Quality of martensitic stainless steel type AISI-420 utilized in the manufacture surgical implements

Até o presente momento, o aço inoxidável martensítico do tipo AISI-420 é muito utilizado na confecção de ferramentas cirúrgicas. Tais ferramentas vêm apresentando corrosão prematura, identificada após processo de limpeza e esterilização, perda de corte e/ou quebra durante os processos cirúrgicos. Esse trabalho avalia esse aço sobre à composição química, dureza, microestrutura e resistência à corrosão por pite em solução de detergente enzimático diluída em água por polarização cíclica anódica. Essa mistura é utilizada na limpeza das ferramentas que são submersas por 2h nessa solução antes da lavagem e esterilização. Os resultados mostram aços com microestrutura composta de martensita com fase ferrita e impurezas. Os referidos aços apresentam baixos valores de potencial de pite em comparação aos aços com microestrutura totalmente martensítica que possuem maiores valores.Until now the martensitic stainless steel type AISI-420 is widely used in the manufacture of surgical implements. These implements present premature corrosion problems identified after cleaning, , sterilization and cutting edge loss and/or rupture during the surgical processes. This study evaluates the steel as to the chemical composition, hardness, microstructure and pitting corrosion resistance in a solution of enzyme detergent diluted in water by anodic cyclic polarization. This mixture is used in the cleaning of surgical implements that are submerged in this solution for 2 h before cleaning and sterilization. The results show steels with martensite microstructures in the ferrite phase, together wth impurities. These presented low pitting potential values in compariston to steels with a fully martensitic microstructure.Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)CNPqFAPES

A Incidência de depressão em pacientes pós-infartados

O infarto agudo do miocárdio (IAM) pode estar intimamente relacionado à depressão, podendo esta ser tanto um fator agravante para doenças coronarianas quanto uma consequência desse tipo de acometimento. Por isso, é de extrema importância um correto e precoce diagnóstico dos quadros de depressão em cardíacos, valendo-se de parâmetros e escalas padrão-ouro como a Escala de Hamilton (HAM-D), o Inventário de depressão de Beck (BDI), e o Inventário de Ansiedade de Beck (BAI). Além disso, a responsabilidade e a adesão ao tratamento do paciente depressivo devem ser de todos que o cercam, uma vez que o apoio emocional tem um papel tão importante quanto a medicação administrada

Memórias de Oeiras: preservação e divulgação do património histórico-cultural

A apresentação assenta no propósito de dar a conhecer o projeto co-financiado pela Fundação Calouste Gulbenkian, Memórias de Oeiras – Coleção Pombalina e Obras do século XVIII: Recuperação, Tratamento e Organização de Acervos Documentais. Como introdução, apresentam-se os principais objetivos do projeto ao contribuir para a divulgação de obras da Rede de Bibliotecas Municipais de Oeiras relacionadas com o Século das Luzes e uma das personalidades mais proeminente deste período da história nacional com ligação profunda ao concelho de Oeiras: o Marquês de Pombal. A enquadrar, identificam-se os requisitos de implementação, nomeadamente, o modelo geral de organização e a estrutura de consolidação de coleções e recursos digitais dedicados à história e cultural local. Descrevem-se as práticas de tratamento aplicadas às coleções especiais, as modalidades de reutilização de informação e de partilha de recursos digitais, assim como a forma de interligação à plataforma de divulgação de colecções, actividades, serviços e conteúdos, o sítio web «Memória de Oeiras». Evidencia-se a vertente pioneira de repositório cooperativo ao permitir a integração dinâmica e o acesso contínuo a novas colecções e conteúdos digitais das bibliotecas, arquivos, centros de documentação, galerias, museus e monumentos do concelho de Oeiras e seu património histórico material e imaterial.

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