Two Iranian families with a novel mutation in GJB2 causing autosomal dominant nonsyndromic hearing loss

Mutations in GJB2 , encoding connexin 26 (Cx26), cause both autosomal dominant and autosomal recessive nonsyndromic hearing loss (ARNSHL) at the DFNA3 and DFNB1 loci, respectively. Most of the over 100 described GJB2 mutations cause ARNSHL. Only a minority has been associated with autosomal dominant hearing loss. In this study, we present two families with autosomal dominant nonsyndromic hearing loss caused by a novel mutation in GJB2 (p.Asp46Asn). Both families were ascertained from the same village in northern Iran consistent with a founder effect. This finding implicates the D46N missense mutation in Cx26 as a common cause of deafness in this part of Iran mandating mutation screening of GJB2 for D46N in all persons with hearing loss who originate from this geographic region. © 2011 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83755/1/33209_ftp.pd

Modeling and Predicting the Risk of Coronary Artery Disease Using Data Mining Algorithms

Introduction: Coronary artery disease (CAD) is one of the most common causes of death in adults while accurate and early diagnosis can lead to treatment and survival of patients to a great extent. Therefore, the objective of this study was to identify the effective factors leading to this disease and develop a data-driven model to assist physicians in predicting and diagnosing it. Method: This is an applied research, considering 2038 medical records, collected from Shahid Rajaei Heart Hospital in Tehran, during 5 years. A data preprocessing was carried out and random balanced sampling reduced the dataset into 1000 records, with 500 CAD and 500 Normal. Literature review, consultation with specialist physicians, and weighting using the Chi-square method led to the determination of important features. Support Vector Machine, Neural Network and Random Forest algorithms were applied in RapidMiner and Python. Results: Among the 35 identified variables, the most important features included VHD, Chest pain, LDL, RWMA, TG, Na, K, BP, and weight. The F-measure, precision, accuracy, and recall for random forest algorithm were calculated as 82.11%, 81.40%, 79.07%, and 85.40%, respectively, and the error rate was 18.6%. Conclusion: Random Forest predicted the risk of CAD with a reasonable precision. In comparison, due to the large number of input nodes, the error rate of the Neural Network model was relatively higher (23.6%)