We present an analysis and comparison study of genetic variants and mutations of about 1200 genomes of SARS-CoV-2 virus sampled across the first seven months of 2020. The study includes 12 sets of about 100 genomes each collected between January and September . We analyzed the mutations, mutation frequency and count trends over time, and genomes trends over time from January through September. We show that certain mutations in the SARS-CoV-2 genome are not occurring randomly as it has been commonly believed. This finding is in agreement with other recently published research in this domain. Therefore, this validates other findings in this direction. This study includes approximately 1000 genomes and was able to identify over 35 different mutations most of which are common to almost all genomes groups. Some mutations' ratios (frequency percentage) fluctuate over time to adapt the virus to various environmental factors, climate, and populations. One of the interesting findings in this paper is that the coding region, at the nucleotide level for NSP13 protein is relatively conserved compared with other protein regions in the ORF1ab gene which makes this protein a good candidate for developing drug targets and treatment for the COVID-19 disease. Although this outcome was already reported by other researchers, we corroborated their result with our work in a different approach and another experimental setting with almost one thousand complete genome sequences. We presented and discussed all these results and findings with tables of results and illustrating figures.
Background In multidisciplinary education, different perspectives from more than one discipline are used to illustrate a certain topic. The aim of this study was to evaluate the effectiveness of an online, multidisciplinary radiology curriculum to teach radiology to medical students in Egypt. A multidisciplinary team of radiologists, surgeons, and internists taught a series of 5 case-based radiology sessions on a web conference platform. Topics included common clinical case scenarios for various body systems. Undergraduate medical students across Egypt were enrolled in the course. A pre-test–post-test design was used to evaluate the efficacy of each session. Upon course completion, students filled out a subjective survey to assess the radiology education series. Results On average, 1000 students attended each session. For each session, an average of 734 students completed both the pre-test and post-test. There was a statistically significant increase in post-test scores compared to pre-test scores across all 5 sessions (p < 0.001) with an overall average score improvement of 63%. A subjective survey at the end of the course was completed by 1027 students. Over 96% of students found the lecture series to be a worthwhile experience that increased their imaging knowledge and interest in radiology, and that the use of a multidisciplinary approach added educational value. About 66% of students also reported that the session topics were “excellent and clinically important.” There was a marked increase in reported confidence levels in radiology competencies before and after attendance of the sessions. Conclusions An online radiology curriculum with a multidisciplinary approach can be implemented successfully to reach a large group of medical students and meet their educational objectives.
In the recent years, people are becoming more dependent on the Internet as their main source of information about healthcare. A number of research projects in the past few decades examined and utilized the internet data for information extraction in healthcare including disease surveillance and monitoring. In this paper, we investigate and study the potential of internet data like internet search keywords and search query patterns in the healthcare domain for disease monitoring and detection. Specifically, we investigate search keyword patterns for disease outbreak detection. Accurate prediction and detection of disease outbreaks in a timely manner can have a big positive impact on the entire health care system. Our method utilizes machine learning in identifying interesting patterns related to target disease outbreak from search keyword logs. We conducted experiments on the flu disease, which is the most searched disease in the interest of this problem. We showed examples of keywords that can be good predictors of outbreaks of the flu. Our method proved that the correlation between search queries and keyword trends are truly reliable in the sense that it can be used to predict the outbreak of the disease.
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