Transmission dynamics and evolutionary history of 2019‐nCoV

To investigate the time origin, genetic diversity, and transmission dynamics of the recent 2019‐nCoV outbreak in China and beyond, a total of 32 genomes of virus strains sampled from China, Thailand, and the USA with sampling dates between 24 December 2019 and 23 January 2020 were analyzed. Phylogen...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of medical virology 2020-05, Vol.92 (5), p.501-511
Hauptverfasser: Li, Xingguang, Wang, Wei, Zhao, Xiaofang, Zai, Junjie, Zhao, Qiang, Li, Yi, Chaillon, Antoine
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 511
container_issue 5
container_start_page 501
container_title Journal of medical virology
container_volume 92
creator Li, Xingguang
Wang, Wei
Zhao, Xiaofang
Zai, Junjie
Zhao, Qiang
Li, Yi
Chaillon, Antoine
description To investigate the time origin, genetic diversity, and transmission dynamics of the recent 2019‐nCoV outbreak in China and beyond, a total of 32 genomes of virus strains sampled from China, Thailand, and the USA with sampling dates between 24 December 2019 and 23 January 2020 were analyzed. Phylogenetic, transmission network, and likelihood‐mapping analyses of the genome sequences were performed. On the basis of the likelihood‐mapping analysis, the increasing tree‐like signals (from 0% to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We identified three phylogenetic clusters using the Bayesian inference framework and three transmission clusters using transmission network analysis, with only one cluster identified by both methods using the above genome sequences of 2019‐nCoV strains. The estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10−3 to 1.8266 × 10−3 substitutions per site per year. On the basis of our study, undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real‐time. Highlights On the basis of our results in the present study, the increasing tree‐like signals (from 0 to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We only find one cluster identified by two methods (phylogenetic clusters using the Bayesian inference framework and transmission clusters using transmission network analysis). We estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10‐3 to 1.8266 × 10‐3 substitutions per site per year. Our study also highlights that undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real time.
doi_str_mv 10.1002/jmv.25701
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7166881</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2352050942</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4711-ca251830cd29b3015f515fe0db68234ad90099880862d0c3ff433cbc976941eb3</originalsourceid><addsrcrecordid>eNp1kctKxDAUhoMoOl4WvoAU3Oiiek7Sps1CQQavKG7UbUjTVDu0iTbTkdn5CD6jT2LGUVHBRTiQ8_HxH35CNhH2EIDuj9rJHk0zwAUyQBA8FpDhIhkAJjzmHNMVsur9CAByQekyWWEUaAYsHZCDm05Z39be185G5dSqttY-UraMzMQ1_Th8q24aPdR-7MJ0VUQBxdvLqx26u3WyVKnGm43PuUZuT45vhmfx5fXp-fDoMtZJhhhrRVPMGeiSioIBplUanoGy4DlliSoFgBB5DjmnJWhWVQljutAi4yJBU7A1cjj3PvZFa0pt7LhTjXzs6jaEk07V8vfG1g_y3k1khpznOQbBzqegc0-98WMZTtamaZQ1rveSspRCCiKhAd3-g45c39lwXqCyjCICzqjdOaU7531nqu8wCHJWigylyI9SArv1M_03-dVCAPbnwHPdmOn_JnlxdTdXvgMpsZXV</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2377211012</pqid></control><display><type>article</type><title>Transmission dynamics and evolutionary history of 2019‐nCoV</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Li, Xingguang ; Wang, Wei ; Zhao, Xiaofang ; Zai, Junjie ; Zhao, Qiang ; Li, Yi ; Chaillon, Antoine</creator><creatorcontrib>Li, Xingguang ; Wang, Wei ; Zhao, Xiaofang ; Zai, Junjie ; Zhao, Qiang ; Li, Yi ; Chaillon, Antoine</creatorcontrib><description>To investigate the time origin, genetic diversity, and transmission dynamics of the recent 2019‐nCoV outbreak in China and beyond, a total of 32 genomes of virus strains sampled from China, Thailand, and the USA with sampling dates between 24 December 2019 and 23 January 2020 were analyzed. Phylogenetic, transmission network, and likelihood‐mapping analyses of the genome sequences were performed. On the basis of the likelihood‐mapping analysis, the increasing tree‐like signals (from 0% to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We identified three phylogenetic clusters using the Bayesian inference framework and three transmission clusters using transmission network analysis, with only one cluster identified by both methods using the above genome sequences of 2019‐nCoV strains. The estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10−3 to 1.8266 × 10−3 substitutions per site per year. On the basis of our study, undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real‐time. Highlights On the basis of our results in the present study, the increasing tree‐like signals (from 0 to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We only find one cluster identified by two methods (phylogenetic clusters using the Bayesian inference framework and transmission clusters using transmission network analysis). We estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10‐3 to 1.8266 × 10‐3 substitutions per site per year. Our study also highlights that undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real time.</description><identifier>ISSN: 0146-6615</identifier><identifier>EISSN: 1096-9071</identifier><identifier>DOI: 10.1002/jmv.25701</identifier><identifier>PMID: 32027035</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>2019‐nCoV ; Bayes Theorem ; Bayesian analysis ; Betacoronavirus - genetics ; Biological evolution ; China ; Cluster analysis ; Coronavirus Infections - epidemiology ; Coronavirus Infections - transmission ; Coronavirus Infections - virology ; COVID-19 ; Disease Outbreaks ; Epidemiology ; Evolution ; evolutionary rate ; Gene mapping ; Gene sequencing ; Genetic diversity ; Genome, Viral ; Genomes ; Health surveillance ; Humans ; Identification methods ; Likelihood Functions ; Mapping ; Models, Genetic ; Mutation Rate ; Network analysis ; phylogenetic cluster ; Phylogenetics ; Phylogeny ; Pneumonia, Viral - epidemiology ; Pneumonia, Viral - transmission ; Prevention ; Public health ; SARS-CoV-2 ; Statistical inference ; Strains (organisms) ; Surveillance ; Thailand ; time to most recent common ancestor ; TMRCA ; transmission cluster ; United States ; Viral diseases ; Virology ; Viruses</subject><ispartof>Journal of medical virology, 2020-05, Vol.92 (5), p.501-511</ispartof><rights>2020 Wiley Periodicals, Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4711-ca251830cd29b3015f515fe0db68234ad90099880862d0c3ff433cbc976941eb3</citedby><cites>FETCH-LOGICAL-c4711-ca251830cd29b3015f515fe0db68234ad90099880862d0c3ff433cbc976941eb3</cites><orcidid>0000-0002-3470-2196</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmv.25701$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmv.25701$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,776,780,881,1411,27903,27904,45553,45554</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32027035$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Xingguang</creatorcontrib><creatorcontrib>Wang, Wei</creatorcontrib><creatorcontrib>Zhao, Xiaofang</creatorcontrib><creatorcontrib>Zai, Junjie</creatorcontrib><creatorcontrib>Zhao, Qiang</creatorcontrib><creatorcontrib>Li, Yi</creatorcontrib><creatorcontrib>Chaillon, Antoine</creatorcontrib><title>Transmission dynamics and evolutionary history of 2019‐nCoV</title><title>Journal of medical virology</title><addtitle>J Med Virol</addtitle><description>To investigate the time origin, genetic diversity, and transmission dynamics of the recent 2019‐nCoV outbreak in China and beyond, a total of 32 genomes of virus strains sampled from China, Thailand, and the USA with sampling dates between 24 December 2019 and 23 January 2020 were analyzed. Phylogenetic, transmission network, and likelihood‐mapping analyses of the genome sequences were performed. On the basis of the likelihood‐mapping analysis, the increasing tree‐like signals (from 0% to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We identified three phylogenetic clusters using the Bayesian inference framework and three transmission clusters using transmission network analysis, with only one cluster identified by both methods using the above genome sequences of 2019‐nCoV strains. The estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10−3 to 1.8266 × 10−3 substitutions per site per year. On the basis of our study, undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real‐time. Highlights On the basis of our results in the present study, the increasing tree‐like signals (from 0 to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We only find one cluster identified by two methods (phylogenetic clusters using the Bayesian inference framework and transmission clusters using transmission network analysis). We estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10‐3 to 1.8266 × 10‐3 substitutions per site per year. Our study also highlights that undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real time.</description><subject>2019‐nCoV</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Betacoronavirus - genetics</subject><subject>Biological evolution</subject><subject>China</subject><subject>Cluster analysis</subject><subject>Coronavirus Infections - epidemiology</subject><subject>Coronavirus Infections - transmission</subject><subject>Coronavirus Infections - virology</subject><subject>COVID-19</subject><subject>Disease Outbreaks</subject><subject>Epidemiology</subject><subject>Evolution</subject><subject>evolutionary rate</subject><subject>Gene mapping</subject><subject>Gene sequencing</subject><subject>Genetic diversity</subject><subject>Genome, Viral</subject><subject>Genomes</subject><subject>Health surveillance</subject><subject>Humans</subject><subject>Identification methods</subject><subject>Likelihood Functions</subject><subject>Mapping</subject><subject>Models, Genetic</subject><subject>Mutation Rate</subject><subject>Network analysis</subject><subject>phylogenetic cluster</subject><subject>Phylogenetics</subject><subject>Phylogeny</subject><subject>Pneumonia, Viral - epidemiology</subject><subject>Pneumonia, Viral - transmission</subject><subject>Prevention</subject><subject>Public health</subject><subject>SARS-CoV-2</subject><subject>Statistical inference</subject><subject>Strains (organisms)</subject><subject>Surveillance</subject><subject>Thailand</subject><subject>time to most recent common ancestor</subject><subject>TMRCA</subject><subject>transmission cluster</subject><subject>United States</subject><subject>Viral diseases</subject><subject>Virology</subject><subject>Viruses</subject><issn>0146-6615</issn><issn>1096-9071</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kctKxDAUhoMoOl4WvoAU3Oiiek7Sps1CQQavKG7UbUjTVDu0iTbTkdn5CD6jT2LGUVHBRTiQ8_HxH35CNhH2EIDuj9rJHk0zwAUyQBA8FpDhIhkAJjzmHNMVsur9CAByQekyWWEUaAYsHZCDm05Z39be185G5dSqttY-UraMzMQ1_Th8q24aPdR-7MJ0VUQBxdvLqx26u3WyVKnGm43PuUZuT45vhmfx5fXp-fDoMtZJhhhrRVPMGeiSioIBplUanoGy4DlliSoFgBB5DjmnJWhWVQljutAi4yJBU7A1cjj3PvZFa0pt7LhTjXzs6jaEk07V8vfG1g_y3k1khpznOQbBzqegc0-98WMZTtamaZQ1rveSspRCCiKhAd3-g45c39lwXqCyjCICzqjdOaU7531nqu8wCHJWigylyI9SArv1M_03-dVCAPbnwHPdmOn_JnlxdTdXvgMpsZXV</recordid><startdate>202005</startdate><enddate>202005</enddate><creator>Li, Xingguang</creator><creator>Wang, Wei</creator><creator>Zhao, Xiaofang</creator><creator>Zai, Junjie</creator><creator>Zhao, Qiang</creator><creator>Li, Yi</creator><creator>Chaillon, Antoine</creator><general>Wiley Subscription Services, Inc</general><general>John Wiley and Sons Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7TK</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3470-2196</orcidid></search><sort><creationdate>202005</creationdate><title>Transmission dynamics and evolutionary history of 2019‐nCoV</title><author>Li, Xingguang ; Wang, Wei ; Zhao, Xiaofang ; Zai, Junjie ; Zhao, Qiang ; Li, Yi ; Chaillon, Antoine</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4711-ca251830cd29b3015f515fe0db68234ad90099880862d0c3ff433cbc976941eb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>2019‐nCoV</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Betacoronavirus - genetics</topic><topic>Biological evolution</topic><topic>China</topic><topic>Cluster analysis</topic><topic>Coronavirus Infections - epidemiology</topic><topic>Coronavirus Infections - transmission</topic><topic>Coronavirus Infections - virology</topic><topic>COVID-19</topic><topic>Disease Outbreaks</topic><topic>Epidemiology</topic><topic>Evolution</topic><topic>evolutionary rate</topic><topic>Gene mapping</topic><topic>Gene sequencing</topic><topic>Genetic diversity</topic><topic>Genome, Viral</topic><topic>Genomes</topic><topic>Health surveillance</topic><topic>Humans</topic><topic>Identification methods</topic><topic>Likelihood Functions</topic><topic>Mapping</topic><topic>Models, Genetic</topic><topic>Mutation Rate</topic><topic>Network analysis</topic><topic>phylogenetic cluster</topic><topic>Phylogenetics</topic><topic>Phylogeny</topic><topic>Pneumonia, Viral - epidemiology</topic><topic>Pneumonia, Viral - transmission</topic><topic>Prevention</topic><topic>Public health</topic><topic>SARS-CoV-2</topic><topic>Statistical inference</topic><topic>Strains (organisms)</topic><topic>Surveillance</topic><topic>Thailand</topic><topic>time to most recent common ancestor</topic><topic>TMRCA</topic><topic>transmission cluster</topic><topic>United States</topic><topic>Viral diseases</topic><topic>Virology</topic><topic>Viruses</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Xingguang</creatorcontrib><creatorcontrib>Wang, Wei</creatorcontrib><creatorcontrib>Zhao, Xiaofang</creatorcontrib><creatorcontrib>Zai, Junjie</creatorcontrib><creatorcontrib>Zhao, Qiang</creatorcontrib><creatorcontrib>Li, Yi</creatorcontrib><creatorcontrib>Chaillon, Antoine</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Neurosciences Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of medical virology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Xingguang</au><au>Wang, Wei</au><au>Zhao, Xiaofang</au><au>Zai, Junjie</au><au>Zhao, Qiang</au><au>Li, Yi</au><au>Chaillon, Antoine</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Transmission dynamics and evolutionary history of 2019‐nCoV</atitle><jtitle>Journal of medical virology</jtitle><addtitle>J Med Virol</addtitle><date>2020-05</date><risdate>2020</risdate><volume>92</volume><issue>5</issue><spage>501</spage><epage>511</epage><pages>501-511</pages><issn>0146-6615</issn><eissn>1096-9071</eissn><abstract>To investigate the time origin, genetic diversity, and transmission dynamics of the recent 2019‐nCoV outbreak in China and beyond, a total of 32 genomes of virus strains sampled from China, Thailand, and the USA with sampling dates between 24 December 2019 and 23 January 2020 were analyzed. Phylogenetic, transmission network, and likelihood‐mapping analyses of the genome sequences were performed. On the basis of the likelihood‐mapping analysis, the increasing tree‐like signals (from 0% to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We identified three phylogenetic clusters using the Bayesian inference framework and three transmission clusters using transmission network analysis, with only one cluster identified by both methods using the above genome sequences of 2019‐nCoV strains. The estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10−3 to 1.8266 × 10−3 substitutions per site per year. On the basis of our study, undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real‐time. Highlights On the basis of our results in the present study, the increasing tree‐like signals (from 0 to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We only find one cluster identified by two methods (phylogenetic clusters using the Bayesian inference framework and transmission clusters using transmission network analysis). We estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10‐3 to 1.8266 × 10‐3 substitutions per site per year. Our study also highlights that undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real time.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>32027035</pmid><doi>10.1002/jmv.25701</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-3470-2196</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0146-6615
ispartof Journal of medical virology, 2020-05, Vol.92 (5), p.501-511
issn 0146-6615
1096-9071
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7166881
source MEDLINE; Wiley Online Library Journals Frontfile Complete
subjects 2019‐nCoV
Bayes Theorem
Bayesian analysis
Betacoronavirus - genetics
Biological evolution
China
Cluster analysis
Coronavirus Infections - epidemiology
Coronavirus Infections - transmission
Coronavirus Infections - virology
COVID-19
Disease Outbreaks
Epidemiology
Evolution
evolutionary rate
Gene mapping
Gene sequencing
Genetic diversity
Genome, Viral
Genomes
Health surveillance
Humans
Identification methods
Likelihood Functions
Mapping
Models, Genetic
Mutation Rate
Network analysis
phylogenetic cluster
Phylogenetics
Phylogeny
Pneumonia, Viral - epidemiology
Pneumonia, Viral - transmission
Prevention
Public health
SARS-CoV-2
Statistical inference
Strains (organisms)
Surveillance
Thailand
time to most recent common ancestor
TMRCA
transmission cluster
United States
Viral diseases
Virology
Viruses
title Transmission dynamics and evolutionary history of 2019‐nCoV
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T13%3A24%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Transmission%20dynamics%20and%20evolutionary%20history%20of%202019%E2%80%90nCoV&rft.jtitle=Journal%20of%20medical%20virology&rft.au=Li,%20Xingguang&rft.date=2020-05&rft.volume=92&rft.issue=5&rft.spage=501&rft.epage=511&rft.pages=501-511&rft.issn=0146-6615&rft.eissn=1096-9071&rft_id=info:doi/10.1002/jmv.25701&rft_dat=%3Cproquest_pubme%3E2352050942%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2377211012&rft_id=info:pmid/32027035&rfr_iscdi=true