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<?covid-19-tdm?>
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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Immunol.</journal-id>
<journal-title>Frontiers in Immunology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Immunol.</abbrev-journal-title>
<issn pub-type="epub">1664-3224</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fimmu.2024.1341168</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Immunology</subject>
<subj-group>
<subject>Systematic Review</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Clinical and immunological comparison of COVID-19 disease between critical and non-critical courses: a systematic review and meta-analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Hedayati-Ch</surname>
<given-names>Mojtaba</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/project-administration/"/>
<role content-type="https://credit.niso.org/contributor-roles/resources/"/>
<role content-type="https://credit.niso.org/contributor-roles/supervision/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ebrahim-Saraie</surname>
<given-names>Hadi Sedigh</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/investigation/"/>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
<role content-type="https://credit.niso.org/contributor-roles/software/"/>
<role content-type="https://credit.niso.org/contributor-roles/validation/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Bakhshi</surname>
<given-names>Arash</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2405713"/>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Department of Microbiology, Virology and Microbial Toxins, School of Medicine, Guilan University of Medical Sciences</institution>, <addr-line>Rasht</addr-line>, <country>Iran</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Microbial Toxins Physiology Group (MTPG), Universal Scientific Education and Research Network (USERN)</institution>, <addr-line>Rasht</addr-line>, <country>Iran</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Member of Research Committee, School of Medicine, Guilan University of Medical Sciences</institution>, <addr-line>Rasht</addr-line>, <country>Iran</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Athanasia Mouzaki, University of Patras, Greece</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Stelvio Tonello, University of Eastern Piedmont, Italy</p>
<p>Maria Lagadinou, University of Patras, Greece</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Arash Bakhshi, <email xlink:href="mailto:A.bakhshi.b13@gmail.com">A.bakhshi.b13@gmail.com</email>
</p>
</fn>
<fn fn-type="other" id="fn003">
<p>&#x2020;ORCID: Arash Bakhshi, <uri xlink:href="https://orcid.org/0000-0002-0642-4651">orcid.org/0000-0002-0642-4651</uri>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>16</day>
<month>04</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>15</volume>
<elocation-id>1341168</elocation-id>
<history>
<date date-type="received">
<day>19</day>
<month>11</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>28</day>
<month>03</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Hedayati-Ch, Ebrahim-Saraie and Bakhshi</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Hedayati-Ch, Ebrahim-Saraie and Bakhshi</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Introduction</title>
<p>Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which appeared in 2019, has been classified as critical and non-critical according to clinical signs and symptoms. Critical patients require mechanical ventilation and intensive care unit (ICU) admission, whereas non-critical patients require neither mechanical ventilation nor ICU admission. Several factors have been recently identified as effective factors, including blood cell count, enzymes, blood markers, and underlying diseases. By comparing blood markers, comorbidities, co-infections, and their relationship with mortality, we sought to determine differences between critical and non-critical groups.</p>
</sec>
<sec>
<title>Method</title>
<p>We used Scopus, PubMed, and Web of Science databases for our systematic search. Inclusion criteria include any report describing the clinical course of COVID-19 patients and showing the association of the COVID-19 clinical courses with blood cells, blood markers, and bacterial co-infection changes. Twenty-one publications were eligible for full-text examination between 2019 to 2021.</p>
</sec>
<sec>
<title>Result</title>
<p>The standard difference in WBC, lymphocyte, and platelet between the two clinical groups was 0.538, -0.670, and -0.421, respectively. Also, the standard difference between the two clinical groups of CRP, ALT, and AST was 0.482, 0.402, and 0.463, respectively. The odds ratios for hypertension and diabetes were significantly different between the two groups. The prevalence of co-infection also in the critical group is higher.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>In conclusion, our data suggest that critical patients suffer from a suppressed immune system, and the inflammation level, the risk of organ damage, and co-infections are significantly high in the critical group and suggests the use of bacteriostatic instead of bactericides to treat co-infections.</p>
</sec>
</abstract>
<abstract abstract-type="graphical">
<title>Graphical Abstract</title>
<p>Summary of the comparison of two clinical course of COVID-19</p>
<p><graphic xlink:href="fimmu-15-1341168-g007.tif" position="anchor"/></p>
</abstract>
<kwd-group>
<kwd>COVID-19</kwd>
<kwd>critical</kwd>
<kwd>co-infection</kwd>
<kwd>mortality</kwd>
<kwd>sever</kwd>
</kwd-group>
<counts>
<fig-count count="6"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="54"/>
<page-count count="10"/>
<word-count count="3576"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Viral Immunology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>The SARS Coronavirus 2 (SARS-CoV-2) originated in China and spread to most countries worldwide in 2019. Generally, more than 200 million confirmed cases and more than 4 million deaths have been reported. SARS-CoV-2 is more infectious than SARS-CoV (<xref ref-type="bibr" rid="B1">1</xref>&#x2013;<xref ref-type="bibr" rid="B3">3</xref>). The new Coronavirus is classified into critical and non-critical cases based on symptoms. Description of critical patients require mechanical ventilation and intensive care unit (ICU) admission, and Non-critical patients don&#x2019;t require mechanical ventilation and ICU admission (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B4">4</xref>). Accordingly, comparing critical and non-critical groups can describe the difference between the presence and absence of co-infection. After three years of the first appearance of COVID-19, researchers examined essential factors for evaluating COVID-19 disease.</p>
<p>As a first step, the blood cell count was evaluated. Some papers suggest that the number of white blood cells (WBCs), lymphocytes, and platelets may vary as a result of COVID-19, including critical, mild, moderate, and severe cases (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B6">6</xref>). The difference between these cells may show the infection and inflammation in critical and non-critical groups (<xref ref-type="bibr" rid="B7">7</xref>, <xref ref-type="bibr" rid="B8">8</xref>). Another factor associated with COVID-19 may be enzymes and proteins. Alanine aminotransferase (ALT), Aspartate aminotransferase (AST), and C reactive protein (CRP) levels were inconsistent between critical and non-critical groups. The level of these markers can determine the prognosis of the two groups (<xref ref-type="bibr" rid="B9">9</xref>, <xref ref-type="bibr" rid="B10">10</xref>).</p>
<p>Underlying diseases such as high blood pressure, diabetes, cardiovascular disease, and dyslipidemia can play a crucial role in the clinical course of COVID-19 patients. Critical and non-critical patients exhibit varying levels of comorbidities; these have a different impact on morbidity (<xref ref-type="bibr" rid="B11">11</xref>). Moreover, there is evidence to suggest that the mortality rate can be affected by comorbidities (<xref ref-type="bibr" rid="B12">12</xref>, <xref ref-type="bibr" rid="B13">13</xref>).</p>
<p>Co-infection is an essential factor in morbidity and mortality. Globally, the prevalence of bacterial co-infection in COVID-19 patients is unknown and different micro-organisms lead to co-infection (<xref ref-type="bibr" rid="B14">14</xref>). Most of the organisms differ in their distribution in different organs, such as the respiratory, blood, and urinary tracts (<xref ref-type="bibr" rid="B15">15</xref>). As a result, the reaction of critical and non-critical groups to co-infections will be considerably variable. Bacterial co-infection plays a vital role in the clinical course of the COVID-19 disease, which can be treated with various antibiotics.</p>
<p>The mortality rate of COVID-19 disease can be affected by factors such as blood cell count, blood markers, comorbidities, and co-infections (<xref ref-type="bibr" rid="B4">4</xref>). Therefore, the mortality rate can differ between critical and non-critical groups. In this study, blood markers, comorbidities, co-infections, and their relationship to mortality rates were compared between critical and non-critical patients.</p>
</sec>
<sec id="s2">
<title>Method</title>
<sec id="s2_1">
<title>Search strategy</title>
<p>We reported this systematic review meta-analysis using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The studies were identified using the following PICOS principle: <bold>P</bold>atients = Patients with COVID-19, <bold>I</bold>ntervention = dividing patients into critical and non-critical based on ICU admission and mechanical ventilation, <bold>C</bold>ontrol, <bold>O</bold>utcome = Comparison of immunological and clinical factors between critical and non-critical groups, <bold>S</bold>tudy design = case-control, prospective or retrospective studies (<xref ref-type="bibr" rid="B16">16</xref>). We used Scopus, PubMed, and Web of Science databases for our systematic search. The search terms used in the database were included (&#x201c;COVID-19&#x201d; OR &#x201c;SARS COV-2&#x201d; OR &#x201c;Coronavirus infection&#x201d;) AND (&#x201c;critical&#x201d; OR &#x201c;Non-sever&#x201d;) AND (&#x201c;non-critical&#x201d;) AND (&#x201c;Co-infection&#x201d; OR &#x201c;Secondary infection&#x201d; OR &#x201c;bacterial infection&#x201d;). We searched English publications and stored and checked articles using Endnote software as a citation manager. All selected articles were published in the 2019 to Jan 2022 date range. We reviewed the search results&#x2019; titles, abstracts, and full text for screening and study selection based on the inclusion criteria. Inclusion criteria include any original study that evaluated differences in 1) blood cells and blood markers, 2) bacterial co-infection, 3) Comorbidities, and 4) mortality Rate between critical and non-critical COVID-19 patients. Viral co-infection, case reports, reviews, and duplicate studies are generally excluded from this systematic review.</p>
</sec>
<sec id="s2_2">
<title>Quality assessment and data extraction</title>
<p>Rayyan platform was used for screening and data extraction of included studies. Using the nine-point Joanna Briggs Institute critical appraisal checklist for studies, two researchers conducted the quality assessment (A. B and M. H) and disagreements were resolved by consensus (H. S). The included studies met more than half of the quality assessment parameters. Based on <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>, we (A. B. and M. H.) extracted the publication year, country, the number of patients, the clinical course (ICU admission or mechanical ventilation), and physiological data from the studies. The prevalence of bacteria and co-infections were determined using nasal and pharyngeal swabs, blood serum, and urine analysis samples for the respiratory, bloodstream, and urinary systems, respectively in selected studies. As a part of our investigation, the following information was obtained: publication year, study design and research question, number of articles, number of each type of study, language, and country of study, device used, patient characteristics, and statistical methodology.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>The Main Characteristics of Studies Included in the Meta-analysis.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="bottom" align="right">Study name</th>
<th valign="bottom" align="right">Year</th>
<th valign="bottom" align="right">Country</th>
<th valign="bottom" align="right">Study design</th>
<th valign="bottom" align="right">Critical (%)</th>
<th valign="bottom" align="right">Non-Critical (%)</th>
<th valign="top" align="right">JBI score (RoB)</th>
<th valign="top" align="right">References</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="bottom" align="center">Tao Zuo</td>
<td valign="bottom" align="center">2020</td>
<td valign="bottom" align="center">China</td>
<td valign="bottom" align="center">Prospective</td>
<td valign="bottom" align="center">2 (20%)</td>
<td valign="bottom" align="center">8 (80%)</td>
<td valign="top" align="center">5 (Moderate)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B17">17</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Tang</td>
<td valign="bottom" align="center">2019</td>
<td valign="bottom" align="center">China</td>
<td valign="bottom" align="center">Cohort</td>
<td valign="bottom" align="center">18 (48%)</td>
<td valign="bottom" align="center">19 (52%)</td>
<td valign="top" align="center">4 (Moderate)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B10">10</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Shi</td>
<td valign="bottom" align="center">2020</td>
<td valign="bottom" align="center">China</td>
<td valign="bottom" align="center">Case-control</td>
<td valign="bottom" align="center">6 (30%)</td>
<td valign="bottom" align="center">14 (70%)</td>
<td valign="top" align="center">8 (Low)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B7">7</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">M. McKeigue</td>
<td valign="bottom" align="center">2021</td>
<td valign="bottom" align="center">Scotland</td>
<td valign="bottom" align="center">Case-control</td>
<td valign="bottom" align="center">702 (16.5%)</td>
<td valign="bottom" align="center">3533 (83.4%)</td>
<td valign="top" align="center">6 (Moderate)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B18">18</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Liaqat</td>
<td valign="bottom" align="center">2021</td>
<td valign="bottom" align="center">Pakistan</td>
<td valign="bottom" align="center">Retrospective</td>
<td valign="bottom" align="center">57 (28.3%)</td>
<td valign="bottom" align="center">144 (71.6%)</td>
<td valign="top" align="center">4 (Moderate)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B4">4</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Liu</td>
<td valign="bottom" align="center">2021</td>
<td valign="bottom" align="center">China</td>
<td valign="top" align="center">Retrospective</td>
<td valign="bottom" align="center">16 (18.5%)</td>
<td valign="bottom" align="center">69 (81.1%)</td>
<td valign="top" align="center">9 (Low)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B9">9</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Tian</td>
<td valign="bottom" align="center">2020</td>
<td valign="bottom" align="center">China</td>
<td valign="top" align="center">Retrospective</td>
<td valign="bottom" align="center">45 (50%)</td>
<td valign="bottom" align="center">45 (50%)</td>
<td valign="top" align="center">8 (Low)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B12">12</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Jieyu He</td>
<td valign="bottom" align="center">2020</td>
<td valign="bottom" align="center">China</td>
<td valign="bottom" align="center">Prospective</td>
<td valign="bottom" align="center">49 (43.7%)</td>
<td valign="bottom" align="center">63 (56.2%)</td>
<td valign="top" align="center">7 (Low)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B19">19</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Yuan Cen</td>
<td valign="bottom" align="center">2020</td>
<td valign="bottom" align="center">China</td>
<td valign="top" align="center">Retrospective</td>
<td valign="bottom" align="center">22 (10%)</td>
<td valign="bottom" align="center">200 (90%)</td>
<td valign="top" align="center">6 (Moderate)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B20">20</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Jianfeng Wu</td>
<td valign="bottom" align="center">2020</td>
<td valign="bottom" align="center">China</td>
<td valign="top" align="center">Retrospective</td>
<td valign="bottom" align="center">697 (30%)</td>
<td valign="bottom" align="center">1690 (70)</td>
<td valign="top" align="center">9 (Low)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B21">21</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Fukushima</td>
<td valign="bottom" align="center">2021</td>
<td valign="bottom" align="center">Japan</td>
<td valign="top" align="center">Retrospective</td>
<td valign="bottom" align="center">41 (18%)</td>
<td valign="bottom" align="center">193 (82%)</td>
<td valign="top" align="center">8 (Low)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B22">22</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Wang</td>
<td valign="bottom" align="center">2020</td>
<td valign="bottom" align="center">China</td>
<td valign="top" align="center">Retrospective</td>
<td valign="bottom" align="center">50 (41%)</td>
<td valign="bottom" align="center">73 (59%)</td>
<td valign="top" align="center">8 (Low)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B13">13</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Liu</td>
<td valign="bottom" align="center">2020</td>
<td valign="bottom" align="center">China</td>
<td valign="top" align="center">Retrospective</td>
<td valign="bottom" align="center">30 (32%)</td>
<td valign="bottom" align="center">65 (68%)</td>
<td valign="top" align="center">7 (Low)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B23">23</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Cheng</td>
<td valign="bottom" align="center">2020</td>
<td valign="bottom" align="center">China</td>
<td valign="top" align="center">Retrospective</td>
<td valign="bottom" align="center">52 (21%)</td>
<td valign="bottom" align="center">200 (79%)</td>
<td valign="top" align="center">6 (Moderate)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B24">24</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Zhihua Lv</td>
<td valign="bottom" align="center">2020</td>
<td valign="bottom" align="center">China</td>
<td valign="top" align="center">Retrospective</td>
<td valign="bottom" align="center">84 (42%)</td>
<td valign="bottom" align="center">115 (58%)</td>
<td valign="top" align="center">8 (Low)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B25">25</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Cam&#xe9;l&#xe9;na</td>
<td valign="bottom" align="center">2021</td>
<td valign="bottom" align="center">France</td>
<td valign="bottom" align="center">Prospective</td>
<td valign="bottom" align="center">43 (100%)</td>
<td valign="bottom" align="center">&#x2013;</td>
<td valign="top" align="center">5 (Moderate)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B26">26</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Contou</td>
<td valign="bottom" align="center">2020</td>
<td valign="bottom" align="center">France</td>
<td valign="top" align="center">Retrospective</td>
<td valign="bottom" align="center">92 (100%)</td>
<td valign="bottom" align="center">&#x2013;</td>
<td valign="top" align="center">6 (Moderate)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B27">27</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Rothe</td>
<td valign="bottom" align="center">2020</td>
<td valign="bottom" align="center">Germany</td>
<td valign="top" align="center">Retrospective</td>
<td valign="bottom" align="center">&#x2013;</td>
<td valign="bottom" align="center">56 (100%)</td>
<td valign="top" align="center">5 (Moderate)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B28">28</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">THOMSEN</td>
<td valign="bottom" align="center">2021</td>
<td valign="bottom" align="center">Scandinavian</td>
<td valign="bottom" align="center">Cohort</td>
<td valign="bottom" align="center">34 (100%)</td>
<td valign="bottom" align="center">&#x2013;</td>
<td valign="top" align="center">3 (High)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B29">29</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Amaravati</td>
<td valign="bottom" align="center">2021</td>
<td valign="bottom" align="center">Indonesia</td>
<td valign="top" align="center">Retrospective</td>
<td valign="bottom" align="center">52 (56%)</td>
<td valign="bottom" align="center">40 (44%)</td>
<td valign="top" align="center">3 (high)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B30">30</xref>)</td>
</tr>
<tr>
<td valign="bottom" align="center">Yang</td>
<td valign="bottom" align="center">2021</td>
<td valign="bottom" align="center">China</td>
<td valign="top" align="center">Retrospective</td>
<td valign="bottom" align="center">58 (60%)</td>
<td valign="bottom" align="center">38 (40%)</td>
<td valign="top" align="center">8 (Low)</td>
<td valign="top" align="center">(<xref ref-type="bibr" rid="B31">31</xref>)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>RoB, Risk of Bias.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s2_3">
<title>Data analysis</title>
<p>The statistical analysis and construction of graphs were performed with a comprehensive meta-analysis (CMA) version 3 (Biostat Inc., Englewood, NJ) with a random effect model plotted on forest plots since this model is more reasonable in the presence of heterogeneity than the fixed model. The pooled standard difference in mean with 95% CI gave the summary estimate. To test heterogeneity, we used the I-squared (<italic>I<sup>2</sup>
</italic>). Visual bias was assessed using a funnel plot, and Egger&#x2019;s regression test confirmed it (p &lt; 0.05 was considered a statistically significant publication bias).</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Result</title>
<sec id="s3_1">
<title>Search outcome and study characteristics</title>
<p>Considering the objectives of this study, we identified 746 publications in Scopus, PubMed, and Web of Science databases. After removing duplicate studies and screening based on inclusion and exclusion, 21 publications were eligible for full-text examination (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>). Studies have been conducted in the following countries: China (13), France (2), and one study from each of the following: Germany, Scotland, Pakistan, Japan, Scandinavian, and Indonesia. Among the studies, there were 14 retrospective studies, three prospective studies, two case-control studies, and two cohort studies.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>PRISMA flow chart of study selection.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1341168-g001.tif"/>
</fig>
</sec>
<sec id="s3_2">
<title>Comparison of blood cell count between critical and non-critical groups</title>
<p>One of the basic factors in the clinical course of COVID-19 is blood cells. We analyzed WBC, lymphocyte, and platelet differences between the two groups. As shown in <xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>, the standard difference in means indicated that lymphocytes and platelets were significantly higher in non-critical patients than in critical patients, while WBCs were higher in critical patients (std: -0.670, -0.421, and 0.538, respectively, 95% CI, <italic>P &lt;</italic>0.001).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Comparison of blood cells between critical and non-critical courses.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1341168-g002.tif"/>
</fig>
</sec>
<sec id="s3_3">
<title>Comparison biomarkers level between critical and non-critical groups</title>
<p>Biomarkers and enzymes, such as CRP, ALT, and AST are other factors in the clinical course of COVID-19. As shown in <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>, there was a significant difference in blood markers between the two groups. the standard difference of the mean for ALT was 0.403 (95% CI: 0.212, 0.593. P <italic>&lt;</italic>0.001), while for AST it was 0.461 (95% CI: 0.099, 0.823. P = 0.013). In addition, the standard difference of the mean for CRP was 0.482 (95% CI: 0.178, 0.786. P = 0.002). The critical group had significantly higher levels of each of these factors than the non-critical group.</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Comparison of blood markers between critical and non-critical courses.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1341168-g003.tif"/>
</fig>
</sec>
<sec id="s3_4">
<title>Comparison of mortality and comorbidities between critical and non-critical groups</title>
<p>Comorbidities such as hypertension and diabetes are important factors related to mortality and complications of COVID-19 patients. Pooled results in <xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4</bold>
</xref> showed that there was a significantly higher prevalence of hypertension, diabetes, and subsequent mortality rate in the critical group ((OR: 0.446, 95% CI: 0.243, 0.818. P = 0.009), (OR: 0.565, 95% CI: 0.336, 0.949. P = 0.031), and (OR: 0.043, 95% CI: 0.011, 0.161. P &lt; 0.001) respectively).</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Comparison of comorbidities between critical and non-critical courses.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1341168-g004.tif"/>
</fig>
</sec>
<sec id="s3_5">
<title>Comparison of bacterial co-infection between critical and non-critical groups</title>
<p>The probability of bacterial co-infection differs significantly between groups, as illustrated in <xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5</bold>
</xref>. As a result, the difference in co-infection prevalence between critical (Event rate:57.7%, 95% CI: 0.296, 0.816) and non-critical (Event rate:25.7%, 95% CI: 0.074, 0.60) groups was 32%.</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Comparison of co-infection prevalence between critical and non-critical courses.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1341168-g005.tif"/>
</fig>
</sec>
<sec id="s3_6">
<title>Publication bias</title>
<p>A funnel plot was used for visual evaluation (<xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6</bold>
</xref>, 5S) and Egger&#x2019;s test was used to determine bias (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). Egger&#x2019;s test indicated publication bias for three of the ten variables. According to Egger&#x2019;s test, we found significant bias in WBC and lymphocyte mean and mortality rate differences between the two groups. Using <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>, it appears that variables with a <italic>P value &lt; 0.05</italic> are heterogeneous in terms of heterogeneity analysis. Although heterogeneous data does not necessarily indicate bias, the Egger test must be significant (<italic>P&lt; 0.05</italic>). We have also attached the results of one removed study plots as <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figures&#xa0;1&#x2013;3</bold>
</xref>.</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Funnel plot for comparison of blood markers between critical and non-critical courses.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1341168-g006.tif"/>
</fig>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>The Complete Results of Heterogeneity and Publication Bias Examination.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" rowspan="2" align="center">Variable</th>
<th valign="top" rowspan="2" align="center">Number<break/>of report/s</th>
<th valign="top" rowspan="2" align="center">Standard error</th>
<th valign="top" colspan="2" align="center">95% CI</th>
<th valign="top" colspan="3" align="center">Heterogeneity</th>
<th valign="top" colspan="2" align="center">Egger&#x2019;s<break/>regression</th>
</tr>
<tr>
<th valign="top" align="center">Lower<break/>limit</th>
<th valign="top" align="center">Upper<break/>limit</th>
<th valign="top" align="center">X<sup>2</sup>
</th>
<th valign="top" align="center">
<italic>p</italic>-value</th>
<th valign="top" align="center">I<sup>2</sup>
</th>
<th valign="top" align="center">
<italic>P</italic>-value</th>
<th valign="top" align="center">
<italic>t</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="center">Mean of WBC difference between Critical and non-Critical</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">0.148</td>
<td valign="top" align="center">0.247</td>
<td valign="top" align="center">0.828</td>
<td valign="top" align="center">55.46</td>
<td valign="top" align="center">&lt;0.001</td>
<td valign="top" align="center">81.96</td>
<td valign="top" align="center">0.023</td>
<td valign="top" align="center">2.72</td>
</tr>
<tr>
<td valign="top" align="center">Mean of lymphocyte difference between Critical and non-Critical</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">0.08</td>
<td valign="top" align="center">-0.826</td>
<td valign="top" align="center">-0.514</td>
<td valign="top" align="center">16.48</td>
<td valign="top" align="center">0.087</td>
<td valign="top" align="center">39.335</td>
<td valign="top" align="center">0.001</td>
<td valign="top" align="center">20.69</td>
</tr>
<tr>
<td valign="top" align="center">Mean of platelet difference between Critical and non-Critical</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">0.122</td>
<td valign="top" align="center">-0.659</td>
<td valign="top" align="center">-0.182</td>
<td valign="top" align="center">25.4</td>
<td valign="top" align="center">0.001</td>
<td valign="top" align="center">68.54</td>
<td valign="top" align="center">0.24</td>
<td valign="top" align="center">1.28</td>
</tr>
<tr>
<td valign="top" align="center">Mean of ALT difference between Critical and non-Critical</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">0.097</td>
<td valign="top" align="center">0.212</td>
<td valign="top" align="center">0.593</td>
<td valign="top" align="center">13.9</td>
<td valign="top" align="center">0.084</td>
<td valign="top" align="center">42.45</td>
<td valign="top" align="center">0.062</td>
<td valign="top" align="center">2.21</td>
</tr>
<tr>
<td valign="top" align="center">Mean of AST difference between Critical and non-Critical</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">0.185</td>
<td valign="top" align="center">0.099</td>
<td valign="top" align="center">0.823</td>
<td valign="top" align="center">24.70</td>
<td valign="top" align="center">0.001&gt;</td>
<td valign="top" align="center">75.71</td>
<td valign="top" align="center">0.72</td>
<td valign="top" align="center">0.36</td>
</tr>
<tr>
<td valign="top" align="center">Mean of CRP difference between Critical and non-Critical</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">0.155</td>
<td valign="top" align="center">.0178</td>
<td valign="top" align="center">0.786</td>
<td valign="top" align="center">41.74</td>
<td valign="top" align="center">&lt;0.001</td>
<td valign="top" align="center">80.83</td>
<td valign="top" align="center">0.42</td>
<td valign="top" align="center">0.84</td>
</tr>
<tr>
<td valign="top" align="center">Hypertension event difference between Critical and non-Critical</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">0.243</td>
<td valign="top" align="center">0.818</td>
<td valign="top" align="center">69.179</td>
<td valign="top" align="center">&lt;0.001</td>
<td valign="top" align="center">85.54</td>
<td valign="top" align="center">0.658</td>
<td valign="top" align="center">0.457</td>
</tr>
<tr>
<td valign="top" align="center">Diabetes event difference between Critical and non-Critical</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">0.336</td>
<td valign="top" align="center">0.949</td>
<td valign="top" align="center">64.59</td>
<td valign="top" align="center">&lt;0.001</td>
<td valign="top" align="center">84.52</td>
<td valign="top" align="center">0.328</td>
<td valign="top" align="center">1.03</td>
</tr>
<tr>
<td valign="top" align="center">Death event difference between Critical and non-Critical</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">0.011</td>
<td valign="top" align="center">0.161</td>
<td valign="top" align="center">56.265</td>
<td valign="top" align="center">&lt;0.001</td>
<td valign="top" align="center">85.78</td>
<td valign="top" align="center">0.0002</td>
<td valign="top" align="center">6.75</td>
</tr>
<tr>
<td valign="top" align="center">Prevalence of Co-infection between Critical and non-Critical cases</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">0.242</td>
<td valign="top" align="center">0.664</td>
<td valign="top" align="center">124.46</td>
<td valign="top" align="center">&lt;0.001</td>
<td valign="top" align="center">92.76</td>
<td valign="top" align="center">0.317</td>
<td valign="top" align="center">1.06</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>The COVID-19 pandemic has reached a global scale, and medical systems in many countries are experiencing severe problems as a result (<xref ref-type="bibr" rid="B32">32</xref>, <xref ref-type="bibr" rid="B33">33</xref>). The COVID-19 pandemic in 2019 has caused significant hospitalizations and deaths. According to the clinical course on COVID-19, we can classify patients with COVID-19 into critical and non-critical groups (<xref ref-type="bibr" rid="B4">4</xref>). The results showed that there are several differences between critical and non-critical groups. In this study, we examined the blood cell count, blood markers, and the comorbidities difference between critical and non-critical groups.</p>
<p>Due to the correlation between the immune system function and the clinical course of COVID-19, we compared the blood cell count between groups (<xref ref-type="bibr" rid="B34">34</xref>). The production of cytokines is crucial for the growth and specialization of immune cells. In COVID-19 pneumonia patients, certain inflammatory cytokines like IL-6 and IL-10 were found to be elevated in critical cases (<xref ref-type="bibr" rid="B35">35</xref>). However, IL-2 levels were increased in non-critical patients but decreased in critical ones. When present in low concentrations, IL-2 can prevent CD4+ T and CD8+ T-cell activation by maintaining T regulatory cell activity and survival (<xref ref-type="bibr" rid="B36">36</xref>). As a result, this could lead to a significant drop in CD8+ T-cells and lymphocytes in COVID-19 critical patients (<xref ref-type="bibr" rid="B37">37</xref>). Furthermore, critical patients had significantly lower T-cell, B-cell, and NK cell counts compared to controls (<xref ref-type="bibr" rid="B38">38</xref>). A gradual decrease in peripheral blood lymphocytes is a common early indicator of adult patients with non-critical and critical illnesses (<xref ref-type="bibr" rid="B39">39</xref>). IL-6 can stimulate T cell differentiation, and its increased levels are associated with producing acute-phase proteins like CRP and inflammatory cytokines. It is also possible that increased WBC in critical patients with low lymphocytes may be caused by an increase in PMNs, which can be indicated by an increase in CRP levels.</p>
<p>This study analysis indicated the higher platelet count in non-critical patients. Platelets and other related indicators play a crucial role in inflammation and prothrombotic responses during numerous viral infections (<xref ref-type="bibr" rid="B19">19</xref>). Apart from their traditional function in hemostasis and thrombosis, platelets also contribute significantly to the immune and inflammatory processes. Research suggests that platelets express surface receptors that enable them to bind and allow entry to various viruses. Furthermore, the rise in platelets and neutrophils could be due to anti-apoptotic cytokines and stimulation by specific pro-inflammatory cytokines (<xref ref-type="bibr" rid="B40">40</xref>). In addition to the immune system, enzymes and inflammation markers play an essential role in the course of COVID-19 disease (<xref ref-type="bibr" rid="B9">9</xref>). As a result of this study, ALT, AST, and CRP levels are significantly higher in the critical group than in the non-critical group. During acute inflammatory responses to COVID-19, there is usually a rapid and significant increase in serum CRP levels. Elevated CRP fluctuation during hospitalization has been identified as the primary cause of ICU admission with a poor prognosis (<xref ref-type="bibr" rid="B41">41</xref>). Analysis revealed that critical patients have higher CRP levels, indicating a more significant inflammatory response than non-critical patients (<xref ref-type="bibr" rid="B13">13</xref>). Although CRP is a sensitive indicator of disease activity and an independent risk factor for various diseases, studies have shown that CRP fluctuation is a better indicator of inflammation severity for guiding treatment in sepsis, systemic inflammatory response syndrome (SIRS), and community-acquired pneumonia (<xref ref-type="bibr" rid="B9">9</xref>, <xref ref-type="bibr" rid="B42">42</xref>).</p>
<p>An elevated CRP level in critical patients may hint to SIRS and multi organ damage. An elevated level of ALT and AST in critically ill patients may indicate liver damage and a change in bacterial co-infection in COVID-19 disease, both of which are associated with mortality (<xref ref-type="bibr" rid="B10">10</xref>). Lipopolysaccharides (LPS) are always considered a major contributor to liver damage (<xref ref-type="bibr" rid="B43">43</xref>). In critical patients with elevated liver enzymes that are indicative of acute liver damage, LPS may be one of the contributing factors. Our analysis of critical patients reveals a high prevalence of bacterial co-infection and LPS is predominantly present in bacterial cell walls. LPS is generally released from bacterial walls during bacterial proliferation or destruction (<xref ref-type="bibr" rid="B44">44</xref>). Therefore, it is possible that the overused broad-spectrum antibiotics in COVID-19 patients may suddenly destroy gram-negative bacteria and induce liver damage with a large amount of LPS toxin (<xref ref-type="bibr" rid="B45">45</xref>). Bactericide antibiotics may cause bacteria to release LPS, so bacteriostatic are recommended instead. The bacteriostatic inhibits the proliferation of bacteria, but does not kill them, therefore the level of LPS remains low until the body can recover from COVID-19. Once COVID-19 has been eliminated, bactericide antibiotics can be used. ALT, AST, and CRP levels are associated with ICU admission risk based on the results of this study and according to the definition of critical patients.</p>
<p>In univariable analysis, hypertension, diabetes, cardiovascular disease, and cancer were associated with critical illnesses (<xref ref-type="bibr" rid="B24">24</xref>). In this study, a statistical meta-analysis revealed that comorbidities, such as hypertension and diabetes, are more prevalent in critical groups than in non-critical groups. However, some previous studies state that comorbidities are common in non-critical groups, contrary to recent studies and our meta-analysis (<xref ref-type="bibr" rid="B46">46</xref>). A common element of COVID-19 patients with hypertension and diabetes is the use of angiotensin-converting enzyme inhibitors (ACEI). A membrane receptor known as ACE2 is responsible for binding SARS-CoV-2 to cells and promoting its entry into the respiratory tract. The downregulation of ACE2 by SARS-Cov-2 spike protein binding reduces the protective effects of ACE2 during acute inflammation (<xref ref-type="bibr" rid="B47">47</xref>). ACE inhibitors may induce the ACE2 expression, the cellular receptor for SARS-Cov-2, and can aggravate the disease course (<xref ref-type="bibr" rid="B48">48</xref>). It has been identified that SARS-CoV-2 is able to invade cells via this previously established cell receptor which is facilitating the invasion of SARS-CoV-2 cells (<xref ref-type="bibr" rid="B49">49</xref>). The higher incidence of diabetes in critically ill patients can be attributed to three well-defined mechanisms (<xref ref-type="bibr" rid="B50">50</xref>): 1) The direct entry of viruses through various receptors in &#x3b2;-cells can directly cause &#x3b2;-cell dysfunction and apoptosis or trigger &#x3b2;-cell autoimmunity. Alternatively, viruses can enter pancreatic cells that express viral receptors, leading to structural and functional changes, local inflammation, and the creation of a pro-diabetic environment. This can disrupt the integrity of nearby non-infected &#x3b2;-cells in a paracrine manner, potentially leading to loss or dysfunction of these cells (<xref ref-type="bibr" rid="B51">51</xref>). 2) Targeting putative viral receptor-expressing cells in metabolic organs like the liver and adipose tissue can induce insulin resistance and result in the loss of disease tolerance mechanisms (<xref ref-type="bibr" rid="B52">52</xref>). 3) Induction of systemic inflammation and accumulation of prediabetic metabolites can lead to metabolic derangement and maladaptive functions (<xref ref-type="bibr" rid="B53">53</xref>).</p>
<p>Critical patients with COVID-19 pneumonia exhibit a state of immune deficiency and hypo immunity. These factors can further worsen the situation by causing severe infection and leading to fatal outcomes (<xref ref-type="bibr" rid="B54">54</xref>). The prevalence of bacterial co-infection in COVID-19 patients can also be another difference between critical and non-critical patients. Our meta-analysis showed bacterial co-infection is more common in critical than non-critical patients. Bronchoalveolar lavage (BAL) and sputum are usually collected in the first week of ICU admission. The majority of COVID-19 patients with bacterial co-infection previously received antibiotics. Overall, our results revealed that the frequency of bacterial co-infection is higher in critical patients following ICU admission than in non-critical patients.</p>
<p>Therefore, the risk of inflammation, organ damage, and previous disease is significantly higher in the critical group. According to the comparison of co-infection rates, critical patients are more likely to have co-infections than non-critical patients. Also, the critical group had a higher death rate than the non-critical group (Graphical abstract).</p>
<p>In conclusion, our findings suggested that critical patients have a suppressed immune system and that inflammation, organ damage, and co-infections are significantly higher. Due to these factors, critical groups have a worsened course of the disease and a high mortality rate, so these patients require rapid diagnosis and careful management. Additionally, bactericide antibiotics may cause liver failure in critical patients due to the risk of liver damage. Therefore, we suggest that this relationship be fully evaluated in future studies.</p>
<sec id="s4_1">
<title>Limitations</title>
<p>Incomplete and vague definitions of some articles about critical and non-critical phases.</p>
<p>More than three-quarters of the studies we included were from China describing patients at the start of the pandemic.</p>
<p>Most patients with COVID-19 patients do not require hospitalization but patients in the studies included in this review were predominantly hospitalized.</p>
</sec>
</sec>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Material</bold>
</xref>. Further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>MH-C: Project administration, Resources, Supervision, Writing &#x2013; review &amp; editing. HE-S: Investigation, Methodology, Software, Validation, Writing &#x2013; review &amp; editing. AB: Conceptualization, Methodology, Writing &#x2013; original draft.</p>
</sec>
</body>
<back>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.</p>
</sec>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s10" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s11" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fimmu.2024.1341168/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fimmu.2024.1341168/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="DataSheet_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
</sec>
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