CovidSeniorV1, Main, Exploration, bibRecord, 000642

Dynamic changes in clinical and CT characteristics of COVID-19 cases with different exposure histories: a retrospective study.

Identifieur interne : 000642 ( Main/Exploration ); précédent : 000641; suivant : 000643

Dynamic changes in clinical and CT characteristics of COVID-19 cases with different exposure histories: a retrospective study.

Auteurs : Ruili Li [République populaire de Chine] ; Guangxue Liu [République populaire de Chine] ; Xiaojie Huang [République populaire de Chine] ; Cuiyu Jia [République populaire de Chine] ; Zhenying Xia [République populaire de Chine] ; Wenyan Song [République populaire de Chine] ; Xueqin Li [République populaire de Chine] ; Xing Wang [République populaire de Chine] ; Hongjun Li [République populaire de Chine]

Source :

BMC infectious diseases [ 1471-2334 ] ; 2020.

RBID : pubmed:32746805

Descripteurs français

KwdFr :
- Adulte (MeSH), Adulte d'âge moyen (MeSH), Betacoronavirus (MeSH), Femelle (MeSH), Fièvre (virologie), Fièvre (épidémiologie), Humains (MeSH), Infections à coronavirus (imagerie diagnostique), Infections à coronavirus (virologie), Lymphopénie (diagnostic), Mâle (MeSH), Pandémies (MeSH), Pneumopathie virale (imagerie diagnostique), Pneumopathie virale (virologie), Pronostic (MeSH), Protéine C-réactive (analyse), Sortie du patient (MeSH), Sujet âgé (MeSH), Sérum-albumine humaine (analyse), Sérum-globulines (analyse), Tomodensitométrie (MeSH), Toux (virologie), Toux (épidémiologie), Voyage (MeSH), Études de suivi (MeSH), Études rétrospectives (MeSH), Évolution de la maladie (MeSH).
MESH :
- analyse : Protéine C-réactive, Sérum-albumine humaine, Sérum-globulines.
- diagnostic : Lymphopénie.
- imagerie diagnostique : Infections à coronavirus, Pneumopathie virale.
- virologie : Fièvre, Infections à coronavirus, Pneumopathie virale, Toux.
- épidémiologie : Fièvre, Toux.
- Adulte, Adulte d'âge moyen, Betacoronavirus, Femelle, Humains, Mâle, Pandémies, Pronostic, Sortie du patient, Sujet âgé, Tomodensitométrie, Voyage, Études de suivi, Études rétrospectives, Évolution de la maladie.

English descriptors

KwdEn :
- Adult (MeSH), Aged (MeSH), Betacoronavirus (MeSH), C-Reactive Protein (analysis), Coronavirus Infections (diagnostic imaging), Coronavirus Infections (virology), Cough (epidemiology), Cough (virology), Disease Progression (MeSH), Female (MeSH), Fever (epidemiology), Fever (virology), Follow-Up Studies (MeSH), Humans (MeSH), Lymphopenia (diagnosis), Male (MeSH), Middle Aged (MeSH), Pandemics (MeSH), Patient Discharge (MeSH), Pneumonia, Viral (diagnostic imaging), Pneumonia, Viral (virology), Prognosis (MeSH), Retrospective Studies (MeSH), Serum Albumin, Human (analysis), Serum Globulins (analysis), Tomography, X-Ray Computed (MeSH), Travel (MeSH).
MESH :
- chemical , analysis : C-Reactive Protein, Serum Albumin, Human, Serum Globulins.
- diagnosis : Lymphopenia.
- diagnostic imaging : Coronavirus Infections, Pneumonia, Viral.
- epidemiology : Cough, Fever.
- virology : Coronavirus Infections, Cough, Fever, Pneumonia, Viral.
- Adult, Aged, Betacoronavirus, Disease Progression, Female, Follow-Up Studies, Humans, Male, Middle Aged, Pandemics, Patient Discharge, Prognosis, Retrospective Studies, Tomography, X-Ray Computed, Travel.

Abstract

BACKGROUND

To assess the dynamic changes in clinical and CT characteristics of COVID-19 patients with different epidemiology histories.

METHODS

Fifty-three discharged COVID-19 patients were enrolled at Beijing YouAn Hospital, Capital Medical University, between January 21 and March 10, 2020. Spearman correlation analysis was performed between CT scores and laboratory indicators. Patients were divided into the Wuhan group (lived in or with travel to Wuhan, numbering 30 cases) and non-Wuhan group (close contacts or unknown exposure, totaling 23 cases). The CT and laboratory findings were compared between and within groups during the clinical process.

RESULTS

Fever (88.7%), cough (64.2%), fatigue (34%), and abnormal laboratory indicators, including lymphopenia, reduced albumin, albumin/globulin (A/G), and elevated C-reactive protein (CRP), were mainly observed. Subpleural ground-glass opacities (86.8%) were usually detected at admission. The CT scores were highly correlated with lymphocytes, CRP, albumin, and A/G at initial and follow-ups (all p < 0.05). Four days after admission, most patients (66.7% Wuhan, 47.8% non-Wuhan) showed progression, and the CT scores of Wuhan significantly increased (p = 0.015). Eight days after admission, the vast majority of patients (69.2% Wuhan, 100% non-Wuhan, p = 0.006) presented improvement, and the CT scores of non-Wuhan were significantly lower than Wuhan (p = 0.006). Pneumonia was completely absorbed in most patients 2-4 weeks after discharge.

CONCLUSIONS

CT plays a crucial role in the early diagnosis and monitoring of changes in COVID-19. Lymphocytes, CRP, albumin, and A/G are expected to predict disease severity and prognosis. Viral pathogenicity in non-endemic areas may be weaker than core-infected areas. In most patients, lung lesions can disappear around 4 weeks after discharge.

DOI: 10.1186/s12879-020-05306-x
PubMed: 32746805
PubMed Central: PMC7397456

Affiliations:

République populaire de Chine

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000856
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Le document en format XML

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<term>Middle Aged (MeSH)</term>
<term>Pandemics (MeSH)</term>
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<term>Pneumonia, Viral (virology)</term>
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<term>Fièvre (épidémiologie)</term>
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<term>Pneumopathie virale</term>
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<keywords scheme="MESH" xml:lang="en"><term>Adult</term>
<term>Aged</term>
<term>Betacoronavirus</term>
<term>Disease Progression</term>
<term>Female</term>
<term>Follow-Up Studies</term>
<term>Humans</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Pandemics</term>
<term>Patient Discharge</term>
<term>Prognosis</term>
<term>Retrospective Studies</term>
<term>Tomography, X-Ray Computed</term>
<term>Travel</term>
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<term>Adulte d'âge moyen</term>
<term>Betacoronavirus</term>
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<term>Humains</term>
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<front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b>
</p>
<p>To assess the dynamic changes in clinical and CT characteristics of COVID-19 patients with different epidemiology histories.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>METHODS</b>
</p>
<p>Fifty-three discharged COVID-19 patients were enrolled at Beijing YouAn Hospital, Capital Medical University, between January 21 and March 10, 2020. Spearman correlation analysis was performed between CT scores and laboratory indicators. Patients were divided into the Wuhan group (lived in or with travel to Wuhan, numbering 30 cases) and non-Wuhan group (close contacts or unknown exposure, totaling 23 cases). The CT and laboratory findings were compared between and within groups during the clinical process.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>Fever (88.7%), cough (64.2%), fatigue (34%), and abnormal laboratory indicators, including lymphopenia, reduced albumin, albumin/globulin (A/G), and elevated C-reactive protein (CRP), were mainly observed. Subpleural ground-glass opacities (86.8%) were usually detected at admission. The CT scores were highly correlated with lymphocytes, CRP, albumin, and A/G at initial and follow-ups (all p < 0.05). Four days after admission, most patients (66.7% Wuhan, 47.8% non-Wuhan) showed progression, and the CT scores of Wuhan significantly increased (p = 0.015). Eight days after admission, the vast majority of patients (69.2% Wuhan, 100% non-Wuhan, p = 0.006) presented improvement, and the CT scores of non-Wuhan were significantly lower than Wuhan (p = 0.006). Pneumonia was completely absorbed in most patients 2-4 weeks after discharge.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b>
</p>
<p>CT plays a crucial role in the early diagnosis and monitoring of changes in COVID-19. Lymphocytes, CRP, albumin, and A/G are expected to predict disease severity and prognosis. Viral pathogenicity in non-endemic areas may be weaker than core-infected areas. In most patients, lung lesions can disappear around 4 weeks after discharge.</p>
</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">32746805</PMID>
<DateCompleted><Year>2020</Year>
<Month>08</Month>
<Day>21</Day>
</DateCompleted>
<DateRevised><Year>2020</Year>
<Month>08</Month>
<Day>21</Day>
</DateRevised>
<Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2334</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>20</Volume>
<Issue>1</Issue>
<PubDate><Year>2020</Year>
<Month>Aug</Month>
<Day>03</Day>
</PubDate>
</JournalIssue>
<Title>BMC infectious diseases</Title>
<ISOAbbreviation>BMC Infect Dis</ISOAbbreviation>
</Journal>
<ArticleTitle>Dynamic changes in clinical and CT characteristics of COVID-19 cases with different exposure histories: a retrospective study.</ArticleTitle>
<Pagination><MedlinePgn>567</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1186/s12879-020-05306-x</ELocationID>
<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">To assess the dynamic changes in clinical and CT characteristics of COVID-19 patients with different epidemiology histories.</AbstractText>
<AbstractText Label="METHODS" NlmCategory="METHODS">Fifty-three discharged COVID-19 patients were enrolled at Beijing YouAn Hospital, Capital Medical University, between January 21 and March 10, 2020. Spearman correlation analysis was performed between CT scores and laboratory indicators. Patients were divided into the Wuhan group (lived in or with travel to Wuhan, numbering 30 cases) and non-Wuhan group (close contacts or unknown exposure, totaling 23 cases). The CT and laboratory findings were compared between and within groups during the clinical process.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">Fever (88.7%), cough (64.2%), fatigue (34%), and abnormal laboratory indicators, including lymphopenia, reduced albumin, albumin/globulin (A/G), and elevated C-reactive protein (CRP), were mainly observed. Subpleural ground-glass opacities (86.8%) were usually detected at admission. The CT scores were highly correlated with lymphocytes, CRP, albumin, and A/G at initial and follow-ups (all p < 0.05). Four days after admission, most patients (66.7% Wuhan, 47.8% non-Wuhan) showed progression, and the CT scores of Wuhan significantly increased (p = 0.015). Eight days after admission, the vast majority of patients (69.2% Wuhan, 100% non-Wuhan, p = 0.006) presented improvement, and the CT scores of non-Wuhan were significantly lower than Wuhan (p = 0.006). Pneumonia was completely absorbed in most patients 2-4 weeks after discharge.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">CT plays a crucial role in the early diagnosis and monitoring of changes in COVID-19. Lymphocytes, CRP, albumin, and A/G are expected to predict disease severity and prognosis. Viral pathogenicity in non-endemic areas may be weaker than core-infected areas. In most patients, lung lesions can disappear around 4 weeks after discharge.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Li</LastName>
<ForeName>Ruili</ForeName>
<Initials>R</Initials>
<AffiliationInfo><Affiliation>Department of Radiology, Beijing YouAn Hospital, Capital Medical University, No.8 Xi Tou Tiao Youanmen Wai, Fengtai District, Beijing, 100069, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Liu</LastName>
<ForeName>Guangxue</ForeName>
<Initials>G</Initials>
<AffiliationInfo><Affiliation>Department of Natural Medicines, School of Pharmaceutical Sciences, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, Beijing, 100191, China. guangxl@bjmu.edu.cn.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Huang</LastName>
<ForeName>Xiaojie</ForeName>
<Initials>X</Initials>
<AffiliationInfo><Affiliation>Center for Infectious Diseases, Beijing YouAn Hospital, Capital Medical University, No.8 Xi Tou Tiao Youanmen Wai, Fengtai District, Beijing, 100069, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Jia</LastName>
<ForeName>Cuiyu</ForeName>
<Initials>C</Initials>
<AffiliationInfo><Affiliation>Department of Radiology, Beijing YouAn Hospital, Capital Medical University, No.8 Xi Tou Tiao Youanmen Wai, Fengtai District, Beijing, 100069, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Xia</LastName>
<ForeName>Zhenying</ForeName>
<Initials>Z</Initials>
<AffiliationInfo><Affiliation>Department of Radiology, Beijing YouAn Hospital, Capital Medical University, No.8 Xi Tou Tiao Youanmen Wai, Fengtai District, Beijing, 100069, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Song</LastName>
<ForeName>Wenyan</ForeName>
<Initials>W</Initials>
<AffiliationInfo><Affiliation>Department of Radiology, Beijing YouAn Hospital, Capital Medical University, No.8 Xi Tou Tiao Youanmen Wai, Fengtai District, Beijing, 100069, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Li</LastName>
<ForeName>Xueqin</ForeName>
<Initials>X</Initials>
<AffiliationInfo><Affiliation>Department of Radiology, Beijing YouAn Hospital, Capital Medical University, No.8 Xi Tou Tiao Youanmen Wai, Fengtai District, Beijing, 100069, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Wang</LastName>
<ForeName>Xing</ForeName>
<Initials>X</Initials>
<AffiliationInfo><Affiliation>Department of Radiology, Beijing YouAn Hospital, Capital Medical University, No.8 Xi Tou Tiao Youanmen Wai, Fengtai District, Beijing, 100069, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Li</LastName>
<ForeName>Hongjun</ForeName>
<Initials>H</Initials>
<AffiliationInfo><Affiliation>Department of Radiology, Beijing YouAn Hospital, Capital Medical University, No.8 Xi Tou Tiao Youanmen Wai, Fengtai District, Beijing, 100069, China. lihongjun00113@126.com.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<GrantList CompleteYN="Y"><Grant><GrantID>81771806</GrantID>
<Agency>National Natural Science Foundation of China</Agency>
<Country></Country>
</Grant>
<Grant><GrantID>61936013</GrantID>
<Agency>National Natural Science Foundation of China</Agency>
<Country></Country>
</Grant>
<Grant><GrantID>BMU2018MX027</GrantID>
<Agency>Peking University Medicine Seed Fund for Interdisciplinary Research</Agency>
<Country></Country>
</Grant>
<Grant><GrantID>PYZ19162</GrantID>
<Agency>Capital medical university research and incubation funding</Agency>
<Country></Country>
</Grant>
<Grant><GrantID>2018000021469G290</GrantID>
<Agency>Beijing Excellent Talent Plan</Agency>
<Country></Country>
</Grant>
<Grant><GrantID>2020-TG-002</GrantID>
<Agency>Beijing Municipal Health Commission, technology achievements and appropriate technology promotion project</Agency>
<Country></Country>
</Grant>
<Grant><GrantID>BJYAYY-2020YC-03</GrantID>
<Agency>YouAn Medical Development Project of COVID-19 Emergency Prevention and Control Public</Agency>
<Country></Country>
</Grant>
</GrantList>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2020</Year>
<Month>08</Month>
<Day>03</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>England</Country>
<MedlineTA>BMC Infect Dis</MedlineTA>
<NlmUniqueID>100968551</NlmUniqueID>
<ISSNLinking>1471-2334</ISSNLinking>
</MedlineJournalInfo>
<ChemicalList><Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D012712">Serum Globulins</NameOfSubstance>
</Chemical>
<Chemical><RegistryNumber>9007-41-4</RegistryNumber>
<NameOfSubstance UI="D002097">C-Reactive Protein</NameOfSubstance>
</Chemical>
<Chemical><RegistryNumber>ZIF514RVZR</RegistryNumber>
<NameOfSubstance UI="D000075462">Serum Albumin, Human</NameOfSubstance>
</Chemical>
</ChemicalList>
<SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName>
<SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome coronavirus 2</SupplMeshName>
</SupplMeshList>
<CitationSubset>IM</CitationSubset>
<MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="Y">Betacoronavirus</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D002097" MajorTopicYN="N">C-Reactive Protein</DescriptorName>
<QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName>
<QualifierName UI="Q000000981" MajorTopicYN="Y">diagnostic imaging</QualifierName>
<QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D003371" MajorTopicYN="N">Cough</DescriptorName>
<QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName>
<QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D018450" MajorTopicYN="N">Disease Progression</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D005334" MajorTopicYN="N">Fever</DescriptorName>
<QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName>
<QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D005500" MajorTopicYN="N">Follow-Up Studies</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008231" MajorTopicYN="N">Lymphopenia</DescriptorName>
<QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D010351" MajorTopicYN="N">Patient Discharge</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName>
<QualifierName UI="Q000000981" MajorTopicYN="Y">diagnostic imaging</QualifierName>
<QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D011379" MajorTopicYN="N">Prognosis</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012189" MajorTopicYN="N">Retrospective Studies</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000075462" MajorTopicYN="N">Serum Albumin, Human</DescriptorName>
<QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012712" MajorTopicYN="N">Serum Globulins</DescriptorName>
<QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D014057" MajorTopicYN="N">Tomography, X-Ray Computed</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D014195" MajorTopicYN="N">Travel</DescriptorName>
</MeshHeading>
</MeshHeadingList>
<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword>
<Keyword MajorTopicYN="N">CT</Keyword>
<Keyword MajorTopicYN="N">Clinical characteristic</Keyword>
<Keyword MajorTopicYN="N">Different exposure histories</Keyword>
<Keyword MajorTopicYN="N">Dynamic change</Keyword>
</KeywordList>
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<Month>03</Month>
<Day>16</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2020</Year>
<Month>07</Month>
<Day>27</Day>
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<Month>8</Month>
<Day>5</Day>
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<affiliations><list><country><li>République populaire de Chine</li>
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<tree><country name="République populaire de Chine"><noRegion><name sortKey="Li, Ruili" sort="Li, Ruili" uniqKey="Li R" first="Ruili" last="Li">Ruili Li</name>
</noRegion>
<name sortKey="Huang, Xiaojie" sort="Huang, Xiaojie" uniqKey="Huang X" first="Xiaojie" last="Huang">Xiaojie Huang</name>
<name sortKey="Jia, Cuiyu" sort="Jia, Cuiyu" uniqKey="Jia C" first="Cuiyu" last="Jia">Cuiyu Jia</name>
<name sortKey="Li, Hongjun" sort="Li, Hongjun" uniqKey="Li H" first="Hongjun" last="Li">Hongjun Li</name>
<name sortKey="Li, Xueqin" sort="Li, Xueqin" uniqKey="Li X" first="Xueqin" last="Li">Xueqin Li</name>
<name sortKey="Liu, Guangxue" sort="Liu, Guangxue" uniqKey="Liu G" first="Guangxue" last="Liu">Guangxue Liu</name>
<name sortKey="Song, Wenyan" sort="Song, Wenyan" uniqKey="Song W" first="Wenyan" last="Song">Wenyan Song</name>
<name sortKey="Wang, Xing" sort="Wang, Xing" uniqKey="Wang X" first="Xing" last="Wang">Xing Wang</name>
<name sortKey="Xia, Zhenying" sort="Xia, Zhenying" uniqKey="Xia Z" first="Zhenying" last="Xia">Zhenying Xia</name>
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Dynamic changes in clinical and CT characteristics of COVID-19 cases with different exposure histories: a retrospective study.

Dynamic changes in clinical and CT characteristics of COVID-19 cases with different exposure histories: a retrospective study.

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