Showing 5 results for Kalhor
Etaati Z, Moazzami Godarzi R, Kalhori F, Sobhani Sa, Solati M, Alavi A, Tashnizi Sh, Naderi N,
Volume 70, Issue 1 (3 2012)
Abstract
Background: Diabetes mellitus (DM) is a group of metabolic disorders such as DM I, DM II, secondary causes of DM and gestational diabetes mellitus characterized by hyperglycemic phonotype. The etiology of gestational diabetes mellitus is unknown. Recent studies address the chronic activity of immune system against infections (not autoimmunity) as an important cause of gestational diabetes mellitus. This study aimed to compare T-helper cells 1 and 2 cytokines and associated antibodies in patients with gestational diabetes mellitus and normal pregnant women.
Methods: This cross-sectional study was performed on 45 female patients with GDM and 45 healthy pregnant women in Bandar Abbas, Iran, from 2008- 2009. The exclusion criteria were presence of any infectious diseases or autoimmune disorders such as SLE or RA. Present and past medical histories were taken from the participants thorough physical examination. Blood samples (10 mL) were drawn and sent to laboratory for measuring serum IgE, IgG1, IgG2, IgG3, IgG4, interleukin-10 (IL-10), interleukin-12 (IL-12), transforming growth factor-beta (TGF1), and interferon-gamma (IFN) measurements. T-test and Kolmogorov-Smirnov test were used for data analysis.
Results: The mean age of the patients with GDM and healthy pregnant women was 32.5 and 27.9 yrs, respectively. T-helper 1 and 2 associated antibodies and cytokines had no significant differences between the case and control groups.
Conclusion: The changes in T-helper 1 and 2 associated antibodies and cytokines are not associated with gestational diabetes mellitus and could not be considered as a predictor for gestational diabetes mellitus.
Mahmoud Akbarian , Khadijeh Paydar, Sharareh R Ostam Niakan Kalhori , Abbas Sheikhtaheri ,
Volume 73, Issue 4 (July 2015)
Abstract
Background: Pregnancy in women with systemic lupus erythematosus (SLE) is still introduced as a major challenge. Consulting before pregnancy in these patients is essential in order to estimating the risk of undesirable maternal and fetal outcomes by using appropriate information. The purpose of this study was to develop an artificial neural network for prediction of pregnancy outcomes including spontaneous abortion and live birth in SLE.
Methods: In a retrospective study, forty-five variables were identified as effective factors for prediction of pregnancy outcomes in systemic lupus erythematosus. Data of 104 pregnancies in women with systemic lupus erythematosus in Shariati Hospital and 45 pregnancies in a private specialized center in Tehran from 1982 to 2014 in August and September, 2014 were collected and analyzed. For feature selection, information of the 149 pregnancies was analyzed with a binary logistic regression model in SPSS software, version 20 (SPSS, Inc., Chicago, IL, USA). These selected variables were used for inputs of neural networks in MATLAB software, version R2013b (MathWorks Inc., Natick, MA, USA). A Multi-Layer Perceptron (MLP) network with scaled conjugate gradient (trainscg) back propagation learning algorithm has been designed and evaluated for this purpose. We used confusion matrix for evaluation. The accuracy, sensitivity and specificity were calculated from the confusion matrix.
Results: Twelve features with P<0.05 and four features with P<0.1 were identified by using binary logistic regression as effective features. These sixteen features were used as input variables in artificial neural networks. The accuracy, sensitivity and specificity of the test data for the MLP network were 90.9%, 80.0%, and 94.1% respectively and for the total data were 97.3%, 93.5%, and 99.0% respectively.
Conclusion: According to the results, we concluded that feed-forward Multi-Layer Perceptron (MLP) neural network with scaled conjugate gradient (trainscg) back propagation learning algorithm can help physicians to predict the pregnancy outcomes (spontaneous abortion and live birth) among pregnant women with lupus by using identified effective variables.
Rohollah Kalhor , Asghar Mortezagholi , Fatemeh Naji, Saeed Shahsavari, Mohammad Zakaria Kiaei ,
Volume 76, Issue 12 (March 2019)
Abstract
Background: Diabetes mellitus has several complications. The Late diagnosis of diabetes in people leads to the spread of complications. Therefore, this study has been done to determine the possibility of predicting diabetes type 2 by using data mining techniques.
Methods: This is a descriptive-analytic study that was conducted as a cross-sectional study. The study population included people referring to health centers in Mohammadieh City in Qazvin Province, Iran, from April to June 2015 for screening for diabetes. The 5-step CRISP method was used to implement this study. Data were collected from March 2015 to June 2015. In this study, 1055 persons with complete information were included in the study. Of these, 159 were healthy and 896 were diabetic. A total of 11 characteristics and risk factors were examined, including the age, sex, systolic and diastolic blood pressure, family history of diabetes, BMI, height, weight, waistline, hip circumference and diagnosis. The results obtained by support vector machine (SVM), decision tree (DT) and the k-nearest neighbors algorithm (k-NN) were compared with each other. Data was analyzed using MATLAB® software, version 3.2 (Mathworks Inc., Natick, MA, USA).
Results: Data analysis showed that in all criteria, the best results were obtained by decision tree with accuracy (0.96) and precision (0.89). The k-NN methods were followed by accuracy (0.96) and precision (0.83) and support vector machine with accuracy (0.94) and precision (0.85). Also, in this study, decision tree model obtained the highest degree of class accuracy for both diabetes classes and healthy in the analysis of confusion matrix.
Conclusion: Based on the results, the decision tree represents the best results in the class of test samples which can be recommended as a model for predicting diabetes type 2 using risk factor data.
Atefeh Sedighnia , Sharareh Rostam Niakan Kalhori, Mahshid Nasehi , Ahmad Ali Hanafi-Bojd ,
Volume 77, Issue 4 (July 2019)
Abstract
Background: Tuberculosis (TB) is an important infectious disease with high mortality in the world. None of the countries stay safe from TB. Nowadays, different factors such as Co-morbidities, increase TB incidence. World Health Organization (WHO) last report about Iran's TB status shows rising trend of multidrug-resistant tuberculosis (MDR-TB) and HIV/TB. More than 95% illness and death of TB cases are in developing countries. The most infections are in South East Asia and West Pacific that 56% of them are new cases in the world. The incidence is actually new cases of each year. Incidence prediction is affecting TB prevention, management and control. The purpose of this study is designing and creating a system to predict TB incidence by time series artificial neural networks (ANN) in Iran.
Methods: This study is a retrospective analytic. 10651 TB cases that registered on Iran’s Stop TB System from March 2014 to March 2016, Were analyzed. Most of reliable data used directly, some of them merged together and create new indicators and two columns used to compute a new indicator. At first, effective variables were evaluating with correlation coefficient tests then extracting by linear regression on SPSS statistical software, version 20 (IBM, Armonk, NY, USA). We used different algorithms and number of neurons in hidden layer and delay in time series neural network. R, MSE (mean squared error) and regression graph were used for compare and select the best network. Incidence prediction neural network were designed on MATLAB® software, version R2014a (Mathworks Inc., Natick, MA, USA).
Results: At first, 23 independent variables entered to study. After correlation coefficient and regression, 12 variables with P≤0.01 in Spearman and P≤0.05 in Pearson were selected. We had the best value of R, MSE (mean squared error) and also regression graph in train, validation and tested by Bayesian regularization algorithm with 10 neuron in hidden layer and two delay.
Conclusion: This study showed that artificial neural network had acceptable function to extract knowledge from TB raw data; ANN is beneficial to TB incidence prediction.
Mohsen Sheykhhasan, Hossein Bakhtiari Pak , Mohammad Bakhtiari Pak , Naser Kalhor ,
Volume 77, Issue 12 (March 2020)
Abstract
Background: One of the most significant factors in the success of dental implant procedures, can be mentioned by the quality and quantity of jaw bone. The occurrence of some problems such as trauma, infection, pathological lesions and the long-term absence of teeth in patients, it causes irregularities in the jaw bone and can get bone resorption. Sever defects after trauma or tumor resection needs bone reconstruction. Sticky bone is a new biological agent that provides stabilization of bone graft in the defect, and therefore, ameliorates tissue repairing and decreases bone loss during healing period. In this study, the evaluation of sticky bone performance to reconstruction of defects in two patients jaw's bone was considered.
Case Presentation: Two patients (1 male, 1 female) with an average age of 50 years underwent surgery, due to the history of tooth extraction. They had resorption of jaw bone for implant surgery. The teeth were 11 and 37. This study was performed in Al-Mortaza's Clinic, Qom province, Iran, from May 2016 to January 2017. 10 ml of blood were taken individually and centrifuged at 1300 revolutions per minute (rpm) for 8 minutes to separate the platelet-rich fibrin. Then, platelet-rich fibrin was combined with allogeneic bone to form sticky bone. Sticky bone prepared during implant surgery, with implant inside the patient's jaw bone was used.
Conclusion: The use of sticky bone to stimulate and induce bone resorption in toothless area was associated with increased implant's success. Sticky bone due to multiple growth factors, such as TGF-β1 and VEGF, usability is an appropriate and efficiency method for stimulation of bone resorption.
