Mosquiteiros impregnados com inseticida em Rondônia, Brasil: avaliação do impacto na incidência da malária

O uso de mosquiteiros impregnados com inseticida de longa duração (MILD), quando obedecidas as orientações da Organização Mundial da Saúde, é medida de controle de vetores da malária que pode apresentar excelentes resultados. Em 2012 foram instalados aproximadamente 150.000 MILDs em nove municípios do estado de Rondônia. Concomitantemente não houve estudo de avaliação de impacto na redução da incidência. O presente estudo analisou dados secundários da incidência, na expectativa de avaliar o impacto dos MILDs na incidência parasitária anual (IPA). Os resultados estatísticos mostram que, no período de um ano após a instalação dos MILDs, não houve diferença estatisticamente significativa na variação da IPA em relação a outros municípios que não receberam os MILDs. A adoção de medidas de controle vetorial deve ser acompanhada de estudos epidemiológicos e de avaliação de uso e eficácia para oferecer subsídios mais robustos que justifiquem a adoção desta medida de controle da malária na Região Amazônica.Mosquito nets treated with long-lasting insecticide (LLINs), when used in compliance with guidelines of the World Health Organization, may be effective for malaria vector control. In 2012, approximately 150,000 LLINs were installed in nine municipalities in the state of Rondônia. However, no studies have assessed their impact on the reduction of malaria incidence. This study analyzed secondary data of malaria incidence, in order to assess the impact of LLINs on the annual parasite incidence (API). The results showed no statistically significant differences in API one year after LLIN installation when compared to municipalities without LLINs. The adoption of measures for malaria vector control should be associated with epidemiological studies and evaluations of their use and efficiency, with the aim of offering convincing advantages that justify their implementation and limit malaria infection in the Amazon Region

Avaliação do paciente com acidente vascular encefálico na era da COVID-19 – inovações no exame neurológico observacional

A pandemia de ‘Coronavirus disease’ 2019 (COVID-19) vem impondo mudanças radicais nos diversos sistemas de saúde, na estrutura médico-hospitalar, até no relacionamento médico-paciente. Medidas extremas visando a contenção da COVID-19 incluem o isolamento de populações (medidas de ‘lockdown’), a suspensão do atendimento ambulatorial de rotina e cirurgias eletivas, a conversão de andares inteiros em enfermarias dedicadas à quarentena de casos COVID-19, com importante impacto no manejo de diversas doenças agudas e crônicas. Independentemente das medidas acima, a doença encefalovascular continua afetando a vida de milhares de brasileiros, constituindo um dos principais motivos de atendimento emergencial em hospital-geral, exigindo assistência médica continuada e tempo-sensível. Com os atuais indícios de que esta pandemia continuará se prolongando globalmente, médicos-neurologistas e de outras especialidades responsáveis pelo atendimento emergencial do acidente vascular encefálico (AVE) estão sob constante risco de exposição ao COVID-19. Visando minimizar esses riscos, mas mantendo a eficiência e acurácia diagnóstica do exame, inovações no exame neurológico emergencial para o paciente com suspeita de AVE foram implementadas em diversos centros de referência para o atendimento de AVE. É objetivo deste projeto, portanto, apresentar um vídeo (https://youtu.be/5Sh1PnpeKmk), demonstrando o exame neurológico observacional na era da COVID-19, visando extrair o máximo de informações de forma eficiente, rápida, e minimizando os riscos de contaminação para o médico-assistente

Contribuição para o ensino de Ortopedia da primeira liga da especialidade em Rondônia

Among the various forms of medical education, the participation in academic leagues has great importance in theoretical and technical training of students, due to extracurricular activities exercised by members. In view of this situation, the first league of orthopedics and traumatology of Rondônia was created, with a commitment to provide theoretical and practical knowledge to its members. Thus, this paper aims to report the experience of orthopedics teaching through the creation of the league.Dentre as diversas formas de ensino médico, a participação em ligas acadêmicas tem grande importância na formação técnica e teórica dos alunos, devido às atividades extracurriculares exercidas pelos membros. Em vista dessa situação, foi criada a primeira liga de ortopedia e traumatologia do estado de Rondônia, com o compromisso de fornecer conhecimento teórico e prático aos seus membros. Com isso, esse artigo tem como objetivo relatar a experiência do ensino da ortopedia através da criação da liga

INSECTICIDE-TREATED BED NETS IN RONDÔNIA, BRAZIL: EVALUATION OF THEIR IMPACT ON MALARIA CONTROL

Mosquito nets treated with long-lasting insecticide (LLINs), when used in compliance with guidelines of the World Health Organization, may be effective for malaria vector control. In 2012, approximately 150,000 LLINs were installed in nine municipalities in the state of Rondônia. However, no studies have assessed their impact on the reduction of malaria incidence. This study analyzed secondary data of malaria incidence, in order to assess the impact of LLINs on the annual parasite incidence (API). The results showed no statistically significant differences in API one year after LLIN installation when compared to municipalities without LLINs. The adoption of measures for malaria vector control should be associated with epidemiological studies and evaluations of their use and efficiency, with the aim of offering convincing advantages that justify their implementation and limit malaria infection in the Amazon Region

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