Introduction: Some communicators resort to negative appeals such as fear to encourage consumers to healthy behaviors. Nonetheless, the effectiveness of this type of content is yet questioned. Present study has been conducted to investigate how fear intensity and fear type applied in anti-obesity advertisements prompt preventive behavior among consumers.
Methods: 208 college students in Tehran province were randomly classified in 7 groups (6 experimental and 1 control group) based on a factorial design; 2 (fear intensity: high and low) x 3 (fear type: physical, social, and reappraisal). Research hypotheses were tested applying appropriate statistical tests including structural equation modeling and analysis of variance, based on data gathered through questionnaire and interventions such as presenting participants with visual contents.
Findings: it was revealed that there is a significant and positive relationship between the perceived severity and perceived fear, as well as between perceived susceptibility and perceived fear, and also between perceived fear and behavioral intention. Analysis of variance confirmed the significant main effect of fear intensity on behavioral intention. The main effect of the fear type on behavioral intention was not approved. However, findings indicate the significant interaction effect of fear intensity and fear type on behavioral intention so that highly intense messages that representing physical harm and lowly intense messages that representing social harm stimulate more behavioral intention.
Conclusion: It is concluded that applying fear appeal in health warning advertisements is effective in inviting consumers to healthy behavior, especially once a proper combination of the type and intensity of fear is applied in messages.

Introduction: Missing values are frequently seen in data sets of research studiesespecially in medical studies.Therefore, it is essential that the data, especially in medical research should evaluate in terms of the structure of missingness.This study aims to provide new statistical methods for analyzing such data.
Methods:Multiple imputation (MI) and inverse-probability weighting (IPW)aretwo common methods whichused to deal with missing data. MI method is more effectiveand complexthan IPW.MI requires a model for the joint distribution of the missing data given the observed data.While IPW need only a model for the probability that a subject has fulldata .Inefficacy in each of these models may causeto serious bias if missingness in dataset is large .Anothermethod that combines these approaches to give a doubly robust estimator.In addition, using of these methodswill demonstrate in the clinical trial data related to postpartum bleeding.
Results:In this article, we examine the performance of IPW/MI relative to MI and IPW alone in terms of bias and efficiency.According to the results of simulation can be said that that IPW/MI have advantages over alternatives.Also results of real data showed that,results of MI/MI doesnot differ with the results of IPW/MIsignificantly.
Conclusion:Problem of missing data are in many studies that causes bias and decreasing efficacy inmodel.In this study, after comparing the results of these techniques,it was concludedthat IPW/MI method has better performance than other methods.

Introduction:A very limited evidences are available that considered the relationship between Shift Work (SW) and lipid variables.
Objectives: As the importance of this subject, this prospectively cohort study examines the association between SW and Body Mass Index (BMI) and lipid profiles among male workers using multilevel ordinal model
Methods: This five years prospective cohort study has been conducted in random selected workers (using cluster sampling) who work in Esfahan’s Mobarakeh Steel Company (EMSC) (Iran) between 2011 and 2015.
Results: The study sample included 1626 male workers (mean age=41.5). Among these subjects, 652 (40.01%), 183 (11.3%) and 791 (48.6%) were day workers, weekly rotating shift workers and routinely rotating.  After controlling for several confounding variables, except HDL and Cholesterol, the odds ratio for high HDL was decreased by 26% (OR=0.74, P<0.001) and increased for TG by 36% in weekly rotating shift workers compared to day workers.  
Conclusion: Since weekly rotating shift workers had higher mean value of TG in their serum compared to day workers, they should limit eating high-fat diets in order to decrease risk of CVD.

Background: Menopause can have adverse effects on the quality of life of postmenopausal women. The main purpose of this study was to determine the Validity and Reliability of the Persian version of the Utian Quality of Life Scale (UQOL) in iranian postmenopausal women in 2019
Methods: The questionnaire was first translated into Persian. After its adaptation with the original version and backward translating it into English, the face and content validity were assessed by a group of experienced experts. To this end, exploratory factor analysis was performed by Principal Factor Analysis method with varimax rotation. Convergent validity was assessed by correlating the Persian version of UQOL and the 36-Item Short Form Health Survey (SF-36). Finally, the mean quality of life score of postmenopausal women in different domains as well as its mean in different domains based on age, education level, menopausal age, and number of children were calculated.
Results: Regarding reliability, the alpha coefficient was obtained 0.66 for occupation domain, 0.52 for health domain, 0.50 for emotional domain, and 0.90 for sex domain. There was a significant relationship between quality of life and age, menopausal age, occupation and number of children in all domains in menopausal women. There was a significant relationship between BMI and quality of life score in postmenopausal women only in physical health domain.
Conclusion: it seems this questionnaire can’t be used in research on the quality of life of postmenopausal women.

Background: Data mining as an integral part of the knowledge discovery in database (KDD) has gained significant attention over the past few years. By and large, data mining is the process of finding interesting structures in a considerably voluminous amount of data. Owing to its methods and algorithms supporting variable types of data, the data mining approach has been applied in many scientific areas, including the healthcare industry.
Regarding this matter, in this paper, we elaborate on the latest papers, including data mining techniques and algorithms in the healthcare field of research.
Results: We present a data mining review based on the newest researches. Afterward, we categorize data mining papers in healthcare based on supervised and unsupervised learning paradigms as well as classifying them in terms of their applications in the healthcare domain.
Conclusions: In every healthcare application, we propose some summary points of the papers. At last, we delve into the absence and hence, the necessity of existing some novel methods in healthcare domains in this researches.

Introduction: Papain-like protease (PLpro) of SARS-CoV in association with 3Chemotrypsin-like protease (3CLpro or Mpro) are two proteases which auto-proteolyze replicase polyproteins pp1a/pp1ab. These poly-proteins are translated from ORF1a/ORF1b of the virus genome. Cleavage of pp1a/pp1ab releases non-structural proteins of the virus which orchestrate viral replication. In addition, PLpro as a deubiquitinase and deISGylase modifies the proteins involved in recognition of the virus by the sensors of host cell innate immunity system. In this manner, the virus reforms the ubiquitination and ISGylation of the cell proteins to progress its own replication without any interference from host cell restrictive strategies against the viruses. Furthermore, PLpro blocks IRF3 activation independent of deubiquinating processes. Besides, PLpro induces pulmonary fibrosis through pathways involving ROS and MAPK.
Conclusion: Inhibition of PLpro allows innate immunity to sense and react against the invasion of SARS-CoV and to activate IRF3 to induce type I IFN expression. Thenceforth, proper development and signaling of innate immunity result in a long-term efficient cell/humoral adaptive immunity. Moreover, suppression of PLpro prevents cleavage of nsp3 and hence replication of the virus and through abolishing ubiquitin-proteasome/MAPK/ERK- and ROS/MAPK-mediated pathways prevent pulmonary fibrosis.

Background: Small P-values have been conventionally considered as evidence to reject a null hypothesis in empirical studies. However, there is widespread criticism of P-values now and the threshold we use for statistical significance is questioned.
Methods: This communication is on contrarian view and explains why P-value and its threshold are still useful for ruling out sampling fluctuation as a source of the findings.
Results: The problem is not with P-values themselves but it is with their misuse, abuse, and over-use, including the dominant role they have assumed in empirical results. False results may be mostly because of errors in design, invalid data, inadequate analysis, inappropriate interpretation, accumulation of Type-I error, and selective reporting, and not because of P-values per se.
Conclusion: A threshold of P-values such as 0.05 for statistical significance is helpful in making a binary inference for practical application of the result. However, a lower threshold can be suggested to reduce the chance of false results. Also, the emphasis should be on detecting a medically significant effect and not zero effect.

Introduction: Nowadays Big Data Analytics has attracted students for research due to its very high capabilities, but there are also obstacles to analyses that need to be addressed. Therefore, the purpose of this study is to investigate the viewpoints of students of different disciplines at Mashhad universities on the challenges of this analysis.
Method: This study is a cross-sectional study conducted on students of different universities and fields such as computer engineering, pharmacy, industry and biology in Mashhad, Iran. A questionnaire based on literature review in Pubmed, Google scholar, and science direct databases was designed by 10 experts from different disciplines using Delphi method. 185 students participated in the study. Students' viewpoints on the challenges were also collected. Descriptive and analytical results were reported using SPSS 21 and Maxqda software.
Results: The age range of most students was 25 - 34 years. 54.2% were female. Most of the participants in this study were students of engineering and medical informatics. Of the participants in this study, 96.4% considered big data analytics necessary, 50.6% were familiar with the benefits of analytics. Lack of awareness, inadequate management, lack of managers' knowledge, lack of expertise, and lack of priority were the most important challenges for students.
Conclusion: Despite the importance and benefits of big data analytics, challenges are a major barrier to use that need to be addressed.