Compare the effects of atorvastatin and omega-3 on index of lipid oxidation in patients with polycystic ovary syndrome

Background and aims: Oxidative stress in patients with polycystic ovary syndrome causes a lot of problems and oxidized lipids increase the risk of many diseases. The aim of this study was to evaluate changes in malondialdehyde (MDA) following treatment with atorvastatin and omega-3. To identify the factors causing the decline lipid oxidation are particularly important in the management and treatment of these patients. Methods: In this clinical trial study, patients with this syndrome were divided into three groups based on the visit. The first group, 26 patients consumed 4g per day omega -3. The second group, 27 patients consumed 20 mg per day atorvastatin and control group, 29 patients received placebo. After gathering all the samples using standard method for measuring lipid profile, the level of malondialdehyde was detected by the HPLC, insulin and testosterone were measured by ELISA. Data were analyzed with using paired t-test and ANOVA in SPSS software. Results: Omega-3 supplementation decreased malondialdehyde and testosterone. It also had a positive effect on raising HDL-C. Atorvastatin only decreased malondialdehyde in the atorvastatin recipients. Conclusion: Omega-3 supplements or atorvastatin in patients with polycystic ovary syndrome reduces the lipid oxidation and MDA level. It is expected to decrease the risk of cardiovascular diseases in patients with polycystic ovary syndrome by reduction of lipid oxidation

A Survey of Students’ Attitudes to Big Data Analysis in Iranian Universities

Today, with the emergence of new technologies and massive data, big data analysis has attracted the attention of researchers, industries and universities on a global scale. The present research aims to explore students’ attitude to big data analysis in different fields of study. The present cross-sectional study was conducted with students at different universities and fields of study in Iran. A questionnaire was developed. This questionnaire explored students’ attitude toward big data analysis. To this aim, 359 university students participated in the research. The data were analyzed using descriptive and inferential statistics. The age of the students ranged between 25 and 34 years. 55.2% were female and 54% were economically active. 40.9% had a work experience of less than a year. The academic degree of the majority of participants was master’s degree. 93.9% of the participants believed that big data analysis was essential for the country. 43.2% maintained that big data mostly belonged to the communication industry. 28.1% perceived MATLAB useful software for analysis. 40.9% were familiar with the benefits of analysis. Engage in economic activities, less than 1 year of experience and studies for a Master’s degree showed to be significantly correlated with familiarity with the benefits of big data (p≤0.01). Such issues as high costs, managers’ unfamiliarity and lack of expertise and complexity were raised by the respondents. Considering the undeniable benefits of big data analysis, it seems essential to familiarize university students with these analyses through particular training courses, conferences and so on