Melatonin versus chloral hydrate on sleep induction for recording electroencephalography in children: a randomized clinical trial.

Background: Electroencephalography (EEG) plays an essential role in the diagnosis of seizures. EEG recording in children is done with partial sleep deprivation and sedative drugs. To compare the effectiveness of melatonin and chloral hydrate on sleep induction and EEG recording in children. Materials and methods: In a parallel blinded randomized clinical trial study, 78 patients (6 months-5 years) were included to record EEG. Patients were randomly divided into two groups to receive melatonin (0.4 mg/kg) or chloral hydrate (0.5 ml/kg). After receiving the sedative drug, the start and duration of sedation, recovery time, side effects, and epileptiform waves in the EEG were recorded. The data was analyzed using SPSS version 16, and the significance level was determined to be less than 0.05. Results: A total of 78 children, including 34 girls (43.6%) and 44 boys (56.4%) (average age of 27.15±17.15 months), were examined. Success in the induction of sedation was reported by melatonin in 36 patients (92%) and chloral hydrate in 37 patients (95%), which was similar between the two drugs (P=0.5). The start time (P=0.134) and the duration of sedation (P=0.408) were alike between the two drugs. However, compared to the chloral hydrate, the recovery time in the melatonin group was significantly shorter (P<0.001). Side effects were not seen in melatonin, while six children (15%) using chloral hydrate had mild side effects (P=0.013). Epileptiform waves in EEGs were reported to be similar and positive for melatonin in 18 children (50%) and chloral hydrate in 16 children (43%) (P=0.410). Conclusion: The findings show that using melatonin in the dose prescribed in this study had similar effects to success in inducing sedation with the minimum quantity of chloral hydrate. Regardless of the start time and duration of sedation, the shorter recovery time and the absence of side effects are the advantages of using melatonin.

STO: Stroke Ontology for Accelerating Translational Stroke Research.

INTRODUCTION: Ontology-based annotation of evidence, using disease-specific ontologies, can accelerate analysis and interpretation of the knowledge domain of diseases. Although many domain-specific disease ontologies have been developed so far, in the area of cardiovascular diseases, there is a lack of ontological representation of the disease knowledge domain of stroke. METHODS: The stroke ontology (STO) was created on the basis of the ontology development life cycle and was built using Protégé ontology editor in the ontology web language format. The ontology was evaluated in terms of structural and functional features, expert evaluation, and competency questions. RESULTS: The stroke ontology covers a broad range of major biomedical and risk factor concepts. The majority of concepts are enriched by synonyms, definitions, and references. The ontology attempts to incorporate different users' views on the stroke domain such as neuroscientists, molecular biologists, and clinicians. Evaluation of the ontology based on natural language processing showed a high precision (0.94), recall (0.80), and F-score (0.78) values, indicating that STO has an acceptable coverage of the stroke knowledge domain. Performance evaluation using competency questions designed by a clinician showed that the ontology can be used to answer expert questions in light of published evidence. CONCLUSIONS: The stroke ontology is the first, multiple-view ontology in the domain of brain stroke that can be used as a tool for representation, formalization, and standardization of the heterogeneous data related to the stroke domain. Since this is a draft version of the ontology, the contribution of the stroke scientific community can help to improve the usability of the current version.

Prevalence of Stroke Risk Factors and Their Distribution Based on Stroke Subtypes in Gorgan: A Retrospective Hospital-Based Study-2015-2016.

BACKGROUND: Stroke is a leading cause of death and disability worldwide. According to the Iranian Ministry of Medical Health and Education, out of 100,000 stroke incidents in the country, 25,000 lead to death. Thus, identifying risk factors of stroke can help healthcare providers to establish prevention strategies. This study was conducted to investigate the prevalence of stroke risk factors and their distribution based on stroke subtypes in Sayad Shirazi Hospital, Gorgan, Northeastern Iran. MATERIAL AND METHODS: A retrospective hospital-based study was conducted at Sayad Shirazi Hospital in Gorgan, the only referral university hospital for stroke patients in Gorgan city. All medical records with a diagnosis of stroke were identified based on the International Classification of Diseases, Revision 10, from August 23, 2015, to August 22, 2016. A valid and reliable data gathering form was used to capture data about demographics, diagnostics, lifestyle, risk factors, and medical history. RESULTS: Out of 375 cases, two-thirds were marked with ischemic stroke with mean ages (standard deviation) of 66.4 (14.2) for men and 64.6 (14.2) for women. The relationship between stroke subtypes and age groups (P=0.008) and hospital outcome (P=0.0001) was significant. Multiple regression analysis showed that hypertension (Exp. (B) =1.755, P=0.037), diabetes mellitus (Exp. (B) =0.532, P=0.021), and dyslipidemia (Exp. (B) =2.325, P=0.004) significantly increased the risk of ischemic stroke. CONCLUSION: Overall, hypertension, diabetes mellitus, and dyslipidemia were the major risk factors of stroke in Gorgan. Establishment of stroke registry (population- or hospital-based) for the province is recommended.

Differential Diagnosis of Erythmato-Squamous Diseases Using Classification and Regression Tree.

INTRODUCTION: Differential diagnosis of Erythmato-Squamous Diseases (ESD) is a major challenge in the field of dermatology. The ESD diseases are placed into six different classes. Data mining is the process for detection of hidden patterns. In the case of ESD, data mining help us to predict the diseases. Different algorithms were developed for this purpose. OBJECTIVE: we aimed to use the Classification and Regression Tree (CART) to predict differential diagnosis of ESD. METHODS: we used the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For this purpose, the dermatology data set from machine learning repository, UCI was obtained. The Clementine 12.0 software from IBM Company was used for modelling. In order to evaluation of the model we calculate the accuracy, sensitivity and specificity of the model. RESULTS: The proposed model had an accuracy of 94.84% (. STANDARD DEVIATION: 24.42) in order to correct prediction of the ESD disease. CONCLUSIONS: Results indicated that using of this classifier could be useful. But, it would be strongly recommended that the combination of machine learning methods could be more useful in terms of prediction of ESD.

Data Standards in Tele-radiology.

Data standards play an important role to provide interoperability among different system. As other applications of telemedicine, the tele-radiology needs these standards to work properly. In this article, we conducted a review to introduce some data standards about tele-radiology. By searching PUBMED and Google Scholar database, we find more relevant articles about data standards in tele-radiology. Three categories of standards identified, including data interchange, document and terminology standards. Data interchange standards, including those which facilitate the understanding of the format of a massage between systems, such as DICOM and HL7. Document standards, including those which facilitate the contents of a massage, such as DICOM SR and HL7 CDA. And terminology standards, including those which facilitate the understanding of concepts of the domain. Since, the harmonization between different standards are important to meet interoperability, so the more effort is needed to conduct harmonization between tele-radiology standards and other domain.

Nurses readiness and electronic health records.

BACKGROUND: The importance of the electronic health records in health care well known to everybody, as well as, the role of nurses to provide clinical care; they have a valuable role in successful implementation of electronic systems. The aim of this paper is to assess the nurses' readiness for EHR implementation. METHODS AND MATERIALS: This was a descriptive cross sectional study, conducted in 2013. Using cluster sampling, 310 nurses selected from teaching hospitals at Tehran University of Medical Sciences (TUMS). A self-structured questionnaire was used for gathering data. Data management and analysis was performed using SPSS for windows by using descriptive statistics. RESULTS: 85.9% of nurses, participated in the study. The Microsoft Word (58.8%) was the higher level of skill according to ICDL. The mean of computer skills, knowledge and attitude of nurses towards EHR was 43.4%, 51.2% and 65.2%, respectively. In overall, the mean of readiness of nurses was 57.2%. Establish proper communication among providers and prevent duplications was the most positive attitude and complexity of service delivery was the most negative attitude toward EHR. CONCLUSION: The most obvious finding to emerge from this study is that it should be considered in the education, training and participation of nurses, it should be ensured the level of knowledge and attitude toward EHR and finally, some related courses in Health Information Systems suggested including the curriculum of nursing.