Comparison of lifestyle in fertile and infertile couples in Kermanshah during 2013

Infertility is a major reproductive health in gynecology. According to the world health organization, there are currently 50-80 million infertile couples in the world. Considering the critical effects of lifestyle on reproductive health, this study aimed to compare the lifestyle of fertile and infertile couples in Kermanshah during 2013. This research is a descriptive cross sectional study that was done on 216 fertile and infertile couples attending Infertility Center and six medical centers that were selected through the convenience sampling. Data were collected using a researcher-made questionnaire containing demographic and fertility-related information and also lifestyle items on nutrition, physical activity, perceived social support, responsibility for health, and inappropriate health behaviors. Descriptive statistics, logistic regression analysis, independent t, chi-square and Generalized Estimating equation were performed to analyze the data. Fertile and infertile women [86.1% and 73. 1% respectively, p= 0. 03] as well as fertile and infertile men were significantly different in terms of physical activity [87% and 96.3% p<0.001, respectively] and perceived social support [p<0.001]. Moreover, there was a significant difference between fertile and infertile women in nutrition [p<0.001]. Similar differences were observed in responsibility for health and inappropriate health behaviors between fertile and infertile men. However, all of the dimensions of lifestyle, except nutrition, were significantly different between fertile and infertile couples. As lifestyle plays a crucial role in reproductive health, the inappropriate lifestyle of infertile couples has to be modified through effective measures such as awareness promotion, behavioral changes, and development of a healthy environment

Biclustering algorithm for embryonic tumor gene expression dataset: LAS algorithm

An important step in considering of gene expression data is obtained groups of genes that have similarity patterns. Biclustering methods was recently introduced for discovering subsets of genes that have coherent values across a subset of conditions. The LAS algorithm relies on a heuristic randomized search to find biclusters. In this paper, we introduce biclustering LAS algorithm and then apply this procedure for real value gene expression data. In this study after normalized data, LAS performed. 31 biclusters were discovered that 26 of them were for positive gene expression values and others were for negative. Biological validity for LAS procedure in biological process, in molecular function and in cellular component were 77.96%, 62.28% and 74.39% respictively. The result of biological validation of LAS algorithm in this study had shown LAS algorithm effectively convenient in discovering good biclusters