Tehran University Medical Journal

Background: The application of mesenchymal stem cells in the healing of chronic wounds is one of the most challenging issues in cell therapy. The present study investigated the efficacy of intradermal injection of umbilical cord Wharton's Jelly-derived mesenchymal stem cells in diabetic wound healing using ultrasound imaging in an animal model.
Methods: During this experimental laboratory study that was performed in the Skin and Stem Cell Research Center, Tehran University of Medical Sciences between October 2017 and October 2016, mesenchymal stem cells were isolated from umbilical cord Wharton's jelly of 10 neonates. The cells were passage. The differentiation potential of cells to osteocyte and adipose cells was evaluated. The expression of specific markers of mesenchymal stem cells was evaluated using flow cytometry. The viability and quality of cells were evaluated before transplantation. The diabetes model was developed by intraperitoneal injection of streptozotocin in 42 male Wistar rats. The animals were randomly divided into two groups: normal saline injection (control) and cell injection. Cell transplantation was performed intradermally. Skin thickness and density were assessed using ultrasound imaging on days 7, 14 and 21. Finally, the data were analyzed using a t-test and analysis of variance.

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: Long-term and intensive physical exercise can change the function of different cells in the immune system in athletes, predisposing them to viral infections such as coronavirus disease (COVID-19). The purpose of this brief report was to provide protocols related to the immune system in athletes to prevent infectious diseases.

Background: Gender differences in scientometric indicators among medical faculty members in Iran was investigated.
Methods: The Research performance of the faculty at all universities and institutes of medical sciences in Iran was assessed using the Iranian Scientometric Information Database (ISID) on June 12, 2021. Selected variables in our study were name, gender, university, degree, academic rank, type of faculty, the total number of articles, the total number of citations, self-citation percentage, h-index, citation per article, international cooperation percentage, and the number of research collaborators. The Kruskal-Wallis test was used. The extracted data were analyzed using R v4.0.1 statistical software.
Results: A total of 21064 faculty members in 77 universities and institutes of medical sciences were registered in the ISID database, of which 12093 (57.4%) were men. Men faculty members outnumbered women in all academic ranks, except for the “instructor” rank (1134 female instructors against 835 male). In both sexes, most faculty members were assistant professors. There were more articles (346837 vs. 146024) and citations (5177060 vs. 1639246) by men than women. Among the 1789 faculty members with zero articles, 902 (50.4%) were men. One-hundred-and-fifty-four people were among ESI's top researchers' list, with a majority of men (124 people equal to 80.5%). The medians of all the scientometric indicators were higher in men than women. Men had a higher number of articles, the number of citations, h-index, citation per article, percentage of international articles, and co-authors, however, women had lower self-citations (1.56% vs. 2.51%). In all academic ranks, men had higher scientometric indicators. The only exception was the associate professors’ self-citation, in which women’s citations were higher than that of men (3.5 vs. 3.3). The highest mean h-index was in men with a Ph.D. in Pharmacy (13), men with a Ph.D. by Research (12) and women with a Ph.D. by Research (8.5), respectively.
Conclusion: Gender differences were evident in research performance in Iran. Women faculty members of medical sciences in Iran generally had lower scientometric indicators.

Background: Early outcome prediction of hospitalized patients is critical because the intensivists are constantly striving to improve patients' survival by taking effective medical decisions about ill patients in Intensive Care Units (ICUs). Despite rapid progress in medical treatments and intensive care technology, the analysis of outcomes, including mortality prediction, has been a challenge in ICUs. Hence, this study aims to predict the mortality of patients admitted to ICUs using data mining techniques.
Methods: In this study, among the cases of patients who were admitted to ICUs of the Rasoul Akram and Firoozgar hospitals of Tehran City, Iran, from December 2017 to March 2018, the first 24 hours of the ICUs admission data of 874 cases were gathered. A new model based on the standard methodology CRISP was developed. In the modeling section, two well-known data mining techniques called artificial neural network (ANN), K nearest neighbor (KNN) and decision tree (DT) were used. WEKA 3.9.2 open-source software was implemented for data analysis. Finally, according to the accuracy, sensitivity, specificity criteria and AUC-ROC Curve, the superior model was introduced.
Results: Based on the WEKA results, 19 variables had the most impact on the mortality prediction of patients admitted to ICUs including Glasgow Coma Scale (GCS), mechanical ventilation, surgical service at ICUs admission, gender, temperature, serum creatinine, diabetes, Blood urea nitrogen (BUN), age, addiction, International Normalized Ratio (INR), PH, Partial Thromboplastin Time (PTT), albumin, hemoglobin, glucose, pulse rate, hematocrit (HCT), PO2.  Based on the created models, some rules have been extracted which can be used as a pattern to predict the probability of mortality. Although the AUC of the three models was acceptable (KNN 81.5%, ANN 77.5% and DT 74.3%), but the accuracy of decision tree J48 (74.2%) was higher.

Background: Cholecystectomy is one of the most common abdominal surgeries and its preferred method is laparoscopy. The difficulty of laparoscopic cholecystectomy in diabetic patients is not clear and the preferred method of cholecystectomy in these patients is still under controversy. Therefore, this study was performed to evaluate the difficulty of laparoscopic cholecystectomy in diabetic and non-diabetic patients.
Methods: This retrospective analytical study was performed in Shahid Beheshti Hospital and Forghani Educational and Medical Center from April 2019 to April 2020. Samples were easily selected and 86 people in two groups of diabetic and non-diabetic patients were included in the study. All patient records were reviewed based on inclusion and exclusion criteria for factors such as age, sex, diet, duration of surgery, bleeding, adhesions, and open surgery, and finally, diabetes as a risk factor. It was compared between the two groups. Data were analyzed in SPSS software version 22, an independent t-test was used to analyze quantitative data and the chi-square test was used to analyze qualitative data. In this study, a significance level of less than 0.05 was considered.
Results: Abdominal scar, palpable gallbladder and gallstone were not significantly different between the two groups (P=0.33). But the history of cholecystectomy attacks was significantly different between the two groups (P<0.05). Laboratory values were not significant (P>0.05) . Hard operations in diabetic patients were more than nondiabetic
patients and even two cases of open surgery were seen in the group of diabetic
patients, but there was no significant relationship (P=0.09). Intraoperative bleeding was
statistically significant between the two groups (P=0.02), But adhesion during the
operation was not related (P=0.38).
Conclusion: Finally, our study showed that diabetes can be a predictive risk factor for the difficulty of cholecystectomy.

Background: The approach of medical ethics training courses at Tehran University of Medical Sciences to change the attitude and promote medical ethics knowledge of learners has challenges. This study aims to identify the challenges in teaching medical ethics at Tehran University of Medical Sciences.
Methods: This is a qualitative study with semi-structured interviews conducted in April 2018 at Tehran University of Medical Sciences. Using purposive sampling, 23 participants were selected from the clinicians and faculty members of medical ethics and medical students. Data were analyzed by the content analysis method.

Conclusion: The effectiveness and efficiency of medical ethics courses to change the behavior and attitude of learners are not acceptable. Dealing with the existing challenges requires the efforts of the Medical Ethics Department to make maximum use of available resources and interact effectively with other academic departments.

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.

Background: Vesicoureteral reflux (urinary reflux) is one of the most common congenital anomalies in children. This study aimed to compare the ureteral opening distance from the midline in children with vesicoureteral reflux (VUR) and healthy children
Methods: This cross-sectional study was performed on the children with pyelonephritis who were referred to Afzalipour Hospital in Kerman, Iran from August 2019 to August 2020. Twenty children with vesicoureteral reflux (urinary reflux) and twenty healthy children were randomly selected. Then children with vesicoureteral reflux (urinary reflux) and healthy children were divided into two groups. The distance from the ureter to the midline in these children was then measured by a radiologist with a Philips affinity 70 ultrasound machine.

Background: Early prediction of the outcome situation of COVID-19 patients can decrease mortality risk by assuring efficient resource allocation and treatment planning. This study introduces a very accurate and fast system for the prediction of COVID-19 outcomes using demographic, vital signs, and laboratory blood test data.
Methods: In this analytic study, which is done from May 2020 to June 2021 in Tehran, 41 features of 244 COVID-19 patients were recorded on the first day of admission to the Masih Daneshvari Hospital. These features were categorized into eight different groups, demographic and patient history features, vital signs, and six different groups of laboratory blood tests including complete blood count (CBC), coagulation, kidney, liver, blood gas, and general. In this study, first, the significance of each of the extracted features and then the eight groups of features for prediction of mortality outcomes were considered, separately. Finally, the best combination of different groups of features was assessed. The statistical methods including the area under the receiver operating characteristic curve (AUC-ROC) based on binary Logistic Regression classification algorithm were used for evaluation.

Background: Cataracts are the leading diseases that cause blindness. Cataracts can be treated with surgery. Pain, itching, burning, and a foreign body sensation in the eye are common ocular complaints of patients in recovery. In this study, we compared the effect of Ketorolac eye drops and naphazoline antazoline eye drops on reducing complaints of surgery.
Methods: This randomized clinical trial study was performed from February 2016 to March 2017 on patients who were candidates for cataract surgery at Feyz University Hospital in Isfahan, Iran.  In this study, 54 patients were enrolled in two groups receiving Ketorolac eye drops and naphazoline antazoline (NA) eye drops. After the operation, patients' ocular complaints such as burning, itching, pain, and severity were evaluated every 15 minutes in recovery and every half hour in the ward until discharge from the hospital.
Results: In this study, 54 patients in two groups of 27 were studied. In the post-anesthesia care unit, the burning sensation in the naphazoline-antazoline group was significantly lower than in the ketorolac group (P=0.02). Patients in the NA group had less foreign body sensation in the eye in recovery (P=0.97) and the ward (P<0.001) than in the ketorolac group. Mean Pain intensity in the recovery unit (P=0.39) and ward unit in the ketorolac group were less than NA (P<0.001). In the recovery unit in the NA group, the eye-burning sensation was significantly lower than in the ketorolac group. The foreign body sensation in the NA group was less than the ketorolac group but there was no significant difference between the two groups. In the NA group, itching of the eyes was less than in the ketorolac group.
Conclusion: It can be concluded that ketorolac drops have been more effective in reducing post-operative eye pain than NA and NA drops have been more effective in reducing burning, itching and foreign body sensation in the eyes than Ketorolac.

Conclusion: The concurrent prevalence of upper respiratory tract disorders with gastrointestinal infections of H. pylori was observed to be almost equal and significant in elderly and young patients. Due to the fact that the simultaneous occurrence of two types of infections and diseases in people is very dangerous, screening of patients with each of these infections is necessary to identify the other infection and prescribe appropriate drugs.