<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article">
 <front>
  <journal-meta>
   <journal-id journal-id-type="publisher-id">
    ojepi
   </journal-id>
   <journal-title-group>
    <journal-title>
     Open Journal of Epidemiology
    </journal-title>
   </journal-title-group>
   <issn pub-type="epub">
    2165-7459
   </issn>
   <issn publication-format="print">
    2165-7467
   </issn>
   <publisher>
    <publisher-name>
     Scientific Research Publishing
    </publisher-name>
   </publisher>
  </journal-meta>
  <article-meta>
   <article-id pub-id-type="doi">
    10.4236/ojepi.2024.143036
   </article-id>
   <article-id pub-id-type="publisher-id">
    ojepi-135444
   </article-id>
   <article-categories>
    <subj-group subj-group-type="heading">
     <subject>
      Articles
     </subject>
    </subj-group>
    <subj-group subj-group-type="Discipline-v2">
     <subject>
      Medicine 
     </subject>
     <subject>
       Healthcare
     </subject>
    </subj-group>
   </article-categories>
   <title-group>
    Pattern of Potential Laboratory Markers for COVID-19 in Eastern Sudan
   </title-group>
   <contrib-group>
    <contrib contrib-type="author" xlink:type="simple">
     <name name-style="western">
      <surname>
       Nazik Sir El Khatim Bakhit
      </surname>
      <given-names>
       Suliman
      </given-names>
     </name> 
     <xref ref-type="aff" rid="aff1"> 
      <sup>1</sup>
     </xref>
    </contrib>
    <contrib contrib-type="author" xlink:type="simple">
     <name name-style="western">
      <surname>
       Mohammed Omer Abaker
      </surname>
      <given-names>
       Gibreel
      </given-names>
     </name> 
     <xref ref-type="aff" rid="aff2"> 
      <sup>2</sup>
     </xref>
    </contrib>
    <contrib contrib-type="author" xlink:type="simple">
     <name name-style="western">
      <surname>
       Mervat Sir El Khatim Bakhit
      </surname>
      <given-names>
       Suliman
      </given-names>
     </name> 
     <xref ref-type="aff" rid="aff3"> 
      <sup>3</sup>
     </xref>
    </contrib>
   </contrib-group> 
   <aff id="aff1">
    <addr-line>
     aDepartment of Clinical Chemistry, College of Medical Laboratory Science, Eastern Sudan University of Medical Science and Technology, Port Sudan, Sudan
    </addr-line> 
   </aff> 
   <aff id="aff2">
    <addr-line>
     aDean of the Secretariat of Academic Affairs, Port Sudan Ahlia University, Port Sudan, Sudan
    </addr-line> 
   </aff> 
   <aff id="aff3">
    <addr-line>
     aCardiology Department, Cath Lab, Fujaira Hospital, Fujaira, UAE
    </addr-line> 
   </aff> 
   <pub-date pub-type="epub">
    <day>
     18
    </day> 
    <month>
     06
    </month>
    <year>
     2024
    </year>
   </pub-date> 
   <volume>
    14
   </volume> 
   <issue>
    03
   </issue>
   <fpage>
    508
   </fpage>
   <lpage>
    516
   </lpage>
   <history>
    <date date-type="received">
     <day>
      4,
     </day>
     <month>
      June
     </month>
     <year>
      2024
     </year>
    </date>
    <date date-type="published">
     <day>
      20,
     </day>
     <month>
      June
     </month>
     <year>
      2024
     </year> 
    </date> 
    <date date-type="accepted">
     <day>
      20,
     </day>
     <month>
      August
     </month>
     <year>
      2024
     </year> 
    </date>
   </history>
   <permissions>
    <copyright-statement>
     © Copyright 2014 by authors and Scientific Research Publishing Inc. 
    </copyright-statement>
    <copyright-year>
     2014
    </copyright-year>
    <license>
     <license-p>
      This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/
     </license-p>
    </license>
   </permissions>
   <abstract>
    <b>Background</b>: Coronavirus disease 2019 (COVID-19) is a recent global health crisis. One of the major issues of COVID-19 is its unpredictable manifestations and serious outcomes. Many hematological parameters are thought to change dramatically during the course of the disease. These include white blood cells, red blood cells, and platelets. This study aimed at evaluating certain laboratory results; peripheral blood lymphopenia, relative neutrophilia, high neutrophil-lymphocyte ratio, and elevated C-reactive protein as potential laboratory markers of COVID-19 in Eastern Sudanese patients. 
    <b>Methods: </b>We, retrospectively, aimed at the evaluation of peripheral blood leucocytes count, neutrophil-lymphocyte ratio NLR and C-reactive protein (CRP) levels in confirmed COVID-19 eastern Sudanese patients during the course of the disease. 
    <b>Results: </b>The mean total leucocytes count, % neutrophils count, absolute neutrophils count and C-reactive protein (CRP) were significantly higher (P. value = 0.000) in COVID-19 patients than in the control group while the mean % lymphocytes count and % mixed cells count were found to be significantly lower in COVID-19 patients than in the control group (P. value 0.000). 
    <b>Conclusion:</b> Peripheral blood leucocyte alterations (simultaneous presence of lymphopenia, relative neutrophilia and high neutrophil lymphocyte ratio (NLR) along with elevated CRP levels may be valuable biomarkers associated with COVID-19 in Port Sudan city, Red Sea state, Sudan. These markers might be important in prediction, inspection of disease progression and prognosis.
   </abstract>
   <kwd-group> 
    <kwd>
     COVID-19
    </kwd> 
    <kwd>
      SARS 2
    </kwd> 
    <kwd>
      WBCs
    </kwd> 
    <kwd>
      CRP
    </kwd> 
    <kwd>
      NLR
    </kwd> 
    <kwd>
      Port Sudan
    </kwd>
   </kwd-group>
  </article-meta>
 </front>
 <body>
  <sec id="s1">
   <title>1. Introduction</title>
   <p>The name severe acute respiratory coronavirus 2 (SARS2) was applied to the novel b-corona virus discovered in 2019 that is now known to cause respiratory disease that causes mild, moderate or severe life-threatening disorder <xref ref-type="bibr" rid="scirp.135444-1">
     [1]
    </xref>. Two strains of COVID-19; SARS-CoV and its divergent (the zoonotic positive-strand RNA virus known as SARS-CoV-2) have been associated with high incidences of morbidity and mortality worldwide during the recent decade <xref ref-type="bibr" rid="scirp.135444-2">
     [2]
    </xref>. The World Health Organization (WHO) declared that COVID-19 is a global pandemic the spread of which has impacted all aspects of life and has negatively affected healthcare, medical activity and research <xref ref-type="bibr" rid="scirp.135444-3">
     [3]
    </xref>. SARS2 is responsible for the acute respiratory distress syndrome (ARDS) that is associated with multi-organ failure and death experienced in affected patients <xref ref-type="bibr" rid="scirp.135444-1">
     [1]
    </xref>. Predicting the risk factors for severe COVID-19 infection can greatly help manage critical cases and save lives <xref ref-type="bibr" rid="scirp.135444-4">
     [4]
    </xref>. It is important that the majority of human populations in the world are at risk of the infection. Alterations that happen in peripheral blood leucocytes including lymphocytes, eosinophils and neutrophils in COVID-19 patients are suggested as a potential indicator for both disease progression and response to therapeutic procedures <xref ref-type="bibr" rid="scirp.135444-2">
     [2]
    </xref>. In 2020, the WHO declared SARS2 a global health emergency with more than 800,000 confirmed cases, more than 2700 deaths and affecting at least 37 countries at that time <xref ref-type="bibr" rid="scirp.135444-5">
     [5]
    </xref>. One of the best-recognized leucocytes change in COVID-19 patients is lymphopenia (absolute lymphocytes count less than 1.0 × 109/L) which is correlated with disease outcome. A more severe course of COVID-19 is often accompanied by leucocytosis with neutrophilia in a majority of patients. As the disease progresses neutrophilia increases and is thus regarded as a marker of respiratory disease and poor outcome. Reversible morphological neutrophil alterations including toxic granulation and hypo-lobulation are also encountered in SARS 2 infected patients <xref ref-type="bibr" rid="scirp.135444-1">
     [1]
    </xref>. Significant correlations were found between WBCs count and death in hospitalized patients <xref ref-type="bibr" rid="scirp.135444-6">
     [6]
    </xref>. Neutrophil lymphocyte ratio (NLR) obtained by the division of absolute neutrophil count over the absolute lymphocyte count has been recognized as a useful marker in the prediction of many inflammatory processes including ischemic heart disease, acute pancreatitis along with some malignant conditions. NLR might be a better predictor of COVID-19 infection than neutrophil count alone <xref ref-type="bibr" rid="scirp.135444-1">
     [1]
    </xref>. COVID-19 pandemic expresses a state of rapid spread that rendered health service providers in all countries to make efforts to provide tools of rapid diagnosis ranging from simple biological markers to DNA analysis. Complete blood Count (CBC) is the most utilized laboratory test worldwide; hence, markers associated with CBC would give valuable information for both detection and prognosis of the disease. C-reactive protein (CRP) is considered a sensitive marker of both infection and inflammation <xref ref-type="bibr" rid="scirp.135444-7">
     [7]
    </xref>. It is often requested by physicians in the counseling of COVID-19 patients. C-reactive protein can be used to predict the cases that are most susceptible to progress unfavorably <xref ref-type="bibr" rid="scirp.135444-8">
     [8]
    </xref>. Local researches that assess the sensitivity and specificity of hematology laboratory tests in the diagnosis and prognosis of COVID-19 are scanty. We have tried in this study to verify hematological changes associated with peripheral blood leucocyte counts and CRP that may add valuable information as laboratory markers for the prediction and prognosis of COVID-19 infection in the Red Sea state, Sudan.</p>
  </sec><sec id="s2">
   <title>2. Patients and Methods</title>
   <p>A total of 50 adult Eastern Sudanese COVID-19 patients (confirmed by RT PCR assay of nasal and pharyngeal swab specimens) living in Port Sudan City and aged between 11 and 90 years old of whom 29 (58%) were males and 21 (42%) were females (<xref ref-type="fig" rid="fig1">
     Figure 1
    </xref>) along with other 50 healthy appearing adults aged between 18 and 82 years of whom 28 (56%) were males and 22 (44%) were females (<xref ref-type="fig" rid="fig2">
     Figure 2
    </xref>) as a control group were enrolled into this cross-sectional retrospective study. Venous blood specimens were collected and analyzed during the period from September to November 2021. All specimens from participants were subjected to the determination of Complete Blood Count (CBC) using the Sysmex XP-300 apparatus in the Hematology department at the Eastern Sudan University of Medical Science and Technology, College of Medical Laboratory Science. CRP level was estimated in patients and the control group using the Biosystems A25 chemistry analyzer. In respect to relative illiteracy, only verbal consent was obtained from patients and was approved by the ethics committee at the Eastern Sudan University of Medical Science and Technology, Port Sudan. Data were statistically analyzed by the Statistical Product and Service Solutions IBM SPSS 24 program. Descriptive statistics in the form of frequencies and percentages were used to facilitate the interpretation of results. Degree of confidence adhered to was 95% where P. values lower than 0.05 considered significant.</p>
  </sec><sec id="s3">
   <title>3. Results</title>
   <p>Of COVID-19 patients included in this study, 64% were vaccinated, 80% were home-isolated, 22% were hospitalized, and 90% had received oxygen supplements. Most of the patients had been suffering from an accompanying chronic illness (<xref ref-type="table" rid="table1">
     Table 1
    </xref>). 46% of the individuals in the control group were also vaccinated.</p>
   <table-wrap id="table1">
    <label>
     <xref ref-type="table" rid="table1">
      Table 1
     </xref></label>
    <caption>
     <title>
      <xref ref-type="bibr" rid="scirp.135444-"></xref>Table 1. Frequency of chronic illness among patients.</title>
    </caption>
    <table class="MsoTableGrid custom-table" border="0" cellspacing="0" cellpadding="0"> 
     <tr> 
      <td class="acenter" width="31.00%">Item<p style="text-align:center"></p></td> 
      <td class="acenter" width="31.75%">COVID-19 patients (N = 50) Frequency<p style="text-align:center"></p></td> 
      <td class="acenter" width="35.48%">COVID-19 patients (N = 50) Percent<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="31.00%">Diabetes Mellitus<p style="text-align:center"></p></td> 
      <td class="acenter" width="31.75%">24<p style="text-align:center"></p></td> 
      <td class="acenter" width="35.48%">48<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="31.00%">Cardiac disorder<p style="text-align:center"></p></td> 
      <td class="acenter" width="31.75%">03<p style="text-align:center"></p></td> 
      <td class="acenter" width="35.48%">06<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="31.00%">Hypertension<p style="text-align:center"></p></td> 
      <td class="acenter" width="31.75%">10<p style="text-align:center"></p></td> 
      <td class="acenter" width="35.48%">20<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="31.00%">Renal disease<p style="text-align:center"></p></td> 
      <td class="acenter" width="31.75%">04<p style="text-align:center"></p></td> 
      <td class="acenter" width="35.48%">08<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="31.00%">Arteriosclerosis<p style="text-align:center"></p></td> 
      <td class="acenter" width="31.75%">02<p style="text-align:center"></p></td> 
      <td class="acenter" width="35.48%">04<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="31.00%">Hyperthyroidism<p style="text-align:center"></p></td> 
      <td class="acenter" width="31.75%">01<p style="text-align:center"></p></td> 
      <td class="acenter" width="35.48%">02<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="31.00%">No chronic disease<p style="text-align:center"></p></td> 
      <td class="acenter" width="31.75%">06<p style="text-align:center"></p></td> 
      <td class="acenter" width="35.48%">12<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="31.00%">Total<p style="text-align:center"></p></td> 
      <td class="acenter" width="31.75%">50<p style="text-align:center"></p></td> 
      <td class="acenter" width="35.48%">100<p style="text-align:center"></p></td> 
     </tr> 
    </table>
   </table-wrap>
   <p>Statistically significant differences were detected between COVID-19 patients and controls in the measured parameters except for one. The mean total leucocytes count, % neutrophils count, absolute neutrophils count and neutrophil lymphocyte ratio were significantly higher (P. value = 0.000) in the patient group than in the control group while the mean % lymphocytes count and % mixed cells count were found to be significantly lower in the patient group than in the control group (P. value = 0.000) (<xref ref-type="table" rid="table2">
     Table 2
    </xref>).</p>
   <p>
    <xref ref-type="table" rid="table3">
     Table 3
    </xref> illustrates the clinical presentations observed in the two groups which range from mild to vast distributed symptoms.</p>
   <table-wrap id="table2">
    <label>
     <xref ref-type="table" rid="table2">
      Table 2
     </xref></label>
    <caption>
     <title>
      <xref ref-type="bibr" rid="scirp.135444-"></xref>Table 2. Comparison between Patients and control based on the measured parameters.</title>
    </caption>
    <table class="MsoTableGrid custom-table" border="0" cellspacing="0" cellpadding="0"> 
     <tr> 
      <td class="acenter">Item<p style="text-align:center"></p></td> 
      <td class="acenter">Reference Range<p style="text-align:center"></p></td> 
      <td class="acenter">Patients Mean ± SD (N = 50)<p style="text-align:center"></p></td> 
      <td class="acenter">Control Mean ± SD (N = 50)<p style="text-align:center"></p></td> 
      <td class="acenter">P. value<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter">TWBCs count/µL<p style="text-align:center"></p></td> 
      <td class="acenter">4000 - 11000<p style="text-align:center"></p></td> 
      <td class="acenter">9364 ± 4446<p style="text-align:center"></p></td> 
      <td class="acenter">5958 ± 1818<p style="text-align:center"></p></td> 
      <td class="acenter">0.000<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter">% Lymphocytes count<p style="text-align:center"></p></td> 
      <td class="acenter">20 - 45<p style="text-align:center"></p></td> 
      <td class="acenter">11.25 ± 10.80<p style="text-align:center"></p></td> 
      <td class="acenter">38.20 ± 13.53<p style="text-align:center"></p></td> 
      <td class="acenter">0.000<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter">% Neutrophils count<p style="text-align:center"></p></td> 
      <td class="acenter">40 - 60<p style="text-align:center"></p></td> 
      <td class="acenter">79.04 ± 12.62<p style="text-align:center"></p></td> 
      <td class="acenter">14.20 ± 08.95<p style="text-align:center"></p></td> 
      <td class="acenter">0.000<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter">% Mixed cells count<p style="text-align:center"></p></td> 
      <td class="acenter">05 - 20<p style="text-align:center"></p></td> 
      <td class="acenter">03.84 ± 02.24<p style="text-align:center"></p></td> 
      <td class="acenter">50.38 ± 16.28<p style="text-align:center"></p></td> 
      <td class="acenter">0.000<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter">Absolute lymphocytes ×10<sup>3</sup>/µL<p style="text-align:center"></p></td> 
      <td class="acenter">1.2 – 4.9<p style="text-align:center"></p></td> 
      <td class="acenter">05.70 ± 12.85<p style="text-align:center"></p></td> 
      <td class="acenter">03.25 ± 01.73<p style="text-align:center"></p></td> 
      <td class="acenter">0.186<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter">Absolute neutrophils ×10<sup>3</sup>/µL<p style="text-align:center"></p></td> 
      <td class="acenter">1.8 – 6.8<p style="text-align:center"></p></td> 
      <td class="acenter">06.03 ± 05.13<p style="text-align:center"></p></td> 
      <td class="acenter">01.67 ± 01.25<p style="text-align:center"></p></td> 
      <td class="acenter">0.000<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter">Neutrophil lymphocyte ratio<p style="text-align:center"></p></td> 
      <td class="acenter">1.38 – 1.50<p style="text-align:center"></p></td> 
      <td class="acenter">5.43 ± 7.46<p style="text-align:center"></p></td> 
      <td class="acenter">0.63 ± 0.59<p style="text-align:center"></p></td> 
      <td class="acenter">0.000<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter">C-Reactive protein mg/L<p style="text-align:center"></p></td> 
      <td class="acenter">0-5<p style="text-align:center"></p></td> 
      <td class="acenter">95.09 ± 1.78<p style="text-align:center"></p></td> 
      <td class="acenter">0.84 ± 3.36<p style="text-align:center"></p></td> 
      <td class="acenter">0.000<p style="text-align:center"></p></td> 
     </tr> 
    </table>
   </table-wrap>
   <table-wrap id="table3">
    <label>
     <xref ref-type="table" rid="table3">
      Table 3
     </xref></label>
    <caption>
     <title>
      <xref ref-type="bibr" rid="scirp.135444-"></xref>Table 3. Frequencies of clinical remarks.</title>
    </caption>
    <table class="MsoTableGrid custom-table" border="0" cellspacing="0" cellpadding="0"> 
     <tr> 
      <td class="acenter" width="36.25%">Symptoms<p style="text-align:center"></p></td> 
      <td class="acenter" width="34.38%">Patients % (N = 50)<p style="text-align:center"></p></td> 
      <td class="acenter" width="34.23%">Control % (N = 50)<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="36.25%">Fever<p style="text-align:center"></p></td> 
      <td class="acenter" width="34.38%">68<p style="text-align:center"></p></td> 
      <td class="acenter" width="34.23%">2<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="36.25%">Headache<p style="text-align:center"></p></td> 
      <td class="acenter" width="34.38%">56<p style="text-align:center"></p></td> 
      <td class="acenter" width="34.23%">4<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="36.25%">Loss of taste<p style="text-align:center"></p></td> 
      <td class="acenter" width="34.38%">22<p style="text-align:center"></p></td> 
      <td class="acenter" width="34.23%">0<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="36.25%">Cough<p style="text-align:center"></p></td> 
      <td class="acenter" width="34.38%">100<p style="text-align:center"></p></td> 
      <td class="acenter" width="34.23%">0<p style="text-align:center"></p></td> 
     </tr> 
     <tr> 
      <td class="acenter" width="36.25%">Loss of smell<p style="text-align:center"></p></td> 
      <td class="acenter" width="34.38%">36<p style="text-align:center"></p></td> 
      <td class="acenter" width="34.23%">2<p style="text-align:center"></p></td> 
     </tr> 
    </table>
   </table-wrap>
   <fig id="fig1" position="float">
    <label>Figure 1</label>
    <caption>
     <title>Figure 1. Gender distribution in the patients group.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/1890766-rId12.jpeg?20240904015324" />
   </fig>
   <fig id="fig2" position="float">
    <label>Figure 2</label>
    <caption>
     <title>Figure 2. Gender distribution in the control group.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/1890766-rId13.jpeg?20240904015324" />
   </fig>
   <fig id="fig3" position="float">
    <label>Figure 3</label>
    <caption>
     <title>Figure 3. Comparison between the two groups in TWBCs count.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/1890766-rId14.jpeg?20240904015324" />
   </fig>
   <fig id="fig4" position="float">
    <label>Figure 4</label>
    <caption>
     <title>Figure 4. Comparison between the two groups in absolute lymphocytes count.</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/1890766-rId15.jpeg?20240904015324" />
   </fig>
   <fig id="fig5" position="float">
    <label>Figure 5</label>
    <caption>
     <title>Figure 5. Absolute neutrophils count in the patient group. (Ab = Absolute, N = Neutrophils).</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/1890766-rId16.jpeg?20240904015325" />
   </fig>
   <fig id="fig6" position="float">
    <label>Figure 6</label>
    <caption>
     <title>Figure 6. Absolute neutrophils count in the control group. (Ab = Absolute, N = Neutrophils).</title>
    </caption>
    <graphic mimetype="image" position="float" xlink:type="simple" xlink:href="https://html.scirp.org/file/1890766-rId17.jpeg?20240904015325" />
   </fig>
   <p>Peripheral blood leucocytosis was more prominent in the patient group than in the controls (<xref ref-type="fig" rid="fig3">
     Figure 3
    </xref>). A relatively low percent lymphocytes count was seen most frequently in the patient group (<xref ref-type="fig" rid="fig4">
     Figure 4
    </xref>). Similarly, a relatively elevated absolute neutrophils count was also seen more frequently in the patient group than in the control (<xref ref-type="fig" rid="fig5">
     Figure 5
    </xref> and <xref ref-type="fig" rid="fig6">
     Figure 6
    </xref>).</p>
  </sec><sec id="s4">
   <title>4. Discussion</title>
   <p>The clinical and laboratory spectrum of COVID-19 is not yet totally verified despite the huge data concerning the documentation of epidemiological and clinical characteristics. The state of dramatically rapid human-to-human spread of the virus and that both middle-aged and elderly patients are susceptible to acute respiratory failure associated with poor prognosis <xref ref-type="bibr" rid="scirp.135444-6">
     [6]
    </xref> necessitate the provision of further markers to predict the disease. The provision of biomedical and/or statistical markers will help physicians to make proper decisions concerning the prediction, progression and prognosis of the disease. Our present study tried to assess the alterations in peripheral blood leucocytes count and CRP levels in COVID-19 patients as compared to healthy-appearing adult controls. Lymphopenia and neutrophilia which are significantly associated with our patient group (P. value = 0.000), is in concordance to <xref ref-type="bibr" rid="scirp.135444-1">
     [1]
    </xref> and <xref ref-type="bibr" rid="scirp.135444-2">
     [2]
    </xref> who both reported results that are associated with COVID-19 infection and severity among an Italian and Saudi Arabian patient populations, respectively. Furthermore, neutrophilia detected in our present study in COVID-19 patients are in concordance with <xref ref-type="bibr" rid="scirp.135444-9">
     [9]
    </xref>, <xref ref-type="bibr" rid="scirp.135444-10">
     [10]
    </xref> and <xref ref-type="bibr" rid="scirp.135444-11">
     [11]
    </xref> where white cell alterations in COVID-19 patients were evaluated retrospectively. The fact that patients with higher white blood cells at admission were facing a much higher death possibility, <xref ref-type="bibr" rid="scirp.135444-6">
     [6]
    </xref>, makes our present study which aims at searching for biological markers linked to peripheral blood white cell alterations valuable. Moreover, the whole clinical and diagnostic picture for COVID-19 is not yet completed owing to the continuous evolvement of new convergent copies of the virus. These facts, collectively, necessitate the presence of close open eye inspection concerning COVID-19 infection. Much remains unknown about COVID-19 in spite of the information emerged on the viral genome and epidemiology. Liu et al. <xref ref-type="bibr" rid="scirp.135444-12">
     [12]
    </xref> reported that lymphocytopenia was detected in about 72.3% of patients while nearly 80% had normal or decreased WBCs counts. Again these findings agree with our observations. Our findings regarding CRP are matched with what was reported by Manalu E. et al. <xref ref-type="bibr" rid="scirp.135444-7">
     [7]
    </xref> who reported that the average CRP levels in patients with moderate symptoms was 63.705 mg/l while the average level in patients with severe symptoms was 132.050 mg/l. A strong association (P value ˂ 0.001) between first CRP level and mortality had been reported by <xref ref-type="bibr" rid="scirp.135444-13">
     [13]
    </xref> in records of 10 hospitals at Common Spirit Health, USA. Again, statistically significant difference in CRP levels (P. value 0.000) between COVID-19 patients and the healthy control group was detected by Kurt N. et al., <xref ref-type="bibr" rid="scirp.135444-14">
     [14]
    </xref>. Chandran RT. and Vadhul PB, <xref ref-type="bibr" rid="scirp.135444-15">
     [15]
    </xref> have also reported that elevated levels of CRP was associated with high ICU mortality in COVID-19 infected (Adult Respiratory Distress Syndrome (ARDS)) patients. We greatly hope that our results might provide significant local medical data that will help physicians to predict COVID-19 through these laboratory markers. Nonetheless, some limitations are to be noticed such as our smaller sample size and relatively heterogeneous group of patients. So, future research is needed to investigate more biological and laboratory markers associated with COVID-19 infection.</p>
  </sec><sec id="s5">
   <title>Limitations</title>
   <p>This study was primarily conducted in an Eastern Sudanese patient population. So, geographical, environmental and ethnic considerations may differ from other areas. Further studies with larger sample sizes are recommended that clarify the correlation between COVID-19 infection and other dynamic hematological changes.</p>
  </sec><sec id="s6">
   <title>Ethical approval and consent to participate</title>
   <p>Permission for this study was obtained from the Medical Laboratory Science College, Eastern Sudan University of Medical Science and Technology and Ministry of Health issued by the local ethics committee. Verbal consent was also obtained from participants.</p>
  </sec><sec id="s7">
   <title>Conclusion</title>
   <p>The hematopoietic system is one of the organs that are affected by COVID-19 manifestation. Many alterations in the components of the hematopoietic system occur including lymphopenia, neutrophilia, raised neutrophil lymphocyte ratio NLR and elevated levels of CRP in COVID-19 patients. To some extent, these alterations may act as laboratory markers that assist prediction and/or prognosis of the disease.</p>
  </sec>
 </body><back>
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