Insomnia and restless leg syndrome in patients undergoing chronic hemodialysis in Rafsanjan ali ibn abitaleb hospital

Background: Sleep is one of the most fundamental human needs; without any doubt sleep is even more essential for sick patients, especially for patients with chronic illnesses. Sleep disturbance may lead to anxiety and reduced quality of life. Restless leg syndrome (RLS) is a sensory-motor disorder accompanied by a strong desire to move the legs or other parts of the body, which can cause sleep disturbance. Its etiology is unknown, but increased urea and creatinine levels before dialysis, iron deficiency due to kidney failure and end-stage renal disease (ESRD) are mentioned as causes. Objectives: This study is designed to examine the prevalence of insomnia and restless leg syndrome in patients undergoing chronic hemodialysis in Rafsanjan Ali Ibn Abitaleb Hospital. Patients and Methods: In this study we used two questionnaires to evaluate the presence of RLS and insomnia in ESRD patients who were undergoing hemodialysis treatment as kidney replacement therapy. Results: According to our results, 54.5 of patients were diagnosed with RLS, and of those 65.2 and 42.9 were women and men, respectively. RLS is seen more often among patients with blood group type A, but this result was not statistically significant. There was a statistically significant correlation between RLS and a positive family history of RLS, between RLS and the number of hemodialysis treatments per week and also between RLS and the Insomnia Severity Index. Unlike previous studies, in this study we did not find any statistically significant correlation between RLS and biochemical factors such as serum iron, TIBC, BUN, creatinine, potassium, calcium and phosphorous levels. Conclusions: The frequency of RLS among our patients was remarkable and we conclude that all patients who are undergoing hemodialysis should be screened for RLS, which can assist in providing proper attention and treatment. © 2016, Nephrology and Urology Research Center

Simulation of Elastic Properties of Polymer- Clay Nanocomposite

In this research, stiffness of polymer-clay nanocomposites was simulated by Mori-Tanaka and two and three dimensional finite element models. Nanoclays were dispersed into polymer matrix in two ways, namely parallel and random orientations toward loading direction. Effects of microstructural parameters including volume fraction of nanoclays, elastic modulus of nanoclays and interphase, thickness of interphase, aspect ratio of nanoclays and random orientation of nanoclays on elastic modulus of the nanocomposite were investigated by finite element model. Comparing the simulation with experimental results showed that the Mori-Tanak simulation results were closer to the experimental results. Analysis of results showed that the volume fraction of nanoclay, elastic modulus of nanoclay, deviation of nanoclay layers with respect to loading direction, nanoclays aspect ratio, thickness of interphase and the elastic modulus of interphase had respectively the most to the least effect on elastic modulus of nanocomposite

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

This study challenges the use of three nature-inspired algorithms as learning frameworks of the adaptive-neuro-fuzzy inference system (ANFIS) machine learning model for short-term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography-based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R2 parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R2), root-mean-square error (RMSE), Nash–Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all-nature-inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS-PSO and ANFIS-BOA provide slightly better results than the other ANFIS models. </div

The Effect of Zoledronic Acid on BSP Expression and Methylation during Osteoblastic Differentiation of Mesenchymal Stem Cells

Background and Aim: Bone sialoprotein (BSP) is a specific marker of osteoblastic differentiation. In this research, the effect of Zoledronic Acid on BSP expression and methylation status during osteoblastic differentiation of mesenchymal stem cells (MSCs) was evaluated.Materials and Methods: In this experimental study, MSCs were isolated from human bone marrow. For osteogenic differentiation, hMSCs were pulse treated with zoledronic acid, and were incubated in osteogenic differentiation medium for 3 weeks. The DNA and RNA were extracted after the first, second and third weeks of culture and also from undifferentiated MSCs. After Sodium bisulfate (SBS) treatment, gene specific methylation analysis for BSP was carried out using Methylation Specific PCR technique.Results: BSP expression was observed in osteoblastic differentiated cells whereas it was not seen in MSCs. MSP showed that BSP was unmethylated during osteoblastic differentiation.Conclusion: BSP was expressed from the first week of differentiation. This confirms that zoledronic acid accelerates osteoblastic differentiation. Unmethylation status of BSP indicates that zoledronic acid does not have any effect on BSP methylation status. Other genetic or epigenetic mechanisms may control BSP expression during osteoblastic differentiation induction by zoledronic acid