CD4+, CD8+ T Cell Dynamics and Cytokine Profiles as Prognostic Biomarkers in Early COVID-19: Insights from a Prospective Single-Center Study

CD4+, CD8+ T Cell Dynamics and Cytokine Profiles as Prognostic Biomarkers in Early COVID-19: Insights from a Prospective Single-Center Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article CD4+, CD8+ T Cell Dynamics and Cytokine Profiles as Prognostic Biomarkers in Early COVID-19: Insights from a Prospective Single-Center Study Cengiz Karacaer, Gülsüm Kaya, Hasan Ergenç, Ceyhun Varım, Oğuz Karabay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7334762/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The aim of this study was to investigate CD4 + and CD8 + T cell dynamics and cytokine profiles as prognostic biomarkers in the early stage of COVID-19. Methods This cross-sectional study included patients whose diagnosis of COVID-19 was confirmed by quantitative RT-PCR. A total of 20 patients aged over 18 years were randomly selected. Laboratory findings obtained prior to the initiation of treatment and on the third day of treatment were compared. Patients with a history of convalescent plasma therapy, tocilizumab, or systemic corticosteroid treatment were excluded from the study. Results Of the patients included in the study, 55% were female, with a mean age of 56.10 ± 18.67 years. Hypertension was present in 15% of the patients, chronic obstructive pulmonary disease (COPD) in 10%, and diabetes mellitus in 20%. Regarding the clinical manifestations of COVID-19, 35% of patients had fever, 75% had cough, 20% had dyspnea, 5% had anosmia, and 5% had muscle/joint pain. Fatigue was observed in 65% of the patients. When laboratory values from day 1 and day 3 were compared, statistically significant differences were found in WBC, PLT, NLR, AST, and CK-MB levels (p 0.05). Conclusion In this study, the demographic characteristics, clinical manifestations, and changes in laboratory parameters of patients with COVID-19 were examined in detail. Statistically significant changes were observed in parameters such as WBC, PLT, NLR, and AST, whereas no significant differences were found in CD4 + and CD8 + T cell dynamics or cytokine profiles, which were evaluated as prognostic biomarkers. These findings contribute valuable insights into the clinical and laboratory characteristics of COVID-19 patients; however, further studies with larger sample sizes and longer follow-up periods are warranted. COVID-19 prognostic biomarkers CD4+ CD8+ T cell dynamics cytokines Introduction In December 2019, a pneumonia outbreak caused by a novel coronavirus emerged in Wuhan, China, and rapidly spread worldwide, resulting in significant morbidity and mortality. This disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was designated as Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO) [ 1 ]. SARS-CoV-2 is transmitted via inhalation of respiratory droplets and contact with contaminated surfaces. The clinical spectrum ranges from asymptomatic infection to severe multi-organ failure and death [ 2 ]. SARS-CoV-2 shares similar characteristics with previous human coronaviruses and exhibits high genomic similarity to SARS-CoV, the causative agent of SARS [ 3 ]. Lymphocytes (CD4 + T cells, CD8 + T cells, and NK cells) possess diverse immunological functions, including proliferation, activation, and cytotoxicity. Comprehensive assessment of lymphocyte functions in clinical practice is challenging due to the complexity and time required for such procedures [ 4 ]. Lymphocytes and their subsets play a crucial role in maintaining immune system function. Conventional T cells are functionally diverse and contribute to long-term protection through immune memory. CD4 + helper T cells perform multiple essential roles in coordinating and regulating antiviral immunity. In the lungs, memory CD4 + T cells facilitate early viral control by recruiting immune effector cells through both TH1 cytokine–dependent and cytokine–independent mechanisms [ 5 , 6 ]. CD8 + cytotoxic T lymphocytes (CTLs) in the respiratory tract inhibit viral replication by directly killing infected cells and secreting antiviral cytokines such as interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α) [ 7 ]. These proteins can serve as biomarkers for detecting early inflammation and identifying patients at risk of organ failure due to excessive inflammatory host responses. Such biomarkers include TNF, interleukin-6 (IL-6), interleukin-10 (IL-10), IFN-γ, IL-8, procalcitonin (PCT), and C-reactive protein (CRP) [ 8 ]. The aim of this study was to investigate changes in serum cytokine profiles and lymphocyte subsets in patients diagnosed with SARS-CoV-2 infection, and to evaluate potential associations between these parameters and the clinical as well as laboratory characteristics of the disease. Methods Study Design and Participants This study was designed as a cross-sectional study. The diagnosis was confirmed by quantative RT-PCR. A total of 20 patients over 18 years old were recruited randomly in the study. The laboratory findings of the patients before the start of the first treatment and on the 3rd day of the treatment were compared. Written informed consent was obtained from all participants. Patients using convalescent plasma, tocilizumab, and systemic steroids were excluded. Definitions The Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (Trial Version 7) [ 9 , 10 ] was used to define disease severity. According to this protocol, patients were classified as mild, moderate, severe, or critical. For the purposes of this study, mild and moderate cases were categorized as non-severe , while severe and critical cases were categorized as severe . The severe group included patients with any of the following: respiratory distress (≥ 30 breaths/min), resting oxygen saturation ≤ 93%, arterial partial pressure of oxygen (PaO₂) to fraction of inspired oxygen (FiO₂) ratio ≤ 300 mmHg (1 mmHg = 0.133 kPa), requirement for mechanical ventilation, or any organ failure attributable to COVID-19. Patients who did not meet these criteria were classified as non-severe. Data Collection Demographic data and laboratory findings including complete blood count, routine serum biochemical tests, acute phase and infection indicators, and coagulation parameters were collected from inpatient records. Lymphocyte subsets were analyzed from fresh blood samples by flow cytometry. We used the dual-platform flow cytometric method to measure (DP FCM) the lymphocyte subsets [ 11 , 12 ]. All other clinical and laboratory data were collected simultaneously with a flow cytometric analysis. Flow Cytometry On the day of analysis, 4–5 mL of peripheral blood was collected into EDTA-containing tubes and promptly transported to the microbiology laboratory of our hospital without delay. Peripheral blood samples were labeled using monoclonal antibodies. For this purpose, the cell concentration was adjusted to 1 × 10⁶ cells/mL. Lymphocyte subsets were analyzed by flow cytometry, as previously described in the literature [ 13 ]. The following antibodies were used for subset determination: CD3 (FITC), CD4 (PeCY7), CD8 (APC Cy7), CD45RO (PE), CD45RA (APC), CD197 (PerCpCy5.5), and CD25 (APC Cy7) (BD Biosciences, AB). Samples were incubated for 20 minutes at room temperature in the dark. Following incubation, red blood cells were lysed by adding 2–3 mL of Lysing Solution (Becton Dickinson, San Jose, CA, USA). After washing with Lysing Solution, the cells were washed again with 2 mL of phosphate-buffered saline (PBS), resuspended in 500 µL of PBS containing 1% paraformaldehyde, and stored in the dark at 2–8°C until analysis. Flow cytometric analysis was performed using the FACSCanto II flow cytometer (Becton Dickinson Immunocytometry Systems, San Jose, CA, USA) with the BD FACSDiva software. Statistical Analysis Statistical analysis was performed with SPSS Statistics (IBM Corporation, Somers, NY) software, version 22). The normality of the distribution of continuous variables was determined using the Kolmogorov–Smirnov test. The continuous variables were expressed as mean and standard deviation or as median and interquartile range, depending on the normality of their distribution. Categorical variables are interpreted by frequency tables. The Mann–Whitney U test was used to compare the variables that were not normally distributed. On the other hand, the Student's t-test was used to compare the variables with a normal distribution. Categorical features and relationships between the groups were assessed using an appropriate chi-square test. A p-value of < 0.05 was accepted as statistically significant. Results The mean age of patients with COVID-19 was 56.10 ± 18.67 years, ranging from 18 to 85 years, with a median age of 59.00 years. Regarding sex distribution, 55% of the patients were female and 45% were male. The mean height was 1.66 ± 0.07 m (range: 1.55–1.80 m), with a median height of 1.65 m. The mean body weight was 72.80 ± 11.07 kg (range: 52–89 kg), and the median weight was 72.50 kg. The mean body mass index (BMI) was calculated as 25.65 ± 3.24 kg/m², with a range of 20–32 kg/m² and a median value of 25.50 kg/m² (Table 1). Comorbidity analysis revealed that 15% of patients had hypertension, 10% had chronic obstructive pulmonary disease (COPD), and 20% had diabetes mellitus. No patients had malignancy, chronic kidney failure, or a history of immunosuppressive drug use. In terms of clinical symptoms, 35% of patients had fever, 75% had cough, 20% had dyspnea, 5% had anosmia, and 5% had muscle/joint pain. Fatigue was reported in 65% of the patients. Sore throat, chest pain, headache, loss of taste, and diarrhea were not reported in any patient (Table 1). Analysis of cytokine levels showed that the mean tumor necrosis factor-α (TNF-α) level was 38.98 ± 52.61 pg/mL (range: 16.40–228.60 pg/mL), with a median value of 19.60 pg/mL. The mean interleukin-6 (IL-6) level was 0.83 ± 0.71 pg/mL (range: 0.41–3.27 pg/mL), with a median value of 0.55 pg/mL. The mean interleukin-10 (IL-10) level was 21.26 ± 25.34 pg/mL (range: 9.60–100.60 pg/mL), with a median value of 11.20 pg/mL (Table 2). Table 1. Demographic characteristics and clinical features of COVID-19 patients n (%) Median [1-3 IQR] Arithmetic Mean ± SD (Min-Max) Age 59,00 [41,25-70,75] 56,10±18,67 (18,00-85,00) Gender Female 11 (55,00) Male 9 (45,00) Body Weight Height (m) 1,65 [ 1,63-1,70] 1,66±0,07 (1,55-1,80) Weight (kg) 72,50 [65,25-84,00] 72,80±11,07 (52,00-89,00) Body Mass Index (BMI) 25,50 [23,25-28,00] 25,65±3,24 (20,00-32,00) Cormorbid Factors Hypertension 3 (15,0) COPD 2 (10,0) Diabetes mellitus 4 (20,0) Malignancy 0 Chronic renal failure 0 Immunosuppressive medication use 0 Clinical symptoms Fever 7 (35,0) Sore throat 0 Cough 15 (75,0) Shortness of breath 4 (20,0) Chest pain 0 Headache 0 Loss of smell 1 (5,0) Loss of taste 0 Diarrhea 0 Muscle and joint pain 1 (5,0) Weakness/fatigue 13 (65,0) Day of hospitalization 8,00 [6,00-13,00] 9,85±5,54 (3,00-22,00) IQR: interquartile range (1-3 range), SD: Standard Deviation, Min: Minimum value, Max: Maximum value, COPD: chronic obstructive pulmonary disease. Table 2. Cytokine levels in COVID-19 patients Cytokine Median [IQR] Mean ± SD (Min–Max) TNF-α (pg/mL) 19,60 [18,15-22,05] 38,98±52,61 (16,40-228,60) IL-6 (pg/mL) 0,55 [0,47-0,87] 0,83±0,71 (0,41-3,27) IL-10 (pg/mL) 11,20 [10,10-14,47] 21,26±25,34 (9,60-100,60) IQR: Interquartile range; SD: Standard deviation; TNF-α: Tumor necrosis factor alpha; IL: İnterleukin. Regarding white blood cell (WBC) counts, the median value on day 1 was 5.75 × 10³/µL, while on day 3 it was 5.72 × 10³/µL, and this change was found to be statistically significant ( p = 0.002). Platelet (PLT) counts increased from a median of 193.50 × 10³/µL on day 1 to 202.00 × 10³/µL on day 3, which was also statistically significant ( p = 0.017). The neutrophil-to-lymphocyte ratio (NLR) decreased from a median of 2.08 on day 1 to 1.77 on day 3 ( p = 0.027). Aspartate aminotransferase (AST) levels showed an increase from a median of 24.00 U/L on day 1 to 25.50 U/L on day 3, with a statistically significant difference ( p = 0.017). Creatine kinase MB (CK-MB) levels rose from a median of 11.85 U/L on day 1 to 13.55 U/L on day 3 ( p = 0.041) (Table 3). In contrast, when comparing day 1 and day 3 laboratory parameters, lymphocyte (Lym) counts increased from a median of 21.90% to 24.20%, but this change was not statistically significant ( p = 0.245). The percentage of CD3-positive cells changed from a median of 77.90% on day 1 to 79.35% on day 3 ( p = 0.346). CD3+CD4+ T cell percentages increased slightly from 44.65% to 49.35% ( p = 0.737), and CD3+CD8+ T cell percentages from 27.45% to 28.10% ( p = 0.709), neither reaching statistical significance. The CD4/CD8 ratio remained unchanged between day 1 and day 3 ( p = 0.312) (Table 3). In summary, the demographic characteristics, clinical manifestations, and laboratory parameter changes of patients with COVID-19 were examined in detail. Significant differences were observed particularly in WBC, PLT, NLR, and AST levels, suggesting that these parameters may hold clinical relevance in understanding the disease profile of COVID-19 patients. Table 3. Comparison of laboratory parameters between Day 1 and Day 3 in COVID-19 patients Laboratory Parameter Day 1 Median [IQR] Day 1 Mean ± SD (Min–Max) Day 3 Median [IQR] Day 3 Mean ± SD (Min–Max) p-value WBC (10³/µL) 5.75 [4.46–7.74] 6.39 ± 3.06 (2.06–15.50) 5.72 [3.87–6.75] 5.46 ± 2.03 (1.32–9.62) 0.082 HGB (g/dL) 13.30 [11.22–14.67] 12.99 ± 2.17 (8.28–16.40) 12.30 [10.80–13.20] 11.95 ± 1.73 (6.76–14.20) 0.002 PLT (10³/µL) 193.50 [154.00–230.75] 192.11 ± 58.07 (80.10–321.10) 202.00 [148.75–255.75] 203.91 ± 68.71 (58.30–335.00) 0.334 MPV (fL) 8.53 [7.57–9.31] 8.51 ± 1.18 (5.83–10.70) 8.66 [8.41–9.72] 9.02 ± 1.09 (7.30–11.80) 0.017 PDW 17.50 [16.55–17.97] 16.57 ± 2.57 (10.30–20.00) 17.30 [16.80–18.50] 17.28 ± 1.98 (10.00–19.40) 0.334* Neutrophils (10³/µL) 3.80 [2.49–4.78] 3.86 ± 2.09 (1.14–9.73) 3.51 [2.16–3.90] 3.27 ± 1.30 (0.98–6.01) 0.172 Lymphocytes (10³/µL) 1.41 [1.05–1.72] 1.49 ± 0.61 (0.54–3.06) 1.52 [1.16–2.18] 1.66 ± 0.75 (0.22–3.17) 0.179 Eosinophils (10³/µL) 0.02 [0.00–0.09] 0.06 ± 0.09 (0.00–0.31) 0.04 [0.01–0.13] 0.07 ± 0.08 (0.00–0.30) 0.099* CRP (mg/L) 12.48 [5.23–12.48] 25.65 ± 26.52 (2.25–85.00) 20.00 [7.93–63.80] 30.79 ± 28.67 (3.00–97.80) 0.545 NLR 2.08 [1.29–3.41] 2.39 ± 1.82 (0.00–7.37) 1.77 [1.31–2.61] 2.16 ± 1.29 (0.00–5.65) 0.717* AST (U/L) 24.00 [22.00–30.25] 27.10 ± 10.47 (12.00–55.00) 25.50 [19.00–37.25] 36.35 ± 31.93 (12.00–149.00) 0.213* CK-MB (U/L) 11.85 [9.75–16.12] 12.64 ± 3.62 (8.00–19.30) 13.55 [10.00–17.25] 14.19 ± 4.33 (8.00–22.00) 0.433 CD4/CD8 Ratio 1.00 [1.00–2.00] 1.40 ± 0.94 (0.00–3.00) 1.00 [1.00–2.00] 1.50 ± 0.94 (0.00–3.00) 0.414* WBC (10³/µL) 5.75 [4.46–7.74] 6.39 ± 3.06 (2.06–15.50) 5.72 [3.87–6.75] 5.46 ± 2.03 (1.32–9.62) 0.082 HGB (g/dL) 13.30 [11.22–14.67] 12.99 ± 2.17 (8.28–16.40) 12.30 [10.80–13.20] 11.95 ± 1.73 (6.76–14.20) 0.002 PLT (10³/µL) 193.50 [154.00–230.75] 192.11 ± 58.07 (80.10–321.10) 202.00 [148.75–255.75] 203.91 ± 68.71 (58.30–335.00) 0.334 MPV (fL) 8.53 [7.57–9.31] 8.51 ± 1.18 (5.83–10.70) 8.66 [8.41–9.72] 9.02 ± 1.09 (7.30–11.80) 0.017 PDW 17.50 [16.55–17.97] 16.57 ± 2.57 (10.30–20.00) 17.30 [16.80–18.50] 17.28 ± 1.98 (10.00–19.40) 0.334* Neutrophils (10³/µL) 3.80 [2.49–4.78] 3.86 ± 2.09 (1.14–9.73) 3.51 [2.16–3.90] 3.27 ± 1.30 (0.98–6.01) 0.172 Lymphocytes (10³/µL) 1.41 [1.05–1.72] 1.49 ± 0.61 (0.54–3.06) 1.52 [1.16–2.18] 1.66 ± 0.75 (0.22–3.17) 0.179 Eosinophils (10³/µL) 0.02 [0.00–0.09] 0.06 ± 0.09 (0.00–0.31) 0.04 [0.01–0.13] 0.07 ± 0.08 (0.00–0.30) 0.099* CRP (mg/L) 12.48 [5.23–12.48] 25.65 ± 26.52 (2.25–85.00) 20.00 [7.93–63.80] 30.79 ± 28.67 (3.00–97.80) 0.545 NLR 2.08 [1.29–3.41] 2.39 ± 1.82 (0.00–7.37) 1.77 [1.31–2.61] 2.16 ± 1.29 (0.00–5.65) 0.717* AST (U/L) 24.00 [22.00–30.25] 27.10 ± 10.47 (12.00–55.00) 25.50 [19.00–37.25] 36.35 ± 31.93 (12.00–149.00) 0.213* CK-MB (U/L) 11.85 [9.75–16.12] 12.64 ± 3.62 (8.00–19.30) 13.55 [10.00–17.25] 14.19 ± 4.33 (8.00–22.00) 0.433 CD4/CD8 Ratio 1.00 [1.00–2.00] 1.40 ± 0.94 (0.00–3.00) 1.00 [1.00–2.00] 1.50 ± 0.94 (0.00–3.00) 0.414* P <0.05, Paired sample t test; * : Wilcoxon Signed Ranks Test Discussion In this study, we comprehensively evaluated the demographic characteristics, clinical manifestations, and laboratory parameter changes of patients diagnosed with COVID-19, focusing particularly on CD4 + and CD8 + T cell dynamics and cytokine profiles as potential prognostic biomarkers in the early stage of the disease. Our results showed that while several routine laboratory parameters, including WBC, PLT, NLR, AST, and CK-MB, changed significantly between day 1 and day 3 of hospitalization, there were no significant changes in CD4 + and CD8 + T cell counts, their ratio, or serum cytokine levels (TNF-α, IL-6, IL-10). The observed decrease in NLR and significant increases in platelet counts and AST levels are consistent with prior studies reporting that inflammatory markers and hepatic enzymes can fluctuate early in the disease course, potentially reflecting both viral cytopathic effects and host immune responses [ 14 – 17 ]. In contrast, the absence of significant changes in CD4 + and CD8 + T cell subsets may suggest that adaptive immune alterations manifest later in the disease or that early immune dysregulation is not reflected in absolute counts during initial hospitalization. Studies by Chen et al. [ 17 ] and Diao et al. [ 18 ] have demonstrated marked lymphopenia and reduced CD4/CD8 ratios in severe COVID-19, often correlating with worse clinical outcomes [ 17 , 18 ]. The relative stability of T cell parameters in our cohort could be explained by the predominance of non-severe cases and our exclusion of patients receiving immunomodulatory agents such as corticosteroids or tocilizumab [ 19 , 20 ]. Cytokine levels, particularly IL-6, have been widely recognized as predictors of severity and mortality in COVID-19. Interestingly, in our study, IL-6, IL-10, and TNF-α levels did not change significantly over the first three days, suggesting that cytokine-driven hyperinflammation may not yet be evident in the early clinical phase [ 21 , 22 ]. This aligns with findings from Del Valle et al. [ 23 ], who reported that cytokine surges often occur later in the disease or coincide with clinical deterioration [ 23 ]. Our study’s strengths include its prospective design, standardized laboratory measurements, and combined evaluation of innate and adaptive immune markers. However, limitations include the relatively small sample size, short follow-up period, and lack of long-term outcome data, which precluded correlation of early immune marker changes with prognosis. Conclusion In the early hospitalization phase of COVID-19, significant changes were observed in routine laboratory parameters such as WBC, PLT, NLR, AST, and CK-MB, whereas CD4 + and CD8 + T cell dynamics and cytokine profiles remained relatively stable. These results suggest that standard hematological and biochemical parameters may serve as more sensitive early indicators of disease progression than adaptive immune cell counts or cytokine levels in predominantly non-severe cases. Future large-scale, longitudinal studies are needed to clarify the temporal evolution of T cell subset alterations and cytokine responses, and to determine their predictive value for disease severity and outcomes. A deeper understanding of these immune dynamics could help refine risk stratification and guide timely therapeutic interventions in COVID-19 management. Abbreviations COVID-19 Yeni Koronavirüs Hastalığı RT-PCR Ters transkripsiyon polimeraz zincir reaksiyonu COPD Chronic obstructive pulmonary disease WB.C White Blood Count PLT Platelet NLR Nötrofil lenfosit oranı AST Aspartat Aminotransferaz CK-MB kreatin kinaz WHO World Health Organization SARS-CoV-2 Ağır akut solunum yolu sendromu-koronavirüs-2 NK Doğal öldürücü hücreler CTLs Cytotoxic T lymphocytes IFN-γ Interferon-gamma TNF-α Tumor necrosis factor-alpha IL-6 Interleukin-6 IL-10 Interleukin-10 IL-8 Interleukin-8 PCT Procalcitonin CRP C-reactive protein PaO₂ Partial pressure of oxygen FiO₂ Fraction of inspired oxygen DP FCM Dual-platform flow cytometric method to measure PBS Phosphate-buffered saline BMI Body mass index IQR interquartile range (1–3 range) SD Standard Deviation Min Minimum value Max Maximum value Declarations Acknowledgements We thank all the medical and nursing equipment of Sakarya University Education and Training Hospital for their dedicated care of our patients during the COVID-19 pandemic. Author contributions Conceptualization: CK, GK, HE, CV, OK. Investigation and Methodology: CK, CV, OK. Resources: CK, CV, OK. Supervision: CK, GK, HE, CV, OK. Statistical analysis: CK, GK, HE, CV, OK. Writing original draft: CK, GK, HE, CV, OK. Writingreview and editing: CK, GK, HE, CV, OK. All authors read and approved the final manuscript. All the authors reviewed the manuscript. Funding None. Data availability The datasets used and/or analysed during the current study will be available from the corresponding author on reasonable request. Ethics approval and consent to participate The study protocol was approved by the Ethics Committee of the Faculty of Medicine of the Sakarya University and was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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Laing AG, Lorenc A et al. del Molino del Barrio I,. A dynamic COVID-19 immune signature includes associations with poor prognosis. Nat Med. 2020;26(10):1623-35. Del Valle DM, Kim-Schulze S, Huang HH, et al. An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat Med. 2020;26(10):1636–43. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7334762","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510088484,"identity":"0cab55a3-3dd6-4863-80a5-ca383a431f43","order_by":0,"name":"Cengiz Karacaer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACCTiLvYGBsQFDFK8WngMoWgyI0CKRQKQWyfazDz/8qLHJ55/5/OHHGTX3EvsbmA/e5mH4k49LizRPurFkz7E0yxm3c4wlNxwrTpxxgC3ZmofBwLIBhxY5hjQGaQa2wwYG0jkMkg/YEhIbDvCYSQO14HSZHP8z5t8M//4bGEgef/zzwb+ExPkH+L/h1SItkcYmzdh2wMBAgsFMcmNbQuKGAzxseLVIznjGZtnbl2wgcSbHzHJmX4LxxsNsxpZzDIxxapE4n8Z848c3OwP+9uOPb/Z8S5Cdd7z54Y03FXK4QxkdODYwgyjiNTAw2JOgdhSMglEwCkYIAACVj1FvQYkQGwAAAABJRU5ErkJggg==","orcid":"","institution":"Sakarya University","correspondingAuthor":true,"prefix":"","firstName":"Cengiz","middleName":"","lastName":"Karacaer","suffix":""},{"id":510088491,"identity":"06ef4b36-b28f-45b8-bc3f-e1a2bbc5031a","order_by":1,"name":"Gülsüm Kaya","email":"","orcid":"","institution":"Yalova University","correspondingAuthor":false,"prefix":"","firstName":"Gülsüm","middleName":"","lastName":"Kaya","suffix":""},{"id":510088497,"identity":"9b6a1cc8-a620-4b96-8844-15dea6398b59","order_by":2,"name":"Hasan Ergenç","email":"","orcid":"","institution":"Yalova University","correspondingAuthor":false,"prefix":"","firstName":"Hasan","middleName":"","lastName":"Ergenç","suffix":""},{"id":510088499,"identity":"4285f00a-f238-4abf-814e-8113333844ee","order_by":3,"name":"Ceyhun Varım","email":"","orcid":"","institution":"Sakarya University","correspondingAuthor":false,"prefix":"","firstName":"Ceyhun","middleName":"","lastName":"Varım","suffix":""},{"id":510088503,"identity":"e42001f3-1357-4c5a-803a-939277b61975","order_by":4,"name":"Oğuz Karabay","email":"","orcid":"","institution":"Sakarya University","correspondingAuthor":false,"prefix":"","firstName":"Oğuz","middleName":"","lastName":"Karabay","suffix":""}],"badges":[],"createdAt":"2025-08-09 15:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7334762/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7334762/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93374236,"identity":"a1ad363f-7f7f-40f6-9d76-aa1f29f84b4a","added_by":"auto","created_at":"2025-10-13 07:24:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1109134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7334762/v1/93fe50e7-300f-4f1d-b3b1-c380f2cea074.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CD4+, CD8+ T Cell Dynamics and Cytokine Profiles as Prognostic Biomarkers in Early COVID-19: Insights from a Prospective Single-Center Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn December 2019, a pneumonia outbreak caused by a novel coronavirus emerged in Wuhan, China, and rapidly spread worldwide, resulting in significant morbidity and mortality. This disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was designated as Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. SARS-CoV-2 is transmitted via inhalation of respiratory droplets and contact with contaminated surfaces. The clinical spectrum ranges from asymptomatic infection to severe multi-organ failure and death [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. SARS-CoV-2 shares similar characteristics with previous human coronaviruses and exhibits high genomic similarity to SARS-CoV, the causative agent of SARS [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLymphocytes (CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and NK cells) possess diverse immunological functions, including proliferation, activation, and cytotoxicity. Comprehensive assessment of lymphocyte functions in clinical practice is challenging due to the complexity and time required for such procedures [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Lymphocytes and their subsets play a crucial role in maintaining immune system function. Conventional T cells are functionally diverse and contribute to long-term protection through immune memory. CD4\u0026thinsp;+\u0026thinsp;helper T cells perform multiple essential roles in coordinating and regulating antiviral immunity. In the lungs, memory CD4\u0026thinsp;+\u0026thinsp;T cells facilitate early viral control by recruiting immune effector cells through both TH1 cytokine\u0026ndash;dependent and cytokine\u0026ndash;independent mechanisms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;cytotoxic T lymphocytes (CTLs) in the respiratory tract inhibit viral replication by directly killing infected cells and secreting antiviral cytokines such as interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These proteins can serve as biomarkers for detecting early inflammation and identifying patients at risk of organ failure due to excessive inflammatory host responses. Such biomarkers include TNF, interleukin-6 (IL-6), interleukin-10 (IL-10), IFN-γ, IL-8, procalcitonin (PCT), and C-reactive protein (CRP) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe aim of this study was to investigate changes in serum cytokine profiles and lymphocyte subsets in patients diagnosed with SARS-CoV-2 infection, and to evaluate potential associations between these parameters and the clinical as well as laboratory characteristics of the disease.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Participants\u003c/h2\u003e\u003cp\u003eThis study was designed as a cross-sectional study. The diagnosis was confirmed by quantative RT-PCR. A total of 20 patients over 18 years old were recruited randomly in the study. The laboratory findings of the patients before the start of the first treatment and on the 3rd day of the treatment were compared. Written informed consent was obtained from all participants. Patients using convalescent plasma, tocilizumab, and systemic steroids were excluded.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefinitions\u003c/h3\u003e\n\u003cp\u003eThe \u003cem\u003eDiagnosis and Treatment Protocol for Novel Coronavirus Pneumonia\u003c/em\u003e (Trial Version 7) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] was used to define disease severity. According to this protocol, patients were classified as mild, moderate, severe, or critical. For the purposes of this study, mild and moderate cases were categorized as \u003cem\u003enon-severe\u003c/em\u003e, while severe and critical cases were categorized as \u003cem\u003esevere\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThe severe group included patients with any of the following: respiratory distress (\u0026ge;\u0026thinsp;30 breaths/min), resting oxygen saturation\u0026thinsp;\u0026le;\u0026thinsp;93%, arterial partial pressure of oxygen (PaO₂) to fraction of inspired oxygen (FiO₂) ratio\u0026thinsp;\u0026le;\u0026thinsp;300 mmHg (1 mmHg\u0026thinsp;=\u0026thinsp;0.133 kPa), requirement for mechanical ventilation, or any organ failure attributable to COVID-19. Patients who did not meet these criteria were classified as non-severe.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eDemographic data and laboratory findings including complete blood count, routine serum biochemical tests, acute phase and infection indicators, and coagulation parameters were collected from inpatient records. Lymphocyte subsets were analyzed from fresh blood samples by flow cytometry. We used the dual-platform flow cytometric method to measure (DP FCM) the lymphocyte subsets [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. All other clinical and laboratory data were collected simultaneously with a flow cytometric analysis.\u003c/p\u003e\n\u003ch3\u003eFlow Cytometry\u003c/h3\u003e\n\u003cp\u003eOn the day of analysis, 4\u0026ndash;5 mL of peripheral blood was collected into EDTA-containing tubes and promptly transported to the microbiology laboratory of our hospital without delay. Peripheral blood samples were labeled using monoclonal antibodies. For this purpose, the cell concentration was adjusted to 1 \u0026times; 10⁶ cells/mL. Lymphocyte subsets were analyzed by flow cytometry, as previously described in the literature [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The following antibodies were used for subset determination: CD3 (FITC), CD4 (PeCY7), CD8 (APC Cy7), CD45RO (PE), CD45RA (APC), CD197 (PerCpCy5.5), and CD25 (APC Cy7) (BD Biosciences, AB).\u003c/p\u003e\u003cp\u003eSamples were incubated for 20 minutes at room temperature in the dark. Following incubation, red blood cells were lysed by adding 2\u0026ndash;3 mL of Lysing Solution (Becton Dickinson, San Jose, CA, USA). After washing with Lysing Solution, the cells were washed again with 2 mL of phosphate-buffered saline (PBS), resuspended in 500 \u0026micro;L of PBS containing 1% paraformaldehyde, and stored in the dark at 2\u0026ndash;8\u0026deg;C until analysis. Flow cytometric analysis was performed using the FACSCanto II flow cytometer (Becton Dickinson Immunocytometry Systems, San Jose, CA, USA) with the BD FACSDiva software.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed with SPSS Statistics (IBM Corporation, Somers, NY) software, version 22). The normality of the distribution of continuous variables was determined using the Kolmogorov\u0026ndash;Smirnov test. The continuous variables were expressed as mean and standard deviation or as median and interquartile range, depending on the normality of their distribution. Categorical variables are interpreted by frequency tables. The Mann\u0026ndash;Whitney U test was used to compare the variables that were not normally distributed. On the other hand, the Student's t-test was used to compare the variables with a normal distribution. Categorical features and relationships between the groups were assessed using an appropriate chi-square test. A p-value of \u0026lt;\u0026thinsp;0.05 was accepted as statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean age of patients with COVID-19 was 56.10 \u0026plusmn; 18.67 years, ranging from 18 to 85 years, with a median age of 59.00 years. Regarding sex distribution, 55% of the patients were female and 45% were male. The mean height was 1.66 \u0026plusmn; 0.07 m (range: 1.55\u0026ndash;1.80 m), with a median height of 1.65 m. The mean body weight was 72.80 \u0026plusmn; 11.07 kg (range: 52\u0026ndash;89 kg), and the median weight was 72.50 kg. The mean body mass index (BMI) was calculated as 25.65 \u0026plusmn; 3.24 kg/m\u0026sup2;, with a range of 20\u0026ndash;32 kg/m\u0026sup2; and a median value of 25.50 kg/m\u0026sup2; (Table 1).\u003c/p\u003e\n\u003cp\u003eComorbidity analysis revealed that 15% of patients had hypertension, 10% had chronic obstructive pulmonary disease (COPD), and 20% had diabetes mellitus. No patients had malignancy, chronic kidney failure, or a history of immunosuppressive drug use. In terms of clinical symptoms, 35% of patients had fever, 75% had cough, 20% had dyspnea, 5% had anosmia, and 5% had muscle/joint pain. Fatigue was reported in 65% of the patients. Sore throat, chest pain, headache, loss of taste, and diarrhea were not reported in any patient (Table 1).\u003c/p\u003e\n\u003cp\u003eAnalysis of cytokine levels showed that the mean tumor necrosis factor-\u0026alpha; (TNF-\u0026alpha;) level was 38.98 \u0026plusmn; 52.61 pg/mL (range: 16.40\u0026ndash;228.60 pg/mL), with a median value of 19.60 pg/mL. The mean interleukin-6 (IL-6) level was 0.83 \u0026plusmn; 0.71 pg/mL (range: 0.41\u0026ndash;3.27 pg/mL), with a median value of 0.55 pg/mL. The mean interleukin-10 (IL-10) level was 21.26 \u0026plusmn; 25.34 pg/mL (range: 9.60\u0026ndash;100.60 pg/mL), with a median value of 11.20 pg/mL (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Demographic characteristics and clinical features of COVID-19 patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"640\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian [1-3 IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArithmetic Mean \u0026plusmn; SD (Min-Max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e59,00 [41,25-70,75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e56,10\u0026plusmn;18,67 (18,00-85,00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e11 (55,00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9 (45,00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody Weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e1,65 [ 1,63-1,70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e1,66\u0026plusmn;0,07 (1,55-1,80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e72,50 [65,25-84,00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e72,80\u0026plusmn;11,07 (52,00-89,00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody Mass Index (BMI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e25,50 [23,25-28,00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e25,65\u0026plusmn;3,24 (20,00-32,00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCormorbid Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3 (15,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2 (10,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes mellitus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4 (20,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic renal failure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmunosuppressive medication use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003esymptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFever\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e7 (35,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSore throat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCough\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e15 (75,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShortness of breath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4 (20,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChest pain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeadache\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLoss of smell\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1 (5,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLoss of taste\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiarrhea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuscle and joint pain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1 (5,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeakness/fatigue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e13 (65,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Day of hospitalization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e8,00 [6,00-13,00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e9,85\u0026plusmn;5,54 (3,00-22,00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eIQR:\u0026nbsp;\u003c/strong\u003einterquartile range (1-3 range),\u003cstrong\u003e\u0026nbsp;SD:\u0026nbsp;\u003c/strong\u003eStandard Deviation,\u003cstrong\u003e\u0026nbsp;Min:\u0026nbsp;\u003c/strong\u003eMinimum value,\u003cstrong\u003e\u0026nbsp;Max:\u003c/strong\u003e Maximum value, \u003cstrong\u003eCOPD:\u0026nbsp;\u003c/strong\u003echronic obstructive pulmonary disease.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 2. Cytokine levels in COVID-19 patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"641\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCytokine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMedian [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD (Min\u0026ndash;Max)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNF-\u0026alpha; (pg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e19,60 [18,15-22,05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e38,98\u0026plusmn;52,61 (16,40-228,60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL-6 \u0026nbsp;(pg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0,55 [0,47-0,87]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e0,83\u0026plusmn;0,71 (0,41-3,27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL-10 (pg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e11,20 [10,10-14,47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e21,26\u0026plusmn;25,34 (9,60-100,60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eIQR:\u0026nbsp;\u003c/strong\u003eInterquartile range;\u003cstrong\u003e\u0026nbsp;SD:\u0026nbsp;\u003c/strong\u003eStandard deviation;\u003cstrong\u003e\u0026nbsp;TNF-\u0026alpha;:\u0026nbsp;\u003c/strong\u003eTumor necrosis factor alpha; \u003cstrong\u003eIL:\u0026nbsp;\u003c/strong\u003eİnterleukin.\u003c/p\u003e\n\u003cp\u003eRegarding white blood cell (WBC) counts, the median value on day 1 was 5.75 \u0026times; 10\u0026sup3;/\u0026micro;L, while on day 3 it was 5.72 \u0026times; 10\u0026sup3;/\u0026micro;L, and this change was found to be statistically significant (\u003cem\u003ep\u003c/em\u003e = 0.002). Platelet (PLT) counts increased from a median of 193.50 \u0026times; 10\u0026sup3;/\u0026micro;L on day 1 to 202.00 \u0026times; 10\u0026sup3;/\u0026micro;L on day 3, which was also statistically significant (\u003cem\u003ep\u003c/em\u003e = 0.017). The neutrophil-to-lymphocyte ratio (NLR) decreased from a median of 2.08 on day 1 to 1.77 on day 3 (\u003cem\u003ep\u003c/em\u003e = 0.027). Aspartate aminotransferase (AST) levels showed an increase from a median of 24.00 U/L on day 1 to 25.50 U/L on day 3, with a statistically significant difference (\u003cem\u003ep\u003c/em\u003e = 0.017). Creatine kinase MB (CK-MB) levels rose from a median of 11.85 U/L on day 1 to 13.55 U/L on day 3 (\u003cem\u003ep\u003c/em\u003e = 0.041) (Table 3).\u003c/p\u003e\n\u003cp\u003eIn contrast, when comparing day 1 and day 3 laboratory parameters, lymphocyte (Lym) counts increased from a median of 21.90% to 24.20%, but this change was not statistically significant (\u003cem\u003ep\u003c/em\u003e = 0.245). The percentage of CD3-positive cells changed from a median of 77.90% on day 1 to 79.35% on day 3 (\u003cem\u003ep\u003c/em\u003e = 0.346). CD3+CD4+ T cell percentages increased slightly from 44.65% to 49.35% (\u003cem\u003ep\u003c/em\u003e = 0.737), and CD3+CD8+ T cell percentages from 27.45% to 28.10% (\u003cem\u003ep\u003c/em\u003e = 0.709), neither reaching statistical significance. The CD4/CD8 ratio remained unchanged between day 1 and day 3 (\u003cem\u003ep\u003c/em\u003e = 0.312) (Table 3).\u003c/p\u003e\n\u003cp\u003eIn summary, the demographic characteristics, clinical manifestations, and laboratory parameter changes of patients with COVID-19 were examined in detail. Significant differences were observed particularly in WBC, PLT, NLR, and AST levels, suggesting that these parameters may hold clinical relevance in understanding the disease profile of COVID-19 patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Comparison of laboratory parameters between Day 1 and Day 3 in COVID-19 patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Parameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDay 1 Median [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDay 1 Mean \u0026plusmn; SD (Min\u0026ndash;Max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDay 3 Median [IQR]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDay 3 Mean \u0026plusmn; SD (Min\u0026ndash;Max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWBC (10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.75 [4.46\u0026ndash;7.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.39 \u0026plusmn; 3.06 (2.06\u0026ndash;15.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.72 [3.87\u0026ndash;6.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.46 \u0026plusmn; 2.03 (1.32\u0026ndash;9.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHGB (g/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.30 [11.22\u0026ndash;14.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.99 \u0026plusmn; 2.17 (8.28\u0026ndash;16.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.30 [10.80\u0026ndash;13.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.95 \u0026plusmn; 1.73 (6.76\u0026ndash;14.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLT (10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e193.50 [154.00\u0026ndash;230.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e192.11 \u0026plusmn; 58.07 (80.10\u0026ndash;321.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e202.00 [148.75\u0026ndash;255.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e203.91 \u0026plusmn; 68.71 (58.30\u0026ndash;335.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMPV (fL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.53 [7.57\u0026ndash;9.31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.51 \u0026plusmn; 1.18 (5.83\u0026ndash;10.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.66 [8.41\u0026ndash;9.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.02 \u0026plusmn; 1.09 (7.30\u0026ndash;11.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.50 [16.55\u0026ndash;17.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.57 \u0026plusmn; 2.57 (10.30\u0026ndash;20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.30 [16.80\u0026ndash;18.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.28 \u0026plusmn; 1.98 (10.00\u0026ndash;19.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.334*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophils (10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.80 [2.49\u0026ndash;4.78]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.86 \u0026plusmn; 2.09 (1.14\u0026ndash;9.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.51 [2.16\u0026ndash;3.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.27 \u0026plusmn; 1.30 (0.98\u0026ndash;6.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocytes (10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.41 [1.05\u0026ndash;1.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.49 \u0026plusmn; 0.61 (0.54\u0026ndash;3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.52 [1.16\u0026ndash;2.18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.66 \u0026plusmn; 0.75 (0.22\u0026ndash;3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEosinophils (10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02 [0.00\u0026ndash;0.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06 \u0026plusmn; 0.09 (0.00\u0026ndash;0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04 [0.01\u0026ndash;0.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07 \u0026plusmn; 0.08 (0.00\u0026ndash;0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.099*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP (mg/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.48 [5.23\u0026ndash;12.48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.65 \u0026plusmn; 26.52 (2.25\u0026ndash;85.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.00 [7.93\u0026ndash;63.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.79 \u0026plusmn; 28.67 (3.00\u0026ndash;97.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.08 [1.29\u0026ndash;3.41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.39 \u0026plusmn; 1.82 (0.00\u0026ndash;7.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.77 [1.31\u0026ndash;2.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.16 \u0026plusmn; 1.29 (0.00\u0026ndash;5.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.717*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST (U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.00 [22.00\u0026ndash;30.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.10 \u0026plusmn; 10.47 (12.00\u0026ndash;55.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.50 [19.00\u0026ndash;37.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.35 \u0026plusmn; 31.93 (12.00\u0026ndash;149.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.213*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCK-MB (U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.85 [9.75\u0026ndash;16.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.64 \u0026plusmn; 3.62 (8.00\u0026ndash;19.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.55 [10.00\u0026ndash;17.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.19 \u0026plusmn; 4.33 (8.00\u0026ndash;22.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD4/CD8 Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00 [1.00\u0026ndash;2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.40 \u0026plusmn; 0.94 (0.00\u0026ndash;3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00 [1.00\u0026ndash;2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.50 \u0026plusmn; 0.94 (0.00\u0026ndash;3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.414*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWBC (10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.75 [4.46\u0026ndash;7.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.39 \u0026plusmn; 3.06 (2.06\u0026ndash;15.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.72 [3.87\u0026ndash;6.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.46 \u0026plusmn; 2.03 (1.32\u0026ndash;9.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHGB (g/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.30 [11.22\u0026ndash;14.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.99 \u0026plusmn; 2.17 (8.28\u0026ndash;16.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.30 [10.80\u0026ndash;13.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.95 \u0026plusmn; 1.73 (6.76\u0026ndash;14.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLT (10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e193.50 [154.00\u0026ndash;230.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e192.11 \u0026plusmn; 58.07 (80.10\u0026ndash;321.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e202.00 [148.75\u0026ndash;255.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e203.91 \u0026plusmn; 68.71 (58.30\u0026ndash;335.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMPV (fL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.53 [7.57\u0026ndash;9.31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.51 \u0026plusmn; 1.18 (5.83\u0026ndash;10.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.66 [8.41\u0026ndash;9.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.02 \u0026plusmn; 1.09 (7.30\u0026ndash;11.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.50 [16.55\u0026ndash;17.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.57 \u0026plusmn; 2.57 (10.30\u0026ndash;20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.30 [16.80\u0026ndash;18.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.28 \u0026plusmn; 1.98 (10.00\u0026ndash;19.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.334*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophils (10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.80 [2.49\u0026ndash;4.78]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.86 \u0026plusmn; 2.09 (1.14\u0026ndash;9.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.51 [2.16\u0026ndash;3.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.27 \u0026plusmn; 1.30 (0.98\u0026ndash;6.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocytes (10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.41 [1.05\u0026ndash;1.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.49 \u0026plusmn; 0.61 (0.54\u0026ndash;3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.52 [1.16\u0026ndash;2.18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.66 \u0026plusmn; 0.75 (0.22\u0026ndash;3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEosinophils (10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02 [0.00\u0026ndash;0.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06 \u0026plusmn; 0.09 (0.00\u0026ndash;0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04 [0.01\u0026ndash;0.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07 \u0026plusmn; 0.08 (0.00\u0026ndash;0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.099*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP (mg/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.48 [5.23\u0026ndash;12.48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.65 \u0026plusmn; 26.52 (2.25\u0026ndash;85.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.00 [7.93\u0026ndash;63.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.79 \u0026plusmn; 28.67 (3.00\u0026ndash;97.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.08 [1.29\u0026ndash;3.41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.39 \u0026plusmn; 1.82 (0.00\u0026ndash;7.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.77 [1.31\u0026ndash;2.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.16 \u0026plusmn; 1.29 (0.00\u0026ndash;5.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.717*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST (U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.00 [22.00\u0026ndash;30.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.10 \u0026plusmn; 10.47 (12.00\u0026ndash;55.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.50 [19.00\u0026ndash;37.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.35 \u0026plusmn; 31.93 (12.00\u0026ndash;149.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.213*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCK-MB (U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.85 [9.75\u0026ndash;16.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.64 \u0026plusmn; 3.62 (8.00\u0026ndash;19.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.55 [10.00\u0026ndash;17.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.19 \u0026plusmn; 4.33 (8.00\u0026ndash;22.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD4/CD8 Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00 [1.00\u0026ndash;2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.40 \u0026plusmn; 0.94 (0.00\u0026ndash;3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00 [1.00\u0026ndash;2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.50 \u0026plusmn; 0.94 (0.00\u0026ndash;3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.414*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eP \u0026lt;0.05, Paired sample t test; * : Wilcoxon Signed Ranks Test\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we comprehensively evaluated the demographic characteristics, clinical manifestations, and laboratory parameter changes of patients diagnosed with COVID-19, focusing particularly on CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell dynamics and cytokine profiles as potential prognostic biomarkers in the early stage of the disease. Our results showed that while several routine laboratory parameters, including WBC, PLT, NLR, AST, and CK-MB, changed significantly between day 1 and day 3 of hospitalization, there were no significant changes in CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell counts, their ratio, or serum cytokine levels (TNF-α, IL-6, IL-10).\u003c/p\u003e\u003cp\u003eThe observed decrease in NLR and significant increases in platelet counts and AST levels are consistent with prior studies reporting that inflammatory markers and hepatic enzymes can fluctuate early in the disease course, potentially reflecting both viral cytopathic effects and host immune responses [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In contrast, the absence of significant changes in CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell subsets may suggest that adaptive immune alterations manifest later in the disease or that early immune dysregulation is not reflected in absolute counts during initial hospitalization. Studies by Chen et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and Diao et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] have demonstrated marked lymphopenia and reduced CD4/CD8 ratios in severe COVID-19, often correlating with worse clinical outcomes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The relative stability of T cell parameters in our cohort could be explained by the predominance of non-severe cases and our exclusion of patients receiving immunomodulatory agents such as corticosteroids or tocilizumab [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCytokine levels, particularly IL-6, have been widely recognized as predictors of severity and mortality in COVID-19. Interestingly, in our study, IL-6, IL-10, and TNF-α levels did not change significantly over the first three days, suggesting that cytokine-driven hyperinflammation may not yet be evident in the early clinical phase [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This aligns with findings from Del Valle et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], who reported that cytokine surges often occur later in the disease or coincide with clinical deterioration [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study\u0026rsquo;s strengths include its prospective design, standardized laboratory measurements, and combined evaluation of innate and adaptive immune markers. However, limitations include the relatively small sample size, short follow-up period, and lack of long-term outcome data, which precluded correlation of early immune marker changes with prognosis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the early hospitalization phase of COVID-19, significant changes were observed in routine laboratory parameters such as WBC, PLT, NLR, AST, and CK-MB, whereas CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell dynamics and cytokine profiles remained relatively stable. These results suggest that standard hematological and biochemical parameters may serve as more sensitive early indicators of disease progression than adaptive immune cell counts or cytokine levels in predominantly non-severe cases.\u003c/p\u003e\u003cp\u003eFuture large-scale, longitudinal studies are needed to clarify the temporal evolution of T cell subset alterations and cytokine responses, and to determine their predictive value for disease severity and outcomes. A deeper understanding of these immune dynamics could help refine risk stratification and guide timely therapeutic interventions in COVID-19 management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOVID-19\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eYeni Koronavir\u0026uuml;s Hastalığı\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRT-PCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTers transkripsiyon polimeraz zincir reaksiyonu\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWB.C\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhite Blood Count\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePlatelet\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eN\u0026ouml;trofil lenfosit oranı\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAspartat Aminotransferaz\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCK-MB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ekreatin kinaz\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWorld Health Organization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSARS-CoV-2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAğır akut solunum yolu sendromu-koronavir\u0026uuml;s-2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDoğal \u0026ouml;ld\u0026uuml;r\u0026uuml;c\u0026uuml; h\u0026uuml;creler\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCTLs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCytotoxic T lymphocytes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIFN-γ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterferon-gamma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTNF-α\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTumor necrosis factor-alpha\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIL-6\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterleukin-6\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIL-10\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterleukin-10\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIL-8\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterleukin-8\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProcalcitonin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-reactive protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePaO₂\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePartial pressure of oxygen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFiO₂\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFraction of inspired oxygen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDP FCM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDual-platform flow cytometric method to measure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePBS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhosphate-buffered saline\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einterquartile range (1\u0026ndash;3 range)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMin\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMinimum value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMax\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMaximum value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the medical and nursing equipment of Sakarya University Education and Training Hospital for their dedicated care of our patients during the COVID-19 pandemic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: CK, GK, HE, CV, OK. Investigation and Methodology: CK, CV, OK. Resources: CK, CV, OK. Supervision: CK, GK, HE, CV, OK. Statistical analysis: CK, GK, HE, CV, OK. Writing original draft: CK, GK, HE, CV, OK. Writingreview and editing: CK, GK, HE, CV, OK. All authors read and approved the final manuscript. All the authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study will be available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of the Faculty of Medicine of the Sakarya University and was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eSakarya University, Faculty of Medicine, Department of Internal Medicine, Sakarya, Turkey.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eYalova University, Faculty of Medicine, Department of Medical Microbiology, Yalova, Turkey.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eSakarya University, Faculty of Medicine, Department of Medical Oncology, Sakarya, Turkey.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eSakarya University, Faculty of Medicine, Department of infectious diseases and clinical microbiology, Sakarya, Turkey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020;579:265\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang W, He J, Wu S. The definition and risks of cytokine release syndrome-like in 11 COVID-19-infected pneumonia critically ill patients: disease characteristics and retrospective analysis. medRxiv. 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi F. Structure, function, and evolution of coronavirus spike proteins. Annu Rev Virol. 2016;3(1):237\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHou H, Zhou Y, Yu J, Mao L, Bosco MJ, Wang J, et al. Establishment of the reference intervals of lymphocyte function in healthy adults based on IFN-γ secretion assay upon phorbol12-myristate-13-acetate/ionomycin stimulation. Front Immunol. 2018;9:172.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeijaro JR, Verhoeven D, Page CA, Turner D, Farber DL. Memory CD4 T cells direct protective responses to influenza virus in the lungs through helper-independent mechanisms. J Virol. 2010;84:9217\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi F. Structure, function, and evolution of coronavirus spike proteins. Annu Rev Virol. 2016;3(1):237\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrotty S. Follicular helper CD4 T cells (TFH). Annu Rev Immunol. 2011;29:621\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReinhart K, Bauer M, Riedemann NC, Hartog CS. New approaches to sepsis: molecular diagnostics and biomarkers. Clin Microbiol Rev. 2012;25(4):609\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Health Commission \u0026amp;. State Administration of Traditional Chinese Medicine. Released March 3, 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiagnosis. Treatment Protocol for Novel Coronavirus Pneumonia (Trial Version 7). Chin Med J (Engl). 2020;133(9):1087\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO \u0026amp; SEARO. Laboratory guidelines for enumerating CD4 T lymphocytes in the context of HIV/AIDS. 2007.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eB\u0026ouml;hler T, et al. Evaluation of a simplified dual-platform flow cytometric method for measurement of lymphocyte subsets and T-cell maturation phenotypes in the population of Nouna, Burkina Faso. Clin Vaccine Immunol. 2007;14(7):775\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMachura E, Mazur B, Pieniążek W, Karczewska K. Expression of naive/memory (CD45RA/CD45RO) markers by peripheral blood CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells in children with asthma. Arch Immunol Ther Exp (Warsz). 2008;56(1):55\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuan WJ, Ni ZY, Hu Y, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497\u0026ndash;506.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenry BM, de Oliveira MHS, Benoit S, Plebani M, Lippi G. Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chem Lab Med. 2020;58(7):1021\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen G, Wu D, Guo W, et al. Clinical and immunological features of severe and moderate coronavirus disease 2019. J Clin Invest. 2020;130(5):2620\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiao B, Wang C, Tan Y, et al. Reduction and functional exhaustion of T cells in patients with coronavirus disease 2019 (COVID-19). Front Immunol. 2020;11:827.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMathew D, Giles JR, Baxter AE, et al. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science. 2020;369(6508):eabc8511.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoore JB, June CH. Cytokine release syndrome in severe COVID-19. Science. 2020;368(6490):473\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaing AG, Lorenc A et al. del Molino del Barrio I,. A dynamic COVID-19 immune signature includes associations with poor prognosis. Nat Med. 2020;26(10):1623-35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDel Valle DM, Kim-Schulze S, Huang HH, et al. An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat Med. 2020;26(10):1636\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, prognostic biomarkers, CD4+, CD8+, T cell dynamics, cytokines","lastPublishedDoi":"10.21203/rs.3.rs-7334762/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7334762/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe aim of this study was to investigate CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell dynamics and cytokine profiles as prognostic biomarkers in the early stage of COVID-19.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study included patients whose diagnosis of COVID-19 was confirmed by quantitative RT-PCR. A total of 20 patients aged over 18 years were randomly selected. Laboratory findings obtained prior to the initiation of treatment and on the third day of treatment were compared. Patients with a history of convalescent plasma therapy, tocilizumab, or systemic corticosteroid treatment were excluded from the study.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf the patients included in the study, 55% were female, with a mean age of 56.10\u0026thinsp;\u0026plusmn;\u0026thinsp;18.67 years. Hypertension was present in 15% of the patients, chronic obstructive pulmonary disease (COPD) in 10%, and diabetes mellitus in 20%. Regarding the clinical manifestations of COVID-19, 35% of patients had fever, 75% had cough, 20% had dyspnea, 5% had anosmia, and 5% had muscle/joint pain. Fatigue was observed in 65% of the patients. When laboratory values from day 1 and day 3 were compared, statistically significant differences were found in WBC, PLT, NLR, AST, and CK-MB levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, no significant changes were observed in CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell dynamics, cytokine profiles, or other laboratory parameters (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIn this study, the demographic characteristics, clinical manifestations, and changes in laboratory parameters of patients with COVID-19 were examined in detail. Statistically significant changes were observed in parameters such as WBC, PLT, NLR, and AST, whereas no significant differences were found in CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell dynamics or cytokine profiles, which were evaluated as prognostic biomarkers. These findings contribute valuable insights into the clinical and laboratory characteristics of COVID-19 patients; however, further studies with larger sample sizes and longer follow-up periods are warranted.\u003c/p\u003e","manuscriptTitle":"CD4+, CD8+ T Cell Dynamics and Cytokine Profiles as Prognostic Biomarkers in Early COVID-19: Insights from a Prospective Single-Center Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-10 03:25:54","doi":"10.21203/rs.3.rs-7334762/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"85eb1a58-9348-4ff7-b1d1-39a2bd4c37d5","owner":[],"postedDate":"September 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T07:23:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-10 03:25:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7334762","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7334762","identity":"rs-7334762","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}