Analysis of the factors affecting the interval between blood donations using log-normal hazard model with gamma correlated frailty

Background: Time to donating blood plays a major role in a regular donor to becoming continues one. The aim of this study was to determine the effective factors on the interval between the blood donations. Methods: In a longitudinal study in 2008, 864 samples of first-time donors in Shahrekord Blood Transfusion Center, capital city of Chaharmahal and Bakhtiari Province, Iran were selected by a systematic sampling and were followed up for five years. Among these samples, a subset of 424 donors who had at least two successful blood donations were chosen for this study and the time intervals between their donations were measured as response variable. Sex, body weight, age, marital status, education, stay and job were recorded as independent variables. Data analysis was performed based on log-normal hazard model with gamma correlated frailty. In this model, the frailties are sum of two independent components assumed a gamma distribution. The analysis was done via Bayesian approach using Markov Chain Monte Carlo algorithm by OpenBUGS. Convergence was checked via Gelman-Rubin criteria using BOA program in R. Results: Age, job and education were significant on chance to donate blood (P<0.05). The chances of blood donation for the higher-aged donors, clericals, workers, free job, students and educated donors were higher and in return, time intervals between their blood donations were shorter. Conclusions: Due to the significance effect of some variables in the log-normal correlated frailty model, it is necessary to plan educational and cultural program to encourage the people with longer inter-donation intervals to donate more frequently. © 2016, Health Hamadan University of Medical Sciences. All rights reserved

Optimization removal of ciprofloxacin with photo fenton process using Response Surface

زمینه و هدف: ترکیبات دارویی در منابع آب آشامیدنی، علاوه بر تهدید سلامت محیط زیست باعث گسترش مقاومت باکتری &zwnj;ها در محیط&zwnj;های آبی می&zwnj;شوند. در این پژوهش، حذف سیپروفلوکساسین با فرآیند اکسیداسیون فتوشبه&zwnj; فنتون و بهینه&zwnj;سازی شرایط حذف به روش سطح پاسخ مورد بررسی قرار گرفت. روش بررسی: در این مطالعه تجربی در pH بهینه 3، اثر غلظت اولیه سیپروفلوکساسین (250-50 میلی گرم بر لیتر)، زمان تماس (60-10 دقیقه)، مقدار نیترات آهن (5/0-1/0 میلی مول) و مقدار H2O2 (12-1 میلی مول)، نسبت مولی واکنشگرها بر راندمان حذف آنتی &zwnj;بیوتیک با روش طراحی مرکب مرکزی و با استفاده از نرم افزار Design Expert مورد ارزیابی قرار گرفت. برای تحلیل آماری نتایج از آزمایشات ANOVA و P-value استفاده شد. غلظت سیپروفلوکساسین با استفاده از دستگاه HPLC اندازه&zwnj;گیری گردید. یافته&zwnj;ها: نتایج نشان داد که کارآیی فرآیند با افزایش غلظت سیپروفلوکساسین، کاهش یافت و با افزایش مقدار نیترات آهن، پراکسید هیدروژن و زمان تماس افزایش یافت. در طرح مرکب مرکزی، حداکثر کارآیی حذف (8/85) در 3=pH، غلظت سیپروفلوکساسین 5/88 میلی گرم در لیتر، نیترات آهن 35/0 میلی مول، 54/11 میلی مول پراکسید هیدروژن و زمان تابش 57 دقیقه و نسبت مولی H2O2 به آهن (III) برابر ]35/0 / 54/11 به دست آمد. بررسی روابط سینتیک نشان داد که فرآیند حدف سیپروفلوکساسین با (953/0=R2) از واکنش&zwnj; درجه دوم تبعیت کرد. نتیجه&zwnj;گیری: نتایج به دست آمده از این پژوهش نشان داد که فرآیند فتوشبه فنتون، روش موثری جهت حذف سیپروفلوکساسین از پساب است و با بهینه سازی عوامل موثر می&zwnj;توان از این فرآیند جهت تصفیه فاضلاب دارای آنتی بیوتیک استفاده نمود

The association between fever and pyuria in children older than one month

Introduction: Some assumptions have been made on the probable association between fever and pyuria. Objectives: The present study aimed to investigate the association between fever and pyuria. Patients and Methods: In this case-control study, 90 febrile and 90 non-febrile children aged more than one month who were admitted to the pediatric ward were included. Urine specimens of children less than 2 years of age were collected by urine bag. Midstream urine samples were collected and immediately sent to the laboratory for complete urinalysis and urine culture. Results: Overall, 6.7% in febrile children and 2.2% in control group had pyuria however there was no significant association between fever and pyuria (P>0.05). Additionally, no association between the presence of pyuria and type of disease was detected (P>0.40). Conclusion: The present study could not reveal any association between fever and pyuria in children older than one month. Keywords: Pyuria, Interstitial nephritis, Feve

Using principal component analysis to increase accuracy of prediction of metabolic syndrome in artificial neural network and logistic regression models

زمینه و هدف: در فرآیند مدل‌سازی، زمانی‌که بین متغیرهای کمکی همبستگی‌های نسبتا قوی وجود داشته باشد، هم‌خطی‌چندگانه ایجاد شده و باعث کاهش کارآیی مدل می‌گردد. هدف از این مطالعه استفاده از تحلیل مولفه‌های اصلی برای تعدیل اثر هم‌خطی‌چندگانه در مدل‌های رگرسیون لجستیک و شبکه عصبی مصنوعی و بررسی تاثیر آن بر صحت و دقت پیش‌بینی سندرم متابولیک بود. روش بررسی: در این مطالعه توصیفی – تحلیلی تعداد 347 نفر از افراد شرکت کننده در مطالعه آینده نگر قند و لیپید تهران که در فاز اول مطالعه بر اساس تعریف پانل درمان بالغین (ATPIII) مبتلا به سندرم متابولیک نبودند انتخاب شدند. ابتدا مدل‌های رگرسیون لجستیک و شبکه عصبی مصنوعی با استفاده از متغیرهای کمکی اولیه و سپس با استفاده از مولفه‌های اصلی به داده‌ها برازش گردید و پیش‌بینی بر اساس این مدل‌ها انجام شد. از تحلیل راک و آماره کاپا برای مقایسه قدرت پیش‌بینی مدل‌ها استفاده گردید. یافته‌ها: برای مدل‌های رگرسیون لجستیک، رگرسیون لجستیک با مولفه‌های اصلی، شبکه عصبی مصنوعی و شبکه عصبی مصنوعی با مولفه‌های اصلی به‌ترتیب مساحت زیر منحنی راک 749/0، 790/0، 890/0 و 927/0 به‌دست آمد، میزان حساسیت مدل‌ها 483/0، 435/0، 836/0 و 919/0، ویژگی آن‌ها 857/0، 919/0، 892/0 و 964/0 و اندازه آماره کاپا برای مدل‌ها 322/0، 386/0، 712/0 و 886/0 به‌دست آمد. نتیجه‌گیری: تحقیق نشان داد که صحت پیش‌بینی مدل‌های بر اساس مولفه‌های اصلی از مدل‌های مبتنی بر متغیرهای کمکی اولیه بیشتر بوده و بنابراین در هنگام وجود هم‌خطی‌چندگانه، مدل‌های مبتنی بر مولفه‌های اصلی برای پیش‌بینی سندرم متابولیک کاراتر هستند

Optimization of removal of COD and color from baker’s yeast wastewater by Fenton oxidation

Background and Objectives: Bakery’s yeast industry wastewater contains various pollutants and is generally characterized with high chemical oxygen demand (COD), dark color, high-nitrogen and sulfate and non-biodegradable organic pollutants. Having persistent soluble colored compounds (called melanoidins), effluent from yeast industry is a major source of water and soil pollution. The aim of this study was to evaluate advanced oxidation efficiency using Fenton process for COD and color removal from bakery’s yeast wastewater. Materials and Methods: This was an experimental- laboratory scale study. In this study, the effect of time and Fenton concentrations were tested for COD and color removal from bakery’s yeast wastewater. The sample used for this study was yeast effluent from Separator 2 with initial concentrations of COD and color of 5300 mg/L and 6950 pt-co respectively. In order to obtain the optimum operating conditions of the process, Taguchi analysis method was used. Experiments were carried out in five stages of the time in the range of 15, 30, 45, 60 and 75 min with various concentrations of hydrogen peroxide (e.g., 0.02, 0.04, 0.06, 0.08, and 0.1 molar) and concentrations of Fe2+ (e.g., 0.01, 0.02, 0.03, 0.04, and 0.05 molar) at pH = 3. Jar test method was used to determine the best operating conditions including: reaction time, dosages of hydrogen peroxide and Fe2+. Results: According to Taguchi method and SN-ratio analysis, the best H2O2/Fe2+ dosages were 0.08/0.04 molar at pH 3 and in reaction time of 30 min for removal of COD and color. For these conditions, the maximum COD and color removal efficiencies were 63 and 69 percent respectively. Based on the results, with increasing reaction time, there was no perceptible change in the removal efficiency. Conclusion: It can be concluded that Fenton’s oxidation method can be used successfully, as an alternative option to the design and choice of color and COD removal from strength industrial wastewaters e.g., bakery’s yeast industry

Comparison of artificial neural network, logistic regression and discriminant analysis methods in prediction of metabolic syndrome.

Introduction: Artificial neural networks as a modern modeling method have received considerable attention in recent years. The models are used in prediction and classification in situations where classic statistical models have restricted application when some, or all of their assumptions are met. This study is aimed to compare the ability of neural network models to discriminant analysis and logistic regression models in predicting the metabolic syndrome. Materials & Methods: A total of 347 participants from the cohort of the Tehran Lipid and Glucose Study (TLGS) were studied. The subjects were free of metabolic syndrome at baseling according to the ATPIII criteria. Demographic characteristics, history of coronary artery disease, body mass index, waist, LDL, HDL, total cholesterol, triglycerides, fasting and 2 hours blood sugar, smoking, systolic and diastolic blood pressure were measured at baseline. Incidence of metabolic syndrome after about 3 years of follow up was considered a dependent variable. Logistic regression, discriminant analysis and neural network models were fitted to the data. The ability of the models in predicting metabolic syndrome was compared using ROC analysis and the Kappa statistic, for which, MATLAB software was used. Results: The areas under receiver operating characteristic (ROC) curve for logistic regression, discriminant analysis and artificial neural network models (15: 8: 1) and (15: 10: 10) were estimated as 0. 749, 0. 739, 0. 748 and 0. 890 respectively. Sensitivity of models were calculated as 0. 483, 0. 677, 0. 453 and 0. 863 and their specificity as 0. 857, 0. 660, 0. 910 and 0. 844 respectively. The Kappa statistics for these models were 0. 322, 0. 363, 0. 372 and 0. 712 respectively. Conclusion: Results of this study indicate that artificial neural network models perform better than classic statistical models in predicting the metabolic syndrome

Joint prediction of occurrence of heart block and death in patient with myocardial infarction with artificial neural network model

When it is desired to examine occurrence of two events simultaneously, it is common to use bivariate statistical models such as bivariate logistic regression. Due to the limitations of classical methods in real situations, other methods such as artificial neural networks (ANN) are concerned. The aim of this study was comparing the predictive accuracy of bivariate logistic regression and artificial neural network models in diagnosis of death occurrence and heart block in myocardial infarction patients. Material and Methods: In this study, data was taken from a census in a cross-sectional study in which 263 patients with myocardial infarction cases who admitted to Hajar hospital heart care in 2013 to 2014. Gender, type of stroke, history of diabetes, previous history of hypertension, lipid disorders, history of heart disease, cardiac output fraction, systolic blood pressure, diastolic blood pressure, fasting and non-fasting blood sugar, cholesterol, triglycerides, low-density cholesterol, smoking, type of treatment, the troponin enzymes and insurant type were considered as explanatory variables and occurrence of death and heart block were used as dependent variables. Bivariate logistic regression and neural network model was fitted. Both models were predicted and the accuracy of them were compared. Models were fitted by MATLAB2013a and Zelig in R3.2.2. Results: Predictive accuracy of bivariate logistic regression model was 77.7% for the training and 78.48% for the test data. In ANN model, LM and OSS algorithms had best performance with 83.69% and 83.15% predictive accuracy for training data and 84.81% and 83.54% for testing data, respectively. Conclusion: This research showed that the neural network method is more accurate than bivariate logistic regression to joint predicting the occurrence of death and heart block in patients with myocardial infarctio

Phytotherapy with hordeum vulgare: A randomized controlled trial on infants with jaundice

Jaundice is one of the most common causes of admission to hospital in newborns which is often associated with several complications. Aim: The present study was conducted to evaluate the effect of H. vulgare in reducing jaundice. Materials and Methods: In this double-blind, randomized controlled trials 70 term infants hospitalized due to jaundice in 2014 were enrolled. Control group was treated with full-time phototherapy alone using LED except when the infants were breastfed and case group with phototherapy, as per the protocol in the control group, along with and topical H. vulgare seed flour three times a day. Data were analysed using and analysis of covariance (ANCOVA) and paired t-test in SPSS version 16.0. Results: There was a significant difference in mean indirect bilirubin level between the two groups p0.05. Conclusion: H. vulgare flour can cause decrease in indirect bilirubin. Because the rate of decrease in indirect bilirubin can be effective in preventing severe complications due to bilirubinemia, H. vulgare can be used as a complementary therapy to treat jaundic

Evaluation of chemical quality in 17 brands of Iranian bottled drinking waters

Background: The purpose of study was to evaluate and compare chemical quality of Iranian bottled drinking water reported on manufacturer's labeling and standards. Methods: This study was a cross-sectional descriptive study and done during July to December 2008. The bottled mineral water collected from shops randomly were analyzed for all parameters address on manufacturer's labeling and the results were compared with the manufacturer's labeling data, WHO Guideline Values, USEPA Maximum Contaminant Levels and the maximum contaminant levels of drinking water imposed by the Iranian legislation. Statistical analysis on data was done with the Kolmogorov-Smirnov test for normal distribution, the paired t-test to compare the data with manufacturer's labeling and the one-sample t-test to compare with standard and MCL values at P < 0.05 of confidence level. Results: The results showed a statistically significant difference with manufacturer's labeling values, however there was no significant difference between the values of magnesium and pH and manufacturer's labeling values (P> 0.05). In addition, pH and calcium values were significantly higher than their proposed values indicated by Iranian National Legislation and international MCLs (P< 0.05). Conclusion: Our results are extremely important for the health supervisory agencies such as Ministry of Health and Institute of Standards & Industrial Research of Iran to have more effective controls on bottled water industries, and to improve periodical the proposed standard values

Particulate Matter Inhalation Exposure Chambers and Parameters Affecting Their Performance: A Systematic Review Study

Exposure to inhalation aerosols and particulate matter (PM) in different concentrations can increase the risk of respiratory, cardiovascular, and other related diseases. The inhalation exposure studies are implemented to assess the biological effects of these hazardous agents in human or animal models, in whole-body (WB) or nose/head-only conditions. Several factors can affect the performance of the inhalation exposure chambers and if left uncontrolled, the results may not be desirable. The current study reviewed the characteristics, structures, and factors affecting the performance of the WB chambers, especially the ones designed for small animal exposure to the PM. At the primary stage, the criteria and the search strategy were determined and the keywords were searched in the scientific electronic databases. Totally, 1051 articles were extracted in the first stage, and finally seven articles were adopted. The technical and design details, materials, coefficient variations (CVs) of concentration, assessment methods, type and number of laboratory animals, procedure, and animals housing conditions were extracted from the selected articles. Then the most desirable WB inhalation exposure chamber was determined based on the criteria for assessing the presented exposure chambers such as the animal housing and least CVs of the concentration in the respiratory zones of the animals under study. It was concluded that the Kimmel design was the best and the most desirable chamber structurally and geometrically, since the concentration of the particle (NaCl) injected into the chamber varied from 3.5% to 5.2%, under standard conditions. Keywords:Inhalation Chamber; Whole-Body; Inhalation Exposure; Particulate Matter