High Prevalence of AmpC β-Lactamases in Clinical Isolates of Escherichia coli in Ilam, Iran

Abbas Maleki,Afra Khosravi,Sobhan Ghafourian,Iraj Pakzad,Shiva Hosseini,Rashid Ramazanzadeh,Nourkhoda Sadeghifard 질병관리본부 2015 Osong Public Health and Research Persptectives Vol.6 No.3

Objectives: Widespread use of β-lactam antibiotics could cause resistance to this group of antibiotics in pathogenic bacteria through the production of the enzyme β-lactamases. The aimof this study is to determine themolecular detection of AmpC β-lactamases among clinical Escherichia coli isolated from Ilam hospitals in Ilam, Iran. Methods: One hundred and twelve clinical isolates of E. coli were collected from hospitalized patients and were identified by biochemical tests. They were evaluated for extended spectrum beta-lactamases (ESBLs) production, and the positive strains were subjected to AmpC enzymes; for detection of AmpC cluster genes, multiplex polymerase chain reaction was applied. Results: The analysis showed 62.5% of isolates were ESBLs positive and that five strains revealed the AmpC cluster genes. This is the first report of FOXM cluster genes in E. coli in Iran. Conclusion: Based on our results, the prevalence of AmpC β-lactamases is increasing in Iran, which caused failure in antibiotic therapy. So, the current study recommended the revision of antibiotic policy in Iranian hospitals.

NN-based Prediction Interval for Nonlinear Processes Controller

Mohammad Anwar Hosen,Abbas Khosravi,H. M. Dipu Kabir,Michael Johnstone,Douglas Creighton,Saeid Nahavandi,Peng Shi 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.9

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

Modification of the effect of ambient air temperature on cardiovascular and respiratory mortality by air pollution in Ahvaz, Iran

Sohrab Iranpour,Soheila Khodakarim,Abbas Shahsavani,Ardeshir Khosravi,Koorosh Etemad 한국역학회 2020 Epidemiology and Health Vol.42 No.-

OBJECTIVES: This study investigated the modification of temperature effects on cardiovascular and respiratory mortality by air pollutants (particulate matter less than 2.5 and 10 μm in diameter [respectively], ozone, nitrogen dioxide, carbon monoxide, and sulfur dioxide). METHODS: Poisson additive models with a penalized distributed lag non-linear model were used to assess the association of air temperature with the daily number of deaths from cardiovascular and respiratory diseases in Ahvaz, Iran from March 21, 2014 to March 20, 2018, controlling for day of the week, holidays, relative humidity, wind speed, air pollutants, and seasonal and long-term trends. Subgroup analyses were conducted to evaluate the effect modification for sex and age group. To assess the modification of air pollutants on temperature effects, the level of each pollutant was categorized as either greater than the median value or less than/equal to the median value. RESULTS: We found no significant associations between temperature and cardiovascular and respiratory mortality. In the subgroup analyses, however, high temperatures were significantly associated with an increased risk of cardiovascular mortality among those 75 years old and older, with the strongest effect observed on day 0 relative to exposure. The results revealed a lack of interactive effects between temperature and air pollutants on cardiovascular and respiratory mortality. CONCLUSIONS: A weak but significant association was found between high temperature and cardiovascular mortality, but only in elderly people. Air pollution did not significantly modify the effect of ambient temperature on cardiovascular and respiratory mortality.

Perceived Psychological Traumatic Childbirth in Iranian Mothers: Diagnostic Value of Coping Strategies

Sedigheh Abdollahpour,Ahmad Khosravi,Habibollah Esmaily,Seyed Abbas Mousavi 질병관리본부 2019 Osong Public Health and Research Persptectives Vol.10 No.2

Objectives: The aim of this study was to investigate the diagnostic value of a stress coping scale for predicting perceived psychological traumatic childbirth in mothers. Methods: This cross-sectional study was performed on 400 new mothers (within 48 hours of childbirth). Psychological traumatic childbirth was evaluated using the 4 diagnostic criteria of Diagnostic and Statistical Manual of Mental Disorders. Coping was measured using Moss and Billings’ Stress Coping Strategies Scale. Results: The overall mean score of stress coping was 29 ± 14.2. There were 193 (43.8%) mothers that had experienced a psychological traumatic childbirth. A stress coping score ≤ 30, with a sensitivity of 90.16 (95% CI = 85.1-94.0), and a specificity of 87.44 (95% CI = 82.1-91.6), was determined as a predictor of psychological traumatic childbirth. So that among mothers with stress coping scores ≤ 30, 87% had experienced a psychological traumatic childbirth. Conclusion: Investigating the degree of coping with stress can be used as an accurate diagnostic tool for psychological traumatic childbirth. It is recommended that during pregnancy, problem-solving and stress management training programs be used as psychological interventions for mothers with low levels of stress control. This will ensure that they can better cope with traumatic childbirth and posttraumatic stress in the postpartum stage.

Comparing the Performance of Feature Selection Algorithms for Wart Treatment Selection

Roohallah Alizadehsani,Moloud Abdar,Seyed Mohammad Jafar Jalali,Mohamad Roshanzamir,Abbas Khosravi,Saeid Nahavandi 한국정보기술학회 2019 Proceedings of The International Workshop on Futur Vol.2018 No.1

Wart is a meat gland that grows on the hands, feet and other parts of the body. The best wart treatment method is still unknown to physicians. Thus, they select the treatment method randomly and change it if unsuccessful. This trial and error approach has multiple drawbacks including but not limited to prolonged treatment and increased cost. To address these issues, this study proposes a machine learning pipeline for finding the best wart treatment method. Six well-known feature selection algorithms are applied in the process of model development. Linear support vector machine (SVM), LIBSVM, and random forest algorithms are implemented as classification methods. Obtained results indicate that information gain is the best feature selection method. Also it is observed that LIBSVM achieves the highest accuracy rates for both Cryotherapy (91.11±6.67%) and Immunotherapy (88.89±6.33%) datasets.