Tehran University Medical Journal

Background: Gestational diabetes mellitus (GDM) is one of the most common medical complications in pregnancy, which is associated with many serious consequences for mother and her fetus. Body mass index (BMI) in pregnant women is considered as one of most effective factor for the incidence of GDM. The aim of this study was to determine the relationship between BMI at pregnant women in the early months of pregnancy and the incidence of GDM.
Methods: In this retrospective cohort study, the case of six hundred fifty-nine pregnant women who referred to health centers in Kermanshah City from September 2010 to September 2012 by convenience sampling method were selected and investigated. This study was sponsored by Kermanshah University of Medical Sciences. Height and weight were measured for each woman at the beginning of pregnancy and maternal body mass index (BMI) was calculated based on height and weight measurements. Then the pregnant women were divided into four groups based on BMI: thin (BMI less than 18.9 kg/m2), normal (BMI between 19 kg/m2 and 24.9 kg/m2), overweight (BMI between 25 kg/m2 and 29.9 kg/m2) and obese (BMI more than 30 kg/m2). Those women who had diabetes at the beginning of pregnancy were excluded from the study. GDM was considered as fasting blood glucose ≥92 between 26-30 weeks of gestation.
Results: The mean±SD age of pregnant women was 27.7±5.85 year and the mean of BMI was 24.4±4.0 kg/m2. The GDM was shown in 30.7% of women. Association between BMI and GDM were statistically significant (P<0.001). The risk of GDM onset was 1.24 times, for each unit increased in BMI, (P<0.001). The risk of GDM was significantly higher in overweight [OR=2.97, CI (2.01-4.39)] and obese [OR=16.89, CI (8.46-33.70)] women. Being underweight increased the risk of GDM onset up to 1.19 times, but not significant.
Conclusion: There is a significant relationship between maternal BMI in pregnant women at the beginning of pregnancy with GDM onset. Increased BMI is correlated with an increase in the incidence of GDM.

Background: Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnancy, which is associated with serious complications. In the event of early diagnosis of this disease, some of the maternal and fetal complications can be prevented. The aim of this study was to early predict gestational diabetes mellitus by two statistical models including artificial neural network (ANN) and decision tree and also comparing these models in the diagnosis of GDM.
Methods: In this modeling study, among the cases of pregnant women who were monitored by health care centers of Kermanshah City, Iran, from 2010 to 2012, four hundred cases were selected, therefore the information in these cases was analyzed in this study. Demographic information, mother's maternal pregnancy rating, having diabetes at the beginning of pregnancy, fertility parameters and biochemical test results of mothers was collected from their records. Perceptron ANN and decision tree with CART algorithm models were fitted to the data and those performances were compared. According to the accuracy, sensitivity, specificity criteria and surface under the receiver operating characteristic (ROC) curve (AUC), the superior model was introduced.
Results: Following the fitting of an artificial neural network and decision tree models to data set, the following results were obtained. The accuracy, sensitivity, specificity and area under the ROC curve were calculated for both models. All of these values were more in the neural network model than the decision tree model. The accuracy criterion for these models was 0.83, 0.77, the sensitivity 0.62, 0.56 and specificity 0.95, 0.87, respectively. The surface under the ROC curve in ANN model was significantly higher than decision tree (0.79, 0.74, P=0.03).
Conclusion: In predicting and categorizing the presence and absence of gestational diabetes mellitus, the artificial neural network model had a higher accuracy, sensitivity, specificity, and surface under the receiver operating characteristic curve than the decision tree model. It can be concluded that the perceptron artificial neural network model has better predictions and closer to reality than the decision tree model.

Background: Multiple sclerosis (MS) is an inflammatory disease affecting the central nervous system. Rituximab (Zytux) is a type of monoclonal antibody against B cells with CD20 antigen that reduces B cells. The present study examined the one-year effectiveness and side effects of Rituximab.
Methods: This quasi-experimental clinical trial was conducted from September 2018 to September 2019 in Imam Reza Hospital in Kermanshah. 44 eligible patients were selected by the available sampling method and entered the study after evaluation in terms of inclusion and exclusion criteria. Patients were treated with rituximab for one year. At the beginning of the study and at the end of the year, the disability score based on the Expanded Disability Status Scale (EDSS) and active lesions based on MRI for patients were evaluated. Also, the patients were followed up in terms of relapse and medication side effects throughout the year. This study was performed with the support of Kermanshah University of Medical Sciences. Informed consent was obtained from all participants. The data were analyzed by SPSS-25 software.

Results: 44 patients with MS including 29 (65.9%) female and 15 (34.1%) male were studied. 22 patients had RRMS and 22 patients had progressive-relapsing MS (PRMS). In patients with RRMS, the EDSS score at the end of the study was significantly reduced compared to the beginning of the study (P=0.010) but in PRMS patients EDSS was increased but this increase was not significant (P=0.148). In both RRMS and PRMS patients, the number of MRI lesions at the end of the study was lower than the beginning of the study and this decrease was not significant (P>0.05). More Immediate side effects occurred in RRMS patients (13.6% vs. 4.5%) and more delayed side effects were observed in PRMS patients (54.5% vs. 36.3%). Conclusion: rituximab caused a greater reduction in EDSS in the treatment of RRMS than PRMS and its use had few side effects.

Background: People with chronic diseases of the immune system, such as multiple sclerosis (MS), are at risk for Covid-19 disease. However, more research is needed with long-term follow-up. The aim of the study was to follow up people with MS (PwMS) for up to three months after AstraZeneca vaccination for the recurrence of MS and Covid-19 infection.
Methods: This study was a case study (descriptive-analytical) of follow-up type. The study population was PwMS over 18 years of age in Kermanshah province who received both doses of the AstraZeneca vaccine. This study was conducted from August to November 2021. Sampling was done with existing methods based on the National MS Registry of Iran (NMSRI). Demographic information of patients was extracted from NMSRI. A researcher-made form was used to collect information by telephone three months after vaccination about clinical characteristics, Covid-19 infection, and recurrence of MS. Data were analyzed using SPSS-25 software.
Results: Study participants were 40 MS patients with a mean (SD) age of 39.27 (8.8) years, including 32 (80.0%) women. A mean of 9.39 (4.6) years had passed since The patients were diagnosed with MS, and 29 (76.4%) had RR type MS. Four patients (10%) relapsed between the second dose and three months later, of whom two (50%) had sensory symptoms, one (25%) had optic nerve involvement, and one (25%) had motor symptoms and pyramidal pathway involvement. The symptoms of Covid-19 were mild in three patients (10%), while severe symptoms developed in one patient (10%) who received rituximab. Among the patients, no cases of thrombosis were observed. Infusion therapy, a leg fracture, and kidney stones were the only hospitalized cases.

Conclusion: Covid-19 and MS relapse prevalence did not differ significantly in the three months before and after vaccination. There is a need for further studies with a longer follow-up period.

Methods: This two-center, double-blind, randomized clinical trial was conducted between December 2021 and July 2022 in the cities of Kermanshah and Hamadan. In this study, 76 migraine patients were randomly divided into two groups. One group received routine medication with Soda capsules and the other group received routine medication with Valproate capsules. Tow group were examined and followed up for three months. Data related to demographic and clinical information of patients were collected and entered into SPSS software version 25. Data analysis was done with a significance level of 0.05.

Conclusion: In terms of frequency, intensity and duration of migraine headaches as well as side effects, there was no significant difference between Sodium Valproate and Sodae groups.