Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Correlation of serum adiponectin level with some biochemical and metabolic factors in stable hemodialysis patients

Introduction: Serum adiponectin is a hormone secreted by the adipose tissue and its level usually increases in patients with renal insufficiency. In uremic condition, it not only loses its protective role against atherosclerosis, but also becomes a risk factor. This hormone is a direct predictor of cardiovascular complications in patients with renal failure. Objectives: This study was designed to assess the association between serum adiponectin with various parameters in in a group of non-diabetic hemodialysis patients. Patients and Methods: In this study, 73 hemodialysis non-diabetic patients were selected and fasting blood samples were taken to measure adiponectin and some other biochemical parameters. Waist circumference, abdominal circumference, weight and body mass index (BMI) were measured. Pearson statistical test was used to find the association between adiponectin and mentioned parameters. Results: Adiponectin level was negatively and significantly associated with weight (P<0.001), BMI (P<0.001), waist circumference (P<0.05), abdominal circumference (P<0.01), and triglycerides (P<0.01). Conclusion: According to the results of our study, serum adiponectin level in hemodialysis patients was negatively associated with weight and BMI which indicates the likely effect of the hormone. As a result, finding of exact connections between this cytokines and the risk factors of atherosclerosis and hypercatabolism may help to introduce serum adiponectin as a measurable and important marker for atherosclerosis and may be used as an index for prognosis of mortality in this type of patients. Keywords: Adiponectin, Kidney failure, Hemodialysis Please cite this paper as: Tamadon MR, Heidari M, Dris F, Mardani S. Correlation of serum adiponectin level with some biochemical and metabolic factors in stable hemodialysis patients. J Parathyr Dis 2015;3(1):20-24. Copyright © 2015 The Author(s); Published by Nickan Research Institute. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Study of barriers to preparation, administration and utilization of research findings from viewpoints of faculty members and active experts in research area in Shahrekord University of Medical Sciences

زمینه و هدف: پژوهش از عناصر کلیدی در توسعه ی هر کشور است و در صورتی که به درستی به آن پرداخته نشود، موجب اتلاف منابع مادی و انسانی خواهد شد. این مطالعه با هدف تعیین موانع موجود بر کاربست پژوهش از دیدگاه فعالان حوزه ی پژوهش اجرا گردید. روش بررسی: مطالعه ی مقطعی حاضر در سال 1393 بر روی 102 نفر از افراد فعال در حوزه ی پژوهش و به صورت نمونه گیری هدفمند انجام شد. ابزار گردآوری داده ها پرسشنامه ی محقق ساخته ی دو بخشی بود که به ارزیابی اطلاعات شخصی و موانع موجود در کاربست پژوهش می پرداخت (738/0=&alpha;). پس از ورود داده ها در نرم افزار SPSS، از آمار توصیفی و تحلیلی برای تحلیل داده ها استفاده شد. یافته ها: میانگین موانع و مشکلات پیش روی کاربست پژوهش به ترتیب در حیطه ی تهیه 48/3، کاربرد نتایج 22/3 و مدیریت و اجرای طرح های تحقیقاتی 91/2 بوده است. میانگین نمره ی موانع کاربست پژوهش در حیطه ی مدیریت و اجرا، در حوزه ی معاونت تحقیقات و فناوری به طور معنی داری از سایر معاونت ها کمتر بود (05/0&gt;P). همبستگی معنی داری میان سن و سابقه کار با نمره موانع کاربست پژوهش در 3 حیطه مذکور وجود نداشت (05/0&lt;P). نتیجه گیری: مهم ترین موانع کاربست پژوهش به ترتیب در حیطه تهیه، کاربرد نتایج و مدیریت طرح های تحقیقاتی می باشد. نتایج این مطالعه می تواند در شناسایی و رفع موانع مربوط به کاربست پژوهش در دستور کار سیاست گزاران و برنامه ریزان قرار گیرد

Intensive Care Nurses' Knowledge of Radiation Safety and Their Behaviors Towards Portable Radiological Examinations

Background: Radiological examinations for patients who are hospitalized at intensive care units are usually performed using portable radiography devices. However they may require knowledge and safety precautions of nurses. Objectives: The aim of the study was to investigate ICU nurses’ knowledge of radiation safety and their behaviors towards portable radiological examinations. Materials and Methods: In total, 44 intensive care nurses were recruited for this cross-sectional descriptive study using census sampling during April and May 2014. The study setting was at intensive care units of Shahid Beheshti Hospital of Kashan, Iran. An eleven-item questionnaire and a five-item checklist were used for evaluating nurses’ radiation protection knowledge and behaviors, respectively. An expert panel consisting of ten nursing and radiology faculty members confirmed the content validity of the questionnaire and the checklist. Moreover, a Geiger-Müller counter was used for measuring ionizing radiation during portable radiological examinations. Study data were analyzed using the SPSS software version 13.0. Mean, standard deviation, frequency and one-sample t test were used for description of the data. The level of significance was set at below 0.05. Results: The mean of participants’ radiation protection knowledge was 4.77 ± 1.38. The most prevalent radiation protection behavior of nurses was leaving the intensive care unit during portable radiological examinations. Only 6.8% of nurses stayed at the nursing station during radiological examinations. The highest dose of radiation was 0.11 micro Sievert per hour (μSv/h), which was much lower than the highest permitted level of radiation exposure i.e. 0.25 μSv/h. Conclusions: Portable radiological examinations did not expose healthcare providers to high doses of ionizing radiation. Nurses’ radiation protection knowledge was limited and hence, they require in-service education programs

Anti-amnesic activity of Citrus aurantium flowers extract against scopolamine-induced memory impairments in rats

Alzheimer's disease (AD) is a progressive neurological disorder that mostly affects the elderly population. Learning and memory impairment as the most characteristic manifestation of dementia could be induced chemically by scopolamine, a cholinergic antagonist. Cholinergic neurotransmission mediated brain oxidative stress. Citrus aurantium (CA) has traditionally been used for the treatment of insomnia, anxiety and epilepsy. The present study was designed to investigate the effect of Citrus aurantium on scopolamine-induced learning and memory deficit in rats. Forty-two Wistar rats were divided into six equal groups. (1) Control (received saline), (2) SCOP (scopolamine at a dose of 1 mg/kg for 15 days), (3) and (4) SCOP + CA (scopolamine and CA extract at doses of 300 and 600 mg/kg per day for 15 days), (5) and (6) intact groups (CA extract at 300 and 600 mg/kg per day for 15 days, respectively). Administration of CA flower extract significantly restored memory and learning impairments induced by scopolamine in the passive avoidance test and also reduced escape latency during trial sessions in the Morris water maze test. Citrus aurantium flower extract significantly decreased the serum malondialdehyde (MDA) levels. Citrus aurantium flower extract has repairing effects on memory and behavioral disorders produced by scopolamine and may have beneficial effects in the treatment of AD

The effect of occupational therapy on some aspects of quality of life in schizophrenic patients

چکیده: زمینه و هدف: بیماری اسکیزوفرنیا شدیدترین و مزمن شونده ترین بیماری روانپزشکی است که با اختلال در تواناییهای اجتماعی و شغلی همراه است. کار درمانی باعث افزایش اعتماد به نفس، خودسازی و تقویت رفتارهای کاری در بیمار می شود. این پژوهش با هدف تعیین تاثیر کاردرمانی بر ابعاد مختلف کیفیت زندگی بیماران اسکیزوفرنیک مزمن بستری در بیمارستان سینا انجام شده است. روش بررسی: این پژوهش یک مطالعه کار آزمایی بالینی است که ابتدا بیماران اسکیزوفرن مزمن بستری در بیمارستان سینای فارسان در استان چهارمحال و بختیاری بصورت سرشماری انتخاب و کیفیت زندگی آنان بوسیله پرسشنامه کیفیت زندگی بررسی و سپس بیماران بصورت تصادفی به دو گروه مورد (32 نفر) و شاهد (30 نفر) تقسیم گردیدند. کاردرمانی به مدت 20 ساعت در هفته در طی 6 ماه برای گروه مورد اجرا شد. بعد از اجرای کاردرمانی مجدداً کیفیت زندگی بیماران بررسی و اطلاعات با استفاده از آمار توصیفی و استنباطی (t مستقل) تجزیه و تحلیل شد. یافته ها: نتایج نشان داد در بدو مطالعه، تفاوت معنی داری بین میانگین نمره کیفیت زندگی گروه مورد و شاهد، وجود نداشت، بعد از مطالعه این تفاوت در حیطه انگیزه و انرژی و نمره کل کیفیت زندگی بین گروه مورد و شاهد معنی دار بود (001/0

Extending the decomposition algorithm for support vector machines training

The Support Vector Machine (SVM) is found to de a capable learning machine. It has the ability to handle difficult pattern recognition tasks such as speech recognition, and has demonstrated reasonable performance. The formulation in a SVM is elegant in that it is simplified to a convex Quadratic IProgramming (QP) problem. Theoretically the training is guaranteed to converge to a global optimal. The training of SVM is not as straightforward as it seems. Numerical problems will cause the training to give non- optimal decision boundaries. Using a conventional optimizer to train SVM is not the ideal solution. One can design a dedicated optimizer that will take full advantage of the specific nature of the QP problem in SVM training. The decomposition algorithm developed by Osuna et al. (1997a) reduces the training cost to an acceptable level. In this paper we have analyzed and developed an extension to Osuna's method in order 110 achieve better performance. The modified method can be used to solve the training of practical SVMs, in which the training might not otherwise converge

Churn classification model for local telecommunication company based on rough set theory

Customer care plays an important role in a company especially in managing churn for Telecommunication Company. Churn is perceived as the behaviour of a customer to leave or to terminate a service. This behaviour causes the loss of profit to companies because acquiring new customer requires higher investment compared to retaining existing ones. Thus, it is necessary to consider an efficient classification model to reduce the rate of churn. Hence, the purpose of this paper is to propose a new classification model based on the Rough Set Theory to classify customer churn. The results of the study show that the proposed Rough Set classification model outperforms the existing models and contributes to significant accuracy improvement.Keywords: customer churn; classification model; telecommunication industry; data mining;rough set