Vehicles play an important role in modern life; however, they also generate hazards. Occupational exposed subjects are in long-term contact with harmful products, which sets these professionals in a susceptible group to air pollutant damage. The aims of this study were to quantify individual exposure to pollutant gases and chemical elements and to evaluate oxidative and genetic damage in professional motorcyclists and office workers. We recruited professional motorcyclists and office workers from Porto Alegre, Brazil, between January and December 2016. Individual exposure to air pollutants was assessed by passive monitoring. Fingernail trace elements were determined by using inductively coupled plasma mass spectrometry. Oxidative stress biomarkers were quantified spectrophotometrically, and genotoxicity was evaluated by micronuclei assay. Individual exposure to NO and O, trace element content (Sb, Pt, As, Cd, V, Mn, and Co), oxidative stress factors, and genetic damage were statistically higher in professional motorcyclists (p < 0.05). Moreover, NO and O levels showed very strong positive correlation with plasmatic lipid peroxidation (p < 0.001 and r = 0.8849 and 0.8995) and strong positive correlation with micronuclei frequency (p < 0.001 and r = 0.7683 and 0.7280). Results suggest that professional motorcyclists are at high risk due to long-term air pollution exposure, which implies in the onset of several harmful effects and worsening of pre-existent diseases.
In the last few decades, obesity has grown exponentially and it progression is imminent contributing to the increase of mortality levels. Artificial Intelligence (AI), which is Computer Science area, could be well applied to management of obesity, such as an important tool to avoid the threat caused by this disease. The aim of this literature review was to show AI applications to obesity management and discuss their effectiveness. The search was performed in the following databases: Public Medline (PubMed), Web of Science, Biblioteca Regional de Medicina (BIREME) and Google Academic, by using following keywords, "artificial intelligence" and "obesity". Our results led to some Artificial Intelligence systems used in obesity handling, which were: the Decision Support System to bariatric surgery patients; the MOPET app to motivate physical activity; Parameter Decreasing Methods and Artificial Neural Network to correlate obesity to cardiovascular disease; Artificial Neural Network to predict resting energy expenditure; a Neuro-Fuzzy Model to refine body mass index result; an Image Processing Algorithm; and a Support Vector Machine that monitors food intake. In this review, all investigated AI systems may have a tendency to more accurate results indicating a promising tool to manage obesity and related diseases.
Tecnologias de Inteligência Artificial no manejo da obesidade
RESUMONas últimas décadas, a obesidade tem crescido de forma exponencial e essa progressão tem contribuído com o aumento dos níveis de mortalidade. Inteligência artificial, uma área da Ciência da Computação, poderia ter uma aplicação benéfica no manejo da obesidade, como uma ferramenta importante para evitar as ameaças à saúde causadas por essa condição. O objetivo desta revisão literária foi investigar as aplicações da AI no manejo da obesidade e discutir a sua eficácia. A busca foi realizada nas seguintes bases de dados: Public Medline (PubMed), Web of Science, Biblioteca Regional de Medicina (BIREME) e Google Academic; utilizando as palavras-chave "inteligência artificial" e "obesidade". Como resultado dessa revisão foi possível observar diferentes sistemas de AI empregados no tratamento da obesidade, os quais são: o "Decision Support System" para pacientes com cirurgia bariátrica; o "MOPET" aplicativo de atividade física; o "Parameter Decreasing Methods" e a "Artificial Neural Network" para correlacionar obesidade com doenças cardiovasculares; "Artificial Neural Network" para prever o gasto energético; a "Neuro-Fuzzy Model" para refinar os resultados fornecidos pelo IMC; um algoritmo para processamento de imagens; e o "Support Vector Machine", que monitora o consumo alimentar. Nesta revisão, todos os sistemas de AI apresentaram uma tendência a resultados com maior acurácia (mais precisos), indicando uma ferramenta promissora para o manejo da obesidade e das doenças relacionadas.
Traffic-related air pollution is an alarming source of pollutants exposure and consequently to the development of several adverse health effects. Otherwise, green spaces are reported to improve health status. Although, in an urban scenario most of these areas are located near air pollutants sources, as vehicle fleet. Thus, the aim of the present study was to determine, during one year, the levels of nitrogen dioxide (NO 2) and ozone (O 3) in the main parks from Porto Alegre-Brazil. This study focused on three urban parks: Germânia, Moinhos de Vento and Marinha do Brasil Park. Nitrogen dioxide and ozone measurements were accessed by passive monitoring in four campaigns including all seasons and performed at distances of 0 m, 15 m, 30 m, 45 m, 60 m and 75 m from the main road at each park. NO 2 and O 3 concentration among the parks was not different (p > 0.05), as well as the mean concentration of NO 2 and O 3 of all parks in the six sites did not differ (p > 0.05). However, season 1 and 3 showed increased NO 2 and O 3 concentration. Temperature were decreased in season 1 and 3 (p < 0.05), while humidity, pressure and insolation showed no difference among seasons (p > 0.05). Traffic flow was higher in Moinhos de Vento Park and Marinha do Brasil Park compared to Germânia Park (p < 0.05). Overall, the seasonal variation could directly interfere in NO 2 and O 3 concentration in urban parks from Porto Alegre.
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