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 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 9  |  Issue : 2  |  Page : 220-224

Hematological Manifestations of COVID-19 and its correlation with outcome – A retrospective study


1 Department of Internal Medicine, Government Villupuram Medical College and Hospital, Villupuram, Tamil Nadu, India
2 Department of Biochemistry, Government Sivagangai Medical College and Hospital, Sivagangai, Tamil Nadu, India

Date of Submission17-Jul-2021
Date of Decision04-Sep-2021
Date of Acceptance23-Sep-2021
Date of Web Publication29-Dec-2021

Correspondence Address:
Dr. Shivkumar Gopalakrishnan
Department of Internal Medicine, Government Villupuram Medical College and Hospital, Villupuram - 605 602, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/amhs.amhs_171_21

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  Abstract 


Background and Aim: The objective was to study the hematological manifestations of disease caused by novel coronavirus 2019 (COVID-19) and evaluate the association between absolute neutrophil count (ANC), absolute lymphocyte count (ALC), neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio, total count, and outcome. Materials and Methods: A hospital-based retrospective study was conducted on COVID-19 adult inpatients admitted between March and August 2020. The patients were classified into three groups based on outcome as mild COVID/uneventful recovery (Group 1), severe COVID/recovered (Group 2), and death (Group 3). Their clinical profile and hematological parameters were compared using both univariate and multivariate analyses. Binary and multinomial logistic regression analyses were used to analyze the chances of survival or death with respect to the variable studied. Results: The median age of nonsurvivors was 62.5 years. For unit increase in age, there were 1.03 times higher chances of severe disease (P = 0.013) and 1.04 times chances of death (P = 0.028). For every 1000/μl increase of ANC, the odds of developing severe disease rose by 1.85 (P = 0.270). For every 1000/μl increase in ALC, there were 94.7% lesser chances of death (P = 0.006) and 51.1% lesser chances of severe disease (P = 0.033). The odds of developing severe disease was 1.16 times per unit rise in NLR and the OR for death was 1.27 (P = 0.053). Conclusion: Advanced age, presence of lymphocytopenia, increased neutrophil count, and elevated NLR were associated with severe disease and high mortality due to COVID-19. Lymphocytopenia and age were the strongest predictors of severe disease and death.

Keywords: Absolute lymphocyte count, absolute neutrophil count, COVID-19, hematological manifestation, mortality, neutrophil–lymphocyte ratio


How to cite this article:
Abraham B, Gopalakrishnan S, Kandasamy S. Hematological Manifestations of COVID-19 and its correlation with outcome – A retrospective study. Arch Med Health Sci 2021;9:220-4

How to cite this URL:
Abraham B, Gopalakrishnan S, Kandasamy S. Hematological Manifestations of COVID-19 and its correlation with outcome – A retrospective study. Arch Med Health Sci [serial online] 2021 [cited 2022 Aug 11];9:220-4. Available from: https://www.amhsjournal.org/text.asp?2021/9/2/220/334003




  Introduction Top


The disease caused by novel coronavirus 2019 (SARS-CoV-2) (COVID-19) pandemic has claimed over a million lives in 6 continents besides economically paralyzing many countries. The unpredictable behavior of SARS-CoV-2 has left the scientific community perplexed and in search of markers of adverse outcomes. With the health-care systems overwhelmed, the need for prioritization is felt more than ever. Hematological parameters such as neutrophil–lymphocyte ratio (NLR), lymphocyte count, platelet–lymphocyte ratio (PLR), and a few other indices have been identified as prognosticators in previous research works.[1],[2],[3],[4] These tests are inexpensive and freely available and do not need expertise to perform or interpret. India is right now facing the pandemic heat, and we are in need of point of care tests to triage. To the best of our knowledge, this is the first regional effort to validate the predictive capacity of blood indices in COVID-19. We therefore, aimed to study the hematological manifestations of COVID-19 among hospitalized patients and derive correlation with outcomes if any.


  Materials and Methods Top


The electronic case records of consecutive reverse transcription–polymerase chain reaction (RT-PCR) confirmed COVID-19 patients admitted to Government Villupuram Medical College and Hospital between March and July 2020 were screened retrospectively for eligibility. The data of 506 adult patients who fulfilled the inclusion criteria were extracted and computed. Pediatric and adolescent (age <18 years) patients were not included. Those referred to other centers, hematological malignancies, and immune-compromised status were excluded. Information retrieved included clinical signs, oxygen saturation, complete blood counts, NLR, and PLR. The study sample was divided into three groups based on outcome (predefined criteria) – mild COVID recovered (Group 1), severe COVID recovered (Group 2), and severe COVID died (Group 3). The outcomes were assessed on day 6 of the hospital stay or at discharge of the patient from the hospital.

Outcome-based criteria

  • Group 1: Patients who had an uneventful recovery and were discharged as per institutional protocol (no signs of dyspnea or respiratory distress, SpO2 >94% in room air, no undue tachycardia, and absent or minimal lung infiltrates [<10%] by radiology on the 6th day of admission)
  • Group 2: Patients with dyspnea or tachypnea (respiratory rate >30/min), respiratory distress, SpO2 ≤93% in room air or those who required oxygen supplementation to maintain SpO2 >93% or PaO2/FiO2 <300, and/or greater than 50% lung infiltrates within 48 h of admission.[5] These patients recovered with treatment and were discharged to home
  • Group 3: Patients who fulfilled the criteria for Group 2 but died during the treatment course in hospital.


Statistical scrutiny was performed using both univariate and multivariate analyses. α < 0.05 was taken as significant. Pearson's rank correlation test was used to study the relation between NLR/PLR and mortality. Binary and multinomial logistic regression analyses were used to analyze the chances of survival or death with respect to the variable in the study. Software used was STATA 14.0 version (Statistical Software: Release 14. College Station, TX: StataCorp LP).


  Results Top


Among 506 patients, 66% (n = 334) were male. 77.7% (n = 393) belonged to Group 1, 15.2% (n = 77) to Group 2 and 7.1% (n = 36) Group 3. The mean age in each group was 47.6 years, 58.7 years and 62.7 years, respectively [Table 1]. 31.2% (n = 158) of patients were over 60 years of age, of which 14.6% (n = 23) died and 26% (n = 41) had severe disease. Among males, 72.2% (n = 241) recovered, 19.2% (n = 62) developed severe disease, and 8.7% (n = 29) died. Out of the 172 females, 88.4% (n = 152) recovered, 7.6% of patients (n = 13) developed severe disease, and 4.1% (n = 7) had fatal outcome. Statistical analysis revealed that for a unit increase in age, there are 1.03 times higher chances of developing severe disease (P = 0.013) and 1.04 times higher chances of death (P = 0.028) which were statistically significant.
Table 1: Sociodemographic and hematological features of COVID-19 patients

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4.7% (n = 24) had TC <4000/μl wherein 87.5% (n = 21) had an uneventful recovery (Group 1) and 12.5% (n = 3) developed clinically severe disease, but no mortality was documented in this subset. 75.9% (n = 384) had a total count (TC) within the normal range of 4000–11,000 per microliter. 12.6% (n = 64) had a TC of 11,000–15,000, among whom 54.7% (n = 35) were in Group 2 and 14.1% (n = 9) in Group 3 (fatalities). 6.7% (n = 34) had TC more than 15,000/μl, of which 38.2% (n = 13) were in Group 2 and 44.1% (n = 15) in Group 3. Statistical analysis revealed that there are 2.38 times higher chances of death per 1000/μl increase in the TC from the baseline (P = 0.143) though it was statistically insignificant. There were no deaths in patients who had TC less than 4000. The case fatality was 2.9% for TC <11000 and 24.5% for TC >11000. Furthermore, 66.6% of total deaths had TC >11000/microliter.

Eighty-two percent (n = 415) of patients had absolute neutrophil count (ANC) within the normal upper limit of 8000 cells/μl. 10.5% (n = 53) had ANC between 8000 and 12000/μl and 7.5% (n = 38) had ANC above 12000/μl. The distribution of three groups based on ANC is depicted in [Table 2]. Out of the 91 patients who had ANC >8000, 47.2% (n = 43) were in Group 2 and 28.5% (n = 26) died. Out of the 38 patients who had ANC >12,000, 84.2% (n = 32) had severe disease, half of whom died (42.1% [n = 16]). Statistical analysis revealed that for every 1000/ul increase of ANC from 8000, the odds of developing severe disease rose by 1.85 (P = 0.270). It was also interesting to note that there was a 42.1% death rate in patients with ANC >12,000/ul in comparison to 2.4% in those with ANC <8000/ul. A comparative graph of death and severe disease matched for ANC values is displayed in [Figure 1].
Table 2: Distribution of patients based on absolute neutrophil count in the three groups

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Figure 1: Distribution of severe COVID and death among three absolute neutrophil count syndicates

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84.8% (n = 429) of patients had absolute lymphocyte count (ALC) within normal levels (1000–4000/μl). Thirteen percent (n = 66) of patients had lymphocytopenia (ALC <1,000), of which 37.9% (n = 25) had severe disease and 27.3% (n = 18) died. 2.17% (n = 11) of patients had ALC over 4000/μl and all of them had an uneventful recovery [Table 3]. Statistical analysis revealed that for an increase in 1000/μl, there are 94.7% lesser chances of death (P = 0.006) and 51.1% lesser chances of severe disease (P = 0.033) which gained statistical significance. The correlation between ALC and death/severe disease is shown in [Figure 2].
Table 3: Distribution of patients based on absolute lymphocyte count in the three groups

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Figure 2: Clustered bar chart for comparison of absolute lymphocyte count between study groups

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91.7% (n = 464) had normal platelet count – the distribution being 79.2% (n = 363) Group 1, 15.1% (n = 70) Group 2, and 6.7% (n = 31) Group 3. 8.1% (n = 41) had platelet count between 50,000–1,50,000/μl, of which 12.2% (n = 5) died and 17.07% (n = 7) developed severe disease. One patient had platelet count <50,000/μl but had mild disease and uneventful recovery. No bleeding manifestations were observed among patients with thrombocytopenia.

69.97% (n = 354) of patients had NLR less than 4, and among them, 95.3% (n = 337) had uneventful recovery. 17.19% (n = 87) of patients had NLR between 4 and 8, of which 34.48% (n = 30) had severe disease and 10.34% (n = 9) died. Twenty-nine patients had NLR between 8 and 12, among whom 55.17% (n = 16) had severe disease and 31.03% (n = 9) died. Thirty-six patients had NLR >12, of which 44.44% (n = 16) developed severe disease and 44.44% (n = 16) had fatal outcome. Statistical analysis showed that the odds of developing severe disease was 1.16 times per unit rise in NLR and the OR for death was 1.27 per unit increase in NLR (P = 0.053). The association between high NLR, low ALC, and mortality risk is depicted in [Figure 3].
Figure 3: Scatter plot of patient distribution according to NLR and ALC

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5.92% (n = 30) of patients of the study population had PLR below 70, of which 93.33% (n = 28) had complete recovery. 52.96% (n = 268) of patients had PLR between 70 and 165 wherein 89.93% (n = 241) had an uneventful disease course, 6.72% (n = 18) had developed severe disease, and 3.36% (n = 9) died. 31.22% (n = 158) of patients had PLR between 166 and 300, of which 67.09% (n = 106) had an uneventful course of disease, 25.32% (n = 40) had severe disease, and 7.59% (n = 12) died. 9.88% (n = 50) of patients had PLR above 300, and of them, 36% (n = 18) had uneventful recovery, 36% (n = 18) developed severe disease, and 28% (n = 14) had fatal outcome. These results did not achieve statistical significance.

Platelet indices

86.56% (n = 438) of patients had normal mean platelet volume (MPV) between 7.2 and 11.2 fl, of which 78.54% (n = 344) had uneventful clinical recovery, 15.98% (n = 70) developed severe disease, and 5.48% (n = 24) died. 13.24% (n = 67) of patients had MPV >11.2 fl, of which 17.91 (n = 12) died and 10.45% (n = 7) had severe disease. 26.08% (n = 132) of patients had plateletcrit (PCT) <0.20, 26.08% (n = 132) of patients had PCT between 0.20 and 0.24, and 47.82% (n = 242) of patients had PCT >0.24. 8.7% (n = 44) of patients had platelet–large cell ratio (PLCR) below 15, 81.42% (n = 412) of patients had PLCR between 15 and 35, and 9.88% (n = 50) of patients had PLCR >35. These results were not statistically significant.


  Discussion Top


COVID-19 pandemic has affected all age groups, breached geopolitical barriers, stunned the health-care, economic and social systems of countries across the globe. The death toll is over a million till date.[6] The case fatality rates show a wide variability from 2.1% to as high as 5.25% in different countries and over different months of 2020.[7] Evidence suggests that 80% of COVID-19 patients suffer mild disease.[8] The focus, however, is on the remaining 20% of patients who develop severe or critical illness. In resource-limited settings, triage could prioritize patients who need hospitalization and intensive monitoring. The unpredictable course of COVID mandates the need for early clinical and laboratory markers of adverse outcome. To date, the search for such specific and cost-effective biomarkers is continuing with limited success.

Complete blood count and its derived parameters like NLR/PLR have been proposed to be predictive of the inflammatory status of the patients with COVID and found to correlate with the outcome.[3],[4] Previously available literature evidence suggests that increase in the neutrophils is associated with increased risk of acute respiratory distress syndrome[4] and lymphocytopenia and neutrophilia are associated with critical illness.[9] In this research, we aimed to study the hematological manifestations of COVID-19 and evaluate the association between ANC, ALC, NLR, PLR, TC, and outcome. The secondary objective was to document the clinical profile of our patients and identify associations with bad outcomes.

Our observations among 506 RT-PCR-confirmed COVID-19 patients revealed that higher age was associated with adverse clinical outcomes. The mean age in groups was 47.7 years (Group 1), 58.7 years (Group 2), and 62.7 years (Group 3), respectively. 31.2% (n = 158) of patients were over 60 years of age, of which 14.6% (n = 23) died and 26% (n = 41) had severe disease. Compared with those under 60 years, the outcome showed striking significance. We identified that for unit increase in age, there are 1.03 times higher chances of developing severe disease (P = 0.013) and 1.04 times higher chances of death (P = 0.028). In general, the vulnerability of elderly people for severe infection is attributed to their diminished immune response and reduced ability to repair the damaged epithelium.[10] The elderly also have reduced mucociliary clearance, and this may allow the virus to spread to the gas exchange units of the lung more readily.[11] From an epidemiological standpoint, prevalent comorbid illnesses among the elderly would also be conducive to bad outcomes.

Although age had a significant bearing on the outcome, the cutoff point did vary in our population. A meta-analysis of 3927 patients done in 6 centers across Europe and the USA identified adverse outcomes more common beyond age 64.[12] The median age for nonsurvivors was 80 years and survivors 64 years.[12] Likewise, the median age of death reported by Roshua et al. was 70 years in the United States.[13] In our observation, the median age of nonsurvivors was 62.5 years. The lowered age threshold for mortality in our population is a matter of speculation. Probable reasons could be the increased prevalence of comorbid illnesses and relative reduction in life expectancy compared to Western counterparts.

Lymphocytopenia was present in 13.04% (n = 66) of patients, among whom 65.2% (43) had bad outcome. Interestingly, the degree of lymphocytopenia in itself was a strong predictor of adverse outcome. For an increase in 1000/μl above baseline, there were 94.7% lesser chances of death (P = 0.006) and 51.1% lesser chances of severe disease (P = 0.033). This was in concordance with previous studies by Terpos et al. which also observed that lymphopenia was associated with critical illness in patients with COVID-19 (1). Lymphocytopenia is likely due to the release of various anti-inflammatory cytokines which induce immunosuppression and apoptosis of lymphoid cells.[14] Furthermore, it has been demonstrated that lymphocytes express ACE2 receptors on their surface which may make them soft targets for direct attack by SARS-CoV-2.[15] A meta-analysis of 23 studies done across the globe also highlighted the predictive value of low lymphocyte count in COVID-19 severity.[2] However, a noteworthy shortcoming of this observation is that all these studies are hospital based and largely retrospective. Data on the prevalence of lymphocytopenia among COVID patients in the community need to be unearthed to further our understanding about the association. Nevertheless, considering the easy availability and high specificity, we strongly recommend ALC for all hospitalized COVID-19 patients as a point of care triage measure.

We observed the utility of high NLR as a marker of bad outcomes. The normal NLR values in an adult population in good health are between 0.78 and 3.53.[16] Thirty-six patients had NLR >12, of which 88.9% (n = 32) suffered either severe disease or death. Statistical analysis showed that the odds of developing severe disease was 1.16 per unit rise in NLR and the OR for death was 1.27 per unit increase in NLR (P = 0.053). NLR and PLR are proposed tools in outcome prediction for patients suffering COVID-19. NLR of >4 was a predictor of admission to the ICU and severe disease course.[3] Since NLR and PLR indirectly reflect a patient's inflammatory state (4), they could serve as predictors of disease severity with reasonable accuracy. Research evidence from previous efforts has proven the value of high NLR as a reliable predictor of adverse outcomes in COVID-19.[17],[18] The reason for borderline significance of NLR in our study was due to inadequate power which perhaps could have been enhanced by a larger sample size. Our data did not reveal any significant association between PLR and severity.

Limitations

The major drawback of our study was its limited sample population. A larger sample would have achieved statistical significance since there is wide variability in hematological parameters among hospitalized patients. Furthermore, we drew the association between onetime hematological parameters upon admission and its correlation with outcome. Although helpful to triage patients, follow-up trend of hematological changes may provide more useful insight into the progression of disease and correlate better with the outcome. As with many other similar studies, retrospective nature is another shortcoming.


  Conclusion Top


To summarize, our study identified higher age, respiratory distress at the time of admission, presence of leukocytosis, lymphocytopenia, increased neutrophil count, and elevated NLR to be associated with severe disease and higher mortality due to COVID-19. Lymphocytopenia and age were the strongest predictors of severe disease and death among hospitalized patients.

Acknowledgments

The authors would like to immensely thank Dr. Alok Ranjan PhD for the data analysis and Dr. Sherin Roy, Dr. A.Muruganantham, and Dr. J. Satheeish for their valuable support.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Terpos E, Ntanasis-Stathopoulos I, Elalamy I, Kastritis E, Sergentanis TN, Politou M, et al. Hematological findings and complications of COVID-19. Am J Hematol 2020;95:834-47.  Back to cited text no. 1
    
2.
Tavakolpour S, Rakhshandehroo T. Lymphopenia during the COVID-19 infection: What it shows and what can be learned. Immunol Lett 2020;225:31-2.  Back to cited text no. 2
    
3.
Chan AS, Rout A. Use of neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios in COVID-19. J Clin Med Res 2020;12:448-53.  Back to cited text no. 3
    
4.
Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med 2020;180:934-43.  Back to cited text no. 4
    
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Marco Cascella; Michael Rajnik; Abdul Aleem; Scott C. Dulebohn; Raffaela Di Napoli . Features, evaluation, and treatment of coronavirus (COVID-19). In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2020. Available from: https:// www.ncbi.nlm.nih.gov/books/NBK554776/. [Last updated on 2020 Aug 10].  Back to cited text no. 5
    
6.
Johns Hopkins University and Medicine. COVID-19 Map. Baltimore, MD: Johns Hopkins University; 2020. Available from: https://coronavirus.jhu.edu/map.html. [Last accessed on 2020 Aug 10].  Back to cited text no. 6
    
7.
Yang S, Cao P, Du P, Wu Z, Zhuang Z, Yang L, et al. Early estimation of the case fatality rate of COVID-19 in mainland China: A data-driven analysis. Ann Transl Med 2020;8:128.  Back to cited text no. 7
    
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World Health Organization. (2020). Clinical management of COVID-19: interim guidance, 27 May 2020. World Health Organization. https://apps.who.int/iris/handle/10665/332196. License: CC BY-NC-SA 3.0 IGO.  Back to cited text no. 8
    
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Zheng HY, Zhang M, Yang CX, Zhang N, Wang XC, Yang XP, et al. Elevated exhaustion levels and reduced functional diversity of T cells in peripheral blood may predict severe progression in COVID-19 patients. Cell Mol Immunol 2020;17:541-3.  Back to cited text no. 9
    
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Mason RJ. Pathogenesis of COVID-19 from a cell biology perspective. Eur Respir J 2020;55:2000607.  Back to cited text no. 10
    
11.
Ho JC, Chan KN, Hu WH, Lam WK, Zheng L, Tipoe GL, et al. The effect of aging on nasal mucociliary clearance, beat frequency, and ultrastructure of respiratory cilia. Am J Respir Crit Care Med 2001;163:983-8.  Back to cited text no. 11
    
12.
Bertsimas D, Lukin G, Mingardi L, Nohadani O, Orfanoudaki A, Stellato B, et al. COVID-19 mortality risk assessment: An international multi-center study. PLoS One 2020;15:e0243262.  Back to cited text no. 12
    
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Goldstein JR, Lee RD. Demographic perspectives on the mortality of COVID-19 and other epidemics. Proc Natl Acad Sci U S A 2020;117:22035-41.  Back to cited text no. 13
    
14.
Adamzik M, Broll J, Steinmann J, Westendorf AM, Rehfeld I, Kreissig C, et al. An increased alveolar CD4 + CD25 + Foxp3 + T-regulatory cell ratio in acute respiratory distress syndrome is associated with increased 30-day mortality. Intensive Care Med 2013;39:1743-51.  Back to cited text no. 14
    
15.
Hao Xu, Liang Zhong, Jiaxin Deng, Jiakuan Peng, Hongxia Dan, Xin Zeng, et al. High expression of ACE2 receptor of 2019-nCoV on the epithelial cells of oral mucosa. Int J Oral Sci 12, 8 (2020). https://doi.org/10.1038/s41368-020-0074-x. https://www.nature.com/articles/s41368-020-0074-x.  Back to cited text no. 15
    
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Forget P, Khalifa C, Defour JP, Latinne D, Van Pel MC, De Kock M. What is the normal value of the neutrophil-to-lymphocyte ratio? BMC Res Notes 2017;10:12.  Back to cited text no. 16
    
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Tatum D, Taghavi S, Houghton A, Stover J, Toraih E, Duchesne J. Neutrophil-to-lymphocyte ratio and outcomes in Louisiana COVID-19 patients. Shock 2020;54:652-8.  Back to cited text no. 17
    
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Yang AP, Liu JP, Tao WQ, Li HM. The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients. Int Immunopharmacol 2020;84:106504.  Back to cited text no. 18
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

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