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Showing 3 results for Zereshki
Echocardiographic wall motion abnormality in posterior myocardial infarction: the diagnostic value of posterior leads
Mohammadzadeh R, Kamal Hedayat D, Mohagheghi A, Tabatabaie A H, Darehzereshki A,
Volume 66, Issue 3 (2 2008)
Abstract

Background: For the purpose of ascertaining myocardial infarction (MI) and ischemia, the sensitivity of the initial 12-lead ECG is inadequate. It is risky to diagnose posterior MI using only precordial reciprocal changes, since the other leads may be more optimally positioned for the identification of electrocardiographic changes. In this study, we evaluated the relationship between electrocardiography changes and wall motion abnormalities in patients with posterior MI for earlier and better diagnosis of posterior MI.
Methods: In this prospective cross-sectional study, we enrolled patients with posterior MI who had come to the Emergency Department of Shariati Hospital with their first episode of chest pain. A 12-lead surface electrocardiogram using posterior leads (V7-V9) was performed for all participants. Patients with ST elevation >0.05 mV or pathologic Q wave in the posterior leads, as well as those with specific changes indicating posterior MI in V1-V2, were evaluated by echocardiography in terms of wall motion abnormalities. All data were analyzed using SPSS and p<0.05 were considered statistically significant.
Results: Of a total 79 patients enrolled, 48 (60.8%) were men, and the mean age was 57.35±8.22 years. Smoking (54.4%) and diabetes (48%) were the most prevalent risk factors. In the echocardiographic evaluation, all patients had wall motion abnormalities in the left ventricle and 19 patients (24.1%) had wall motion abnormalities in the right ventricle. The most frequent segment with motion abnormality among the all patients was the mid-posterior. The posterior leads showed better positive predictive value than the anterior leads for posterior wall motion abnormality.
Conclusion: Electrocardiography of the posterior leads in patients with acute chest pain can help in earlier diagnosis and in time treatment of posterior MI.


Detection of Alzheimer's disease based on magnetic resonance imaging of the brain using support vector machine model
Mansour Rezaei , Ehsan Zereshki , Hamid Sharini , Mohamad Gharib Salehi , Farhad Naleini ,
Volume 76, Issue 6 (September 2018)
Abstract
Background: Alzheimer's disease (AD) is the most common disorder of dementia, which has not been cured after its occurrence. AD progresses indiscernible, first destroy the structure of the brain and subsequently becomes clinically evident. Therefore, the timely and correct diagnosis of these structural changes in the brain is very important and it can prevent the disease or stop its progress. Nowadays, remark to this fact that magnetic resonance imaging (MRI) provides very useful and detailed information, and due to non-invasiveness, this method has been great interest to the researchers. The aim of this study was diagnosis of AD with MRI by support vector machine model (SVM).
Methods: This is an analytical and modeling research which done in School of Public Health, Kermanshah University of Medical Science, Iran, from February 2017 to November 2017. The data used in this study was a database named Miriad containing brain MRI of 69 individuals (46 Alzheimer's disease and 23 healthy subjects) that was collected at the central hospital in London. Individuals were categorized into two groups of healthy and Alzheimer's disease with two criteria: NINCDS-ADRAD and MMSE (as the golden standard). In this paper SVM model with three linear, binomial and Gaussian kernels was used to distinguish Alzheimer`s disease from healthy individuals.
Results: Finally, SVM model with Gaussian kernel, separated AD and healthy subjects with 88.34% accuracy. The most important Areas for Alzheimer were three Area: Right para hippocampal gyrus, Left para hippocampal gyrus and Right hippocampus. The clinical result of this study is to identify the most important ROI for the diagnosis of Alzheimer's by a clinical specialist. Experts should focus on atrophy in the three Areas.
Conclusion: This study showed that the SVM model with Gaussian RBF kernel can separated AD from healthy subjects by high accuracy. Based on results of this study, can make a software to use in MRI centers for screening AD test by people over the age of 50 years.

The effect of diabetes mellitus on coronary artery ectasia: brief report
Mostafa Bahremand, Ehsan Zereshki, Behzad Karami Matin, Samira Mohammadi,
Volume 78, Issue 5 (August 2020)
Abstract
Background: Coronary artery ectasia (CAE) is dilatation of an arterial segment to a diameter at least 1.5 times that of the adjacent normal coronary artery. The incidence of coronary artery ectasia is distinct in different countries that can be found in 1.2% to 5% of angiographic examinations.
Methods: This is a retrospective study that was conducted from September 2019 to February 2020 in Kermanshah University of Medical Sciences and the results were reported briefly. To obtain the desired articles, electronic searches were conducted in databases including the Scopus, PubMed, and Science Direct databases without time limited until October 2019. The keywords used were Coronary Artery Ectasia AND (Diabetes OR "Diabetes Mellitus"). This was done by two individuals separately and the final results were confirmed by a third person. Mixed method appraisal tool (MMAT) was used to evaluate the quality of studies. The structure of writing and the process of performing and reporting the study are based on the PRISMA checklist.
Results: Based on the search strategy carried out at PubMed, Scopus and Science Direct databases, 106 studies were found, which resulted in 24 articles being analyzed based on inclusion and exclusion criteria of which three were conducted in China, 18 in Turkey and one in Sweden, Egypt, and France. Finally, 24 articles were analyzed and the results showed a direct and effective relationship between diabetes mellitus and CAE (OR=1.19, CI: 0.94, 1.51).
Conclusion: Based on these results, the risk of CAE in subjects with diabetes mellitus was 19% higher than in subjects without diabetes mellitus.

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