: Coronaviruses are a large family and a subset of Coronaviridae that include common cold viruses and other severe diseases like severe acute respiratory syndrome (SARS-CoV), Middle East respiratory syndrome (MERS-CoV), and coronavirus disease 2019 (COVID-19). This is an ecological study based on statistics of the prevalence of coronavirus disease until 30 April 2020, based on the reports sent to the World Health Organization (WHO). This study investigates the distribution of the incidence and trend of the incidence rate of COVID-19 in countries, and its relation with the human development index (HDI) until 30 April 2020. The results showed that the most cases of coronavirus disease until the mentioned date were in the United States of America (1,003,947 cases), Spain (212,917 cases), Italy (203,591 cases), the United Kingdom (165,225 cases), and Germany (159,119 cases), in sequence. The results exhibited a significant positive correlation between the incidence of COVID-2019 and HDI in the world (r = 0.470, P < 0.0001).
Introduction: Bioactive encapsulation and drug delivery systems have already found their way to the market as efficient therapeutics to combat infections, viral diseases and different types of cancer. The fields of food fortification, nutraceutical supplementation and cosmeceuticals have also been getting the benefit of encapsulation technologies. Aim: Successful formulation of such therapeutic and nutraceutical compounds requires thorough analysis and assessment of certain characteristics including particle number and surface area without the need to employ sophisticated analytical techniques. Solution: Here we present simple mathematical formulas and equations used in the research and development of drug delivery and controlled release systems employed for bioactive encapsulation and targeting the sites of infection and cancer in vitro and in vivo. Systems covered in this entry include lipidic vesicles, polymeric capsules, metallic particles as well as surfactant- and tocopherol-based micro- and nanocarriers.
IntroductionFlood as the most common kind of the natural disasters has unpleased short, medium, and long-term consequences on the victims’ welfare, relationships, and physical and mental health. One of the most common mental health disorders in these victims is Post-traumatic stress disorder (PTSD). The aim of this study is to investigate the prevalence of PTSD on the flood victims.MethodsData resources including PubMed, Scopus, Web of Science, Science Direct, Embase, Google Scholar, conference and congress papers, key journals, the reference list of selected articles as well as systematic reviews were searched to identify studies that reported the prevalence of PTSD in flood victims. Random Effect Model was used to perform meta-analysis of the studies. Cochran test and I2 indicator were used to explore heterogeneity between the studies. Publication bias of the study was evaluated using Begg’test. Data were analyzed by STATA (version 14) software.ResultsAfter a comprehensive search, 515 papers were extracted. After eliminating duplicates and final screening, 23 studies were selected and entered the meta-analysis phase after qualitative evaluation. The results showed that the prevalence of PTSD in flood victims is 29.48% (95% CI: 18.64–40.31, I2 = 99.3%, p-value < 0.001).ConclusionThe results of the present study showed that the prevalence of PTSD is relatively high in the flood victims. So, it is necessary to take preventive, supportive, therapeutic and effective actions for them.
This article reports the findings of a study designed to investigate the effectiveness of the Roy Adaptation Model, as it relates to improvements in nursing care outcomes for patients undergoing coronary bypass graft surgery. Results revealed that the implementation of a training program based on this model enhanced staff education and led to decreases in the level of fatigue and improved the quality of life for this group of patients.
Background:This study aimed to use social network analysis (SNA) indicators and clique analysis to investigate collaboration between different departments and research centers in Journal of Research in Medical Sciences (JRMS) in 2012–2016.Materials and Methods:The study was a scientometric study using micro- and macro-indicators of SNA to investigate the performance of departments and research centers in JRMS. The population consisted of 1073 articles published in JRMS in 2012–2016. Ravar Matrix, UCINET, and VOSviewer software were used for data analysis.Results:According to the productivity and triple centrality indicators, “Department of Epidemiology and Biostatistics,” “Department of Pathology,” and Department of “Internal Medicine” allocated the first three ranks. Analyzing the cliques of co-authorship network for departments and research centers showed that this network consists of 19 cliques with at least 7 members in each clique. Furthermore, only 30 nodes (8.90% of all nodes in the network) had the presence in minimum clique size of at least 7.Conclusion:Given the importance and position of scientific collaboration in medical research and its effect on other performance indicators such as efficiency, effectiveness, and number of citations, it is necessary for policy-makers to propose new strategies for improving scientific collaboration.
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