ABSTRACT
Background Artificial intelligence (AI) is evolving rapidly and gradually changing the landscape of healthcare and biomedicine. AI has achieved breakthroughs in image-based diagnosis, interpretation of electronic medical records, etc. However, no systematic quantitative analysis has been conducted to provide deeper insights of the status and frontier trends of AI in medicine (AI-MED).Methods We employed a scientometric and visualization approach to analyze the annual publications, countries, journals, keywords, co-citations, and structural variability to establish a knowledge graph that summarizes the hotspots and trends of AI-MED with a quantitative method.Findings There were 30,458 publications screened from the Web of Science (WOS). The number of publications has been growing rapidly. The most prolific countries are the USA and China. Artificial neural networks, machine learning, deep learning, convolutional neural network, image segmentation, and COVID-19 are hotspots in AI-MED.Conclusions This study has made clear the research process, frontier trends and emerging fields of AI-MED, predicting its future and pointing out the path for researchers to grasp the hotspots and directions in AI-MED quickly. © 2022 IEEE.
ABSTRACT
Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, the COVID-19 pandemic has spread rapidly worldwide. Due to the limited virus strains, few key mutations that would be very important with the evolutionary trends of virus genome were observed in early studies. Here, we downloaded 1809 sequence data of SARS-CoV-2 strains from GISAID before April 2020 to identify mutations and functional alterations caused by these mutations. Totally, we identified 1017 nonsynonymous and 512 synonymous mutations with alignment to reference genome NC_045512, none of which were observed in the receptor-binding domain (RBD) of the spike protein. On average, each of the strains could have about 1.75 new mutations each month. The current mutations may have few impacts on antibodies. Although it shows the purifying selection in whole-genome, ORF3a, ORF8 and ORF10 were under positive selection. Only 36 mutations occurred in 1% and more virus strains were further analyzed to reveal linkage disequilibrium (LD) variants and dominant mutations. As a result, we observed five dominant mutations involving three nonsynonymous mutations C28144T, C14408T and A23403G and two synonymous mutations T8782C, and C3037T. These five mutations occurred in almost all strains in April 2020. Besides, we also observed two potential dominant nonsynonymous mutations C1059T and G25563T, which occurred in most of the strains in April 2020. Further functional analysis shows that these mutations decreased protein stability largely, which could lead to a significant reduction of virus virulence. In addition, the A23403G mutation increases the spike-ACE2 interaction and finally leads to the enhancement of its infectivity. All of these proved that the evolution of SARS-CoV-2 is toward the enhancement of infectivity and reduction of virulence.