Occupational exposure to particulate matter and respiratory symptoms in Portuguese swine barn workers

Certain environmental conditions in animal and plant production have been associated with increased frequency in respiratory illnesses, including asthma, chronic bronchitis, and hypersensitivity pneumonitis, in farmers occupationally exposed in swine production. The aim of this study was to characterize particulate matter (PM) contamination in seven Portuguese swine farms and determine the existence of clinical symptoms associated with asthma and other allergy diseases, utilizing the European Community Respiratory Health Survey questionnaire. Environmental assessments were performed with portable direct-reading equipment, and PM contamination including five different sizes (PM0.5, PM1.0, PM2.5, PM5.0, PM10) was determined. The distribution of particle size showed the same trend in all swine farms, with high concentrations of particles with PM5 and PM10. Results from the questionnaire indicated a trend such that subjects with diagnosis of asthma were exposed to higher concentrations of PM with larger size (PM2.5, PM5, and PM10) while subjects with sneezing, runny nose, or stuffy nose without a cold or flu were exposed to higher concentrations of PM with smaller size (PM0.5 and PM1). Data indicate that inhalation of PM in swine farm workers is associated with increased frequency of respiratory illnesses.

Idosos ativos, idosos saudáveis

Mestrado em Fisioterapia

Ansiedade, satisfação e bem-estar em finalistas e profissionais de psicologia em início de carreira

Dissertação de Mestrado, Psicologia da Educação, 11 de Abril de 2014, Universidade dos Açores.

Inclusão de alunos com necessidades educativas especiais no 2º ciclo do ensino básico: a perspectiva dos seus pares

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ciências da Educação, especialidade Educação Especial

Determinants of salivary cotinine level: a population-based study in Brazil

OBJECTIVE: A cross-sectional population-based study was conducted to assess, in active smokers, the relationship of number of cigarettes smoked and other characteristics to salivary cotinine concentrations. METHODS: A random sample of active smokers aged 15 years or older was selected using a stepwise cluster sample strategy, in the year 2000 in Rio de Janeiro, Brazil. The study included 401 subjects. Salivary cotinine concentration was determined using gas chromatography with nitrogen-phosphorus detection. A standard questionnaire was used to collect demographic and smoking behavioral data. The relation between the number of cigarettes smoked in the last 24h and cotinine level was examined by means of a nonparametric fitting technique of robust locally weighted regression. RESULTS: Significantly (p<0.05) higher adjusted mean cotinine levels were found in subjects smoking their first cigarette within five minutes after waking up, and in those smoking 1-20 cigarettes in the last 24h who reported inhaling more than ½ the time. In those smoking 1-20 cigarettes, the slope was significantly higher for those subjects waiting for more than five minutes before smoking their first cigarette after waking up, and those smoking "light" cigarettes when compared with their counterparts. These heterogeneities became negligible and non-significant when subjects with cotinine >40 ng/mL per cigarette were excluded. CONCLUSIONS: There was found a positive association between self-reporting smoking five minutes after waking up, and inhaling more than ½ the time are consistent and higher cotinine levels. These can be markers of dependence and higher nicotine intake. Salivary cotinine proved to be a useful biomarker of recent smoking and can be used in epidemiological studies and smoking cessation programs.

A incidência da avaliação sumativa externa (provas de aferição) nas práticas docentes do 1.º ciclo do Ensino Básico e nos projetos curriculares de turma

Dissertação de Mestrado, Educação (Desenvolvimento e Concepção de Projectos Educativos), 19 de Março de 2013, Universidade dos Açores.

Dietary patterns and risk of oral cancer: a case-control study in São Paulo, Brazil

OBJECTIVE: To analyze the association between dietary patterns and oral cancer. METHODS: The study, part of a Latin American multicenter hospital-based case-control study, was conducted in São Paulo, Southeastern Brazil, between November 1998 and March 2002 and included 366 incident cases of oral cancer and 469 controls, frequency-matched with cases by sex and age. Dietary data were collected using a food frequency questionnaire. The risk associated with the intake of food groups defined a posteriori, through factor analysis (called factors), was assessed. The first factor, labeled "prudent," was characterized by the intake of vegetables, fruit, cheese, and poultry. The second factor, "traditional," consisted of the intake of rice, pasta, pulses, and meat. The third factor, "snacks," was characterized as the intake of bread, butter, salami, cheese, cakes, and desserts. The fourth, "monotonous," was inversely associated with the intake of fruit, vegetables and most other food items. Factor scores for each component retained were calculated for cases and controls. After categorization of factor scores into tertiles according to the distribution of controls, odds ratios and 95% confidence intervals were calculated using unconditional multiple logistic regression. RESULTS: "Traditional" factor showed an inverse association with cancer (OR=0.51; 95% CI: 0.32; 0.81, p-value for trend 0.14), whereas "monotonous" was positively associated with the outcome (OR=1.78; 95% CI: 1.78; 2.85, p-value for trend <0.001). CONCLUSIONS: The study data suggest that the traditional Brazilian diet, consisting of rice and beans plus moderate amounts of meat, may confer protection against oral cancer, independently of any other risk factors such as alcohol intake and smoking.

Perceção dos riscos ocupacionais pelos técnicos de radiologia da Região Autónoma dos Açores

Dissertação de Mestrado, Ambiente, Saúde e Segurança, 23 de Abril de 2013, Universidade dos Açores.

Stresse, burnout e estratégias de coping nos guardas prisionais da Região Autónoma dos Açores

Mestrado, Psicologia da Educação (Contextos Educativos), 18 de Junho de 2014, Universidade dos Açores.

Riscos psicossociais no trabalho: estresse e estratégias de coping em enfermeiros em oncologia

Objetivo: identificar fontes de estresse e estratégias de coping em enfermeiros que exercem funções em três Serviços de Oncologia de Cirurgia Cabeça e Pescoço, de três hospitais centrais de Portugal. Método: estudo transversal, de carácter descritivo e exploratório, cuja amostra foi constituída pelos 96 enfermeiros dos três serviços. Na recolha de dados, foram utilizados: Questionário Sociodemográfico, Questionário de Saúde Geral-12, Inventário de Estressores Ocupacionais e Brief COPE. Resultados: verificaram-se níveis razoáveis de saúde geral. Os estressores mais referidos foram: sobrecarga de trabalho, baixa remuneração salarial, espaço físico onde se desenvolve a profissão, situações emocionalmente perturbadoras e falta de reconhecimento da profissão. As estratégias de coping mais utilizadas foram: planeamento, coping ativo, aceitação e autodistração. Conclusão: os estressores identificados relacionam-se principalmente a aspetos organizacionais e condições de trabalho, e as estratégias de coping escolhidas estão direcionadas para a resolução de problemas e melhoria do bemestar dos enfermeiros. Percentagem expressiva de enfermeiros apresentou níveis elevados de pressão e emoções deprimidas. Os resultados apresentados corroboram estudos anteriores que alertam para a importância do desenvolvimento de estratégias de prevenção dos níveis de estresse.

Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Comportamento das famílias em relação ao consumo de eletricidade: o caso dos Açores

Dissertação de Mestrado em Ciências Económicas e Empresariais