Estudo da aplicação de redes neuronais artificiais para apoio à decisão na liberação do perfil lipídico e de glicemia em jejum.

Orientadora : Profª Drª Jeroniza Nunes MarchaukoskiCo-Orientador: Prof. Dr. Geraldo PichethDissertação (mestrado) - Universidade Federal do Paraná, Setor de Educação profissional e Tecnológica, Programa de Pós-Graduação em Bioinformática. Defesa: Curitiba, 21/02/2011Bibliografia: fls. 84-88Resumo: As determinações do perfil lipídico (colesterol total, HDL-colesterol, LDL-colesterol, triglicérides) e da glicemia em jejum são ensaios de grande demanda nos laboratórios clínicos. A liberação destes resultados por profissionais consome tempo e atenção. O estudo se propõe avaliar a aplicação das redes neuronais, Multilayer Perceptron (MLP) e Free Associative Neurons (FAN), como ferramentas de inteligência artificial para colaborar na liberação dos resultados, em processo designado "segunda opinião". O projeto tem a aprovação do Comitê de Ética em Pesquisa com Seres Humanos do HC-UFPR (CAE: 0253.0.208.000-10). Uma amostra contendo 60.006 registros obtidos do banco de dados do HC-UFPR foi analisada. A idade média dos pacientes foi cerca de 47 anos (amplitude de variação de 2 a 99 anos), com predomínio de mulheres (~65%). Esta amostra foi classificada em "liberado" quando todo os valores dentro dos critérios estabelecidos de normalidade e "retido" quando qualquer analito estudado se mostrou fora da referência. Esta classificação resultou em 62% da amostra classificada no grupo "retido". Quando as redes neuronais foram testadas com arquivos completos (n=30.003) a rede FAN apresentou divergência cerca de 6 vezes superior à rede MLP (7,6% vs. 1,2%) embora ambas tenham um desempenho satisfatório em acurácia (>90%). Foram treinadas e testadas as redes FAN e MLP com arquivos incompletos, aracterizados pela ausência de algum dos parâmetros em estudo com diferentes tamanhos de arquivos (30.536, 65.536 e 120.000 registros). Nesta condição que mimetiza os resultados liberados pelo laboratório clínico, a rede neuronal MLP apresentou desempenho superior à rede FAN. O estudo permitiu concluir que: (1) a rede neuronal FAN perde desempenho com arquivos incompletos, (2) a rede neuronal MLP apresentou desempenho superior à rede FAN quando estudada com arquivos completos ou incompletos, (3) o tamanho amostral utilizado para treinamento e teste não afetaram o desempenho da rede neuronal MLP, enquanto que a rede FAN é afetada por perda de sensibilidade, (4) resultados divergentes da rede neuronal MLP avaliados por especialistas humanos evidenciaram que os ensaios com valores alterados foram o principal elemento de inconsistência. Em síntese, a rede neuronal MLP é recomendada para outros estudos com desenho amostral semelhante e apresenta potencial para aplicação no laboratório clínico como suporte a decisão na liberação de resultados.Abstract: The lipid profile (total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides) and fasting blood glucose are tests of high throughput in clinical laboratories. In the process of liberate these results professionals needs to takes time and attention. The study aims to evaluate the application of neural networks, Multilayer Perceptron (MLP) and Free Associative Neurons (FAN), as artificial intelligence tools to assist in the release of the results, a process called "second opinion". The project was approved by the Ethic Committee in Human Research of the HC-UFPR (CAE: 0253.0.208.000-10). A sample containing 60,006 registers obtained from HC-UFPR Database was analyzed. The mean age of patients was about 47 years (range: 2-99 years) with predominance of women (~65%). This sample was classified as "released", when all values were within the established criteria of normality and "retained" when any studied analyte showed values outside the reference of normality. This classification resulted in 62% of the sample classified as "retained". When neural networks were tested with complete files (n=30,003) FAN network disagreement was about 6 times higher compared with the MLP network (7.6% vs. 1.2%) although both have a satisfactory performance in accuracy (> 90%). The FAN and MLP networks were also trained and tested with incomplete files, characterized by the absence of any of the parameters under study with different file sizes (30,536, 65,536 and 120,000 registers). In this condition that mimics the results released by a clinical laboratory, the MLP neural network showed a superior performance compared to the FAN network. The study concluded that: (1) the FAN neural network loses performance with incomplete files, (2) the performance of MLP neural network was superior to the FAN network when tested with complete or incomplete files, (3) the sample size used to training and testing did not affect the performance of MLP neural network, while FAN network that is affected by loss of sensitivity, (4) divergent results of the MLP neural network evaluated by human experts showed that the tests with high values were the main element of inconsistency. In summary, the MLP neural network is recommended for other studies with similar sample design and presents a potential for application in the clinical laboratory as a decision support system

A epidemia do Diabetes mellitus encontra a pandemia da SARS-CoV-2 (COVID-19) / The Diabetes mellitus epidemic meets the SARS-CoV-2 (COVID-19) pandemic

A pandemia da doença coronavírus 2019 (COVID-19; SARS-Co-2) surgiu como um dos maiores desafios enfrentados pela humanidade. O diabetes mellitus, associado a hiperglicemia crônica, favorece comorbidades que podem ampliar o risco de severidade ou morte quando associado a viremia de COVID-19. A Idade avançada, múltiplas morbidades, hiperglicemia, doença cardíaca e resposta inflamatória severa são preditores de desfechos desfavoráveis na presença da viremia. Os diferentes aspectos da COVID-19 e suas associações com o diabetes mellitus, são abordados. A relação da enzima conversora de angiotensionogênio (ACE2), o controle glicêmico e diferentes aspectos da severidade e prognóstico são descritos em relação ao diabetes-COVID-19. Também são comentadas as medidas para prevenir a propagação da viremia com ênfase em orientações e no gerenciamento de procedimentos hospitalares e ambulatórias para o paciente com diabetes, em particular no sistema público de saúde. Esta revisão resume o conhecimento atual e os desafios percebidos para prevenção e gestão de COVID-19 em pessoas com diabetes

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

Restless legs syndrome: diagnosis and treatment. Opinion of Brazilian experts

This article contains the conclusions of the November 17-18, 2006 meeting of the Brazilian Study Group of Restless Legs Syndrome (GBE-SPI) about diagnosis and management of restless legs syndrome (RLS). RLS is characterized by abnormal sensations mostly but not exclusively in the legs which worsen in the evening and are improved by motion of the affected body part. its diagnosis is solely based on clinical findings. Therapeutic agents with efficacy supported by Class I studies are dopamine agonists, levodopa and gabapentine. Class 11 studies support the use of slow release valproic acid, clonazepan and oxycoclone. The GBE-SPI recommendations for management of SPI are sleep hygiene, withdrawal of medications capable of worsening the condition, treatment of comorbidities and pharmacological agents. The first choice agents are dopaminergic drugs, second choice are gabapentine or oxycodone, and the third choice are clonazepan or slow release valproic acid.Univ Sao Paulo, Hosp Clin, Dept Neurol, Sao Paulo, BrazilHosp Israelita Albert Einstein, Inst Cerebro, Sao Paulo, BrazilPratica privada, Curitiba, Parana, BrazilPractica Privada, Rio De Janeiro, BrazilUniv Fed Minas Gerais, Dept Clin Med, Belo Horizonte, MG, BrazilUniv Fed Sao Paulo, Escola Paulista Med, Dept Neurol, Sao Paulo, BrazilUNESP, Dept Neurol, Botucatu, SP, BrazilUFCE, Fortaleza, Ceara, BrazilPractica Privada, Porto Alegre, RS, BrazilUniv Fed Goias, Hosp Clin, Serv Neurol, Goiania, Go, BrazilUniv Fed Sao Paulo, Escola Paulista Med, Dept Neurol, Sao Paulo, BrazilWeb of Scienc