Med. Sci., Volume 2, Issue 3 (September 2014) – 2 articles , Pages 127-152

Metabolic syndrome (MetS) is chronic inflammatory epidemic state contributing to total and cardiovascular mortality. The current study planned to assess and screen risk factors for MetS and its components. A cross-sectional study conducted to assess age, gender, social status, employment, education, family history, physical activity, dietary habits, alcohol, sleep, body mass index and stress as determinants of MetS. The results were analyzed by Chi Square test with statistical significance of p value <0.05. The frequency of MetS was 17.38% as per modified National Cholesterol Education Program–Adult Treatment Panel III criteria. Females (57.38%), age >50 years (86.90%; p < 0.05), middle socioeconomic status (70.50%), illiteracy (39.35%), and unemployment (81.97%; p < 0.05) were found contributing though to different extents. Subjects with a sedentary lifestyle (72.14%), positive family history (42.63%), omnivore diet (47.55%), stress (78.69%; p < 0.05), insomnia (29.51%) and increased BMI (83.62%; p < 0.001) had shown predisposition to MetS. However, the protective role of alcohol (38.28%), an active lifestyle (36.21%), vegetarian diet (62.07%) and adequate sleep (73.11%) was observed. A significant hypertension (98.37%; p < 0.001), dyslipidemia (77.05%; p < 0.001), dysglycemia (75.41%; p < 0.001) and obesity (59.02%; p < 0.001) was reported in MetS. Common concerns of female gender, increasing age and BMI, sedentary lifestyle, stress and positive family history should be considered for early identification and appropriate intervention to fight the growing MetS epidemic. Full article

Drosophila segmentation as a model organism is one of the most highly studied. Among many maternal segmentation coordinate genes, bicoid protein pattern plays a significant role during Drosophila embryogenesis, since this gradient determines most aspects of head and thorax development. Despite the fact that several models have been proposed to describe the bicoid gradient, due to its association with considerable error, each can only partially explain bicoid characteristics. In this paper, a modified version of singular spectrum analysis is examined for filtering and extracting the bicoid gene expression signal. The results with strong evidence indicate that the proposed technique is able to remove noise more effectively and can be considered as a promising method for filtering gene expression measurements for other applications. Full article