Tehran University Medical Journal

Background: Familial adenomatous polyposis (FAP) is the most common components polyposis syndromes. It incidence is for less than 1 percent of colorectal cancer cases. FAP is characterized by germline mutations in the adenomatous polyposis coli (APC) gene. Generally, there are hundreds to thousands of adenomatous polyps in colon and rectum of patients. The aim of the current study was to evaluate the germline mutation at codon 1309 of the APC gene and its association with extracolonic manifestations in Iranian patients with FAP.
Methods: This Cross-sectional study was conducted at the Gastroenterology and Liver Diseases Research Center, Taleghani Hospital, Tehran, Iran from July 2012 to February 2015. In this study, thirty-three patient with FAP was examined. Demographic and clinical data were gathered from patients. In addition, peripheral blood samples were collected to study the most common mutations of the APC gene and bidirectional sequencing was carried out after genomic DNA extraction by salting out method. Primers were designed by GeneRunner version 5.0.4 (http://www.generunner.com). The samples were run on an applied biosystems 3130XL genetic analyzer. The results were analyzed by SPSS software, version 23 (IBM, Armonk, NY, USA).

Background: Most of colorectal cancers (CRC) have originated from intestinal polyps. Evaluating of the expression level of genes that are involved in tumors growth and development, may consider as diagnostic factor of malignancy in the polyps. AXIN2 regulates the level of nuclear β-catenin in a negative-feedback loop there by being a negative regulator and target gene at the same time. The aims of current study were to examine the expression level of the AXIN2 in the colonic polyps and its linkage with the pathological features of the polyps.
Methods: In the present analytical-descriptive study, the investigated population was chosen from the cases with colonic polyps that referred to the Gastroenterology and Liver Diseases Research Center, Taleghani Hospital, Tehran, Iran, from October 2014 to April 2015. Forty four biopsy polyp samples and 10 normal tissue samples were collected, as well as the demographic and clinical properties of the patients and the expression level of AXIN2 gene was quantified by Real-time PCR. The outcomes were analyzed by the ABI Prism 7500 Sequence Detection System (SDS) software, version 2.1.0 (Applied Biosystems Inc., Foster City, CA, USA) and GraphPad Prism, version 3 (GraphPad Software Inc., La Jolla, CA, USA) Also, the expression changes of the intended gene in target groups were compared with the normal tissues using the 2^-ΔΔCt equation.

Conclusion: The results of the current study show that the expression pattern of AXIN2 gene, was markedly changed during the transformation of the normal tissue to polyp. The increased expression level of this gene could be applied as a diagnostic marker in dissociation of the adenoma polyps from hyperplastic ones. On the other hand, the location of the polyps modulates the AXIN2 gene function. Taking together, evaluating the changes of AXIN2, has a precise diagnostic value in the CRC related studies.

Background: Accurate and early detection of non-alcoholic fatty liver, which is a major cause of chronic diseases is very important and is vital to prevent the complications associated with this disease. Ultrasound of the liver is the most common and widely performed method of diagnosing fatty liver. However, due to the low quality of ultrasound images, the need for an automatic and intelligent classification method based on artificial intelligence methods to accurately detect the amount of liver fat is essential. This paper aims to develop an advanced machine learning model based on texture features to assess liver fat levels based on liver ultrasound images.
Methods: In this analytic study, which is done from April to November 2020 in Tehran, ultrasound images of 55 obese people who have undergone laparoscopic surgery have been used and the histological result of a liver biopsy has been employed as a reference for liver fat. First, 88 texture-based features were extracted from the images using the Gray-Level Co-Occurrence Matrix (GLCM) method. In the next step, using the method of minimum redundancy and maximum correlation, the top features were selected from among 88 features and applied to the classifier input. Finally, using the three classifiers of linear discriminant analysis, support vector machine and AdaBoost, the images were classified into 4 groups based on the amount of liver fat.
Results: The accuracy of the automatic liver fat prediction model from ultrasound images for AdaBoost classification was 92.72%. However, the accuracies obtained for support vector machine and linear discriminant analysis classification were 87.88% and 75.76%, respectively.

Background: Early diagnosis of patients in the early stages of Alzheimer's, known as mild cognitive impairment, is of great importance in the treatment of this disease. If a patient can be diagnosed at this stage, it is possible to treat or delay Alzheimer's disease. Resting-state functional magnetic resonance imaging (fMRI) is very common in the process of diagnosing Alzheimer's disease. In this study, we intend to separate subjects with mild cognitive impairment from healthy control based on fMRI data using brain functional connectivity and graph theory.
Methods: In this article, which was done from April to November 2020 in Tehran, after pre-processing the fMRI data, 116 brain regions were extracted using an Automated Anatomical Labeling atlas. Then, the functional connectivity matrix between the time signals of 116 brain regions was calculated using Pearson correlation and mutual information methods. Using functional connectivity calculations, the brain graph network was formed, followed by thresholding of the brain connectivity network to keep significant and strong edges while eliminating weaker edges that were likely noise. Finally, 11 global features were extracted from the graph network and after performing statistical analyses and selecting optimal features; the classification of 14 healthy individuals and 11 patients with mild cognitive impairment was performed using a support vector machine classifier.

Conclusion: Combining the features of brain graph and functional connectivity by the mutual information method with a machine learning approach, based on fMRI imaging analysis, is very effective in diagnosing mild cognitive impairment in the early stages of Alzheimer’s which consequently allows treating or delaying this disease.

Background: Schizophrenia is a mental disorder that severely affects the perception and relations of individuals. Nowadays, this disease is diagnosed by psychiatrists based on psychiatric tests, which is highly dependent on their experience and knowledge. This study aimed to design a fully automated framework for the diagnosis of schizophrenia from electroencephalogram signals using advanced deep learning algorithms.
Methods: In this analytic study, which is done from April to October 2021 in Tehran, 19-channel electroencephalogram signals from 14 schizophrenia patients and 14 healthy individuals were recorded and pre-processed. Then, the effective connectivity measure using the transfer entropy method is estimated from them and a 19×19 asymmetric connectivity matrix is constructed and represented by a color map as an image. Then, these effective connectivity images are used as inputs to the five pre-trained neural networks of AlexNet, Resnet-50, Shufflenet, Inception, and Xception. Finally, the parameters of these networks are fine-tuned to diagnose schizophrenia patients. All models are fine-tuned based on newly constructed images using the adaptive moment estimation optimizer algorithm and cross-entropy as the loss function. 10-fold cross-validation and subject-independent validation methods are used to evaluate the proposed method.