) é um tumor benigno que tem origem em uma única célula tronco que compõem o miométrio uterino. Essa afecção se manifesta por tumores benignos, que são os mais incidentes do aparelho reprodutor feminino, podendo variar de 20 a 50%. Vale ressaltar os principais fatores de risco, sendo eles: idade avançada, nuliparidade, obesidade, estado pré-menopausa, hipertensão, história familiar e obesidade. A origem fisiopatológica e do desenvolvimento do leiomioma ainda não foram totalmente descobertas, ainda que potenciais genéticos e mecanismos moleculares têm sido exaustivamente debatidos na literatura científica. Dentre as hipóteses do mecanismo patológico dessa doença, a principal delas está ligada às mutações do gene MED12. Em relação às manifestações clínicas, muitas pacientes com LU são assintomáticas, contudo, uma parcela significativa dessa população, por volta de 30%, pode apresentar sintomas, como metrorragia, dificuldade miccional ou fecal. Sabendo que a clínica do LU é variada, exames de imagem como a ultrassonografia são imprescindíveis para a confirmação do diagnóstico, sendo a ultrassonografia transabdominal e a transvaginal as mais utilizadas e classificadas segundo a Federação Internacional de Ginecologia e Obstetrícia. Na perspectiva terapêutica, a histerectomia persiste sendo o único tratamento cirúrgico definitivo para leiomiomas sintomáticos, desde que a mulher já esteja com sua prole definida e que não se oponha à retirada do útero. Tal método propedêutico apresenta resultados favoráveis quando se trata de menor tempo de operação e menor dor pós-operatória em 24 horas

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

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora