Comparison across 12 countries on knowledge, attitude, and behavior scores about medication errors in Intensive Care Units : an international study

OBJECTIVE: The aim of the study was to explore the degree of agreement of intensive care unit nurses working on a set of medication error preventive strategies and to examine possible predictors of nurses’ knowledge, attitude and behavior. MATERIALS AND METHODS: Observational, international, and cross-sectional study. Iran, Malta, Spain, Pakistan, Nepal, Qatar, Ecuador, Australia, Finland, Italy, Egypt, and Jordan were the countries included in this survey. To collect data, the Knowledge, Attitude and Behavior in Medication Errors questionnaire was used. A descriptive statistical analysis was performed for the socio-demographic characteristics of the sample and three multiple logistic regressions were performed. RESULTS: The international sample consists of 1383 nurses, of whom 478 (34.6%) were men and 900 (65.1%) were women and their mean age was 35.61 years with a range of 19-61. Descriptive statistics conducted on the international sample show a medium to high degree of agreement among participants concerning some preventive strategies of medication error. In addition, the results of the present study show a strong relationship between positive nurses’ attitudes and correct behaviors and/or adequate knowledge, as well as between adequate knowledge and correct behaviors (p< 0.01). CONCLUSIONS: Further studies are needed to explore the issue of medication error concerning nurses’ cultural backgrounds, as well as to assess similarities and disparities among international nurses.peer-reviewe

Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review

Farahnaz Sadoughi,1 Zahra Kazemy,1 Farahnaz Hamedan,1 Leila Owji,1 Meysam Rahmanikatigari,2 Tahere Talebi Azadboni1 1Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran; 2Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran Abstract: Breast cancer is the most common cancer among women around the world. Despite enormous medical progress, breast cancer has still remained the second leading cause of death worldwide; thus, its early diagnosis has a significant impact on reducing mortality. However, it is often difficult to diagnose breast abnormalities. Different tools such as mammography, ultrasound, and thermography have been developed to screen breast cancer. In this way, the computer helps radiologists identify chest abnormalities more efficiently using image processing and artificial intelligence (AI) tools. This article examined various methods of AI using image processing to diagnose breast cancer. It was a review study through library and Internet searches. By searching the databases such as Medical Literature Analysis and Retrieval System Online (MEDLINE) via PubMed, Springer, IEEE, ScienceDirect, and Gray Literature (including Google Scholar, articles published in conferences, government technical reports, and other materials not controlled by scientific publishers) and searching for breast cancer keywords, AI and medical image processing techniques were extracted. The results were provided in tables to demonstrate different techniques and their results over recent years. In this study, 18,651 articles were extracted from 2007 to 2017. Among them, those that used similar techniques and reported similar results were excluded and 40 articles were finally examined. Since each of the articles used image processing, a list of features related to the image used in each article was also provided. The results showed that support vector machines had the highest accuracy percentage for different types of images (ultrasound =95.85%, mammography =93.069%, thermography =100%). Computerized diagnosis of breast cancer has greatly contributed to the development of medicine, is constantly being used by radiologists, and is clear in ethical and medical fields with regard to its effects. Computer-assisted methods increase diagnosis accuracy by reducing false positives. Keywords: breast cancer, breast cancer screening techniques, artificial intelligence techniques, medical image processin

Estimation of the isentropic parameter in the compression process of the D/3He plasma based on laser particle acceleration energy

In this research, having applied the modeling of D/3He fuel burning, the isentropic parameter and entropy are studied. It is shown that the Fermi degeneracy plays an important role in reducing the pressure, energy driver and the fractional burn-up. Based on recent progresses in the laser particle accelerators’ research, we have examined the ignition of a D/3He fuel to achieve the gains of order 58. The obtained results show that the energy required to compress a D/3He fuel with a density of 3.9 × 104 g/cm3, is 3.3 × 107 J/g. With the confinement parameter, 80 g/cm2 and a two times reduction of the isentropic parameter, the increased rate of the target gain is 31%