Nutritional knowledge, eating attitudes and chronic dietary restraint among men with eating disorders

We compared nutritional knowledge, eating attitudes and chronic dietary restraint scores among 17 men (10 with bulimia nervosa and 7 with anorexia nervosa) and 50 women (20 with bulimia nervosa and 30 with anorexia nervosa), who were consecutive patients at a major treatment center in Brazil. There were no differences in nutritional knowledge and concern with food between men and women. For both genders, chronic dietary restraint scores were higher among bulimics. Men with eating disorders had better eating attitudes scores than women. Anorexic men tended to have worse eating attitudes scores than bulimic men, while the opposite was observed for women, suggesting an interaction between gender and diagnosis. (C) 2009 Published by Elsevier Ltd.

Nutritional recommendation alone does not change the obesity profile of health professionals

Objective: To evaluate the frequency of overweight and obesity in health professionals, before and after a single specialized dietary recommendation. Methods: Anthropometric measures of 579 workers of a general hospital in the city of Sao Paulo, Brazil were taken. The weight (f), height (h) and waist circumference (wc) were interpreted according to the WHO and NCEP ATP III guidelines. Nutrition specialist provided dietary and behavioral recommendations. The entire sample underwent a new evaluation one year later. Results: At the first evaluation, 79 employees presente WC >= 102 cm (male) or WC >= 88 cm (female). The association between WC >= 102 cm (men) or WC >= 88 cm (women) and BMI >= 30 kg/m(2) was found in 12.8 % (69 subjects). The BMI distribution per age group indicated that the increase in overweight and obesity was directly proportional to the age increase. Physical activities were not practiced by 75% of the subjects studied. A year later, the evaluation indicated lack of statistical differences regarding the BMI and waist circumference of the sample and only 2.8% started to practice a physical activity. Conclusion: Dietary recommendation alone failed to promote changes in the eating habits of health professionals who work at a general hospital or to encourage them to practice exercise.

Mango starch degradation. II. The binding of alpha-amylase and beta-amylase to the starch granule

During mango ripening, soluble sugars that account for mango sweetening are accumulated through carbon supplied by both photosynthesis and starch degradation. The cultivar Keitt has a characteristic dependence on sugar accumulation during starch degradation, which takes place during ripening, only a few days after detachment from the tree. Most knowledge about starch degradation is based on seeds and leaves currently used as models. However, information about the mango fruit is scarce. This work presents the evaluation of alpha- and beta-amylases in the starch granule surface during fruit development and ripening. Extractable proteins were assayed for amylase activity and detected by immunofluorescence microscopy and correlated to gene expression. The results suggest that both amylases are involved in starch degradation during mango ripening, probably under the dependence of another signal triggered by the detachment from the mother-plant.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

Intracultural Diversity in Food Knowledge in Southern Brazil

This research explores the social distribution of food knowledge in Ribeirao Preto, a city in the state of Sao Paulo, Brazil. Through an analysis of the distribution of individual expertise in regard to the cultural model of food along the dimensions of healthfulness, practicality, and prestige, this research demonstrates that knowledge of the cultural model of food is most strongly shared in the upper class of the city. Qualitative and quantitative ethnographic research suggests that the social patterning of health-related food knowledge in Ribeirao Preto may serve to maintain class distinction.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.