Dysregulation of redox-inflammatory markers in COVID-19: A multi-parameter analysis with disease severity

Dysregulation of redox-inflammatory markers in COVID-19: A multi-parameter analysis with disease severity | 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 Dysregulation of redox-inflammatory markers in COVID-19: A multi-parameter analysis with disease severity Tehseen Fatima, Sadaf Khan, Muhammad Wajih Uddin, Rafat Amin, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9558551/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Viral infections are one of the biggest threats ever known to humankind. Viral mutability against the human defense system makes them deadly. Virus manipulates host gene expressions, dictating the course of the disease using inflammation-induced oxidative stress. Characterized by uncontrolled Reactive oxygen species levels and weakened antioxidant defenses, which significantly impact viral disease severity. SARS-CoV-2, a constant threat due to its ability to mutate, showcases this interplay, emphasizing the need to understand host cellular responses. Unraveling these viral modulation patterns offers insight into predicting disease severity and identifying therapeutic targets. The study used infected groups (Severe, Moderate, and Mild) and a control group. The samples were collected using a non-probability stratified sampling approach and were collected early after diagnosis to minimize treatment-related variability: at hospitalization for severe cases, within 12 hours of clinic visit for moderate cases, and within 24 hours of RT-PCR confirmation for mild cases. Disease severity classification was performed by clinicians based on clinical symptoms and oxygen saturation (SpO₂). This research delves into assessing the intricate interplay between the severity of the viral infection and inflammation-induced redox imbalance by expression profile of key antioxidant genes (GPx, NRF2, SOD2), pro-inflammatory genes (ACE2, NOS2), and RNA-binding gene (HuR), by comparing the total hydroperoxides as an indirect markeer for ROS, serum Ang II levels, and clinical data among the groups infected individuals. Significant expressional dysregulation of proinflammatory, oxidative, and anti-oxidative markers is found among the study groups, especially in the severe group. ACE2, GPx, and SOD2 were downregulated, while NOS2, HuR, and Nrf2 were upregulated, which indicates a shift towards a pro-oxidant state, such as twice the amount of ROS in both severe and moderate groups, and Serum Ang II levels are halved in the severe group. Clinical biomarkers were also elevated. These findings establish a strong correlation between redox-inflammatory dysregulation and the severity of infections. COVID-19 oxidative stress inflammation and expression profiling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Viruses, despite their minuscule size and lack of independent replication, pose a major threat to human health through their ability to establish chronic infections 1 . This phenomenon arises from their cunning ability to evade the watchful eyes of our immune system while mutating themselves and effectively turning the body's defenses against itself 2 . Mutations can alter proteins, allowing the virus to evade antibodies generated by vaccines or prior infection. The unpredictable nature of viral evolution makes it difficult to anticipate which mutations will arise next and what their functional consequences will be. At any point in time, a mutation in the genome of SARS-CoV-2 could cause another pandemic, because continuous viral evolution could theoretically lead to reduced efficacy of antiviral drugs over time, mirroring the challenges seen with drug resistance in other viruses 3 . According to “Our World in Data”, by the 11th of May 2025, COVID-19 still had 74,360 confirmed cases when 70.7% of the population was vaccinated by August 2024 4 . Despite this high number of vaccinated individuals, viral infections still pose a serious threat. Rather than targeting viral proteins, targeting host cellular mechanisms can enable us to resist viral infections more effectively without having the complete knowledge of any emerging virus, and can also enable us to design new strategies to combat different stages or severities of viral infections 5 . The Pattern Recognition Receptors (PRR) in the host system stand ready to identify specific “viral signatures”. The PRRs launch an aggressive counterattack upon detecting these signatures, activating signaling pathways that unleash pro-inflammatory cytokines and interferons to generate a response that can lead to cell senescence or Pyroptosis 6 . Increasing viral multiplication leads to an imbalanced redox-inflammatory homeostasis 7 . As a result, Oxidative Stress (OS) brought on by viral infections contributes to a variety of pathogenic processes. A recent study that focused on long-term OS-related effects of SARS-CoV-2 reported that even if the virus is eliminated from the body, the mitochondrial dysfunction and OS caused by the virus can start a feedback loop that stimulates chronic endothelial dysfunction, pyroptosis, and inflammasomic pathway 8 . SARS-CoV-2 envelope (E) protein induces phosphorylation of eIF2α, which ultimately limits host antiviral responses and promotes viral mRNA translation. SARS-CoV-2, instead of being harmed by cellular stress, takes control of these stressed-out cellular processes to make sure it can replicate as efficiently as possible. Inhibition of PKR-like ER kinase, which is responsible for phosphorylating eIF2α, significantly reduces SARS-CoV-2 replication, showing that targeting host systems can be used as a promising therapeutic target 9 . To dissect the molecular foundations of this redox-inflammatory axis, this study focuses on selected genes and biomarkers that are involved in OS, inflammation, and host-viral interactions. The molecular targets include ACE2, the primary receptor involve in mediating viral entry and regulator of the renin-angiotensin system, Low levels of ACE2 are known for elevating NOS2-mediated oxidant formation 10 ; NOS2, a pro-oxidant inflammatory gene, its expression is induced by cytokines 11 ; Nrf2, a key transcription factor responsible for the Induction of Drug Metabolizing Enzymes, along with antioxidants and electrophiles 10 , 12 , 13 ; GPx and SOD2, enzymatic antioxidants that combat ROS and HuR, an RNA-binding protein that stabilizes transcripts of stress-related genes 11 . Studies have reported that binding of HuR with the 3’-UTR of NOS-2 mRNA increases its stability and thus increases its expression 14 , suggesting that HuR can be indirectly involved in increasing OS by promoting the oxidant gene 11 . Additionally, systemic indicators like Angiotensin II (Ang II) and total ROS are evaluated to quantify the infectious physiological burden and to give a more comprehensive view of the host's response. The study also analyzes clinical biomarkers like ferritin, lactate dehydrogenase (LDH), C-reactive protein (CRP), total leukocyte count (WBC), neutrophil and lymphocyte percentages, and hemoglobin levels. By correlating molecular and clinical data across varying severities of the disease, this study aims to discuss the complex interplay between oxidative stress, inflammation, and disease progression in COVID-19, and to identify potential host-targeted strategies for therapeutic intervention. This study is a Multi-parameter Host-Centric Analysis where it focuses on host-centric interventions for future emerging pathogens where viral targets are unknown or rapidly modifying themselves. The goal is to unravel viral modulation patterns and the linkages of biomarkers with oxidative stress and inflammation. Previous studies have explored molecular imbalance, studied clinical findings, and analyzed redox imbalance, but this study offers detailed insights of these parameters in different stages of Covid-19 infection. Such a level of stratified analysis provides a nuanced understanding of disease progression 15 , 16 . Results Interaction and Co-Expression of Target Genes Study investigates six key genes that are directly involved in viral infection-mediated inflammatory redox responses. String interaction networks are used for both co-expression and interactions of protein products, with a confidence threshold of 0.150 (Fig. 1 ). ACE2 exhibited a potential functional interaction with the solute carrier family 6 member 19 (SLC6A19), supported by both co-expression (score: 0.109) and strong experimental/biochemical evidence (score: 0.981). While a weaker co-expression score (0.073) is observed between ACE2 and Nos2, although no direct functional link is identified. Interestingly, putative homologs of Nos2 and Gpx displayed co-expression in other organisms, hinting at a conserved evolutionary relationship that may need further investigation. Similarly, homologs of Gpx showed co-expression with SOD2-2 in other organisms, with a combined score of 0.902, suggesting a potential indirect interplay that needs further investigation. Also, Nrf2 demonstrated a robust interaction with GA-binding protein subunit beta-1 (GABPB1) across co-expression, experimental/biochemical data, and curated databases, with a combined score of 0.999. These results highlight the central role of Nrf2 signaling in regulating cellular stress responses in our system. Angiotensin II Levels Vary with Disease Severity Serum Ang II levels, assessed via ELISA assay (Fig. 3 ), the data weren’t normally distributed; hence, the medians of the groups were compared using Kruskal-Wallis test, which displayed a significant decreasing trend from healthy controls to severe COVID-19 patients (p < 0.0001). Intra-group median variability was assessed using Dunn’s multiple comparison test, revealing that the severe patients had significantly lower Ang II levels than moderate patients and Healthy individuals, aligning with ACE2 downregulation (Fig. 2 ) and inflammation-driven RAS disruption. ROS Accumulation in COVID-19 Patients The FOX assay is used in this study for the ROS estimation (Fig. 4 ). The ROS levels were significantly higher in the infected groups than control. The severe group showed the highest concentration of ROS, with consistently elevated values, but the moderate and mild groups showed lower mean ROS levels, still higher than healthy controls. The moderate group displayed considerable variability, and the mean ROS level in this group was statistically higher than that of the control group ( p < 0.05 ), suggesting that oxidative stress begins early in disease onset. The statistical comparisons revealed a significant difference between the severe and the control group ( p < 0.001 ), a moderate significance between the moderate and control group ( p < 0.05 ), while mild vs. healthy and inter-infection group comparisons remained non-significant (ns). These findings highlight that the ROS levels progressively rise with disease severity, peaking in severe infections, and indicating a strong correlation between oxidative stress and COVID-19 pathophysiology. Clinical Biomarker Trends and Inflammatory Load To evaluate the systemic inflammatory status of COVID-19 patients, key clinical biomarkers were assessed. CRP and LDH levels were elevated in all infected groups, with severe cases showing values well above the healthy range, confirming their role as robust markers of acute inflammation and metabolic stress. Although overall WBC count varied, it was elevated in severe patients, while remaining relatively stable in mild and moderate groups. Neutrophil percentages increased, while lymphocyte percentages declined with severity, reflecting typical COVID-19 hematological shifts. Hb levels remained within normal range across all groups, with no significant variation, indicating no direct anemia in the situation of viral infection. Ferritin levels (Fig. 6 ) were markedly increased in all disease groups compared to healthy individuals (p < 0.0001), underscoring systemic inflammation and iron metabolism dysregulation. Discussion Inflammation and redox imbalance play a pivotal role in modulating molecular health, acting as double-edged swords in host defense. While acute inflammation and ROS are essential components of the innate immune response, chronic or dysregulated activation of these systems exerts harmful effects on cellular health. The oxidative burst generated during inflammation, if not counterbalanced by antioxidant mechanisms, leads to redox imbalance. Elevated ROS and impaired antioxidant capacity damage biomolecules, including DNA, lipids, and proteins, resulting in loss of cellular viability, mitochondrial dysfunction, and ultimately triggering pathways such as pyroptosis and apoptosis 17 , 18 . It is known in literature that enhanced viral infection risk, observed during neurodegeneration, is partly the result of increased ROS in brain cells. The molecular mechanisms of viral infection, occurring during the progression of neurodegeneration, remain unclear. However, it's clear that viral infections can contribute to neurodegeneration through various pathways, including neuroinflammation, oxidative stress, and the disruption of cellular processes 19 – 21 . OS has been suggested as an essential player in COVID-19 15,22 . The intricate interplay between host genes and the SARS-CoV-2 virus plays a pivotal role in determining the course of infection, contributing to the diverse outcomes observed across mild, moderate, and severe stages. This study sheds light on the regulation of key biomarkers during different infection stages, unraveling their intricate linkages with OS and inflammation. The observed elevation in ROS levels suggests a potential link between heightened OS and disease severity in COVID-19 23 . Previous studies have shown that a cascade of biological events related to imbalanced ROS drives the pathological response in the host. Evidence has suggested that elevated ROS levels, as reported in Fig. 4 , play a role in the severity of SARS infection, and a high neutrophil-to-lymphocyte ratio (Fig. 5 ), which is associated with increased ROS production, in critically ill patients of COVID-19, ROS levels have been identified as predictors of in-hospital mortality 24 – 26 . The binding of SARS-CoV-2 and ACE2 drives the viral particles into the host cell, lowering the ACE2 bioavailability 15 . The ACE2 defensive function causes reduced levels of it, which is linked to adverse clinical phenotypes, and its key role in SARS-CoV-2 pathogenesis can actively induce the ‘cytokine storm’ by mediating worsened production and liberation of proinflammatory cytokines 27 . Studies have recently demonstrated that the affinity of SARS-CoV-2 and ACE2 is 5 to 10 folds higher than the forenamed SARS-CoV and ACE2 28 also the co-expression analysis demonstrates potential operational interactions between ACE2, which is involved in the regulation of Ang II, likely contributes to dysregulation of the Renin-Angiotensin System (RAS) 29 , inflammation, and OS 30 , and SLC6A19, which transports various neutral amino acids, essential building blocks for protein synthesis, across the cellular membrane 31 . This aligns with the hypothesis that Ang II levels correlate with disease severity. The observed variations in Ang II levels among disease severity groups further emphasize the intricate regulatory network in play during SARS-CoV-2 infection. The upregulation of NOS2, which is a pro-oxidant enzyme 13 , along with elevated HuR, which stabilizes NOS2 transcripts, supports the hypothesis that viral infection amplifies oxidative responses by modulating host gene expression 32 . This mechanism exacerbates tissue damage and inflammation 33 . On the other hand, antioxidant genes like SOD2 and GPx showed significant downregulation in severe cases, suggesting impaired ROS-scavenging capacity 34 . Nrf2, the master regulator of antioxidant defense, was upregulated in moderate and mild cases, but plateaued in severe disease, indicating possible feedback suppression or exhaustion of the antioxidant system (Fig. 1 ) 35 . Moreover, an ample amount of data is available showing a negative correlation of infectious severity and the expression pattern of these genes 26 , 36 . The levels of OS play a pivotal role in defining the progression of many disease conditions. Cardiovascular, neurodegenerative, metabolic, and autoimmune conditions are known to have a strong connection with the redox imbalance 37 , 38 . The FOX assay for ROS estimation highlights significantly elevated ROS levels in infected subjects compared to healthy individuals, showing a powerful correlation of OS in viral infections as well 39 . The observed variation in ROS levels across infection stages further substantiates the connection between OS and disease severity 40 , aligning with the earlier findings. Finally, the study also incorporates clinical markers, which are used for understanding the redox-inflammatory state of an individual during viral infection, to verify molecular findings. As shown in Fig. 5 , the plots include green shaded regions and dotted lines, which represent the healthy reference ranges extracted from established literature 41 – 45 . Elevated CRP, LDH, WBC, and ferritin in severe patients confirm heightened systemic inflammation and metabolic stress (Fig. 5 ) 46 . CRP and LDH, both markers, are reliable for inflammation and tissue damage. CRP is produced in response to pro-inflammatory cytokines. The progressive increase in the levels of CRP indicates a poor prognosis in viral infection, while LDH, a marker of cellular damage and stress, further underlines the scope of tissue injuries and hypoxia in severely affected patients. Ferritin levels were also markedly increased in the patient group. As an iron-storage protein with pro-inflammatory properties, hyperferritinemia is linked to macrophage activation, cytokine storm, and increased oxidative burden, reinforcing the evidence of a hyperinflammatory state. Hematological shifts, particularly increased neutrophils and reduced lymphocytes, are consistent with established COVID-19 pathophysiology and reinforce the utility of these markers in monitoring disease progression 47 . The comprehensive analysis of this study provides valuable insights into the regulatory mechanisms of biomarkers during SARS-CoV-2 infection. The observed patterns in gene expression, Ang II levels, and ROS estimation contribute to the intricate host-virus interplay, paving the path for probable therapeutic targets and further exploration into the complex dynamics of viral infection’s progression. Anti-inflammatory therapies have also been shown to be effective in treating other viral infections, and they may also be beneficial in treating SARS-CoV-2 infection 48 . However, a major limitation of this study is the relatively small sample size; increasing the participant numbers, particularly in the moderate category, would allow for more robust statistical power and a deeper understanding of host-specific variations in redox-inflammatory responses. A larger dataset potentially uncovers additional molecular patterns, host vulnerabilities, or adaptive responses that remain masked in smaller cohorts. Understanding all this is essential if we are to move towards prophylactic strategies that enhance host antioxidant responses as a first line of defense. Future research should focus on expanding population diversity and integrating longitudinal data to better capture both acute and long-term effects of viral-induced redox dysregulation. Conclusion This study investigated and highlighted the molecular markers of the mechanisms of inflammation and redox imbalance, as well as clinical findings in different disease severities caused by SARS-CoV-2 infection. The findings suggest a complex interplay between various oxidant and anti-oxidant biomarkers that influence disease severity, The study emphasizes the prominence of these molecular players in understanding the pathogenesis of SARS-CoV-2, while underscoring the significance of inflammation and OS in COVID-19, providing insights that could inform future therapeutic approaches, as the number of active cases is still not zero 4 and there is still plausibility of mutation which can give rise to a new wave of epidemical situation. Methodology Subject recruitment: This study was conducted using case control design. A non-probability stratified sampling approach was used to ensure proper representation, forming four distinct groups: healthy, severe, moderate, and mild. For the severe COVID‑19 group, participants were selected through random sampling, while samples from the remaining groups were obtained using convenience sampling. Due to the dynamic nature of the COVID-19 pandemic, particularly the decline in active case numbers during the latter phase of the study period, the acquisition of samples became time-bound. This decline impacted the availability of moderate and mild COVID-19 cases, which in turn influenced the overall number of samples collected. While this approach allowed for timely data collection during a public health crisis, it resulted in a variable distribution of participants across the different disease severity categories. Blood samples were collected shortly after clinical confirmation of COVID-19 to minimize variability due to treatment exposure. For the severe group, samples were obtained upon hospitalization at the Sindh Infectious Disease Hospital. For the moderate group, samples were collected within 12 hours of the patients’ clinic visit. For mild cases, samples were collected within 24 hours following RT-PCR confirmation of SARS-CoV-2 infection. This sampling strategy ensured that specimens were obtained early during clinical evaluation and before prolonged therapeutic interventions. Informed consent was obtained from all participants prior to their inclusion in the study. A total of 159 participants were included: 80 healthy controls and 79 qPCR-confirmed COVID‑19 patients, categorized into severe (n=53)(samples were obtained upon hospitalization), moderate (n=6)(samples were collected within 12 hours of the patients’ clinic visit), and mild (n=20)(samples were collected within 24 hours following RT-PCR confirmation of SARS-CoV-2 infection) groups, this sampling strategy ensured that specimens were obtained early during clinical evaluation and before prolonged therapeutic interventions. Classification of COVID-19 patients into different groups was done by clinicians based on their clinical symptoms and SpO2. Patients with mild illness had any of the different symptoms of COVID-19 (e.g., fever, cough, sore throat, malaise, headache, muscle pain, nausea, vomiting, diarrhea, loss of taste and smell) but didn’t have abnormal chest imaging. Patients with moderate illness had lower respiratory disease during clinical assessments or imaging and had an oxygen saturation of SpO2 ≥94% on room air. Patients with severe illness had SpO2 50% 49,50 . Individuals meeting the study's inclusion and exclusion criteria were recruited after providing informed consent. For each group, samples were collected after the confirmation of the positive result of covid test. Patient demographics and available clinical history data were collected for each participant. The study was conducted in accordance with ethical guidelines and received approval from the DUHS Institutional Review Board (IRB-2256/DUHS/Approval/2021/655). Sample Collection & Processing: The collection site for the mild, moderate, and severe groups in this study was the Sindh Infectious Disease Hospital, Karachi. Blood samples were obtained from both genders with an age limit of 18-60 years, aiming for a diverse representation. For healthy participants with any sort of chronic illness or infection, and in the case of virally infected participants, participants with any other respiratory disorder or any other viral infection were excluded from the study. Collected blood samples underwent serum separation and RNA extraction for subsequent ELISA, Ferrous Oxidation Xylenol-Orange (FOX) assay, and RT-PCR. Blood collected in a yellow top vacutainer was centrifuged for 20 minutes at 2500rpm and 25°C to separate the serum. The serum was separated and aliquoted into microcentrifuge tubes. The isolated serum was stored in micro-centrifuge tubes at -40°C. Molecular expression analysis of ACE2, Nrf2, GPx, NOS-2, SOD-2 and HuR: To analyze the mRNA expression of ACE2, Nrf2, GPx, NOS-2, SOD-2, and HuR, total RNA was extracted using 250 μL of blood per 750 μL of Trizol LS Reagent (Thermo-Fisher – CAT no. 79852404) as per the protocol of the manufacturer. 500ng RNA sample was treated with DNase and subsequently utilized for the synthesis of cDNA using the kit (Thermo Scientific™- CAT no. K1622). To estimate mRNA levels by RT-qPCR, cDNA was amplified for the expression profiling of target genes using respective primers (Table 1), QuantStudio7 Flex system, SYBER Green qPCR Master Mix & 2-ΔΔCT. Protein’s Expressional Analysis by Enzyme-Linked Immunosorbent Assay (ELISA): Serum Ang II levels of each group were analyzed using ELISA. A commercially available ELISA kit (Cat # E1224Hu) was used, and ELISA was performed according to the instructions provided by the kit. ROS Estimation: The FOX assay was employed on serum samples to evaluate hydroperoxides as a proxy of oxidative stress (ROS) in all study groups 51,52 . Serum samples were diluted using deionized water by 1:10, then in the 96-well plate, 100 µL of diluted serum samples were mixed with 900 µL of FOX reagent and allowed to incubate for one hour in the dark. Standards of varying concentrations were made from 10µM and 50µM stock solutions of H 2 O 2, and a standard calibration curve was constructed. The microplate reader was used for recording OD at 560nm. Statistical analysis: Data was analyzed using GraphPad Prism 8.0.1 and Microsoft Excel. Results are presented as mean + SD unless otherwise mentioned. Analysis among different sub-groups of COVID-19 patients is done by using ANOVA, followed by post-hoc comparison (Tukey’s multiple comparison tests) if the data were normally distributed, and for data that were not normally distributed, the Kruskal-Wallis test is used, and Dunn’s multiple comparison test was performed. The P-value, <0.05, is considered statistically significant. The standard curve and charts were prepared by using GraphPad Prism 8.0.1. Study limitations The most prominent limitation of this study is the unequal distribution of participants across disease severity groups, particularly the relatively small number of patients in the moderate category. This imbalance occurred due to the declining number of active COVID-19 cases during the later phase of the study period and the time-bound nature of sample collection at the clinical site. Additionally, strict inclusion criteria and reliance on clinically confirmed cases further limited the availability of eligible moderate cases. Despite this limitation, appropriate statistical methods were applied, including non-parametric tests where required, which are robust for datasets with unequal group sizes. While the findings provide meaningful insights into redox-inflammatory dysregulation across disease severity, future studies with larger and more balanced cohorts would further strengthen these observations. Another limitation, which was actually a technical limitation, was not performing the formal PCR efficiency determination tests using the standard curve, and the melt curve, but the Primers were designed using Primer Express to select sequences with appropriate thermodynamic properties and minimal predicted primer–dimer formation. Statements & Declarations Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request Funding “The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.” Competing Interests “The authors have no relevant financial or non-financial interests to disclose.” Author Contributions Rafat Amin: Conceptualization, Investigation, Methodology, Resources, Validation, Writing - original draft. Sadaf Khan: Conceptualization, Resources, Software, Supervision, Validation, Writing - review and editing. Muhammad Wajih Uddin: Data curation, Investigation, Software, Data analysis, Validation, Writing - original draft. Asif Sheikh: Provision of samples, clinical evaluation, and diagnosis of patients. Naeem Solangi: Data curation, Investigation. Ayesha Khalid: Data curation, Investigation. Salman Ahmed Khan: Investigation, Resources. Tehseen Fatima: Conceptualization, Investigation, Methodology, Project Administration, Resources, Supervision, Visualization, Writing - review and editing Ethics approval “This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the DUHS institutional review board, Dow University of Health Sciences (IRB-2256/DUHS/Approval/2021/655).” Consent to participate “Informed consent was obtained from all individual participants included in the study.” References Luo, G. & Gao, S. J. Global health concerns stirred by emerging viral infections. Journal of Medical Virology 92 , 399-400, doi:10.1002/jmv.25683 (2020). Baker, R. E. et al. Infectious disease in an era of global change. Nature Reviews Microbiology 20 , 193-205, doi:10.1038/s41579-021-00639-z (2022). Thakur, S. et al. SARS-CoV-2 Mutations and Their Impact on Diagnostics, Therapeutics and Vaccines. Frontiers in Medicine 9 , doi:10.3389/fmed.2022.815389 (2022). 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E. & Sen, C. K. Is Low Alveolar Type II CellSOD3in the Lungs of Elderly Linked to the Observed Severity of COVID-19? Antioxidants & Redox Signaling 33 , 59-65, doi:10.1089/ars.2020.8111 (2020). Ramani, S., Pathak, A., Dalal, V., Paul, A. & Biswas, S. Oxidative Stress in Autoimmune Diseases: An Under Dealt Malice. Current Protein & Peptide Science 21 , 611-621, doi:10.2174/1389203721666200214111816 (2020). Rotariu, D. et al. Oxidative stress – Complex pathological issues concerning the hallmark of cardiovascular and metabolic disorders. Biomedicine & Pharmacotherapy 152 , 113238, doi:10.1016/j.biopha.2022.113238 (2022). Alam, M. S. & Czajkowsky, D. M. SARS-CoV-2 infection and oxidative stress: Pathophysiological insight into thrombosis and therapeutic opportunities. Cytokine & Growth Factor Reviews 63 , 44-57, doi:10.1016/j.cytogfr.2021.11.001 (2022). Mohiuddin, M. & Kasahara, K. 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Potential Role of Antioxidant and Anti-Inflammatory Therapies to Prevent Severe SARS-Cov-2 Complications. Antioxidants 10 , 272, doi:10.3390/antiox10020272 (2021). Han, H. et al. Profiling serum cytokines in COVID-19 patients reveals IL-6 and IL-10 are disease severity predictors. Emerging Microbes & Infections 9 , 1123-1130, doi:10.1080/22221751.2020.1770129 (2020). Liu, Y. et al. Viral dynamics in mild and severe cases of COVID-19. The Lancet Infectious Diseases 20 , 656-657, doi:10.1016/s1473-3099(20)30232-2 (2020). Rubio, C. P. & Cerón, J. J. Spectrophotometric assays for evaluation of Reactive Oxygen Species (ROS) in serum: general concepts and applications in dogs and humans. BMC Veterinary Research 17 , doi:10.1186/s12917-021-02924-8 (2021). Katerji, M., Filippova, M. & Duerksen-Hughes, P. Approaches and Methods to Measure Oxidative Stress in Clinical Samples: Research Applications in the Cancer Field. Oxidative Medicine and Cellular Longevity 2019 , 1-29, doi:10.1155/2019/1279250 (2019). Tables Table 1 - Primers used in this study Gene Forward Primer Reverse Primer GPx 5' CGGGACTACACCCAGATGAA 3' 5' TCTCTTCGTTCTTGGCGTTC 3' NRF2 5' TCAGCATGCTACGTGATGAAG 3' 5' TTTGCTGCAGGGAGTATTCA 3' ACE2 5'-ACAGTCCACACTTGCCCAAAT-3' 5'-TGAGAGCACTGAAGACCCATT-3' GAPDH 5’-ACCCAGAAGACTGTGGATGG-3’ 5’- TGACAAAGTGGTCGTTGAGG-3’ NOS-2 5’-ACAAGCCTACCCCTCCAGAT-3’ 5’-TCCCGTCAGTTGGTAGGTTC-3’ SOD-2 5’-GCCATTGCTTTTGGTGTTTT-3’ 5’-AAATGGTGCTGGGAAAACTG-3’ HuR 5’-GTGACATCGGGAGAACGAAT-3’ 5’-GCGGTCACGTAGTTCACAAA-3’ Table 2 - Comparison of RQ values across different severity levels and healthy individuals. Markers ANOVA Tukey's multiple comparisons test Mean Diff. 95.00% CI of diff. P Value NRF2 <0.0001 Severe vs. Moderate -0.7452 -1.149 to -0.3416 0.0004 *** Severe vs. Mild -0.8575 -1.300 to -0.4154 0.0003 *** Severe vs. Healthy 0.08444 -0.2765 to 0.4454 0.9052 ns Moderate vs. Mild -0.1124 -0.5899 to 0.3652 0.9037 ns Moderate vs. Healthy 0.8296 0.4260 to 1.233 0.0001 *** Mild vs. Healthy 0.942 0.4999 to 1.384 0.0001 *** ACE2 <0.0001 Severe vs. Moderate -1.066 -1.998 to -0.1333 0.0224 * Severe vs. Mild -0.4109 -1.290 to 0.4683 0.554 ns Severe vs. Healthy -2.009 -2.851 to -1.168 <0.0001 **** Moderate vs. Mild 0.6549 -0.2776 to 1.587 0.2255 ns Moderate vs. Healthy -0.9435 -1.841 to -0.04622 0.0376 * Mild vs. Healthy -1.598 -2.440 to -0.7567 0.0003 *** SOD2 0.0015 Severe vs. Moderate -0.89 -1.530 to -0.2501 0.0056 ** Severe vs. Mild -1.088 -1.762 to -0.4130 0.0016 ** Severe vs. Healthy -0.5222 -1.138 to 0.09359 0.111 ns Moderate vs. Mild -0.1975 -0.8374 to 0.4424 0.8103 ns Moderate vs. Healthy 0.3679 -0.2098 to 0.9455 0.2959 ns Mild vs. Healthy 0.5654 -0.05038 to 1.181 0.0773 ns NOS2 0.0047 Severe vs. Moderate -0.4097 -0.9620 to 0.1427 0.1861 ns Severe vs. Mild 0.2927 -0.2597 to 0.8450 0.4468 ns Severe vs. Healthy 0.3502 -0.1484 to 0.8488 0.2229 ns Moderate vs. Mild 0.7023 0.1201 to 1.285 0.0159 * Moderate vs. Healthy 0.7598 0.2283 to 1.291 0.0045 ** Mild vs. Healthy 0.0575 -0.4740 to 0.5890 0.9891 ns HuR 0.0099 Severe vs. Moderate -0.1948 -0.8302 to 0.4406 0.8095 ns Severe vs. Mild -0.4332 -1.069 to 0.2022 0.2408 ns Severe vs. Healthy 0.3436 -0.2365 to 0.9236 0.3495 ns Moderate vs. Mild -0.2384 -0.8737 to 0.3970 0.7009 ns Moderate vs. Healthy 0.5384 -0.04163 to 1.118 0.073 ns Mild vs. Healthy 0.7768 0.1967 to 1.357 0.0078 ** Kruskal-Wallis test Dunn's multiple comparisons test Mean rank diff. Adjusted P Value GPX 0.0016 Severe vs. Moderate -1.179 0.0052 ** Severe vs. Mild -1.096 0.0129 * Severe vs. Healthy -1.645 0.0002 *** Moderate vs. Mild 0.08275 0.9925 ns Moderate vs. Healthy -0.4659 0.3922 ns Mild vs. Healthy -0.5487 0.2964 ns Table 3 - Comparison of Angiotensin II protein expression among the healthy and SARS-CoV-2 patient groups. Kruskal-Wallis test Dunn's multiple comparisons test Mean rank diff. Adjusted P Value 0.9999 ns Mild vs. Healthy -5.000 >0.9999 ns Table 4 - Comparison of conc. of ROS values across different severity levels and healthy individuals. Kruskal-Wallis test Dunn's multiple comparisons test Mean rank diff. Adjusted P Value 0.0004 Severe vs. Moderate 1.547 >0.9999 ns Severe vs. Mild 13.05 >0.9999 ns Severe vs. Healthy 36.85 0.0002 *** Moderate vs. Mild 11.50 >0.9999 ns Moderate vs. Healthy 35.30 0.0135 * Mild vs. Healthy 23.80 0.5553 ns Table 5 - Comparison of conc. of Ferritin values across different severity levels and healthy individuals, and other clinical markers among infected groups. Marker Kruskal-Wallis test Dunn's multiple comparisons test Mean rank diff. Adjusted P Value Ferritin 0.9999 ns Severe vs. Mild 0.372 >0.9999 ns Severe vs. Healthy 37.62 0.9999 ns Moderate vs. Healthy 24.31 0.3066 ns Mild vs. Healthy 37.25 0.9999 ns LDH 0.0006 Severe vs. Moderate 20.17 0.0985 ns Severe vs. Mild 18.2 0.0014 ** Moderate vs. Mild -1.963 >0.9999 ns NEUTROPHILS (%) <0.0001 Severe vs. Moderate 20.78 0.0926 ns Severe vs. Mild 26.73 0.9999 ns LYMPHOCYTES (%) 0.0001 Severe vs. Moderate -19.67 0.1231 ns Severe vs. Mild -24.11 0.0002 *** Moderate vs. Mild -4.444 >0.9999 ns Marker ANOVA Holm-Sidak's multiple comparisons test Mean Diff. Adjusted P Value HB 0.7823 Severe vs. Moderate -0.5648 0.8776 * Severe vs. Mild 0.04689 0.9274 ns Moderate vs. Mild 0.6117 0.8776 **** WBC 0.0005 Severe vs. Moderate 5.283 0.0528 ns Severe vs. Mild 5.365 0.001 *** Moderate vs. Mild 0.08167 0.9743 ns Additional Declarations No competing interests reported. Supplementary Files Supplementrydata.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers invited by journal 01 May, 2026 Editor assigned by journal 30 Apr, 2026 Submission checks completed at journal 30 Apr, 2026 First submitted to journal 28 Apr, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9558551","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634350987,"identity":"af5b1dc8-62da-4f2e-baef-da0d2666770e","order_by":0,"name":"Tehseen Fatima","email":"data:image/png;base64,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","orcid":"","institution":"Dow University of Health Sciences","correspondingAuthor":true,"prefix":"","firstName":"Tehseen","middleName":"","lastName":"Fatima","suffix":""},{"id":634350994,"identity":"29bf6245-e944-45c1-a981-f8cfb799be70","order_by":1,"name":"Sadaf Khan","email":"","orcid":"","institution":"Dow University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sadaf","middleName":"","lastName":"Khan","suffix":""},{"id":634351002,"identity":"df1ee231-7c9d-4ca3-b8e2-1fb97e860e7c","order_by":2,"name":"Muhammad Wajih Uddin","email":"","orcid":"","institution":"Dow University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Wajih","lastName":"Uddin","suffix":""},{"id":634351010,"identity":"7cd11dbf-fbe5-41e6-b862-72f4f281f16f","order_by":3,"name":"Rafat Amin","email":"","orcid":"","institution":"Dow University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rafat","middleName":"","lastName":"Amin","suffix":""},{"id":634351020,"identity":"08955d56-5558-4ed3-9e18-809654008b81","order_by":4,"name":"Asif Ali","email":"","orcid":"","institution":"Dow University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Asif","middleName":"","lastName":"Ali","suffix":""},{"id":634351027,"identity":"8d437977-df1f-4f14-baed-9879b76c5e10","order_by":5,"name":"Naeem Solangi","email":"","orcid":"","institution":"Dow University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Naeem","middleName":"","lastName":"Solangi","suffix":""},{"id":634351031,"identity":"e1971b1f-c34e-4e59-a2aa-f6b61f4f06b0","order_by":6,"name":"Ayesha Khalid","email":"","orcid":"","institution":"Dow University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ayesha","middleName":"","lastName":"Khalid","suffix":""},{"id":634351037,"identity":"1120e586-50ea-4f39-904b-955fd3026fef","order_by":7,"name":"Salman Ahmed Khan","email":"","orcid":"","institution":"Dow University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Salman","middleName":"Ahmed","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2026-04-28 22:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9558551/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9558551/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109067862,"identity":"b84f190b-43e5-4fc5-aa56-3a0b23b920b0","added_by":"auto","created_at":"2026-05-12 10:02:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":396903,"visible":true,"origin":"","legend":"\u003cp\u003ea, Co-expression scores based on RNA expression patterns, and on protein co-regulation provided by ProteomeHD. b, shows the interaction between SOD2-2, Nrf2, HuR, GPX6, Nos2, and Ace2 in Homo sapiens from string database, with the average clustering coefficient of 0.8 and PPI enrichment p-value of 0.000781.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9558551/v1/48a4483f3660b0e61db65304.png"},{"id":109204230,"identity":"7832da8b-7236-4c3a-8c34-688061dfe348","added_by":"auto","created_at":"2026-05-13 14:56:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33534,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpressional data of Ace2, HuR, Nrf2, SOD2, GPx, and NOS2 among healthy, mild, moderate, and severe.\u003c/strong\u003e(***P\u0026lt;0.001, **p=0.01, ns=non-significant, p values represent the comparison of infected groups to the healthy group)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9558551/v1/186c8ab23f6bd3c5f532c541.png"},{"id":109014598,"identity":"f365c59c-a803-4510-a5ac-a630900275c8","added_by":"auto","created_at":"2026-05-11 17:20:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26640,"visible":true,"origin":"","legend":"\u003cp\u003eSerum Angiotensin II expression profiling using ELISA, among the healthy and SARS-CoV-2 patient groups.(***P\u0026lt;0.001, **p=0.01, ns= non-significant)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9558551/v1/5152b8f670576b9f05019376.png"},{"id":109014600,"identity":"f62a4490-7dfe-4705-a18e-4931e33c2bc7","added_by":"auto","created_at":"2026-05-11 17:20:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34482,"visible":true,"origin":"","legend":"\u003cp\u003eROS levels in healthy, mild, moderate, and severe SARS-CoV-2 patients, quantified using FOX assay. (***P\u0026lt;0.001, *p=0.01, ns= non-significant)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9558551/v1/80a5c815cdc3778e3f67a499.png"},{"id":109014601,"identity":"aa66a9fa-f6b6-4547-9c47-e203a177c995","added_by":"auto","created_at":"2026-05-11 17:20:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":40624,"visible":true,"origin":"","legend":"\u003cp\u003eClinical biomarker profiles of SARS-CoV-2 patients categorized by disease severity (Mild, Moderate, Severe). Green shaded regions and dotted lines indicate normal healthy reference ranges (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9558551/v1/5e39711006349ce8fc39791e.png"},{"id":109014602,"identity":"20d42b05-ea48-48b8-83ea-a1b58ad8015a","added_by":"auto","created_at":"2026-05-11 17:20:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":16921,"visible":true,"origin":"","legend":"\u003cp\u003eSerum ferritin levels in SARS-CoV-2 patients with varying disease severity (Severe, Moderate, Mild) compared to Healthy controls (****p \u0026lt; 0.0001; ns = not significant).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9558551/v1/39744f7736a3b0eb3bb6dce0.png"},{"id":109206443,"identity":"944146cc-e0d1-4de2-b6aa-6e467f1f8d74","added_by":"auto","created_at":"2026-05-13 15:12:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":867460,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9558551/v1/e6a9305f-1841-48a9-93ef-bbd70e1d12d4.pdf"},{"id":109014597,"identity":"62f71e24-0997-4c42-9b92-63e7122c4b47","added_by":"auto","created_at":"2026-05-11 17:20:49","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27309,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementrydata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9558551/v1/cd2b2f566fe145aef4775c89.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dysregulation of redox-inflammatory markers in COVID-19: A multi-parameter analysis with disease severity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eViruses, despite their minuscule size and lack of independent replication, pose a major threat to human health through their ability to establish chronic infections \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This phenomenon arises from their cunning ability to evade the watchful eyes of our immune system while mutating themselves and effectively turning the body's defenses against itself \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Mutations can alter proteins, allowing the virus to evade antibodies generated by vaccines or prior infection. The unpredictable nature of viral evolution makes it difficult to anticipate which mutations will arise next and what their functional consequences will be. At any point in time, a mutation in the genome of SARS-CoV-2 could cause another pandemic, because continuous viral evolution could theoretically lead to reduced efficacy of antiviral drugs over time, mirroring the challenges seen with drug resistance in other viruses \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. According to \u0026ldquo;Our World in Data\u0026rdquo;, by the 11th of May 2025, COVID-19 still had 74,360 confirmed cases when 70.7% of the population was vaccinated by August 2024 \u003csup\u003e4\u003c/sup\u003e. Despite this high number of vaccinated individuals, viral infections still pose a serious threat.\u003c/p\u003e \u003cp\u003eRather than targeting viral proteins, targeting host cellular mechanisms can enable us to resist viral infections more effectively without having the complete knowledge of any emerging virus, and can also enable us to design new strategies to combat different stages or severities of viral infections \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The Pattern Recognition Receptors (PRR) in the host system stand ready to identify specific \u0026ldquo;viral signatures\u0026rdquo;. The PRRs launch an aggressive counterattack upon detecting these signatures, activating signaling pathways that unleash pro-inflammatory cytokines and interferons to generate a response that can lead to cell senescence or Pyroptosis \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Increasing viral multiplication leads to an imbalanced redox-inflammatory homeostasis \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. As a result, Oxidative Stress (OS) brought on by viral infections contributes to a variety of pathogenic processes. A recent study that focused on long-term OS-related effects of SARS-CoV-2 reported that even if the virus is eliminated from the body, the mitochondrial dysfunction and OS caused by the virus can start a feedback loop that stimulates chronic endothelial dysfunction, pyroptosis, and inflammasomic pathway \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSARS-CoV-2 envelope (E) protein induces phosphorylation of eIF2α, which ultimately limits host antiviral responses and promotes viral mRNA translation. SARS-CoV-2, instead of being harmed by cellular stress, takes control of these stressed-out cellular processes to make sure it can replicate as efficiently as possible. Inhibition of PKR-like ER kinase, which is responsible for phosphorylating eIF2α, significantly reduces SARS-CoV-2 replication, showing that targeting host systems can be used as a promising therapeutic target \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo dissect the molecular foundations of this redox-inflammatory axis, this study focuses on selected genes and biomarkers that are involved in OS, inflammation, and host-viral interactions. The molecular targets include ACE2, the primary receptor involve in mediating viral entry and regulator of the renin-angiotensin system, Low levels of ACE2 are known for elevating NOS2-mediated oxidant formation \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e; NOS2, a pro-oxidant inflammatory gene, its expression is induced by cytokines \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e; Nrf2, a key transcription factor responsible for the Induction of Drug Metabolizing Enzymes, along with antioxidants and electrophiles \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e; GPx and SOD2, enzymatic antioxidants that combat ROS and HuR, an RNA-binding protein that stabilizes transcripts of stress-related genes \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Studies have reported that binding of HuR with the 3\u0026rsquo;-UTR of NOS-2 mRNA increases its stability and thus increases its expression \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, suggesting that HuR can be indirectly involved in increasing OS by promoting the oxidant gene \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Additionally, systemic indicators like Angiotensin II (Ang II) and total ROS are evaluated to quantify the infectious physiological burden and to give a more comprehensive view of the host's response. The study also analyzes clinical biomarkers like ferritin, lactate dehydrogenase (LDH), C-reactive protein (CRP), total leukocyte count (WBC), neutrophil and lymphocyte percentages, and hemoglobin levels. By correlating molecular and clinical data across varying severities of the disease, this study aims to discuss the complex interplay between oxidative stress, inflammation, and disease progression in COVID-19, and to identify potential host-targeted strategies for therapeutic intervention.\u003c/p\u003e \u003cp\u003eThis study is a Multi-parameter Host-Centric Analysis where it focuses on host-centric interventions for future emerging pathogens where viral targets are unknown or rapidly modifying themselves. The goal is to unravel viral modulation patterns and the linkages of biomarkers with oxidative stress and inflammation. Previous studies have explored molecular imbalance, studied clinical findings, and analyzed redox imbalance, but this study offers detailed insights of these parameters in different stages of Covid-19 infection. Such a level of stratified analysis provides a nuanced understanding of disease progression \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInteraction and Co-Expression of Target Genes\u003c/h2\u003e \u003cp\u003eStudy investigates six key genes that are directly involved in viral infection-mediated inflammatory redox responses. String interaction networks are used for both co-expression and interactions of protein products, with a confidence threshold of 0.150 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). ACE2 exhibited a potential functional interaction with the solute carrier family 6 member 19 (SLC6A19), supported by both co-expression (score: 0.109) and strong experimental/biochemical evidence (score: 0.981). While a weaker co-expression score (0.073) is observed between ACE2 and Nos2, although no direct functional link is identified. Interestingly, putative homologs of Nos2 and Gpx displayed co-expression in other organisms, hinting at a conserved evolutionary relationship that may need further investigation. Similarly, homologs of Gpx showed co-expression with SOD2-2 in other organisms, with a combined score of 0.902, suggesting a potential indirect interplay that needs further investigation. Also, Nrf2 demonstrated a robust interaction with GA-binding protein subunit beta-1 (GABPB1) across co-expression, experimental/biochemical data, and curated databases, with a combined score of 0.999. These results highlight the central role of Nrf2 signaling in regulating cellular stress responses in our system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAngiotensin II Levels Vary with Disease Severity\u003c/h3\u003e\n\u003cp\u003eSerum Ang II levels, assessed via ELISA assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the data weren\u0026rsquo;t normally distributed; hence, the medians of the groups were compared using Kruskal-Wallis test, which displayed a significant decreasing trend from healthy controls to severe COVID-19 patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Intra-group median variability was assessed using Dunn\u0026rsquo;s multiple comparison test, revealing that the severe patients had significantly lower Ang II levels than moderate patients and Healthy individuals, aligning with ACE2 downregulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and inflammation-driven RAS disruption.\u003c/p\u003e\n\u003ch3\u003eROS Accumulation in COVID-19 Patients\u003c/h3\u003e\n\u003cp\u003eThe FOX assay is used in this study for the ROS estimation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The ROS levels were significantly higher in the infected groups than control. The severe group showed the highest concentration of ROS, with consistently elevated values, but the moderate and mild groups showed lower mean ROS levels, still higher than healthy controls. The moderate group displayed considerable variability, and the mean ROS level in this group was statistically higher than that of the control group (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e), suggesting that oxidative stress begins early in disease onset. The statistical comparisons revealed a significant difference between the severe and the control group (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e), a moderate significance between the moderate and control group (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e), while mild vs. healthy and inter-infection group comparisons remained non-significant (ns). These findings highlight that the ROS levels progressively rise with disease severity, peaking in severe infections, and indicating a strong correlation between oxidative stress and COVID-19 pathophysiology.\u003c/p\u003e\n\u003ch3\u003eClinical Biomarker Trends and Inflammatory Load\u003c/h3\u003e\n\u003cp\u003eTo evaluate the systemic inflammatory status of COVID-19 patients, key clinical biomarkers were assessed. CRP and LDH levels were elevated in all infected groups, with severe cases showing values well above the healthy range, confirming their role as robust markers of acute inflammation and metabolic stress. Although overall WBC count varied, it was elevated in severe patients, while remaining relatively stable in mild and moderate groups. Neutrophil percentages increased, while lymphocyte percentages declined with severity, reflecting typical COVID-19 hematological shifts. Hb levels remained within normal range across all groups, with no significant variation, indicating no direct anemia in the situation of viral infection. Ferritin levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e) were markedly increased in all disease groups compared to healthy individuals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), underscoring systemic inflammation and iron metabolism dysregulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eInflammation and redox imbalance play a pivotal role in modulating molecular health, acting as double-edged swords in host defense. While acute inflammation and ROS are essential components of the innate immune response, chronic or dysregulated activation of these systems exerts harmful effects on cellular health. The oxidative burst generated during inflammation, if not counterbalanced by antioxidant mechanisms, leads to redox imbalance. Elevated ROS and impaired antioxidant capacity damage biomolecules, including DNA, lipids, and proteins, resulting in loss of cellular viability, mitochondrial dysfunction, and ultimately triggering pathways such as pyroptosis and apoptosis \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. It is known in literature that enhanced viral infection risk, observed during neurodegeneration, is partly the result of increased ROS in brain cells. The molecular mechanisms of viral infection, occurring during the progression of neurodegeneration, remain unclear. However, it's clear that viral infections can contribute to neurodegeneration through various pathways, including neuroinflammation, oxidative stress, and the disruption of cellular processes \u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOS has been suggested as an essential player in COVID-19 \u003csup\u003e15,22\u003c/sup\u003e. The intricate interplay between host genes and the SARS-CoV-2 virus plays a pivotal role in determining the course of infection, contributing to the diverse outcomes observed across mild, moderate, and severe stages. This study sheds light on the regulation of key biomarkers during different infection stages, unraveling their intricate linkages with OS and inflammation. The observed elevation in ROS levels suggests a potential link between heightened OS and disease severity in COVID-19 \u003csup\u003e23\u003c/sup\u003e. Previous studies have shown that a cascade of biological events related to imbalanced ROS drives the pathological response in the host. Evidence has suggested that elevated ROS levels, as reported in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, play a role in the severity of SARS infection, and a high neutrophil-to-lymphocyte ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which is associated with increased ROS production, in critically ill patients of COVID-19, ROS levels have been identified as predictors of in-hospital mortality \u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe binding of SARS-CoV-2 and ACE2 drives the viral particles into the host cell, lowering the ACE2 bioavailability \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The ACE2 defensive function causes reduced levels of it, which is linked to adverse clinical phenotypes, and its key role in SARS-CoV-2 pathogenesis can actively induce the \u0026lsquo;cytokine storm\u0026rsquo; by mediating worsened production and liberation of proinflammatory cytokines \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Studies have recently demonstrated that the affinity of SARS-CoV-2 and ACE2 is 5 to 10 folds higher than the forenamed SARS-CoV and ACE2 \u003csup\u003e28\u003c/sup\u003e also the co-expression analysis demonstrates potential operational interactions between ACE2, which is involved in the regulation of Ang II, likely contributes to dysregulation of the Renin-Angiotensin System (RAS) \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, inflammation, and OS \u003csup\u003e30\u003c/sup\u003e, and SLC6A19, which transports various neutral amino acids, essential building blocks for protein synthesis, across the cellular membrane \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. This aligns with the hypothesis that Ang II levels correlate with disease severity. The observed variations in Ang II levels among disease severity groups further emphasize the intricate regulatory network in play during SARS-CoV-2 infection.\u003c/p\u003e \u003cp\u003eThe upregulation of NOS2, which is a pro-oxidant enzyme \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, along with elevated HuR, which stabilizes NOS2 transcripts, supports the hypothesis that viral infection amplifies oxidative responses by modulating host gene expression \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. This mechanism exacerbates tissue damage and inflammation \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. On the other hand, antioxidant genes like SOD2 and GPx showed significant downregulation in severe cases, suggesting impaired ROS-scavenging capacity \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Nrf2, the master regulator of antioxidant defense, was upregulated in moderate and mild cases, but plateaued in severe disease, indicating possible feedback suppression or exhaustion of the antioxidant system (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Moreover, an ample amount of data is available showing a negative correlation of infectious severity and the expression pattern of these genes \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe levels of OS play a pivotal role in defining the progression of many disease conditions. Cardiovascular, neurodegenerative, metabolic, and autoimmune conditions are known to have a strong connection with the redox imbalance \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The FOX assay for ROS estimation highlights significantly elevated ROS levels in infected subjects compared to healthy individuals, showing a powerful correlation of OS in viral infections as well \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The observed variation in ROS levels across infection stages further substantiates the connection between OS and disease severity \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, aligning with the earlier findings.\u003c/p\u003e \u003cp\u003eFinally, the study also incorporates clinical markers, which are used for understanding the redox-inflammatory state of an individual during viral infection, to verify molecular findings. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the plots include green shaded regions and dotted lines, which represent the healthy reference ranges extracted from established literature \u003csup\u003e\u003cspan additionalcitationids=\"CR42 CR43 CR44\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Elevated CRP, LDH, WBC, and ferritin in severe patients confirm heightened systemic inflammation and metabolic stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. CRP and LDH, both markers, are reliable for inflammation and tissue damage. CRP is produced in response to pro-inflammatory cytokines. The progressive increase in the levels of CRP indicates a poor prognosis in viral infection, while LDH, a marker of cellular damage and stress, further underlines the scope of tissue injuries and hypoxia in severely affected patients. Ferritin levels were also markedly increased in the patient group. As an iron-storage protein with pro-inflammatory properties, hyperferritinemia is linked to macrophage activation, cytokine storm, and increased oxidative burden, reinforcing the evidence of a hyperinflammatory state. Hematological shifts, particularly increased neutrophils and reduced lymphocytes, are consistent with established COVID-19 pathophysiology and reinforce the utility of these markers in monitoring disease progression \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe comprehensive analysis of this study provides valuable insights into the regulatory mechanisms of biomarkers during SARS-CoV-2 infection. The observed patterns in gene expression, Ang II levels, and ROS estimation contribute to the intricate host-virus interplay, paving the path for probable therapeutic targets and further exploration into the complex dynamics of viral infection\u0026rsquo;s progression. Anti-inflammatory therapies have also been shown to be effective in treating other viral infections, and they may also be beneficial in treating SARS-CoV-2 infection \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. However, a major limitation of this study is the relatively small sample size; increasing the participant numbers, particularly in the moderate category, would allow for more robust statistical power and a deeper understanding of host-specific variations in redox-inflammatory responses. A larger dataset potentially uncovers additional molecular patterns, host vulnerabilities, or adaptive responses that remain masked in smaller cohorts. Understanding all this is essential if we are to move towards prophylactic strategies that enhance host antioxidant responses as a first line of defense. Future research should focus on expanding population diversity and integrating longitudinal data to better capture both acute and long-term effects of viral-induced redox dysregulation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study investigated and highlighted the molecular markers of the mechanisms of inflammation and redox imbalance, as well as clinical findings in different disease severities caused by SARS-CoV-2 infection. The findings suggest a complex interplay between various oxidant and anti-oxidant biomarkers that influence disease severity, The study emphasizes the prominence of these molecular players in understanding the pathogenesis of SARS-CoV-2, while underscoring the significance of inflammation and OS in COVID-19, providing insights that could inform future therapeutic approaches, as the number of active cases is still not zero \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and there is still plausibility of mutation which can give rise to a new wave of epidemical situation.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eSubject recruitment:\u003c/p\u003e\n\u003cp\u003eThis study was conducted using case control design. A non-probability stratified sampling approach was used to ensure proper representation, forming four distinct groups: healthy, severe, moderate, and mild. For the severe COVID‑19 group, participants were selected through random sampling, while samples from the remaining groups were obtained using convenience sampling.\u0026nbsp;Due to the dynamic nature of the COVID-19 pandemic, particularly the decline in active case numbers during the latter phase of the study period, the acquisition of samples became time-bound. This decline impacted the availability of moderate and mild COVID-19 cases, which in turn influenced the overall number of samples collected. While this approach allowed for timely data collection during a public health crisis, it resulted in a variable distribution of participants across the different disease severity categories.\u003c/p\u003e\n\u003cp\u003eBlood samples were collected shortly after clinical confirmation of COVID-19 to minimize variability due to treatment exposure. For the severe group, samples were obtained upon hospitalization at the Sindh Infectious Disease Hospital. For the moderate group, samples were collected within 12 hours of the patients\u0026rsquo; clinic visit. For mild cases, samples were collected within 24 hours following RT-PCR confirmation of SARS-CoV-2 infection. This sampling strategy ensured that specimens were obtained early during clinical evaluation and before prolonged therapeutic interventions.\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants prior to their inclusion in the study. A total of 159 participants were included: 80 healthy controls and 79 qPCR-confirmed COVID‑19 patients, categorized into severe (n=53)(samples were obtained upon hospitalization), moderate (n=6)(samples were collected within 12 hours of the patients\u0026rsquo; clinic visit), and mild (n=20)(samples were collected within 24 hours following RT-PCR confirmation of SARS-CoV-2 infection) groups, this sampling strategy ensured that specimens were obtained early during clinical evaluation and before prolonged therapeutic interventions. Classification of COVID-19 patients into different groups was done by clinicians based on their clinical symptoms and SpO2. Patients with mild illness had any of the different symptoms of COVID-19 (e.g., fever, cough, sore throat, malaise, headache, muscle pain, nausea, vomiting, diarrhea, loss of taste and smell) but didn\u0026rsquo;t have abnormal chest imaging. Patients with moderate illness had lower respiratory disease during clinical assessments or imaging and had an oxygen saturation of SpO2 \u0026ge;94% on room air. Patients with severe illness had SpO2 \u0026lt;94% on room air and had lung infiltrates \u0026gt;50%\u0026nbsp;\u003csup\u003e49,50\u003c/sup\u003e. Individuals meeting the study\u0026apos;s inclusion and exclusion criteria were recruited after providing informed consent. For each group, samples were collected after the confirmation of the positive result of covid test. Patient demographics and available clinical history data were collected for each participant. The study was conducted in accordance with ethical guidelines and received approval from the DUHS Institutional Review Board (IRB-2256/DUHS/Approval/2021/655).\u003c/p\u003e\n\u003cp\u003eSample Collection \u0026amp; Processing:\u003c/p\u003e\n\u003cp\u003eThe collection site for the mild, moderate, and severe groups in this study was the Sindh Infectious Disease Hospital, Karachi. Blood samples were obtained from both genders with an age limit of 18-60 years, aiming for a diverse representation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor healthy participants with any sort of chronic illness or infection, and in the case of virally infected participants, participants with any other respiratory disorder or any other viral infection were excluded from the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCollected blood samples underwent serum separation and RNA extraction for subsequent ELISA, Ferrous Oxidation Xylenol-Orange (FOX) assay, and RT-PCR. Blood collected in a yellow top vacutainer was centrifuged for 20 minutes at 2500rpm and 25\u0026deg;C to separate the serum. The serum was separated and aliquoted into microcentrifuge tubes. The isolated serum was stored in micro-centrifuge tubes at -40\u0026deg;C.\u003c/p\u003e\n\u003cp\u003eMolecular expression analysis of ACE2, Nrf2, GPx, NOS-2, SOD-2 and HuR:\u003c/p\u003e\n\u003cp\u003eTo analyze the mRNA expression of ACE2, Nrf2, GPx, NOS-2, SOD-2, and HuR, total RNA was extracted using 250 \u0026mu;L of blood per 750 \u0026mu;L of Trizol LS Reagent (Thermo-Fisher \u0026ndash; CAT no. 79852404) as per the protocol of the manufacturer. 500ng RNA sample was treated with DNase and subsequently utilized for the synthesis of cDNA using the kit (Thermo Scientific\u0026trade;- CAT no. K1622). To estimate mRNA levels by RT-qPCR, cDNA was amplified for the expression profiling of target genes using respective primers (Table 1), QuantStudio7 Flex system, SYBER Green qPCR Master Mix \u0026amp; 2-\u0026Delta;\u0026Delta;CT.\u003c/p\u003e\n\u003cp\u003eProtein\u0026rsquo;s Expressional Analysis by Enzyme-Linked Immunosorbent Assay (ELISA):\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSerum Ang II levels of each group were analyzed using ELISA. A commercially available ELISA kit (Cat # E1224Hu) was used, and ELISA was performed according to the instructions provided by the kit.\u003c/p\u003e\n\u003cp\u003eROS Estimation:\u003c/p\u003e\n\u003cp\u003eThe FOX assay was employed on serum samples to evaluate hydroperoxides as a proxy of oxidative stress (ROS) in all study groups\u003csup\u003e51,52\u003c/sup\u003e. Serum samples were diluted using deionized water by 1:10, then in the 96-well plate, 100 \u0026micro;L of diluted serum samples were mixed with 900 \u0026micro;L of FOX reagent and allowed to incubate for one hour in the dark. Standards of varying concentrations were made from 10\u0026micro;M and 50\u0026micro;M stock solutions of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2,\u003c/sub\u003e and a standard calibration curve was constructed. The microplate reader was used for recording OD at 560nm.\u003c/p\u003e\n\u003cp\u003eStatistical analysis:\u003c/p\u003e\n\u003cp\u003eData was analyzed using GraphPad Prism 8.0.1 and Microsoft Excel. Results are presented as mean + SD unless otherwise mentioned. Analysis among different sub-groups of COVID-19 patients is done by using ANOVA, followed by post-hoc comparison (Tukey\u0026rsquo;s multiple comparison tests) if the data were normally distributed, and for data that were not normally distributed, the Kruskal-Wallis test is used, and Dunn\u0026rsquo;s multiple comparison test was performed. The P-value, \u0026lt;0.05, is considered statistically significant. The standard curve and charts were prepared by using GraphPad Prism 8.0.1.\u003c/p\u003e\n\u003cp\u003eStudy limitations\u003c/p\u003e\n\u003cp\u003eThe most prominent limitation of this study is the unequal distribution of participants across disease severity groups, particularly the relatively small number of patients in the moderate category. This imbalance occurred due to the declining number of active COVID-19 cases during the later phase of the study period and the time-bound nature of sample collection at the clinical site. Additionally, strict inclusion criteria and reliance on clinically confirmed cases further limited the availability of eligible moderate cases. Despite this limitation, appropriate statistical methods were applied, including non-parametric tests where required, which are robust for datasets with unequal group sizes. While the findings provide meaningful insights into redox-inflammatory dysregulation across disease severity, future studies with larger and more balanced cohorts would further strengthen these observations.\u003c/p\u003e\n\u003cp\u003eAnother limitation, which was actually a technical limitation, was not performing the formal PCR efficiency determination tests using the standard curve, and the melt curve, but the Primers were designed using Primer Express to select sequences with appropriate thermodynamic properties and minimal predicted primer\u0026ndash;dimer formation.\u003c/p\u003e"},{"header":"Statements \u0026 Declarations","content":"\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003e“The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.”\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003e“The authors have no relevant financial or non-financial interests to disclose.”\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eRafat Amin:\u003c/strong\u003e Conceptualization, Investigation, Methodology, Resources, Validation, Writing - original draft. \u003cstrong\u003eSadaf Khan:\u0026nbsp;\u003c/strong\u003eConceptualization, Resources, Software, Supervision, Validation, Writing - review and editing. \u003cstrong\u003eMuhammad Wajih Uddin:\u003c/strong\u003e Data curation, Investigation, Software, Data analysis, Validation, Writing - original draft. \u003cstrong\u003eAsif Sheikh:\u003c/strong\u003e Provision of samples, clinical evaluation, and diagnosis of patients. \u003cstrong\u003eNaeem Solangi:\u003c/strong\u003e Data curation, Investigation. \u003cstrong\u003eAyesha Khalid:\u003c/strong\u003e Data curation, Investigation. \u003cstrong\u003eSalman Ahmed Khan:\u003c/strong\u003e Investigation, Resources. \u003cstrong\u003eTehseen Fatima:\u003c/strong\u003e Conceptualization, Investigation, Methodology, Project Administration, Resources, Supervision, Visualization, Writing - review and editing\u003c/p\u003e\n\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003e“This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the DUHS institutional review board, Dow University of Health Sciences (IRB-2256/DUHS/Approval/2021/655).”\u003c/p\u003e\n\u003ch2\u003eConsent to participate\u003c/h2\u003e\n\u003cp\u003e\u0026ldquo;Informed consent was obtained from all individual participants included in the study.\u0026rdquo;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLuo, G. \u0026amp; Gao, S. J. Global health concerns stirred by emerging viral infections. \u003cem\u003eJournal of Medical Virology\u003c/em\u003e \u003cstrong\u003e92\u003c/strong\u003e, 399-400, doi:10.1002/jmv.25683 (2020).\u003c/li\u003e\n\u003cli\u003eBaker, R. 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Spectrophotometric assays for evaluation of Reactive Oxygen Species (ROS) in serum: general concepts and applications in dogs and humans. \u003cem\u003eBMC Veterinary Research\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, doi:10.1186/s12917-021-02924-8 (2021).\u003c/li\u003e\n\u003cli\u003eKaterji, M., Filippova, M. \u0026amp; Duerksen-Hughes, P. Approaches and Methods to Measure Oxidative Stress in Clinical Samples: Research Applications in the Cancer Field. \u003cem\u003eOxidative Medicine and Cellular Longevity\u003c/em\u003e \u003cstrong\u003e2019\u003c/strong\u003e, 1-29, doi:10.1155/2019/1279250 (2019).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 - Primers used in this study\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForward Primer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReverse Primer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eGPx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e5\u0026apos; CGGGACTACACCCAGATGAA 3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003e5\u0026apos; TCTCTTCGTTCTTGGCGTTC 3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eNRF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e5\u0026apos; TCAGCATGCTACGTGATGAAG 3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003e5\u0026apos; TTTGCTGCAGGGAGTATTCA 3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eACE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e5\u0026apos;-ACAGTCCACACTTGCCCAAAT-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003e5\u0026apos;-TGAGAGCACTGAAGACCCATT-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eGAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e5\u0026rsquo;-ACCCAGAAGACTGTGGATGG-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003e5\u0026rsquo;- TGACAAAGTGGTCGTTGAGG-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eNOS-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e5\u0026rsquo;-ACAAGCCTACCCCTCCAGAT-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003e5\u0026rsquo;-TCCCGTCAGTTGGTAGGTTC-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eSOD-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e5\u0026rsquo;-GCCATTGCTTTTGGTGTTTT-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003e5\u0026rsquo;-AAATGGTGCTGGGAAAACTG-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eHuR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e5\u0026rsquo;-GTGACATCGGGAGAACGAAT-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003e5\u0026rsquo;-GCGGTCACGTAGTTCACAAA-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 - Comparison of RQ values across different severity levels and healthy individuals.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"732\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarkers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eANOVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTukey\u0026apos;s multiple comparisons test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Diff.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.00% CI of diff.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 73px;\"\u003e\n \u003cp\u003eNRF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.7452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-1.149 to -0.3416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.8575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-1.300 to -0.4154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.08444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.2765 to 0.4454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.9052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.1124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.5899 to 0.3652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.9037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eModerate vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.8296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.4260 to 1.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMild vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.4999 to 1.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 73px;\"\u003e\n \u003cp\u003eACE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-1.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-1.998 to -0.1333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.4109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-1.290 to 0.4683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-2.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-2.851 to -1.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.6549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.2776 to 1.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.2255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eModerate vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.9435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-1.841 to -0.04622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMild vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-1.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-2.440 to -0.7567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 73px;\"\u003e\n \u003cp\u003eSOD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-1.530 to -0.2501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-1.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-1.762 to -0.4130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.5222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-1.138 to 0.09359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.1975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.8374 to 0.4424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.8103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eModerate vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.3679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.2098 to 0.9455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.2959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMild vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.5654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.05038 to 1.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 73px;\"\u003e\n \u003cp\u003eNOS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.4097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.9620 to 0.1427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.1861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.2927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.2597 to 0.8450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.4468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.3502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.1484 to 0.8488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.2229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.7023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.1201 to 1.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eModerate vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.7598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.2283 to 1.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMild vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.0575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.4740 to 0.5890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.9891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 73px;\"\u003e\n \u003cp\u003eHuR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.1948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.8302 to 0.4406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.8095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.4332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-1.069 to 0.2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.2408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.3436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.2365 to 0.9236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.3495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.2384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.8737 to 0.3970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.7009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eModerate vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.5384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.04163 to 1.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMild vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.7768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.1967 to 1.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKruskal-Wallis test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDunn\u0026apos;s multiple comparisons test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean rank diff.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted P Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 73px;\"\u003e\n \u003cp\u003eGPX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-1.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSevere vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-1.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.08275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.9925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eModerate vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.4659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.3922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMild vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.5487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.2964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 90px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 - Comparison of Angiotensin II protein expression among the healthy and SARS-CoV-2 patient groups.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKruskal-Wallis test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDunn\u0026apos;s multiple comparisons test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean rank diff.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted P Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-32.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-15.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.0615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSevere vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-20.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.0042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e17.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.3614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003eModerate vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026gt;0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003eMild vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-5.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026gt;0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4 - Comparison of conc. of ROS values across different severity levels and healthy individuals.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKruskal-Wallis test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDunn\u0026apos;s multiple comparisons test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean rank diff.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted P Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026gt;0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e13.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026gt;0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSevere vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e36.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e11.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026gt;0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003eModerate vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e35.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.0135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 162px;\"\u003e\n \u003cp\u003eMild vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e23.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.5553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5 - Comparison of conc. of Ferritin values across different severity levels and healthy individuals, and other clinical markers among infected groups.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKruskal-Wallis test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDunn\u0026apos;s multiple comparisons test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean rank diff.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted P Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 116px;\"\u003e\n \u003cp\u003eFerritin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e13.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026gt;0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026gt;0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e37.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e-12.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026gt;0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eModerate vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e24.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.3066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eMild vs. Healthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e37.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e20.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.0912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.1078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026gt;0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003eLDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e20.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.0985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e-1.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026gt;0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003eNEUTROPHILS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e20.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.0926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e26.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e5.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026gt;0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003eLYMPHOCYTES (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e-19.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.1231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e-24.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e-4.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026gt;0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eANOVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHolm-Sidak\u0026apos;s multiple comparisons test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Diff.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted P Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.7823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e-0.5648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.8776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.04689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.9274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.6117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.8776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e****\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e5.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.0528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e5.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eModerate vs. Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.08167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.9743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch1\u003e\u0026nbsp;\u003c/h1\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"COVID-19, oxidative stress, inflammation, and expression profiling","lastPublishedDoi":"10.21203/rs.3.rs-9558551/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9558551/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eViral infections are one of the biggest threats ever known to humankind. Viral mutability against the human defense system makes them deadly. Virus manipulates host gene expressions, dictating the course of the disease using inflammation-induced oxidative stress. Characterized by uncontrolled Reactive oxygen species levels and weakened antioxidant defenses, which significantly impact viral disease severity. SARS-CoV-2, a constant threat due to its ability to mutate, showcases this interplay, emphasizing the need to understand host cellular responses. Unraveling these viral modulation patterns offers insight into predicting disease severity and identifying therapeutic targets.\u003c/p\u003e \u003cp\u003eThe study used infected groups (Severe, Moderate, and Mild) and a control group. The samples were collected using a non-probability stratified sampling approach and were collected early after diagnosis to minimize treatment-related variability: at hospitalization for severe cases, within 12 hours of clinic visit for moderate cases, and within 24 hours of RT-PCR confirmation for mild cases. Disease severity classification was performed by clinicians based on clinical symptoms and oxygen saturation (SpO₂). This research delves into assessing the intricate interplay between the severity of the viral infection and inflammation-induced redox imbalance by expression profile of key antioxidant genes (GPx, NRF2, SOD2), pro-inflammatory genes (ACE2, NOS2), and RNA-binding gene (HuR), by comparing the total hydroperoxides as an indirect markeer for ROS, serum Ang II levels, and clinical data among the groups infected individuals.\u003c/p\u003e \u003cp\u003eSignificant expressional dysregulation of proinflammatory, oxidative, and anti-oxidative markers is found among the study groups, especially in the severe group. ACE2, GPx, and SOD2 were downregulated, while NOS2, HuR, and Nrf2 were upregulated, which indicates a shift towards a pro-oxidant state, such as twice the amount of ROS in both severe and moderate groups, and Serum Ang II levels are halved in the severe group. Clinical biomarkers were also elevated. These findings establish a strong correlation between redox-inflammatory dysregulation and the severity of infections.\u003c/p\u003e","manuscriptTitle":"Dysregulation of redox-inflammatory markers in COVID-19: A multi-parameter analysis with disease severity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 17:20:44","doi":"10.21203/rs.3.rs-9558551/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"230206645706691504621048770344620491257","date":"2026-05-19T01:28:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-01T12:19:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-30T10:25:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T10:25:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Biology Reports","date":"2026-04-28T22:30:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"15003b1e-661a-4781-9dd8-c12d3151f8d2","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"230206645706691504621048770344620491257","date":"2026-05-19T01:28:23+00:00","index":30,"fulltext":""},{"type":"reviewersInvited","content":"20","date":"2026-05-01T12:19:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-30T10:25:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T10:25:45+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T17:20:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 17:20:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9558551","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9558551","identity":"rs-9558551","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}