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Showing 2 results for Mokarram
Microsatellite instability detection using BAT-25 and BAT-26 by Real Time PCR and HPLC in colorectal cancer
Marjan Rismanchi , Pooneh Mokarram , Mahvash Alizadeh Naeeni , Mahdi Paryan , Zohreh Honardar , Soudabeh Kavousipour , Abbas Alipour ,
Volume 71, Issue 12 (March 2014)
Abstract
Background: Colorectal Cancer (CRC) is the third common cancer in the world. One of the pathways in colorectal tumor genesis is Microsatellite Instability (MSI+). MSI is detected in about 15% of all colorectal cancers. Colorectal tumors with MSI have dis-tinctive features compared with Microsatellite Stable (MSS) tumors. Due to the high percentage of MSI+ in patients with CRC in Iran, screening of this type of CRC is im-perative. In current study, two markers (BAT-26 and BAT-25) were used to determine an appropriate screening technique with high sensitivity and specificity to diagnose MSI status in patients with CRC. Methods: Allelic variation in two markers (BAT-26 and BAT-25) was analyzed in tis-sues and sera of 44 normal volunteers and tumor and matched normal mucosal tissues as well as sera of 44 patients with sporadic colorectal cancer by Real Time PCR (Hy-bridization probe) and High-Performance Liquid Chromatography (HPLC) techniques. The sensitivity and specificity of Real Time PCR and HPLC compared with sequencing as gold standard. The data were statistically analyzed using Student’s t-test and 2 or fisher exact test, where applicable with (P<0.05). Receiver-operating-characteristic (ROC) curves were used to evaluate the sensitivity and specificity. Results: The sensitivity and specificity of BAT-26 with Real Time PCR method (Hy-bridization probe) were 100% in comparison with gold standard method. Whereas the sensitivity and specificity of BAT-26 and BAT-25 with HPLC were 83%, 100% and 50%, 97%, respectively. Neither HPLC nor Real time PCR could detect circulating DNA with MSI property in sera. Conclusion: The sensitivity and specificity of real time PCR in MSI detection is the same as sequencing method and more than HPLC. BAT-26 marker is more sensitive than BAT-25 and MSI detection with Real time PCR could be considered as an accu-rate method to diagnose MSI in CRC tissues not sera.
Comparison of ordinary logistic regression and robust logistic regression models in modeling of pre-diabetes risk factors
Fateme Azizi Mayvan , Mehdi Jabbari Nooghabi , Ali Taghipour , Mohammad Taghi Shakeri , Mahsa Mokarram ,
Volume 76, Issue 7 (October 2018)
Abstract
Background: Regarding the increased risk of developing type 2 diabetes in pre-diabetic people, identifying pre-diabetes and determining of its risk factors seems so necessary. In this study, it is aimed to compare ordinary logistic regression and robust logistic regression models in modeling pre-diabetes risk factors.
Methods: This is a cross-sectional study and conducted on 6460 people, over 30 years old, who have participated in the screening of diabetes plan in Mashhad city that it was done by Mashhad University of Medical Sciences from October to December 2010. According to the fasting blood sugar criteria, 5414 individuals were identified as healthy and 1046 individuals were identified as pre-diabetic. Age, gender, body mass index, systolic blood pressure, diastolic blood pressure and waist-to-hip ratio were measured for every participant. The data was entered into the Microsoft Excel 2013 (Microsoft Corp., Redmond, WA, USA) and then analysis of the data was done in R Project for Statistical Computing, Version R 3.1.2 (www.r-project.org). Ordinary logistic regression model was fitted on the data. The outliers were identified. Then Mallow, WBY and BY robust logistic regression models were fitted on the data. And then, the robust models were compared with each other and with ordinary logistic regression model according to goodness of fit and prediction ability using Pearson's chi-square and area under the receiver operating characteristic (ROC) curve respectively.
Results: Among the variables that were included in the ordinary logistic regression model and three robust logistic models, age, body mass index and systolic blood pressure were statistically significant (P< 0.01) but waist-to-hip ratio was not statistically significant (P> 0.1). There were 552 outliers with misclassification error in the ordinary logistic regression model. Pearson's chi-square value and area under the ROC curve value in the Mallow model were almost the same as for ordinary logistic regression model. But it was relatively higher in BY and WBY models.
Conclusion: Based on results of this study age, overweight and hypertension are risk factors of prediabetes. Also, WBY and BY models were better than ordinary logistic regression model, according to goodness of fit criteria and prediction ability.

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