Circulating NET biomarkers as predictors of inflammatory storm escalation and critical illness in COVID-19

Circulating NET biomarkers as predictors of inflammatory storm escalation and critical illness in COVID-19 | 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 Circulating NET biomarkers as predictors of inflammatory storm escalation and critical illness in COVID-19 Wenjuan Liu, Xin Pan, Ruyue Fan, Ying Yang, Na Sun, Peibin Hou, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7475642/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background COVID-19 manifests with significant clinical heterogeneity, ranging from mild respiratory symptoms to ventilator-dependent acute respiratory distress syndrome (ARDS). C, formed by the release of decondensed chromatin to immobilize pathogens, have been implicated; however, their relationship with disease severity and the need for advanced respiratory support remains unclear. Methods Ninety-nine patients who were diagnosed with COVID-19 in 2022–2023 were recruited. The NETs were assessed in plasma by quantifying cell-free deoxyribonucleic acid (cfDNA), protein-DNA complexes, and citrullinated Histone H3 (CitH3). Predictions of severe illness were analyzed with receiver operating characteristic curves. Results Plasma levels of cfDNA, histone-DNA and Myeloperoxidase (MPO)-DNA, Neutrophil Elastase (NE)-DNA and CitH3 were significantly elevated in patients with COVID-19 and increased with disease severity. Moreover, patients requiring mechanical ventilation or high-flow oxygen therapy had significantly higher levels of cfDNA, Histone-DNA and CitH3. Correlation analysis showed that Histone-DNA and CitH3 exhibited significant positive correlation with Procalcitonin (PCT), respectively. Receiver operating characteristic (ROC) curve analysis indicated that CitH3 distinguished severe cases better than the absolute counts or percentage of leukocytes and neutrophil subsets, and superior to the traditional inflammatory indicators, such as C-reactive protein (CRP), and Interleukin (IL)−6. Histone-DNA and MPO-DNA are equal to or better than some clinical indicators in distinguishing disease severity. Conclusion These results highlight the important roles of NETs remnants in viral infections. The CitH3 in plasma represent early predictive biomarkers for the prognosis of COVID-19. NETs cfDNA protein-DNA complexes CitH3 superior respiratory support disease severity Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Neutrophils, the predominant leukocytes in circulation, serve as initial defenders against microorganism invasion through phagocytosis [ 1 , 2 ]. This cell, characterized by a brief lifetime and rapid regeneration, contributes to the pulmonary immune response[ 3 ]. An essential characteristic of activated neutrophils is their capacity to form neutrophil extracellular traps (NETs), which are web-like structures made of chromatin and comprising the modified histone proteins and granule proteins, including myeloperoxidase (MPO) and neutrophil elastase (NE). The formation of NETs occurs during NETosis, an active cell-death mechanism distinct from apoptosis and necrosis [ 4 ]. NETs in pneumonia remain understudied, and they play a double-edged sword role in severe pneumonia. NETs have bactericidal activity, immediately eliminating infections by cytotoxic effects post-capture, or facilitating other neutrophils and phagocytes to phagocytize pathogens, thereby protecting the host. Conversely, NETs may provoke uncontrolled amplification of inflammatory cascades, resulting in lung tissue injury, and immunothrombosis. Current evidence suggests their dual functionality in both host defense and disease development. However, the precise molecular mechanisms by which NETs influenza the pathophysiology of severe pneumonia and their temporal functions in the progression of the disease remain inadequately elucidated. Until recently, neutrophils and NETosis were not regarded as significant contributors to respiratory viral infections[ 5 ]. Growing evidences demonstrates that neutrophils may have both beneficial and detrimental impacts during viral infections. Take influenza virus infection as an example. Elavated circulating levels of NETs are correlated with poor prognosis after influenza A infection, whereas increased NETs levels in bronchoalveolar lavage fluid correspond with lung disease [ 6 ]. Our previous studies highlighted neutrophils activation and NETs formation as the most indicative characteristics of severe influenza[ 7 ]. Several studies indicated that neutrophil depletion exacerbates the severity of these diseases in mice infected with H3N2 influenza[ 8 , 9 ]. Other studies have shown that limiting neutrophil influx after influenza A virus infection reduces the severity of pulmonary damage [ 10 , 11 ]. Arginine-rich histones can inhibit the uptake and replication of influenza virus by directly interacting with virus particles, thereby safeguarding the organism[ 6 ]. These two apparently contradictory statements suggest that the function of neutrophils and their exosomal production in respiratory viral infections remains unclear. Whether these specific viruses hihijacking neutrophils for their own benefit or temporal variations in the antiviral response needs to be elucidated. Millions of patients worldwide are affected by the coronavirus disease in 2019 (COVID-19). Major progress has been accomplished regarding the characterization of neutrophils in patients with COVID-19. COVID-19 parallels influenza, presenting a clinical range from mild upper respiratory symptoms to acute respiratory distress syndrome necessitating ventilatory support[ 12 ]. The excessive recruitment and activation of neutrophils, together with NETosis, are risk factors for acute lung injury (ALI) or acute respiratory distress syndrome (ARDS) caused by SARS-CoV-2 infection [ 13 – 15 ]. Emerging research indicates that NETs may serve as both a predictive biomarker for clinical stratification and a promising therapeutic target, possibly enabling novel immunomodulatory interventions for this life-threatening condition[ 16 , 17 ]. A judicious equilibrium of NETs formation may emerge as a potential avenue for the treatment of severe COVID-19 in the future. The severity of illness is intimately linked to the selection of timely and suitable respiratory support, which is crucial for the early identification of critically ill patients and reduce mortality via appropriate intervention. Nevertheless, research on NETs and the selection of respiratory support strategies after the assessment of illness severity is still lacking. This research aims to determine various plasma NETs indicators in hospitalized patients and determine their association with disease severity and the highest required respiratory support strategy, and their interaction with inflammatory markers. These data provide insights for research and early intervention or treatment of pulmonary infectious diseases. Methods Study population and sample collection Between December 2022 and July 2023, a total of 99 adult COVID-19 patients and 38 healthy controls were recruited from the Department of Pulmonary and Critical Care Medicine at Shandong Provincial Hospital in Jinan, China. The reverse real-time polymerase chain reaction (RT-PCR) examination of throat samples confirmed the presence of SARS-CoV-2 infection in all patients. Patients were diagnosed with pneumonia through chest computed tomography (CT). In accordance with the 10th Diagnosis and Treatment Plan for novel coronavirus Infection released by the National Health Commission of China, moderate pneumonia was characterized by high fever or respiratory tract symptoms (cough, shortness of breath, etc), accompanied by imaging findings indicative of pneumonia. The defining features of severe pneumonia included dyspnea, tachypnea (respiratory rate ≥ 30/min), hypoxemia (SpO₂ ≤ 93% or PaO₂/FiO₂ ratio ≤ 300), and/or extensive lung infiltrates (> 50%) appearing within 24–48 hours; alternatively, it manifested as respiratory failure, septic shock, and/or multiple organ dysfunction syndrome (MODS)/failure. Based on the diagnostic criteria described above and previously reported[ 18 ], 57 patients were diagnosed with severe disease, whereas 42 patients were classified with moderate illness. Fatal outcomes were defined as death from any cause within 60 days of COVID-19 hospitalization. A control group included 38 healthy individuals without signs of viral infection. Blood samples were collected at designated time points, processed within 2 hours, centrifuged at 1500 rpm for 10 minutes at 20°C, and stored as plasma at -80°C until thawed for testing. Chest CT Protocols and image analysis All images were acquired using a multi-detector HiSpeed Dual CT scanner, covering the region from the upper thoracic inlet to the inferior costophrenic angle. Reconstructed at 0.6-mm slice thickness with identical increments, the chest CT images were independently reviewed by three experienced radiologists (5 + years); discrepancies were resolved through consensus discussion. The area of interest was manually delineated by marking the lesion’s most confined area (i.e., the area of highest intensity) on CT images. The predominant chest CT findings in COVID−19 patients included ground-glass opacity (GGO), consolidation, reticulation (with/without septal thickening), linear bands, bronchial wall thickening, nodules, bronchiectasis, and interlobar pleural traction[ 19 , 20 ]. Measurement of cell-free Deoxyribonucleic Acid (cfDNA) The cfDNA levels in the plasma were quantified using the Quant-iT™ PicoGreen™ double-stranded DNA (dsDNA) assay in accordance with the manufacturer’s instructions (Thermo Fisher Scientific). After loading standards/plasma (50 µl) into black 96-well plates, 50 µl Quant-iT PicoGreen working solution was added to each well. The plates were incubated at 20°C for 5 min in light-protected conditions, then fluorescence intensity (emission: 485 nm) was quantified on a Varioskan Flash microplate reader (Thermo Fisher Scientific). Serial dilutions created a 5-point standard curve ranging from 1 ng/mL to 1 µg/mL, which was used to determine sample DNA concentrations. The quantification of histone-DNA and Myeloperoxidase (MPO)-DNA complexes Flat-bottom 96-well plates were coated with anti-histone antibody (Roche Cell Death ELISA PLUS Kit) and anti-MPO antibody (Abcam) at 1:500 dilution. Plasma samples (20 µl) were mixed with 80 µl incubation buffer containing 5% HRP-conjugated anti-DNA antibody (Roche Cell Death Detection ELISA Kit) and incubated (2 h, 20°C). After adding 3-ethylbenzothiazoline-6-sulphonic acid substrate, absorbance was measured at 405 nm. To minimize inter-assay variability, sample optical density (OD) values were normalized to a reference plasma OD (OD index = sample OD/reference OD). Measuring Neutrophil Elastase (NE)-DNA and Citrullinated Histone H3 (CitH3) concentrations The concentrations of NE-DNA (mlbio) and CitH3 (J&l Biological) in the plasma samples were measured according to the manufacturer’s instructions. All the samples were diluted 2 times prior to detection. The protein levels were quantified against standard curves. Statistical analysis Statistical analyses used GraphPad Prism 5.0, applying Student’s t -test, Mann-Whitney U test, chi-square test, or Fisher's exact test as appropriate. * P < 0.05, ** P < 0.01 and *** P < 0.001 represent significant differences. We analyzed the differences in the expression of NETs-associated biomarkers between healthy donors and patients with different degrees of disease severity. Meanwhile, we employed R packages (‘pROC’) to generate Receiver Operating Characteristic (ROC) curves for biomarkers related to NETs and clinical parameters. Results Demographics and clinical characteristics of patients with COVID-19 A total of 137 participants were included in this study, comprising 99 COVID-19 patients and 38 healthy individuals. All COVID-19 patients enrolled had at least one comorbidity, with the most prevalent disease following pneumonia being hypertension. Typical chest CT imaging was shown in Fig. 1 A. Imaging studies conducted at the time of admission can assist in risk stratification of patients with COVID-19. Bilateral lungs showed multiple linear band-shaped ground-glass opacities in moderate patients on chest CT. Conversely, severe patients exhibited bilateral multifocal consolidations with characteristic peripheral predominance on chest CT. And the lesions were predominantly localized across the whole lung area, commonly referred to as the “white lung” sign. The total leukocyte counts, neutrophil counts, and percentage of neutrophils were markedly elevated in severe patients compared to moderate patients, whereas lymphocyte counts and the proportion of lymphocytes were diminished. Both serum albumin and PaO 2 /FiO 2 ratios were significantly decreased in severe patients compared to moderate groups. Hence the highest-level respiratory support strategy was implemented and systematically summarized. Among all patients, 92.93% (92 of 99) required at least one type of supplemental oxygen support. Among severe cases, 50.88% (29/57) required advanced respiratory support, such as high-flow nasal cannula oxygen therapy (HFNC) or mechanical ventilation support, with 14.04% in-hospital mortality observed. The clinical characteristics of all participants are summarized in Table 1 . Table 1 Patient characteristics by disease severity Total ( n = 99) Moderate ( n = 42) Severe ( n = 57) P-value Gender 0.0140 a Male 61 (61.61%) 20 (47.62%) 41 (71.93%) Female 38 (38.38%) 22 (52.38%) 16 (28.07%) Age/years (mean (SD)) 70.12 (14.45) 66.62 (15.58) 72.70 (13.10) 0.0377 b Comorbidity Pneumonia disease 99 (100%) 42 (100%) 57 (100%) - Cancer 17 (17.17%) 6 (14.29%) 11 (19.30%) 0.5134 a Hypertension 42 (42.42%) 12 (28.57%) 30 (52.63%) 0.0167 a Diabetes 21 (21.21%) 9 (21.43%) 12 (21.05%) 0.9639 a Coronary heart disease 33 (33.33%) 14 (33.33%) 19 (33.33%) 0.9999 a Cerebrovascular disease 17 (17.17%) 9 (21.43%) 8 (14.04%) 0.3350 a Laboratory tests (mean (SD)) Total leukocytes (×10 9 /L) 7.97 (3.87) 7.04 (3.66) 8.66 (3.90) 0.0391 b Neutrophil (×10 9 /L) 6.47 (3.72) 5.35 (3.43) 7.30 (3.74) 0.0094 b Proportion of Neutrophil (%) 78.14 (13.36) 72.56 (13.03) 82.24 (12.15) 0.0003 b Lymphocytes (×10 9 /L) 0.98 (0.67) 1.17 (0.79) 0.84 (0.54) 0.0164 b Proportion of Lymphocytes (%) 14.99 (10.68) 19.60 (10.48) 11.59 (9.57) 0.0001 b Platelet (×10 9 /L) 203.16 (94.52) 216.14 (84.99) 193.60 (100.63) 0.2428 b Albumin (g/L) 33.44 (4.74) 35.90 (4.57) 31.63 (4.02) 0.0000 b C-reactive protein (mg/l) 38.10 (48.89) 27.10 (43.08) 46.20 (51.64) 0.0543 b Procalcitonin (ng/ml) 0.15 (0.24) 0.11 (0.10) 0.18 (0.30) 0.1021 b Interleukin-6 (pg/ml) 29.38 (76.96) 14.83 (23.79) 40.10 (98.36) 0.1068 b PaO2/FiO2 grade (mmHg) >300 42 (42.42%) 42 (100%) 0 0.0000 a >200 to ≤ 300 25 (25.25%) 0 25 (43.86%) 0.0000 a >100 to ≤ 200 25 (25.25%) 0 25 (43.86%) 0.0000 a ≤ 100 7 (7.08%) 0 7 (12.28%) 0.0196 c Respiratory support Invasive mechanical ventilation 1 (1.01%) 0 1 (1.75%) 1.0000 c Noninvasive mechanical ventilatory support 5 (5.05%) 0 5 (8.77%) 0.0635 c High-flow nasal cannula oxygen therapy 24 (24.24%) 1 (2.38%) 23 (40.35%) 0.0000 a Low-flow oxygen therapy 62 (62.63%) 34 (80.95%) 28 (49.12%) 0.0012 a Outcome Hospitalization 99 (100%) 42 (100%) 57 (100%) - Admission to ICU 6 (6.06%) 0 6 (10.53%) 0.0371 c Death 8 (8.08%) 0 8 (14.04%) 0.0193 c All the participants resided in Shandong, China. Data are mean (SD) or n (%). p values were calculated by Student's t test, chi-squared test, or Fisher's exact test, as appropriate. a Compared by two-sided chi-squared test. b Compared by two-sided Student’s t test. c Compared by two-sided Fisher's exact test. Elevated plasma NETs correlate with increased COVID-19 severity To investigate and distinguish the functions of NETs following infection, plasma was extracted from COVID-19 patients and healthy donors to analyze various markers of the formation process of NETs (also referred as NETosis). Soluble NETs remnants can exist in the form of cfDNA. Compared with healthy donors, COVID-19 patients exhibited elevated plasma cfDNA concentrations, which were highest in the severe group ( Fig. 1 B ) . And all specific for NETs remnants, histone-DNA, MPO-DNA, and NE-DNA complexes ( Fig. 1 C-E ) displayed similar profiles in COVID-19 patients and healthy controls. CitH3 is a marker of NETosis, and the severely infected patients had higher levels of CitH3 in the plasma than moderate patients and healthy controls ( Fig. 1 F ) . Together, these data demonstrate that plasma NET remnants levels correlate with COVID-19 severity, suggesting their pathogenic role in disease progression. Elevated NETs levels correlated with superior respiratory support in COVID-19 The disease severity can be reflected by the type of respiratory support required; therefore, we next assessed the clinical status corresponding to each accessible plasma sample. We compared samples from individuals necessitating advanced respiratory support (e.g., HFNC or mechanical ventilation, n = 30 samples) with samples from patients receiving less intensive support (oxygen provided via nasal cannula or face mask, n = 62 samples). As compared with patients who received conventional oxygen therapy, patients requiring mechanical ventilation or HFNC had significantly higher levels of cfDNA ( Fig. 2 A ) , histone-DNA ( Fig. 2 B ) and CitH3 ( Fig. 2 C ) , but not MPO-DNA ( Fig. 2 D ) and NE-DNA ( Fig. 2 E ) . Elevated CitH3 levels were specifically observed in severely ill COVID-19 patients requiring escalated respiratory support ( Fig. 2 F ) . Collectively, these data indicate circulating NETs increased as oxygenation deteriorated. Circulating NETs exhibited significant positive correlations with PCT Numerous laboratory markers, including neutrophils, lymphocytes, CRP, procalcitonin (PCT), and IL-6, have been reported to be related to the morbidity and mortality of COVID-19. Thus, the association between circulating NETs and laboratory parameters was further assessed. All available samples (n = 99) underwent subsequent correlation analyses. Significant correlations linked histone-DNA to CitH3 (r = 0.4714, p < 0.0001), and similarly connected MPO-DNA with NE-DNA (r = 0.6192, p < 0.0001) ( Fig. 3 A-B ) . Pearson correlation analyses were conducted between NETs-related biomarker measurements and inflammatory indicators, as shown in Fig. 3 C. The results were also listed in Table S1 and Table S2 . Additionally, scatter plots highlighted results where correlation coefficients exceeded |0.3| and were statistically significant (p < 0.05). Results showed that histone-DNA and CitH3 exhibited significant positive correlation with PCT, respectively ( Fig. 3 D,E ) . In summary, these data indicate a potential correlation between plasma NET levels and dysfunctional inflammatory response. Circulating NETs as putative biomarkers for predicting disease severity Given the central role of neutrophils and NETs in COVID−19 pathogenesis, we posit that quantification of their associated biomarkers could reflect clinical disease progression. ROC curves were used to evaluate the predictive value of cfDNA, histone-DNA, MPO-DNA, NE-DNA, and CitH3 ( Fig. 4 ) . The results indicated an AUC value of 0.767 for CitH3 in predicting severity, exceeding the AUC values of all inflammatory biomarkers for this prediction. Therefore, CitH3 distinguished severe cases better than the absolute counts or percentage of leukocytes and neutrophil subsets, and superior to the traditional inflammatory indicators, such as CRP, PCT, and IL−6. Moreover, the ability of histone-DNA and MPO-DNA to distinguish disease severity was comparable to or superior to that of some clinical indicators. MPO-DNA has a similar predictive value to traditional clinical parameters (percentages of neutrophils and lymphocytes), and is better than all inflammatory indicators ( Fig. 4 ) . Collectively, these data suggest circulating NETs demonstrate potential for predicting outcomes and implicate inflammatory imbalance in poor COVID−19 prognosis. Discussion This research focused on understanding how NETs impact disease progression and clinical outcomes in individuals admitted to the hospital due to COVID-19. Our study revealed a new observation: patients in need of advanced respiratory assistance exhibited markedly increased plasma concentrations of NETs. In addition, our results supported prior evidence suggesting that elevated NET concentrations in the bloodstream are closely linked to disease severity and inflammatory response, which suggested that early and timely monitoring of NETs is beneficial for evaluating the extent of inflammatory response and the status of immune function in these patients. Finally, we have demonstrated that on the day of patient admission, the plasma levels of CitH3 and MPO-DNA have a relatively significant advantage in predicting the disease severity. These results may offer new perspectives for optimizing the clinical application of antibiotics and immunomodulators, ultimately contributing to better patient outcomes. Earlier investigations have shown that fragments derived from neutrophil immune responses-such as extracellular nucleic acids and chromatin-bound particles tend to accumulate in individuals affected by COVID-19[ 21 , 22 ]. Building on these observations, our study not only verified this increase but also uncovered a link between the presence of these immune byproducts and the clinical decision to implement advanced respiratory support. The results suggested that increased levels of circulating NETs were correlated with local pulmonary dysfunction. This finding may be attributed to several reasons. One notable explanation is that a considerable proportion of critically ill patients succumbed to ARDS [ 23 – 25 ]. Acute inflammation, both local and systemic,is a key feature of ARDS and plays a critical role in damaging both the lung epithelium and endothelium. During this process, neutrophils move from the pulmonary circulation into the airway spaces, where they can release various harmful substances [ 26 ]. In our cohort, 14.04% (8/57) of patients diagnosed with ARDS succumbed within a 60-day period. This indicates that ARDS played a more pivotal role in fatal outcomes among critically ill individuals than broader respiratory insufficiency. Accordingly, measurements of extracellular DNA in the bloodstream serve as more accurate biomarkers for COVID-19-related ARDS. Secondly, an elevation in plasma NETs levels may result from the hyperactivation of circulating neutrophils and/or the compromis e of the alveolar-capillary barrier. In clinical practice, infection with SARS-CoV-2 often presents alongside vascular disorders in the lungs, such as the development of embolic events[ 27 , 28 ]. Vascular injury and thrombosis are significant mechanisms of air-blood barrier disruption induced by COVID-19. The extent of clot-related complications, including microvascular thrombosis and deep venous clot formation, has shown a strong association with the concentration of specific indicators linked to NETs [ 29 ]. In ARDS, the formation of NETs may disrupt the equilibrium between inflammatory responses and coagulation processes[ 30 ]. Long-term exposure to this elevated inflammatory equilibrium leads to pathological repair, characterized by persistent damage and repeated repair of normal tissues, which eventually leads to tissue fibrosis, thickening of the air-blood barrier, and obstruction of gas exchange between the alveoli and the microcirculation. These two scenarios may indicate the progression of multi-organ damage driven by widespread inflammatory activity. In conclusion, elevated NET remnant levels in patients with progressive pulmonary function decline further substantiate their role as indicators of severe lung involvement. Notably, in our analysis of various biomarkers linked to NETs,only cfDNA, histone DNA and CitH3 showed significant increases in patients requiring advanced respiratory support strategy, while more targeted indicators like MPO-DNA and NE-DNA remained comparatively unchanged. Cell-free DNA and nucleosome-associated material are not exclusively derived from neutrophils; they may also be released by other immune cells like eosinophils and mastocytes, as well as by structural tissue cells during programmed cell death or unregulated cellular breakdown [ 31 ]. Therefore, DNA fragments found outside cells in the blood of COVID-19 patients could stem from multiple cellular sources, including vascular lining cells, airway epithelium, and immune cells beyond neutrophils. These findings align with the dominant perspective that respiratory impairment in COVID-19 is driven by a combination of contributing factors.[ 32 ]. Despite the uncertainty regarding the cellular origin, the plasma levels of cfDNA and histone-DNA are more likely indicative of pulmonary inflammation and tissue damage. Histone H3 can undergo chemical alteration after protein synthesis, resulting in a modified form known as citrullinated H3 (CitH3). During NETs formation, histones are expelled from the nuclei of neutrophils and subsequently citrullinated through the enzymatic actions of protein arginine deiminase 4 (PAD4) and the calcium-dependent enzyme peptidyl arginine deiminase 2 (PAD2)[ 33 ]. This modification leads to the extrusion of CitH3 into the extracellular environment. Recent studies have linked CitH3 levels to the severity of sepsis[ 34 ] and suggested its potential as a therapeutic target for endotoxic shock[ 35 ]. Histone H3 is recognized for inducing thrombocytopenia and thromboembolic events by activating and aggregating platelets [ 36 – 38 ], and it exerts potent cytotoxic effects via the activation of apoptosis. Nevertheless, the exact function of CitH3 in ARDS caused by COVID-19 has yet to be fully elucidated. This study demonstrates that CitH3 adversely impacts disease progression and serves as a detrimental prognostic indicator. A broad range of clinical biomarkers, such as total leukocyte levels, lymphocyte and neutrophil measurements, along with inflammatory markers like CRP, PCT, and IL-6, have been thoroughly explored in prior research and linked to both the progression and fatal outcomes of COVID-19[ 39 ]. This research focused on plasma levels of NETs and observed that the expression of CitH3 and MPO-DNA could serve as a more reliable indicator for recognizing individuals at risk of severe progression than traditional inflammatory markers such as white cell counts, IL-6, CRP, and PCT. And histone-DNA and CitH3 exhibited significant positive correlation with PCT, respectively. Because PCT elevation is typically a result of concurrent bacterial involvement, its usefulness in evaluating the severity of illness shortly after initial SARS-CoV-2 exposure may be significantly reduced[ 40 ]. Therefore, circulating CitH3 and histone-DNA could act as standalone biomarkers for predicting the likelihood of critical disease progression in individuals infected with COVID-19. There are several constraints to our research. Firstly, it is still unclear whether the detected NET remnants play a direct role in worsening the disease or are merely secondary outcomes of the intense inflammatory response observed in patients. Secondly, whether changes in NETs are specific to SARS-CoV-2 infection remains unknown. Similar to several investigation, this research compared healthy or non-severe individuals to severe COVID-19 patients. Although this does not invalidate findings in SARS-CoV-2 infection, their conclusions may overstate a specific cell or pathway’s role as unique to COVID-19 pathogenesis when it mirrors responses seen in conditions like sepsis from other causes. Finally, the majority of patients enrolled in this study received combination therapies, which included corticosteroids among other medications, which could affect neutrophil number and the generation of NETs. Future research needs to clearly demonstrate that, after eliminating confounding factors, using plasma levels of NETs to assist in predicting and evaluating the disease severity of COVID-19 may have a positive effect. Conclusion Our research demonstrated a significant correlation between the presence of circulating NETs fragments and the severity of illness, as well as systemic inflammatory activity. Notably, patients who needed escalated forms of respiratory assistance showed markedly higher levels of NETs in their blood. Moreover, plasma CitH3 emerged as a promising early biomarker for forecasting disease progression in COVID-19 cases. These outcomes may offer valuable directions for optimizing clinical decisions, particularly in the use of antimicrobial agents and immune-modulating therapies, ultimately enhancing patient recovery and outcomes. Abbreviations ARDS Acute respiratory distress syndrome NETs Neutrophil extracellular traps cfDNA cell-free deoxyribonucleic acid CitH3 Citrullinated Histone H3 MPO Myeloperoxidase NE neutrophil elastase ROC Receiver operating characteristic PCT Procalcitonin CRP C-reactive protein ALI Acute lung injury RT-PCR Reverse real-time polymerase chain reaction CT Computed tomography MODS Multiple organ dysfunction syndrome GGO Ground-glass opacity HFNC High-flow nasal cannula oxygen therapy PAD4 Protein arginine deiminase 4 PAD2 Peptidyl arginine deiminase 2 Declarations Ethics approval and consent to participate This study involving human participants complied with the Declaration of Helsinki and was approved by the Shandong Provincial Hospital Ethics Committee (No. 2023-805). Written informed consent was obtained from all participants. Clinical Trial Not applicable Consent for publication Not applicable Availability of data and material The data used in the current study are accessible from the corresponding authors upon reasonable request. Competing interests The authors declare no competing interests. Funding This work was supported by the Natural Science Foundation of China (grant number 82200014), the Natural Science Foundation of Shandong Province (grant number ZR2024QH025 and ZR2023MH250). Authors’ contributions S.L., W.J.L., and L.L.S. conceived the project. X.P., R.Y.F., Y.Y., N.S., P.B.H., Z.W.C., C.J.H., and S.L. discussed and designed the experiments. W.J.L., X.P., and R.Y.F. conducted the experiments. S. L., and W.J.L. performed statistical analyses. W.J.L., X.P., and L.L.S. wrote the manuscript. All authors interpreted the results and edited the manuscript. All authors reviewed and approved the final manuscript. 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Williams P, McWilliams C, Soomro K, Harding I, Gurney S, Thomas M, Albur M, Martin Williams O. The dynamics of procalcitonin in COVID-19 patients admitted to Intensive care unit - a multi-centre cohort study in the South West of England, UK. J Infect. 2021;82(6):e24–6. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableBMCID.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":707368,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncreased plasma levels of neutrophil extracellular traps in patients with COVID-19 were associated with disease severity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Chest computed tomography (CT) scan features of COVID-19. Chest CT from moderate patients showed bilateral scattered ground glass opacities. And the severe patients showed confluent and predominantly patchy ground glass opacities with pronounced peripheral distribution, and partial consolidation.\u003cstrong\u003e (B-E)\u003c/strong\u003ePlasma levels of \u003cstrong\u003e(B) \u003c/strong\u003ecell-free deoxyribonucleic acid (cfDNA), \u003cstrong\u003e(C) \u003c/strong\u003ehistone-DNA, \u003cstrong\u003e(D) \u003c/strong\u003emyeloperoxidase (MPO)-DNA , \u003cstrong\u003e(E)\u003c/strong\u003e neutrophil elastase (NE)-DNA, and \u003cstrong\u003e(F)\u003c/strong\u003e citrullinated histone H3 (CitH3) in the patients with COVID-19 and healthy donors (HD). Data are displayed as the mean ± SD. Statistical significance was determined by an unpaired \u003cem\u003et\u003c/em\u003e test. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7475642/v1/d0fde0c520f52d52d9f0d6e3.png"},{"id":92471372,"identity":"d62e8223-dff3-4d74-b84f-ca150aa5caec","added_by":"auto","created_at":"2025-09-30 06:49:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":205195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElevated neutrophil extracellular traps levels was correlated with superior respiratory support in COVID-19\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma samples were grouped by specific respiratory support strategies (Common, oxygen provided via nasal cannula or face mask; Superior, high-flow nasal cannula oxygen therapy or mechanical ventilation) and analyzed for \u003cstrong\u003e(A)\u003c/strong\u003e cfDNA, \u003cstrong\u003e(B) \u003c/strong\u003ehistone-DNA, \u003cstrong\u003e(C) \u003c/strong\u003eMPO-DNA, \u003cstrong\u003e(D)\u003c/strong\u003e NE-DNA, and \u003cstrong\u003e(F)\u003c/strong\u003e CitH3. Groups were compared by Mann-Whitney \u003cem\u003eU\u003c/em\u003e test; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; \u003cem\u003ens\u003c/em\u003e, not significant. \u003cstrong\u003e(F)\u003c/strong\u003ePlasma levels of CitH3 in severe patients with different respiratory support strategies. Statistical significance was determined by an unpaired \u003cem\u003et\u003c/em\u003etest. **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7475642/v1/19aaaeadb8ccabed99367304.png"},{"id":92471376,"identity":"2961eb83-a5a7-4bfc-aace-6ac65255f671","added_by":"auto","created_at":"2025-09-30 06:49:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":374011,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between neutrophil extracellular traps and clinical parameter in patients with COVID-19\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A-B)\u003c/strong\u003e Correlation analysis and scatter plot of the neutrophil extracellular traps remnants in the plasma of patients with COVID-19. \u003cstrong\u003e(C)\u003c/strong\u003e Correlation-based heat-map demonstrating the association between the measured neutrophil extracellular traps remnants and clinical parameter in patients with COVID-19. Blue represents positive correlation, and red represents negative correlation. \u003cstrong\u003e(D-E) \u003c/strong\u003eCorrelation analysis between procalcitonin expression levels and key neutrophil extracellular traps remnants \u003cstrong\u003e(D)\u003c/strong\u003e histone-DNA and \u003cstrong\u003e(E)\u003c/strong\u003e CitH3. A Pearson correlation test was used for the association, *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7475642/v1/55e966a46a0d77fae8e53e78.png"},{"id":92471374,"identity":"d61ffbf9-12ac-4e3d-ae5e-7dad08f96622","added_by":"auto","created_at":"2025-09-30 06:49:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82810,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver-operator characteristic (ROC) curves of neutrophil extracellular traps remnants and main clinical parameters to predict disease severity\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7475642/v1/dc524198402eff0a8831efd5.png"},{"id":93812751,"identity":"6a6d4804-79c0-41ba-b976-53f16c0aacdc","added_by":"auto","created_at":"2025-10-17 21:03:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2556639,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7475642/v1/6b96bc14-1617-465b-b5d2-069b4cbde053.pdf"},{"id":92471370,"identity":"e5f29590-7fa4-4910-bc84-b23b5615c118","added_by":"auto","created_at":"2025-09-30 06:49:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17196,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableBMCID.docx","url":"https://assets-eu.researchsquare.com/files/rs-7475642/v1/4d1e9ad878e03d89c34391b4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Circulating NET biomarkers as predictors of inflammatory storm escalation and critical illness in COVID-19","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeutrophils, the predominant leukocytes in circulation, serve as initial defenders against microorganism invasion through phagocytosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This cell, characterized by a brief lifetime and rapid regeneration, contributes to the pulmonary immune response[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. An essential characteristic of activated neutrophils is their capacity to form neutrophil extracellular traps (NETs), which are web-like structures made of chromatin and comprising the modified histone proteins and granule proteins, including myeloperoxidase (MPO) and neutrophil elastase (NE). The formation of NETs occurs during NETosis, an active cell-death mechanism distinct from apoptosis and necrosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNETs in pneumonia remain understudied, and they play a double-edged sword role in severe pneumonia. NETs have bactericidal activity, immediately eliminating infections by cytotoxic effects post-capture, or facilitating other neutrophils and phagocytes to phagocytize pathogens, thereby protecting the host. Conversely, NETs may provoke uncontrolled amplification of inflammatory cascades, resulting in lung tissue injury, and immunothrombosis. Current evidence suggests their dual functionality in both host defense and disease development. However, the precise molecular mechanisms by which NETs influenza the pathophysiology of severe pneumonia and their temporal functions in the progression of the disease remain inadequately elucidated.\u003c/p\u003e\u003cp\u003eUntil recently, neutrophils and NETosis were not regarded as significant contributors to respiratory viral infections[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Growing evidences demonstrates that neutrophils may have both beneficial and detrimental impacts during viral infections. Take influenza virus infection as an example. Elavated circulating levels of NETs are correlated with poor prognosis after influenza A infection, whereas increased NETs levels in bronchoalveolar lavage fluid correspond with lung disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Our previous studies highlighted neutrophils activation and NETs formation as the most indicative characteristics of severe influenza[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Several studies indicated that neutrophil depletion exacerbates the severity of these diseases in mice infected with H3N2 influenza[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Other studies have shown that limiting neutrophil influx after influenza A virus infection reduces the severity of pulmonary damage [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Arginine-rich histones can inhibit the uptake and replication of influenza virus by directly interacting with virus particles, thereby safeguarding the organism[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These two apparently contradictory statements suggest that the function of neutrophils and their exosomal production in respiratory viral infections remains unclear. Whether these specific viruses hihijacking neutrophils for their own benefit or temporal variations in the antiviral response needs to be elucidated.\u003c/p\u003e\u003cp\u003eMillions of patients worldwide are affected by the coronavirus disease in 2019 (COVID-19). Major progress has been accomplished regarding the characterization of neutrophils in patients with COVID-19. COVID-19 parallels influenza, presenting a clinical range from mild upper respiratory symptoms to acute respiratory distress syndrome necessitating ventilatory support[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The excessive recruitment and activation of neutrophils, together with NETosis, are risk factors for acute lung injury (ALI) or acute respiratory distress syndrome (ARDS) caused by SARS-CoV-2 infection [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Emerging research indicates that NETs may serve as both a predictive biomarker for clinical stratification and a promising therapeutic target, possibly enabling novel immunomodulatory interventions for this life-threatening condition[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A judicious equilibrium of NETs formation may emerge as a potential avenue for the treatment of severe COVID-19 in the future. The severity of illness is intimately linked to the selection of timely and suitable respiratory support, which is crucial for the early identification of critically ill patients and reduce mortality via appropriate intervention. Nevertheless, research on NETs and the selection of respiratory support strategies after the assessment of illness severity is still lacking.\u003c/p\u003e\u003cp\u003eThis research aims to determine various plasma NETs indicators in hospitalized patients and determine their association with disease severity and the highest required respiratory support strategy, and their interaction with inflammatory markers. These data provide insights for research and early intervention or treatment of pulmonary infectious diseases.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population and sample collection\u003c/h2\u003e\u003cp\u003e Between December 2022 and July 2023, a total of 99 adult COVID-19 patients and 38 healthy controls were recruited from the Department of Pulmonary and Critical Care Medicine at Shandong Provincial Hospital in Jinan, China. The reverse real-time polymerase chain reaction (RT-PCR) examination of throat samples confirmed the presence of SARS-CoV-2 infection in all patients. Patients were diagnosed with pneumonia through chest computed tomography (CT). In accordance with the 10th Diagnosis and Treatment Plan for novel coronavirus Infection released by the National Health Commission of China, moderate pneumonia was characterized by high fever or respiratory tract symptoms (cough, shortness of breath, etc), accompanied by imaging findings indicative of pneumonia. The defining features of severe pneumonia included dyspnea, tachypnea (respiratory rate\u0026thinsp;\u0026ge;\u0026thinsp;30/min), hypoxemia (SpO₂ \u0026le; 93% or PaO₂/FiO₂ ratio\u0026thinsp;\u0026le;\u0026thinsp;300), and/or extensive lung infiltrates (\u0026gt;\u0026thinsp;50%) appearing within 24\u0026ndash;48 hours; alternatively, it manifested as respiratory failure, septic shock, and/or multiple organ dysfunction syndrome (MODS)/failure. Based on the diagnostic criteria described above and previously reported[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], 57 patients were diagnosed with severe disease, whereas 42 patients were classified with moderate illness. Fatal outcomes were defined as death from any cause within 60 days of COVID-19 hospitalization. A control group included 38 healthy individuals without signs of viral infection. Blood samples were collected at designated time points, processed within 2 hours, centrifuged at 1500 rpm for 10 minutes at 20\u0026deg;C, and stored as plasma at -80\u0026deg;C until thawed for testing.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eChest CT Protocols and image analysis\u003c/h3\u003e\n\u003cp\u003eAll images were acquired using a multi-detector HiSpeed Dual CT scanner, covering the region from the upper thoracic inlet to the inferior costophrenic angle. Reconstructed at 0.6-mm slice thickness with identical increments, the chest CT images were independently reviewed by three experienced radiologists (5\u0026thinsp;+\u0026thinsp;years); discrepancies were resolved through consensus discussion. The area of interest was manually delineated by marking the lesion\u0026rsquo;s most confined area (i.e., the area of highest intensity) on CT images. The predominant chest CT findings in COVID\u0026minus;19 patients included ground-glass opacity (GGO), consolidation, reticulation (with/without septal thickening), linear bands, bronchial wall thickening, nodules, bronchiectasis, and interlobar pleural traction[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eMeasurement of cell-free Deoxyribonucleic Acid (cfDNA)\u003c/h3\u003e\n\u003cp\u003eThe cfDNA levels in the plasma were quantified using the Quant-iT\u0026trade; PicoGreen\u0026trade; double-stranded DNA (dsDNA) assay in accordance with the manufacturer\u0026rsquo;s instructions (Thermo Fisher Scientific). After loading standards/plasma (50 \u0026micro;l) into black 96-well plates, 50 \u0026micro;l Quant-iT PicoGreen working solution was added to each well. The plates were incubated at 20\u0026deg;C for 5 min in light-protected conditions, then fluorescence intensity (emission: 485 nm) was quantified on a Varioskan Flash microplate reader (Thermo Fisher Scientific). Serial dilutions created a 5-point standard curve ranging from 1 ng/mL to 1 \u0026micro;g/mL, which was used to determine sample DNA concentrations.\u003c/p\u003e\n\u003ch3\u003eThe quantification of histone-DNA and Myeloperoxidase (MPO)-DNA complexes\u003c/h3\u003e\n\u003cp\u003eFlat-bottom 96-well plates were coated with anti-histone antibody (Roche Cell Death ELISA\u003csup\u003ePLUS\u003c/sup\u003e Kit) and anti-MPO antibody (Abcam) at 1:500 dilution. Plasma samples (20 \u0026micro;l) were mixed with 80 \u0026micro;l incubation buffer containing 5% HRP-conjugated anti-DNA antibody (Roche Cell Death Detection ELISA Kit) and incubated (2 h, 20\u0026deg;C). After adding 3-ethylbenzothiazoline-6-sulphonic acid substrate, absorbance was measured at 405 nm. To minimize inter-assay variability, sample optical density (OD) values were normalized to a reference plasma OD (OD index\u0026thinsp;=\u0026thinsp;sample OD/reference OD).\u003c/p\u003e\n\u003ch3\u003eMeasuring Neutrophil Elastase (NE)-DNA and Citrullinated Histone H3 (CitH3) concentrations\u003c/h3\u003e\n\u003cp\u003eThe concentrations of NE-DNA (mlbio) and CitH3 (J\u0026amp;l Biological) in the plasma samples were measured according to the manufacturer\u0026rsquo;s instructions. All the samples were diluted 2 times prior to detection. The protein levels were quantified against standard curves.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses used GraphPad Prism 5.0, applying Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test, Mann-Whitney U test, chi-square test, or Fisher's exact test as appropriate. *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 represent significant differences. We analyzed the differences in the expression of NETs-associated biomarkers between healthy donors and patients with different degrees of disease severity. Meanwhile, we employed R packages (\u0026lsquo;pROC\u0026rsquo;) to generate Receiver Operating Characteristic (ROC) curves for biomarkers related to NETs and clinical parameters.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eDemographics and clinical characteristics of patients with COVID-19\u003c/h2\u003e\u003cp\u003eA total of 137 participants were included in this study, comprising 99 COVID-19 patients and 38 healthy individuals. All COVID-19 patients enrolled had at least one comorbidity, with the most prevalent disease following pneumonia being hypertension. Typical chest CT imaging was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. Imaging studies conducted at the time of admission can assist in risk stratification of patients with COVID-19. Bilateral lungs showed multiple linear band-shaped ground-glass opacities in moderate patients on chest CT. Conversely, severe patients exhibited bilateral multifocal consolidations with characteristic peripheral predominance on chest CT. And the lesions were predominantly localized across the whole lung area, commonly referred to as the \u0026ldquo;white lung\u0026rdquo; sign. The total leukocyte counts, neutrophil counts, and percentage of neutrophils were markedly elevated in severe patients compared to moderate patients, whereas lymphocyte counts and the proportion of lymphocytes were diminished. Both serum albumin and PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratios were significantly decreased in severe patients compared to moderate groups. Hence the highest-level respiratory support strategy was implemented and systematically summarized. Among all patients, 92.93% (92 of 99) required at least one type of supplemental oxygen support. Among severe cases, 50.88% (29/57) required advanced respiratory support, such as high-flow nasal cannula oxygen therapy (HFNC) or mechanical ventilation support, with 14.04% in-hospital mortality observed. The clinical characteristics of all participants are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient characteristics by disease severity\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;99)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSevere\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0140\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (61.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (47.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (71.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (38.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (52.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (28.07%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge/years (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.12 (14.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.62 (15.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72.70 (13.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0377\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePneumonia disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (17.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (14.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (19.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5134\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (42.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (28.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (52.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0167\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (21.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (21.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (21.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9639\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary\u0026nbsp;heart\u0026nbsp;disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (33.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (33.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (33.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9999\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrovascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (17.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (21.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (14.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3350\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory tests (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal leukocytes (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.97 (3.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.04 (3.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.66 (3.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0391\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.47 (3.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.35 (3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.30 (3.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0094\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of Neutrophil (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.14 (13.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.56 (13.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.24 (12.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0003\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98 (0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.17 (0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84 (0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0164\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of Lymphocytes (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.99 (10.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.60 (10.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.59 (9.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e203.16 (94.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e216.14 (84.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e193.60 (100.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2428\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.44 (4.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.90 (4.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.63 (4.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0000\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein (mg/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.10 (48.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.10 (43.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.20 (51.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0543\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProcalcitonin (ng/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15 (0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11 (0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.18 (0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1021\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterleukin-6 (pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.38 (76.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.83 (23.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.10 (98.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1068\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePaO2/FiO2 grade (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (42.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;200 to \u0026le;\u0026thinsp;300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (25.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (43.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;100 to \u0026le;\u0026thinsp;200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (25.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (43.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (7.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (12.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0196\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRespiratory support\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInvasive mechanical ventilation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (1.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0000\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNoninvasive mechanical ventilatory support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (5.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (8.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0635\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-flow\u0026nbsp;nasal\u0026nbsp;cannula\u0026nbsp;oxygen therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (24.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (40.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-flow oxygen therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (62.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (80.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (49.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0012\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospitalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmission to ICU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (6.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (10.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0371\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeath\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (8.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (14.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0193\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAll the participants resided in Shandong, China. Data are mean (SD) or n (%). \u003cem\u003ep\u003c/em\u003e values were calculated by Student's t test, chi-squared test, or Fisher's exact test, as appropriate. \u003csup\u003ea\u003c/sup\u003eCompared by two-sided chi-squared test. \u003csup\u003eb\u003c/sup\u003eCompared by two-sided Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e test. \u003csup\u003ec\u003c/sup\u003eCompared by two-sided Fisher's exact test.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eElevated plasma NETs correlate with increased COVID-19 severity\u003c/h2\u003e\u003cp\u003eTo investigate and distinguish the functions of NETs following infection, plasma was extracted from COVID-19 patients and healthy donors to analyze various markers of the formation process of NETs (also referred as NETosis). Soluble NETs remnants can exist in the form of cfDNA. Compared with healthy donors, COVID-19 patients exhibited elevated plasma cfDNA concentrations, which were highest in the severe group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. And all specific for NETs remnants, histone-DNA, MPO-DNA, and NE-DNA complexes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-E\u003cb\u003e)\u003c/b\u003e displayed similar profiles in COVID-19 patients and healthy controls. CitH3 is a marker of NETosis, and the severely infected patients had higher levels of CitH3 in the plasma than moderate patients and healthy controls \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. Together, these data demonstrate that plasma NET remnants levels correlate with COVID-19 severity, suggesting their pathogenic role in disease progression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eElevated NETs levels correlated with superior respiratory support in COVID-19\u003c/h2\u003e\u003cp\u003eThe disease severity can be reflected by the type of respiratory support required; therefore, we next assessed the clinical status corresponding to each accessible plasma sample. We compared samples from individuals necessitating advanced respiratory support (e.g., HFNC or mechanical ventilation, n\u0026thinsp;=\u0026thinsp;30 samples) with samples from patients receiving less intensive support (oxygen provided via nasal cannula or face mask, n\u0026thinsp;=\u0026thinsp;62 samples). As compared with patients who received conventional oxygen therapy, patients requiring mechanical ventilation or HFNC had significantly higher levels of cfDNA \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e, histone-DNA \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e and CitH3 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e, but not MPO-DNA \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e and NE-DNA \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Elevated CitH3 levels were specifically observed in severely ill COVID-19 patients requiring escalated respiratory support \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. Collectively, these data indicate circulating NETs increased as oxygenation deteriorated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCirculating NETs exhibited significant positive correlations with PCT\u003c/h2\u003e\u003cp\u003eNumerous laboratory markers, including neutrophils, lymphocytes, CRP, procalcitonin (PCT), and IL-6, have been reported to be related to the morbidity and mortality of COVID-19. Thus, the association between circulating NETs and laboratory parameters was further assessed. All available samples (n\u0026thinsp;=\u0026thinsp;99) underwent subsequent correlation analyses. Significant correlations linked histone-DNA to CitH3 (r\u0026thinsp;=\u0026thinsp;0.4714, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and similarly connected MPO-DNA with NE-DNA (r\u0026thinsp;=\u0026thinsp;0.6192, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B\u003cb\u003e)\u003c/b\u003e. Pearson correlation analyses were conducted between NETs-related biomarker measurements and inflammatory indicators, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC. The results were also listed in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table S2\u003c/b\u003e. Additionally, scatter plots highlighted results where correlation coefficients exceeded |0.3| and were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Results showed that histone-DNA and CitH3 exhibited significant positive correlation with PCT, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD,E\u003cb\u003e)\u003c/b\u003e. In summary, these data indicate a potential correlation between plasma NET levels and dysfunctional inflammatory response.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eCirculating NETs as putative biomarkers for predicting disease severity\u003c/h2\u003e\u003cp\u003eGiven the central role of neutrophils and NETs in COVID\u0026minus;19 pathogenesis, we posit that quantification of their associated biomarkers could reflect clinical disease progression. ROC curves were used to evaluate the predictive value of cfDNA, histone-DNA, MPO-DNA, NE-DNA, and CitH3 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The results indicated an AUC value of 0.767 for CitH3 in predicting severity, exceeding the AUC values of all inflammatory biomarkers for this prediction. Therefore, CitH3 distinguished severe cases better than the absolute counts or percentage of leukocytes and neutrophil subsets, and superior to the traditional inflammatory indicators, such as CRP, PCT, and IL\u0026minus;6. Moreover, the ability of histone-DNA and MPO-DNA to distinguish disease severity was comparable to or superior to that of some clinical indicators. MPO-DNA has a similar predictive value to traditional clinical parameters (percentages of neutrophils and lymphocytes), and is better than all inflammatory indicators \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Collectively, these data suggest circulating NETs demonstrate potential for predicting outcomes and implicate inflammatory imbalance in poor COVID\u0026minus;19 prognosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research focused on understanding how NETs impact disease progression and clinical outcomes in individuals admitted to the hospital due to COVID-19. Our study revealed a new observation: patients in need of advanced respiratory assistance exhibited markedly increased plasma concentrations of NETs. In addition, our results supported prior evidence suggesting that elevated NET concentrations in the bloodstream are closely linked to disease severity and inflammatory response, which suggested that early and timely monitoring of NETs is beneficial for evaluating the extent of inflammatory response and the status of immune function in these patients. Finally, we have demonstrated that on the day of patient admission, the plasma levels of CitH3 and MPO-DNA have a relatively significant advantage in predicting the disease severity. These results may offer new perspectives for optimizing the clinical application of antibiotics and immunomodulators, ultimately contributing to better patient outcomes.\u003c/p\u003e\u003cp\u003eEarlier investigations have shown that fragments derived from neutrophil immune responses-such as extracellular nucleic acids and chromatin-bound particles tend to accumulate in individuals affected by COVID-19[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Building on these observations, our study not only verified this increase but also uncovered a link between the presence of these immune byproducts and the clinical decision to implement advanced respiratory support. The results suggested that increased levels of circulating NETs were correlated with local pulmonary dysfunction. This finding may be attributed to several reasons. One notable explanation is that a considerable proportion of critically ill patients succumbed to ARDS [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Acute inflammation, both local and systemic,is a key feature of ARDS and plays a critical role in damaging both the lung epithelium and endothelium. During this process, neutrophils move from the pulmonary circulation into the airway spaces, where they can release various harmful substances [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In our cohort, 14.04% (8/57) of patients diagnosed with ARDS succumbed within a 60-day period. This indicates that ARDS played a more pivotal role in fatal outcomes among critically ill individuals than broader respiratory insufficiency. Accordingly, measurements of extracellular DNA in the bloodstream serve as more accurate biomarkers for COVID-19-related ARDS. Secondly, an elevation in plasma NETs levels may result from the hyperactivation of circulating neutrophils and/or the compromis\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ee\u003c/span\u003e of the alveolar-capillary barrier. In clinical practice, infection with SARS-CoV-2 often presents alongside vascular disorders in the lungs, such as the development of embolic events[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Vascular injury and thrombosis are significant mechanisms of air-blood barrier disruption induced by COVID-19. The extent of clot-related complications, including microvascular thrombosis and deep venous clot formation, has shown a strong association with the concentration of specific indicators linked to NETs [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In ARDS, the formation of NETs may disrupt the equilibrium between inflammatory responses and coagulation processes[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Long-term exposure to this elevated inflammatory equilibrium leads to pathological repair, characterized by persistent damage and repeated repair of normal tissues, which eventually leads to tissue fibrosis, thickening of the air-blood barrier, and obstruction of gas exchange between the alveoli and the microcirculation. These two scenarios may indicate the progression of multi-organ damage driven by widespread inflammatory activity. In conclusion, elevated NET remnant levels in patients with progressive pulmonary function decline further substantiate their role as indicators of severe lung involvement.\u003c/p\u003e\u003cp\u003eNotably, in our analysis of various biomarkers linked to NETs,only cfDNA, histone DNA and CitH3 showed significant increases in patients requiring advanced respiratory support strategy, while more targeted indicators like MPO-DNA and NE-DNA remained comparatively unchanged. Cell-free DNA and nucleosome-associated material are not exclusively derived from neutrophils; they may also be released by other immune cells like eosinophils and mastocytes, as well as by structural tissue cells during programmed cell death or unregulated cellular breakdown [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, DNA fragments found outside cells in the blood of COVID-19 patients could stem from multiple cellular sources, including vascular lining cells, airway epithelium, and immune cells beyond neutrophils. These findings align with the dominant perspective that respiratory impairment in COVID-19 is driven by a combination of contributing factors.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Despite the uncertainty regarding the cellular origin, the plasma levels of cfDNA and histone-DNA are more likely indicative of pulmonary inflammation and tissue damage.\u003c/p\u003e\u003cp\u003eHistone H3 can undergo chemical alteration after protein synthesis, resulting in a modified form known as citrullinated H3 (CitH3). During NETs formation, histones are expelled from the nuclei of neutrophils and subsequently citrullinated through the enzymatic actions of protein arginine deiminase 4 (PAD4) and the calcium-dependent enzyme peptidyl arginine deiminase 2 (PAD2)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This modification leads to the extrusion of CitH3 into the extracellular environment. Recent studies have linked CitH3 levels to the severity of sepsis[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and suggested its potential as a therapeutic target for endotoxic shock[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Histone H3 is recognized for inducing thrombocytopenia and thromboembolic events by activating and aggregating platelets [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and it exerts potent cytotoxic effects via the activation of apoptosis. Nevertheless, the exact function of CitH3 in ARDS caused by COVID-19 has yet to be fully elucidated. This study demonstrates that CitH3 adversely impacts disease progression and serves as a detrimental prognostic indicator.\u003c/p\u003e\u003cp\u003eA broad range of clinical biomarkers, such as total leukocyte levels, lymphocyte and neutrophil measurements, along with inflammatory markers like CRP, PCT, and IL-6, have been thoroughly explored in prior research and linked to both the progression and fatal outcomes of COVID-19[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This research focused on plasma levels of NETs and observed that the expression of CitH3 and MPO-DNA could serve as a more reliable indicator for recognizing individuals at risk of severe progression than traditional inflammatory markers such as white cell counts, IL-6, CRP, and PCT. And histone-DNA and CitH3 exhibited significant positive correlation with PCT, respectively. Because PCT elevation is typically a result of concurrent bacterial involvement, its usefulness in evaluating the severity of illness shortly after initial SARS-CoV-2 exposure may be significantly reduced[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Therefore, circulating CitH3 and histone-DNA could act as standalone biomarkers for predicting the likelihood of critical disease progression in individuals infected with COVID-19.\u003c/p\u003e\u003cp\u003eThere are several constraints to our research. Firstly, it is still unclear whether the detected NET remnants play a direct role in worsening the disease or are merely secondary outcomes of the intense inflammatory response observed in patients. Secondly, whether changes in NETs are specific to SARS-CoV-2 infection remains unknown. Similar to several investigation, this research compared healthy or non-severe individuals to severe COVID-19 patients. Although this does not invalidate findings in SARS-CoV-2 infection, their conclusions may overstate a specific cell or pathway\u0026rsquo;s role as unique to COVID-19 pathogenesis when it mirrors responses seen in conditions like sepsis from other causes. Finally, the majority of patients enrolled in this study received combination therapies, which included corticosteroids among other medications, which could affect neutrophil number and the generation of NETs. Future research needs to clearly demonstrate that, after eliminating confounding factors, using plasma levels of NETs to assist in predicting and evaluating the disease severity of COVID-19 may have a positive effect.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur research demonstrated a significant correlation between the presence of circulating NETs fragments and the severity of illness, as well as systemic inflammatory activity. Notably, patients who needed escalated forms of respiratory assistance showed markedly higher levels of NETs in their blood. Moreover, plasma CitH3 emerged as a promising early biomarker for forecasting disease progression in COVID-19 cases. These outcomes may offer valuable directions for optimizing clinical decisions, particularly in the use of antimicrobial agents and immune-modulating therapies, ultimately enhancing patient recovery and outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eARDS \u0026nbsp;Acute respiratory distress syndrome\u003c/p\u003e\n\u003cp\u003eNETs \u0026nbsp;Neutrophil extracellular traps\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ecfDNA \u0026nbsp;cell-free deoxyribonucleic acid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCitH3 \u0026nbsp;Citrullinated Histone H3\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMPO \u0026nbsp;Myeloperoxidase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNE \u0026nbsp;neutrophil elastase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; Receiver operating characteristic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCT \u0026nbsp; Procalcitonin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRP \u0026nbsp;C-reactive protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eALI \u0026nbsp; Acute lung injury\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRT-PCR \u0026nbsp;Reverse real-time polymerase chain reaction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCT \u0026nbsp;Computed tomography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMODS \u0026nbsp;Multiple organ dysfunction syndrome\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGGO \u0026nbsp;Ground-glass opacity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHFNC \u0026nbsp;High-flow nasal cannula oxygen therapy\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePAD4 \u0026nbsp;Protein arginine deiminase 4\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePAD2 \u0026nbsp;Peptidyl arginine deiminase 2\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involving human participants complied with the Declaration of Helsinki and was approved by the Shandong Provincial Hospital Ethics Committee (No. 2023-805). Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in the current study are accessible from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of China (grant number 82200014), the Natural Science Foundation of Shandong Province (grant number ZR2024QH025 and ZR2023MH250).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.L., W.J.L., and L.L.S. conceived the project. X.P., R.Y.F., Y.Y., N.S., P.B.H., Z.W.C., C.J.H., and S.L. discussed and designed the experiments. W.J.L., X.P., and R.Y.F. conducted the experiments. S. L., and W.J.L. performed statistical analyses. W.J.L., X.P., and L.L.S. wrote the manuscript. All authors interpreted the results and edited the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Zhisheng Huang (The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.) for reading and revising the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAroca-Crevill\u0026eacute;n A, Vicanolo T, Ovadia S, Hidalgo A. Neutrophils in Physiology and Pathology. Annu Rev Pathol. 2024;19:227\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurn GL, Foti A, Marsman G, Patel DF, Zychlinsky A. The Neutrophil. 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The dynamics of procalcitonin in COVID-19 patients admitted to Intensive care unit - a multi-centre cohort study in the South West of England, UK. J Infect. 2021;82(6):e24\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"NETs, cfDNA, protein-DNA complexes, CitH3, superior respiratory support, disease severity","lastPublishedDoi":"10.21203/rs.3.rs-7475642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7475642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e COVID-19 manifests with significant clinical heterogeneity, ranging from mild respiratory symptoms to ventilator-dependent acute respiratory distress syndrome (ARDS). C, formed by the release of decondensed chromatin to immobilize pathogens, have been implicated; however, their relationship with disease severity and the need for advanced respiratory support remains unclear.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e Ninety-nine patients who were diagnosed with COVID-19 in 2022\u0026ndash;2023 were recruited. The NETs were assessed in plasma by quantifying cell-free deoxyribonucleic acid (cfDNA), protein-DNA complexes, and citrullinated Histone H3 (CitH3). Predictions of severe illness were analyzed with receiver operating characteristic curves.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e Plasma levels of cfDNA, histone-DNA and Myeloperoxidase (MPO)-DNA, Neutrophil Elastase (NE)-DNA and CitH3 were significantly elevated in patients with COVID-19 and increased with disease severity. Moreover, patients requiring mechanical ventilation or high-flow oxygen therapy had significantly higher levels of cfDNA, Histone-DNA and CitH3. Correlation analysis showed that Histone-DNA and CitH3 exhibited significant positive correlation with Procalcitonin (PCT), respectively. Receiver operating characteristic (ROC) curve analysis indicated that CitH3 distinguished severe cases better than the absolute counts or percentage of leukocytes and neutrophil subsets, and superior to the traditional inflammatory indicators, such as C-reactive protein (CRP), and Interleukin (IL)\u0026minus;6. Histone-DNA and MPO-DNA are equal to or better than some clinical indicators in distinguishing disease severity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e These results highlight the important roles of NETs remnants in viral infections. The CitH3 in plasma represent early predictive biomarkers for the prognosis of COVID-19.\u003c/p\u003e","manuscriptTitle":"Circulating NET biomarkers as predictors of inflammatory storm escalation and critical illness in COVID-19","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 06:49:26","doi":"10.21203/rs.3.rs-7475642/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"68b24c38-1a16-4c9c-862e-719c13dd699d","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T20:55:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 06:49:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7475642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7475642","identity":"rs-7475642","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}