Graphic interpretation and ventilatory monitoring: the knowledge of physiotherapist

Objetivos: avaliar o conhecimento do fisioterapeuta em relação à interpretação gráfica e à monitorização ventilatória. Métodos: estudo quantitativo realizado entre março e abril de 2018, em três hospitais de Fortaleza/CE, com 41 fisioterapeutas intensivistas sobre seu conhecimento no que diz respeito à interpretação gráfica e monitorização ventilatória, bem como sua abordagem terapêutica frente aos achados clínicos. Os dados foram colhidos por meio de um questionário com 10 questões objetivas e analisados por meio do software Statistical Package for Social Sciences (SPSS) 17.0. Resultados: a maioria dos fisioterapeutas tinham entre 6 e 10 anos de atuação na UTI (43,9%). Quando questionados sobre os conhecimentos básicos de pressão e parâmetros de normalidade do Índice de Oxigenação, 80,5% e 70,6% fisioterapeutas ofereceram respoatas corretasrespectivamente. Acerca do conhecimento de monitorização ventilatória, o conceito de drive pressure foi o que apresentou melhor índice de assertiva com 73,2% acertos. Já sobre a análise gráfica, a interpretação da apneia (87,8%) e a abordagem na auto-PEEP (58,5%) foram as questões com maior número de acertos. Conclusão: houve assertividades relevantes no tocante ao conhecimento dos conceitos básicos, da monitorização dos parâmetros ventilatórios e da interpretação gráfica por parte dos profissionais entrevistados, porém sugere-se que haja mais estudos sobre a temática por meio de uma estratégia de educação continuada a fim de gerar um maior suporte teórico-prático para os fisioterapeutas intensivistas.Objectives: To evaluate the knowledge of the physiotherapist in relation to the graphic interpretation and the ventilation monitoring. Methods: A quantitative study was performed between March and April 2018 in three hospitals in Fortaleza / CE, with 41 intensivist physiotherapists about their knowledge regarding graphic interpretation and ventilation monitoring, as well as their therapeutic approach to clinical findings. The data were collected through a questionnaire with 10 objective questions and analyzed through the software Statistical Package for Social Sciences (SPSS) 17.0. Results: The majority of physiotherapists had between 6 and 10 years of ICU performance 43.9%. When questioned about the basic knowledge of pressure and parameters of normality of the Oxygenation Index, 80.5% and 70.6% physiotherapists answered correctly. Regarding the knowledge of ventilation monitoring, the concept of drive pressure was the one that presented the best assertive index with 73.2% correct answers. On the graphical analysis, the interpretation of apnea 87.8% and the auto-PEEP approach with 58.5% were the questions with the highest number of correct answers. Conclusion: There were relevant assertions regarding the knowledge of the basic concepts, the monitoring of ventilation parameters and the graphic interpretation by the professionals interviewed, but it is suggested that there be more studies on the subject through a strategy of continuing education in order to generate more theoretical-practical support for intensivist physiotherapists

Endothelial biomarkers in critically-ill COVID-19 patients: potential predictors of the need for dialysis

Introduction: To evaluate the function of vascular biomarkers to predict need for hemodialysis in critically-ill patients with COVID-19. Methods: This is a prospective study with 58 critically-ill patients due to COVID-19 infection. Laboratory tests in general and vascular biomarkers, such as VCAM-1, Syndecan-1, Angiopoietin-1 and Angiopoeitin-2 were quantified on intensive care unit (ICU) admission. Results: There was a 40% death rate. VCAM and Ang-2/Ang-1 ratio on ICU admission were associated with need for hemodialysis. Vascular biomarkers (VCAM-1, Syndecan-1, angiopoetin-2/ anogiopoetin-1 ratio) were predictors of death and their cut-off values were useful to stratify patients with a worse prognosis. In the multivariate cox regression analysis with adjusted models, VCAM-1 [O.R. 1.13 (C.I. 95%: 1.01 - 1.27); p= 0.034] and Ang-2/Ang-1 ratio [O.R. 4.87 (C.I.95%: 1.732 - 13.719); p= 0.003] were associated with need for dialysis. Conclusion: Vascular biomarkers, mostly VCAM-1 and Ang-2/Ang-1 ratio, showed better efficiency to predict need for hemodialysis in critically-ill COVID-19 patients

Desenvolvimento e perspectivas da propriedade intelectual no Brasil

O desafio das universidades é fazer com que a pesquisa científica estenda-se ao mercado, gerando bem estar social e contribuindo para o desenvolvimento econômico de maneira sustentável e inovadora. A Universidade Federal do Recôncavo da Bahia é a principal Instituição Científica e Tecnológica (ICT) de geração e difusão do conhecimento no recôncavo baiano, mas precisa criar mecanismos para a proteção desse conhecimento, estimulando os pesquisadores – docentes e discentes – a agregar tecnologia e inovação à teoria do conhecimento clássico para exaltar o fruto do intelecto. Este livro reúne informações capazes de direcionar a política de inovação e gestão tecnológica nacional no contexto acadêmico, beneficiando tanto as universidades quanto as empresas e a sociedade, uma vez que permite estreitar parcerias que buscam a formação de profissionais qualificados, capazes de gerar produtos injetores de tecnologia avançada e respeito ao meio ambiente, versando por princípios de responsabilidade e compromisso social

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

Pró-PET-Saúde/Rede Urgência e Emergência: um Relato de Experiência Prática de Ensino-Serviço-Aprendizagem

RESUMO Objetivo Este estudo objetiva relatar a experiência da acadêmica do curso de Fisioterapia e extensionista do Pró-PET-Saúde da Universidade Federal do Ceará no Instituto Dr. José Frota de Fortaleza. Metodologia Trata-se de um relato de experiência de abordagem qualitativa, com descrição de visitas supervisionadas, realizadas pela extensionista do Pró-PET-Saúde da Universidade Federal do Ceará no Instituto Dr. José Frota de Fortaleza durante 26 meses. Resultados Foi vivenciada a rotina hospitalar de diversos profissionais da saúde na unidade concedente. Os procedimentos realizados eram efetuados pelo preceptor/profissional, e o aluno o observava, correlacionando os conhecimentos teóricos adquiridos com a prática visualizada. Conclusões Nesse processo de vivência hospitalar, a visão inter- e multidisciplinar é abordada de maneira a beneficiar a formação acadêmica e de saudável convivência, expondo o aluno a inúmeras situações necessárias e benéficas. Observou-se a importância da inserção da fisioterapia na equipe profissional, auxiliando na reabilitação, bem-estar e melhora da qualidade de vida desses indivíduos