Genetic Polymorphism of Angiotensin II Type 1 Receptors and Their Effect on the Clinical Outcome of Captopril Treatment in Arab Iraqi Patients with Acute Coronary Syndrome (Mid Euphrates)

Genetic variation in the angiotensin II type 1 receptor (AT1R) has an important effect on the outcome of acute coronary syndrome (ACS) initiated treatment with captopril. This study aims to investigate the impact of genetic polymorphism of AT1R (rs5186 and rs275651) on the ACS outcome in Iraqi patients treated with captopril. A total of 250 Iraqi individuals with ACS were included in this case—control study and they were divided into two study groups; Study group 1 included 125 participants who were prescribed captopril, 25 mg twice daily and study group 2 included 125 participants who received no captopril as part of their ACS treatment (control study). The AT1R gene (rs5186) CC genotype was found to be associated with ST-elevation myocardial infarction (STEMI) (Odd’s ratio (O.R) = 1.2, P = 0.7), while AC was associated with Non-ST-elevation myocardial infarction (NSTEMI) and unstable angina (UA) (O.R = 1.2, P = 0.8). AC genotype is more prone to have Percutaneous coronary intervention (PCI) after ACS attack (O.R = 1.2, P = 0.6). CC genotype had a risk to get less improvement (O.R = 1.6, P = 0.5), so might require higher doses of captopril during acute coronary insult. The AT1R gene (rs275651) AA genotype was associated with UA (O.R = 1.3, P = 0.9). AA and AT genotypes were more prone to have PCI after ACS attack (O.R = 3.9 P = 0.2, O.R = 3.5, P = 0.3 respectively) and thus requiring higher doses of captopril. We conclude that the AT1R rs5186, rs275651 genetic polymorphisms might partially affect the clinical outcome of ACS patients treated with captopril and might have captopril resistance which requires higher doses.     

An analysis of review content and reviewer variables that contribute to review helpfulness

Review helpfulness is attracting increasing attention of practitioners and academics. It helps in reducing risks and uncertainty faced by users in online shopping. This study examines uninvestigated variables by looking at not only the review characteristics but also important indicators of reviewers. Several significant review content and two reviewer variables are proposed and an effective review helpfulness prediction model is built using stochastic gradient boosting learning method. This study derived a mechanism to extract novel review content variables from review text. Six popular machine learning models and three real-life Amazon review data sets are used for analysis. Our results are robust to several product categories and along three Amazon review data sets. The results show that review content variables deliver the best performance as compared to the reviewer and state-of-the-art baseline as a standalone model. This study finds that reviewer helpfulness per day and syllables in review text strongly relates to review helpfulness. Moreover, the number of space, aux verb, drives words in review text and productivity score of a reviewer are also effective predictors of review helpfulness. The findings will help customers to write better reviews, help retailers to manage their websites intelligently and aid customers in their product purchasing decisions.