Effect of melatonin in reducing second-generation antipsychotic metabolic effects: A double blind controlled clinical trial

Introduction The use of second-generation atypical antipsychotics has an increasing role in the development of metabolic syndrome. However, these medications due to metabolic disorders can lead to an increased risk of cardiovascular disease and subsequently mortality as well as reduced adherence to treatment. The main objective of current study was to determine the ability of melatonin to reduce the metabolic effects of second-generation antipsychotics. Methods This double blind controlled clinical trial was conducted on 100 patients aged 18–64 years old were treated with the second-generation antipsychotics for the first time. The patients were divided randomly into two groups of 50. The case group received slow-release melatonin at a dose of 3 mg and the control group was given oral placebo at 8 p.m. Results The findings in melatonin group indicated significantly increase of HDL and decreased fasting blood sugar and systolic blood pressure, as well as had statistically significant increase in waist circumference, weight and BMI compared with placebo group. Conclusion According to the findings, it can be claimed that the addition of melatonin to atypical antipsychotics has led to a reduction in some of the metabolic effects of these drugs. In this study, HDL level was increased, and the mean systolic blood pressure and FBS were decreased in the melatonin group. Considering that these factors are contributing to cardiovascular disease as a leading cause of mortality in psychiatric patients, so the use of melatonin can reduce some of the medical effects of long-term treatment of atypical antipsychotics. © 2017 Diabetes Indi

Psychological Violence in the Health Care Settings in Iran: A Cross-Sectional Study

Background: Psychological violence is the most common form of workplace violence that can affect professional performance and job satisfaction of health care workers. Although several studies have been conducted in Iran, but there is no consensus regarding current status of such violence. Objectives: This study aimed to investigate the prevalence of psychological violence among healthcare workers employed at teaching hospitals in Iran. Patients and Methods: In this cross-sectional study, 5874 health professionals were selected using multistage random sampling. Data were collected using a self-administered questionnaire developed by the International Labor Organization, International Council of Nurses, World Health Organization, and Public Services International. Descriptive statistics were used to analyze the data. Results: It was found that 74.7% of the participants were subjected to psychological violence during the past 12 months. Totally, 64.5% of psychological violence was committed by patients’ families, but 50.9% of participants had not reported the violence, and 69.9% of them believed that reporting was useless. Conclusions: The results are indicative of high prevalence of psychological violence against healthcare workers. Considering non-reporting of violence in more than half of participants, use of an appropriate reporting system and providing training programs for health professionals in order to prevent and manage workplace violence are essential

Evaluating equality in prescribing Novel Oral Anticoagulants (NOACs) in England: the protocol of a Bayesian small area analysis

Background Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting about 1.6% of the population in England. Novel oral anticoagulants (NOACs) are approved AF treatments that reduce stroke risk. In this study, we estimate the equality in individual NOAC prescriptions with high spatial resolution in Clinical Commissioning Groups (CCGs) across England from 2014 to 2019. Methods A Bayesian spatio-temporal model will be used to estimate and predict the individual NOAC prescription trend on ‘prescription data’ as an indicator of health services utilisation, using a small area analysis methodology. The main dataset in this study is the “Practice Level Prescribing in England,” which contains four individual NOACs prescribed by all registered GP practices in England. We will use the defined daily dose (DDD) equivalent methodology, as recommended by the World Health Organization (WHO), to compare across space and time. Four licensed NOACs datasets will be summed per 1,000 patients at the CCG-level over time. We will also adjust for CCG-level covariates, such as demographic data, Multiple Deprivation Index, and rural-urban classification. We aim to employ the extended BYM2 model (space-time model) using the RStan package. Discussion This study suggests a new statistical modelling approach to link prescription and socioeconomic data to model pharmacoepidemiologic data. Quantifying space and time differences will allow for the evaluation of inequalities in the prescription of NOACs. The methodology will help develop geographically targeted public health interventions, campaigns, audits, or guidelines to improve areas of low prescription. This approach can be used for other medications, especially those used for chronic diseases that must be monitored over time

Internet of Things; a system for improving the higher education system

Background and Objective:Nowadays, information technology, has affected the production, distribution, and use of knowledge-based processes. Among other things, the Internet of Things as a network of objects connected to each other can bring new capacities in all fields. The aim of the present research is to examine the opportunities and challenges that the Internet of Things can have in relation to the higher education system. The concept of connecting devices and objects to each other is a new development on the Internet; anything anywhere can connect to the Internet and be "smart". Connected devices can communicate with each other and share information, then this information can be processed and lead to making decisions. This concept is called the "Internet of Things (IOT)." The wide range of applications of IOT has enabled educational environments at all levels to benefit from it. In terms of the role of IoT in higher education, this domain includes energy storage, monitoring the health and safety of students, optimizing the physical envriornment of the campus and classrooms, as well as enabling students to attend remotely. The main point that seems to differentiate IOT from other past technologies is that older methods cover a limited range of areas of higher education. With the use of various IOT tools, all these tools and facilities can be made intelligent and use for educational, research and service providing purposes. The aim of this study is to investigate the role and function of the IOT in the processes of knowledge production, transmission and application in higher education system. Methods: For this purpose, the systematic approach and the Chelkland SSM method were used. By refer to several internal and external scientific information databases, 25 related articles were identified. Then, without any sampling, all of these articles were studied by the researchers and their contents are divided into four categories: the introduction of the internet of things, the role of the internet of things in educational functions, the opportunities of the internet of things for higher education and the challenges of the internet of things in higher education, and they were organized in the dimensions of the input, process and output of the higher education system. Findings: The findings of the research indicate that the internet of things, by providing advanced information services, provides a flexible and measurable system for academic community that can be used to personalize training and reinforcement of learning, better management of educational processes, and more effective logistic management etc. The use of the internet of things will also challenge higher education, which violation of privacy, security issues, and rising costs are some of these challenges. Conclusion: In the present study, an attempt was made to introduce IOT and its opportunities and challenges for higher education system by reviewing the related literature. IOT is a technology that covers a wide range of applications in the university, from classrooms to laboratories, colleges and parking lots, and more. Within the system, IOT can be used to support the higher education chain and facilitate communication between input, output, and the process, and facilitate monitoring, control, and management of the university's system. In other words, the IOT operates communication center for the university system. In the dimension of higher education and university process, the IOT can be effective in student interaction and participation, evaluation, mental and physical health, classroom management, satisfaction, attendance, time saving as well as faculty management, energy saving, information searching, improving security in the university environment, providing real learning, personal growth and development for both the university instructors and the students, and so on. Of course, as mentioned, the use of this technology has cetain challenges. These challenges include security and privacy risks, high costs, connection to the Internet, scalability, self-organization and acceptance, etce. But despite this, experts generally see the future of this technology as more practical and important than it is now.   ===================================================================================== COPYRIGHTS  ©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================

Geographical and socioeconomic inequalities in female breast cancer incidence and mortality in Iran: A Bayesian spatial analysis of registry data

Background In Iran, trends in breast cancer incidence and mortality have generally been monitored at national level. The purpose of this study is to examine province-level disparities in age-standardised breast cancer incidence versus mortality from 2000 to 2010 and their association with socioeconomic status. Methods In this study, data from Iran’s national cancer and death registry systems, and covariates from census and household expenditure surveys were used. We estimated the age-standardised incidence and mortality rates in women aged more than 30 years for all 31 provinces in the consecutive time intervals 2000–2003, 2004–2007 and 2008–2010 using a Bayesian spatial model. Results Mean age-standardised breast cancer incidence across provinces increased over time from 15.0 per 100,000 people (95% credible interval 12.0,18.3) in 2000–2003 to 39.6 (34.5,45.1) in 2008–2010. The mean breast cancer mortality rate declined from 10.9 (8.3,13.8) to 9.9 (7.5,12.5) deaths per 100,000 people in the same period. When grouped by wealth index quintiles, provinces in the highest quintile had higher levels of incidence and mortality. In the wealthiest quintile, reductions in mortality over time were larger than those observed among provinces in the poorest quintile. Relative breast cancer mortality decreased by 16.7% in the highest quintile compared to 10.8% in the lowest quintile. Conclusions Breast cancer incidence has increased over time, with lower incidence in the poorest provinces likely driven by underdiagnoses or late-stage diagnosis. Although the reported mortality rate is still higher in wealthier provinces, the larger decline over time in these provinces indicates a possible future reversal, with the most deprived provinces having higher mortality rates. Ongoing analysis of incidence and mortality at sub-national level is crucial in addressing inequalities in healthcare systems and public health both in Iran and elsewhere

Development of specific nanobodies (VHH) for CD19 immunotargeting of human B-Lymphocytes

Objective(s): CD19 is a transmembrane glycoprotein of immunoglobulin superfamily. In order to treat lymphoma, monoclonal antibodies (mAb) can target different antigens, including CD19, CD20 and CD22 on the surface of B-cells. Along with biotechnology progress, a new generation of antibodies is introduced, with the purpose of eliminating the defects of the previous generation. Among the most developed one are nanobodies (Nb). Nbs are a unique kind of camelid single domain antibody fragments with a broad range of medical applications. Unique physicochemical properties of Nbs have made them ideal candidates for therapeutic and diagnostic applications. Materials and Methods: An immune gene library was created, and several CD19 specific Nbs were selected through antigen panning process, and their molecular properties as well as specificity, sensitivity, affinity and immunoreactivity against CD19 positive and negative cells were evaluated. Results: The Nb library was prepared with 7.2 �107 members. We managed to isolate a panel of CD19- specific Nbs after the last round of selection with the affinity of isolated Nbs being estimated at the standard range of 15-35 nM. Sequence analysis of positive clones was indicative of the fact that 12 variable sequences were confirmed. Of all these 12 clones, 2 clones with the greatest level signal in ELISA underwent subsequent analysis. Our sequencing results indicated high sequence homology (approximately 90) between the Nb and Homa variable immunoglobulin domains. Conclusion: Specific Nbs possess the potential to be used as novel therapeutic approaches in order to treat autoimmune diseases and B-cell lymphoma. © 2018, Mashhad University of Medical Sciences. All rights reserved

Development of specific nanobodies (VHH) for CD19 immunotargeting of human B-Lymphocytes

Objective(s): CD19 is a transmembrane glycoprotein of immunoglobulin superfamily. In order to treat lymphoma, monoclonal antibodies (mAb) can target different antigens, including CD19, CD20 and CD22 on the surface of B-cells. Along with biotechnology progress, a new generation of antibodies is introduced, with the purpose of eliminating the defects of the previous generation. Among the most developed one are nanobodies (Nb). Nbs are a unique kind of camelid single domain antibody fragments with a broad range of medical applications. Unique physicochemical properties of Nbs have made them ideal candidates for therapeutic and diagnostic applications. Materials and Methods: An immune gene library was created, and several CD19 specific Nbs were selected through antigen panning process, and their molecular properties as well as specificity, sensitivity, affinity and immunoreactivity against CD19 positive and negative cells were evaluated. Results: The Nb library was prepared with 7.2 �107 members. We managed to isolate a panel of CD19- specific Nbs after the last round of selection with the affinity of isolated Nbs being estimated at the standard range of 15-35 nM. Sequence analysis of positive clones was indicative of the fact that 12 variable sequences were confirmed. Of all these 12 clones, 2 clones with the greatest level signal in ELISA underwent subsequent analysis. Our sequencing results indicated high sequence homology (approximately 90) between the Nb and Homa variable immunoglobulin domains. Conclusion: Specific Nbs possess the potential to be used as novel therapeutic approaches in order to treat autoimmune diseases and B-cell lymphoma. © 2018, Mashhad University of Medical Sciences. All rights reserved

The effect of superparamagnetic iron oxide nanoparticles surface engineering on relaxivity of magnetoliposome

The purpose of this work is evaluating the effect of ultra small superparamagnetic iron oxide nanoparticles (USPIONs) coatings on encapsulation efficiency in liposomes and cellular cytotoxicity assay. Moreover, we assessed the effects of surface engineering on the relaxivity of magnetoliposome nanoparticles in order to create a targeted reagent for the intelligent diagnosis of cancers by MRI. For estimating the effect of nanoparticle coatings on encapsulation, several kinds of USPIONs coated by dextran, PEG5000 and citrate were used. All kinds of samples are monodispersed and below 100 ± 10 nm and the coatings of USPIONs have no significant effect on magnetoliposome diameter. The coating of USPIONs could have effect on percentage of encapsulation. The dextran coated USPIONs have more stability and quality accordingly the encapsulation increased up to 92, then the magnetoliposome nano particles have been targeted by Herceptin and anti-HER2 VHH, separately. Over storage period of four weeks the resulting particles were stable and physico-chemical properties such as size and zetapotential did not show any significant changes. The relaxivity of contrast agents was measured using a 1.5 T MRI. The r2/r1 ratio was more than two for all samples which demonstrate the negative contrast enhancing of all SPION embedded specimens. The high ratio of r2/r1 as well as high r2 is the best combination of a negative contrast agent as it is obtained for pure magnetite. The value of r2/r1 for all other samples including Herceptin targeted magnetoliposome, anti-HER2 VHH targeted magnetoliposome and non-targeted magnetoliposome were between ~21 to ~28, which show the magnetite embedded samples have enough negative contrast to be detectable by MRI. Therefore the HER2 targeted magnetoliposomes are a good and stable candidate as contrast agents in clinical radiology and biomedical research with minimal cytotoxicity and biocompatibility effects. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd

Offline and Online Satisfaction Prediction in Open-Domain Conversational Systems

Predicting user satisfaction in conversational systems has become critical, as spoken conversational assistants operate in increasingly complex domains. Online satisfaction prediction (i.e., predicting satisfaction of the user with the system after each turn) could be used as a new proxy for implicit user feedback, and offers promising opportunities to create more responsive and effective conversational agents, which adapt to the user's engagement with the agent. To accomplish this goal, we propose a conversational satisfaction prediction model specifically designed for open-domain spoken conversational agents, called ConvSAT. To operate robustly across domains, ConvSAT aggregates multiple representations of the conversation, namely the conversation history, utterance and response content, and system- and user-oriented behavioral signals. We first calibrate ConvSAT performance against state of the art methods on a standard dataset (Dialogue Breakdown Detection Challenge) in an online regime, and then evaluate ConvSAT on a large dataset of conversations with real users, collected as part of the Alexa Prize competition. Our experimental results show that ConvSAT significantly improves satisfaction prediction for both offline and online setting on both datasets, compared to the previously reported state-of-the-art approaches. The insights from our study can enable more intelligent conversational systems, which could adapt in real-time to the inferred user satisfaction and engagement.Comment: Published in CIKM '19, 10 page

Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys

Background: Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores, with and without laboratory-based measurements, and the corresponding risk charts for 182 countries to predict 10-year risk of fatal and non-fatal CVD in adults aged 40–74 years. Methods: Based on our previous laboratory-based prediction model (Globorisk), we used data from eight prospective studies to estimate coefficients of the risk equations using proportional hazard regressions. The laboratory-based risk score included age, sex, smoking, blood pressure, diabetes, and total cholesterol; in the non-laboratory (office-based) risk score, we replaced diabetes and total cholesterol with BMI. We recalibrated risk scores for each sex and age group in each country using country-specific mean risk factor levels and CVD rates. We used recalibrated risk scores and data from national surveys (using data from adults aged 40–64 years) to estimate the proportion of the population at different levels of CVD risk for ten countries from different world regions as examples of the information the risk scores provide; we applied a risk threshold for high risk of at least 10% for high-income countries (HICs) and at least 20% for low-income and middle-income countries (LMICs) on the basis of national and international guidelines for CVD prevention. We estimated the proportion of men and women who were similarly categorised as high risk or low risk by the two risk scores. Findings: Predicted risks for the same risk factor profile were generally lower in HICs than in LMICs, with the highest risks in countries in central and southeast Asia and eastern Europe, including China and Russia. In HICs, the proportion of people aged 40–64 years at high risk of CVD ranged from 1% for South Korean women to 42% for Czech men (using a ≥10% risk threshold), and in low-income countries ranged from 2% in Uganda (men and women) to 13% in Iranian men (using a ≥20% risk threshold). More than 80% of adults were similarly classified as low or high risk by the laboratory-based and office-based risk scores. However, the office-based model substantially underestimated the risk among patients with diabetes. Interpretation: Our risk charts provide risk assessment tools that are recalibrated for each country and make the estimation of CVD risk possible without using laboratory-based measurements