Factores relacionados con los síntomas depresivos en estudiantes universitarios / Factors Related to Depressive Symptoms in University Students / Fatores relacionados à sintomatologia depressiva em universitários

Objetivo: identificar e discutir a frequência da sintomatologia depressiva e seus fatores associados em estudantes universitários. Materiais e método: trata-se de um estudo transversal, descritivo com abordagem quantitativa, realizado em uma instituição de ensino superior. O estudo foi desenvolvido por amostra de conveniência com 571 estudantes de graduação de diferentes áreas. Utilizou-se um questionário para a coleta de dados sociodemográficos/acadêmicos, da sintomatologia depressiva e suas associações, dos hábitos de vida, dos aspectos emocionais e dos possíveis distúrbios alimentares. Para a análise dos dados, utilizaram-se a estatística descritiva e o teste de Qui-Quadrado de Pearson para associações, com o nível de significância de 5 % (valor p ≤ 0,05). Resultados: os universitários apresentaram sintomatologia depressiva como sono perturbado (61,1 %) e autoconfiança reduzida (50,9 %). Houve associação da sintomatologia depressiva com o curso, com a satisfação do rendimento acadêmico, com a obesidade, com o consumo de açúcares e doces, com a prática e a frequência de atividade física. Conclusões: espera-se que os resultados desta pesquisa possam contribuir para o pensamento crítico e reflexivo da população, a fim de expandir a visibilidade e os estudos científicos referentes à temática, bem como de aumentar os recursos para o manejo da saúde mental e diminuir os estigmas gerados.

Objective: To identify and discuss the frequency of depressive symptoms and their associated factors in university students. Materials and method: This is a cross-sectional and descriptive study with a quantitative approach, conducted in a Higher Education Institution. The study was developed with a convenience sample consisting of 571 undergraduate students from different areas. A questionnaire was used to collect sociodemographic/academic data, as well as depressive symptoms and their associations, life habits, emotional aspects, and possible eating disorders. For data analysis, descriptive statistics and Pearson's chi-square test were used for associations, with a significance level of 5 % (p-value ≤ 0.05). Results: The university students presented depressive symptoms such as sleep disorders (61.1 %) and reduced self-confidence (50.9 %). There was an association of depressive symptoms with the course, satisfaction with academic performance, obesity, consumption of sugars and sweets, and practice and frequency of physical activity. Conclusions: It is expected that the results of this research contribute to the population's critical and reflective thinking to expand visibility and scientific studies referring to the theme, as well as to increase resources for the management of mental health and reduce the stigmas generated.

Objetivo: identificar y discutir la frecuencia de los síntomas depresivos y sus factores asociados en estudiantes universitarios. Materiales y método: se trata de un estudio transversal, descriptivo con enfoque cuantitativo, realizado en una institución de educación superior. El estudio se desarrolló a partir de un muestreo de conveniencia con 571 estudiantes de diferentes facultades. Se utilizó cuestionario para recolectar los datos sociodemográficos/académicos, de la sintomatología depresiva y sus asociaciones, los hábitos de vida, los aspectos emocionales y los posibles disturbios alimenticios. Para el análisis de los datos, se emplearon la estadística descriptiva y la prueba de Qui-Cuadrado de Pearson para asociaciones, con nivel de significancia del 5 % (valor p ≤ 0,05). Resultados: los universitarios presentaron sintomatología depresiva como sueño perturbado (61,1 %) y autoconfianza reducida (50,9 %). Hubo asociación de la sintomatología depresiva con el grado, con la satisfacción del desempeño académico, con el sobrepeso, con el consumo de azúcares y dulces, con la práctica y la frecuencia de actividad física. Conclusiones: se espera que los resultados de la investigación puedan aportar al pensamiento crítico y reflexivo de la población, con el intuito de expandir la visibilidad y los estudios científicos sobre la temática, así como aumentar los recursos para la gestión de la salud mental y disminuir los estigmas generados.

Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features.

Gliomas are among the most common and aggressive brain tumours. Segmentation of these tumours is important for surgery and treatment planning, but also for follow-up evaluations. However, it is a difficult task, given that its size and locations are variable, and the delineation of all tumour tissue is not trivial, even with all the different modalities of the Magnetic Resonance Imaging (MRI). We propose a discriminative and fully automatic method for the segmentation of gliomas, using appearance- and context-based features to feed an Extremely Randomized Forest (Extra-Trees). Some of these features are computed over a non-linear transformation of the image. The proposed method was evaluated using the publicly available Challenge database from BraTS 2013, having obtained a Dice score of 0.83, 0.78 and 0.73 for the complete tumour, and the core and the enhanced regions, respectively. Our results are competitive, when compared against other results reported using the same database.

Segmentation of small bowel tumor tissue in capsule endoscopy images by using the MAP algorithm.

State of the art algorithms for diagnosis of the small bowel by using capsule endoscopic images usually rely on the processing of the whole frame, hence no segmentation is usually required. However, some specific applications such as three-dimensional reconstruction of the digestive wall, detection of small substructures such as polyps and ulcers or training of young medical staff require robust segmentation. Current state of the art algorithms for robust segmentation are mainly based on Markov Random Fields (MRF) requiring prohibitive computational resources not compatible with applications that generate a great amount of data as is the case of capsule endoscopy. However context information given by MRF is not the only way to improve robustness. Alternatives could come from a more effective use of the color information. This paper proposes a Maximum A Posteriori (MAP) based approach for lesion segmentation based on pixel intensities read simultaneously in the three color channels. Usually tumor regions are characterized by higher intensity than normal regions, where the intensity can be measured as the vectorial sum of the 3 color channels. The exception occurs when the capsule is positioned perpendicularly and too close to the small bowel wall. In this case a hipper intense tissue region appears at the middle of the image, which in case of being normal tissue, will be segmented as tumor tissue. This paper also proposes a Maximum Likelihood (ML) based approach to deal with this situation. Experimental results show that tumor segmentation becomes more effective in the HSV than in the RGB color space where diagonal covariance matrices have similar effectiveness than full covariance matrices.