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Introduction: Antibiotic arbitrary use by increasing resistance changed to a growing issue in the 21st century and its optimal use in hospital could be essential for reducing health system costs and drug side effects. Objectives: The aim of this study was to investigate the pattern of antibiotic use in prescribed prescription. Patients and Methods: This was a cross-sectional study that has been done on 300 prescriptions which selected randomly form Ardabil city hospitals. The anatomical therapeutic chemical classification system with defined daily doses (ATC/DDD) standard measurement method recommended by World Health Organization (WHO) was conducted in the study. The amount uses rate of each medication calculated by DDD/100 bed days. Data collected by a checklist and analyzed by statistical methods in SPSS version 21. Results: The highest prescribed antibiotic was ciprofloxacin with 32% and the total antibiotics uses rate during study period was 44/95 DDD/100 Bed days. The mean number of drugs and prescribed antibiotics in intensive care unit (ICU) ward was more than other wards. However, comparing the rate of antibiotics use between wards based on ATC/DDD method showed that the infection ward with 62.9 was more than other wards. The most form of used antibiotics with 69% was in the form of Ampule and in intravenously method with 64% in all wards. Conclusion: The results of this study showed that the most used antibiotics by considering the studied method, prescribed dose and the hospitalized days were in internal and surgery ward respectively but according ATC/DDD method the most used antibiotics was in infection ward.

Introduction: Death caused by myocardial infarction (MI) usually occurs during the first hours after the onset of symptoms. Therefore, to manage, treat and decrease the mortality rate of these patients, early hospital admission is important. Objectives: We aimed to investigate the impact of the influential factors on mortality of patients with MI. Patients and Methods: In this case-control study, MI patients who were referred to the Ardabil Alavi Hospital, Ardabil university of medical sciences, Ardabil, Iran between April 2017 and April 2018 were included. The patients that had died due to MI were considered as the case group (n=27) and 27 matched patients that had died due to other causes were selected as the control group. Linear logistic regression was employed to analyze the data. Results: The mean age of the patients in the case group was significantly higher than in the control group (75.1±11.7 versus 63.;1±11.6, P=0.001). The history of non-cardiac diseases in the case group (44.4%) was significantly higher than in the control group (7.4%; P=0.002). The number of PCIs (percutaneous coronary interventions) that were conducted in the case group (40.7%) was significantly lower than in the control group (74.1%; p=0.013). The time distance between hospital admission and PCI performance in the case group (110.9 minutes) was significantly higher than in the control group (56 minutes; P=0.001). However, the mean of delay time from the onset of symptoms to hospital admission (the patient delay) and from hospital admission to receiving treatment (the health system delay) was similar between the two groups. Conclusion: This study showed that the number of performed PCIs, history of non-cardiac disorders and the interval between the onset of symptoms and PCI performance are significantly associated with the morality of MI patients.

Introduction: Body mass index (BMI) is an acceptable method to measure overweight and obesity among the population. Objectives: The aim of this study was evaluating the application of machine learning algorithms for classifying body mass index for clinical purposes. Patients and Methods: In this descriptive study, we selected the dataset of 1316 people who selected randomly from all area of Ardabil city in Iran. Dataset included demographic and anthropometric data. Classification algorithms such as random forest (RF), Gaussian Naive Bayes (GNB), decision tree (DT), support vector machines (SVM), multi-layer perceptron (MLP), K-nearest neighbors (KNN) and logistic regression (LR) with 10-fold cross-validation were conducted to classify the data based on BMI. The performance of algorithms was evaluated with precision, recall, mean squared errors (MSE) and accuracy indices. All programing done by Python 3.7 in Jupyter Notebook. Results: According to the BMI, 603(45.8%) of all samples were normal and 713 (54.2%) were at-risk. The precision of RF, GNB, DT, SVM, MLP, KNN and LR for people at risk were 0.93, 0.86, 0.99, 0.82, 100, 0.82 and 0.99 respectively. Additionally, the accuracy of RF, GNB, DT, SVM, MLP, KNN and LR were 95%, 83%, 100%, 82%, 100%, 82% and 100 %. Conclusion: The comparison of the classifying algorithms showed that, the LR, MLP and DT had the higher accuracy than the other algorithms in detecting of people at-risk.

Background: Iron deficiency anemia is common in patients on maintenance hemodialysis (HD). Serum ferritin is increased in HD patients. Vitamin C supplementation has shown to improve functional iron deficiency and anemia in HD patients. Objectives: In this study we aimed to evaluate the effect of vitamin C supplementation on serum ferritin level. Patients and Methods: In this randomized clinical trial, 39 HD patients were randomly assigned to receive routine therapy with (n=24) or without (n=15) vitamin C 500 mg IV twice a week after each dialysis session for 2 months. Changes in serum ferritin, iron, total iron-binding capacity (TIBC) and transferrin saturation (TS) before and after treatment were measured in each group. Results: Serum ferritin levels before treatment in case and control groups were 1859±1398.5 and 2567.5±1907.8 ng/mL which was significantly decreased to 1084.8±727.9 and 1313.5±1151.5 ng/mL, respectively (P=0.006 and P=0.007). Serum iron, TIBC and TS did not change significantly in each group after treatment. Comparing the mean changes in serum ferritin, iron, TIBC and TS, there was no significant difference between groups. Conclusions: Vitamin C had no effects on serum ferritin, iron and TS.