ABSTRACT
The SARS-CoV-2 is a new emerging coronavirus initially reported in China in late December 2019 and rapidly spread to the whole of the world. To date, 1261903 total cases and 55830 deaths are reported from Iran as of 8 January. In this study, we investigated all the complete sequences of SARS-CoV-2 that publicly reported from Iran. Twenty-four sequences between March to September 2020 were analyzed to identify genome variations and phylogenetic relationships. Furthermore, we assessed the amino acid changes related to the spike glycoprotein as an important viral factor associated with the entry to the host cells and as a vaccine target. Most of the variations occur in the ORF1ab, S, N, intergenic, and ORF7 regions. The analysis of spike protein mutations demonstrated that the D614G mutation could be detected from May and beyond. Phylogenetic analysis showed that most of the circulated viruses in Iran are belong to the B.4 lineage. Although, we found a limited number of variants associated with the B.1 lineage carrying D614G mutation. Furthermore, we detected a variant characterize as the B.1.36 lineage with sixteen mutations in the spike protein region. This study showed the frequency of the viral populations in Iran as of September, therefore, there is an emergent need for genomic surveillance to track viral lineage shift in the country beyond September. These data would help to predict the future situation and apply better strategies to control the pandemic.
Subject(s)
DeathABSTRACT
SARS-CoV-2 exploits angiotensin-converting enzyme 2 (ACE2) as a receptor to invade cells. It has been reported that the UK and South African strains may have higher transmission capabilities, eventually due to amino acid substitutions on the SARS-CoV-2 Spike protein. The pathogenicity seems modified but is still under investigation. Here we used the experimental structure of the Spike RBD domain co-crystallized with part of the ACE2 receptor and several in silico methods to analyze the possible impacts of three amino acid replacements (Spike K417N, E484K, N501Y) with regard to ACE2 binding. We found that the N501Y replacement in this region of the interface (present in both UK and South African strains) should be favorable for the interaction with ACE2 while the K417N and E484K substitutions (South African) would seem unfavorable. It is unclear if the N501Y substitution in the South African strain could counterbalance the predicted less favorable (regarding binding) K417N and E484K Spike replacements. Our finding suggests that, if indeed the South African strain has a high transmission level, this could be due to the N501Y replacement and/or to substitutions in regions outside the direct Spike-ACE2 interface.
Subject(s)
Severe Acute Respiratory SyndromeABSTRACT
Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined. Early detection of emotional feelings and their trends help implement timely intervention strategies. Given the limitations of medical diagnosis of early emotional change signs during the quarantine period, artificial intelligence models provide effective mechanisms in uncovering early signs, symptoms and escalating trends. Novelty of the approach presented herein is a multitask methodological framework of text data processing, implemented as a pipeline for meaningful emotion detection and analysis, based on the Plutchik/Ekman approach to emotion detection and trend detection. We present an evaluation of the framework and a pilot system. Results of confirm the effectiveness of the proposed framework for topic trends and emotion detection of COVID-19 tweets. Our findings revealed Stay-At-Home restrictions result in people expressing on twitter both negative and positive emotional semantics (feelings), where negatives are “Anger” (8.5% of tweets), followed by “Fear” (5.2%), “Anticipation” (53.6%) and positive emotional semantics are “Joy” (14.7%) and “Trust” (11.7%). Semantic trends of safety issues related to staying at home rapidly decreased within the 28 days and also negative feelings related to friends dying and quarantined life increased in some days. These findings have potential to impact public health policy decisions through monitoring trends of emotional feelings of those who are quarantined. The framework presented here has potential to assist in such monitoring by using as an online emotion detection tool kit.
Subject(s)
COVID-19ABSTRACT
Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined. Early detection of emotional feelings and their trends help implement timely intervention strategies. Given the limitations of medical diagnosis of early emotional change signs during the quarantine period, artificial intelligence models provide effective mechanisms in uncovering early signs, symptoms and escalating trends. Novelty of the approach presented herein is a multitask methodological framework of text data processing, implemented as a pipeline for meaningful emotion detection and analysis, based on the Plutchik/Ekman approach to emotion detection and trend detection. We present an evaluation of the framework and a pilot system. Results of confirm the effectiveness of the proposed framework for topic trends and emotion detection of COVID-19 tweets. Our findings revealed Stay-At-Home restrictions result in people expressing on twitter both negative and positive emotional semantics. Semantic trends of safety issues related to staying at home rapidly decreased within the 28 days and also negative feelings related to friends dying and quarantined life increased in some days. These findings have potential to impact public health policy decisions through monitoring trends of emotional feelings of those who are quarantined. The framework presented here has potential to assist in such monitoring by using as an online emotion detection tool kit.
Subject(s)
COVID-19ABSTRACT
Background: Qingfei Paidu decoction (QFPDD) is a Chinese medicine compound formula recommended for combating corona virus disease 2019 (COVID-19) by National Health Commission of the People's Republic of China. This study aims to identify the main constituents in QFPDD and the absorbed components (including prototypes and metabolites) in serum and tissues after oral administration of QFPDD to mice.Methods: A practical and sensitive method of UHPLC-Q-Exactive-Orbitrap HRMS was developed to identify the chemical constituents in QFPDD and the absorbed prototypes as well as the metabolites in mice serum and tissues following oral administration of QFPDD.Results: A total of 405 chemicals, including 40 kinds of alkaloids, 162 kinds of flavonoids, 44 kinds of organic acids, 71 kinds of triterpene saponins and 88 kinds of other compounds in the water extract of QFPDD were tentatively identified via comparison with the retention times and MS/MS spectra of the standards or refereed by literature. With the help of the standards and in vitro metabolites, 195 chemical components (including 104 prototypes and 91 metabolites) were identified in mice serum after oral administration of QFPDD. In addition, 165, 177, 112, 120, 44, 53 constituents were identified in the lung, liver, heart, kidney, brain, and spleen of QFPDD-treated mice, respectively.Conclusions: An UHPLC-Q-Orbitrap HRMS based method was established for chemical profiling the constituents in QFPDD, while the absorbed prototypes and metabolites occurring in mice serum and tissues were investigated following oral administration of QFPDD. These findings provided key information and guidance for further investigation on the pharmacologically active substances and clinical applications of QFPDD.
Subject(s)
COVID-19 , Multiple SclerosisABSTRACT
Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19 related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.
Subject(s)
COVID-19ABSTRACT
Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.