Role of CXCL12/CXCR4 pathway and miRNA expression profiles on polymorphonuclear mobilization in COVID-19 patients

Role of CXCL12/CXCR4 pathway and miRNA expression profiles on polymorphonuclear mobilization in COVID-19 patients | 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 Role of CXCL12/CXCR4 pathway and miRNA expression profiles on polymorphonuclear mobilization in COVID-19 patients Matthieu Daniel, Faustine Bernardin, Laetitia Sennsfelder, Melissa Payet, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5662811/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract Introduction: Polymorphonuclear neutrophils (PMN) are actively recruited during COVID-19 and yet dysfunctions are associated with its prognosis. The PMN receptor CXCR4 and its ligand SDF-1/CXCL12 are known to play a role in the recruitment of PMN. The primary objective was to evaluate the modulation of this pathway in COVID-19 patients and after treatment with dexamethasone (DXM). Secondary objectives were to evaluate miRNA expression profiles. Material and Methods We conducted a prospective study comparing patients admitted to the emergency department from December 2022 to April 2023 for SARS-CoV-2 infection with a control population. We studied the PMN surface expression of the CXCR4 receptor, circulating levels of SDF-1 and miR levels. Patients treated with dexamethasone (DXM) were sampled again at H48. Results Forty-four infected patients and 20 controls were analyzed. SDF-1 levels were significantly increased in COVID-19 patients and significantly decreased after treatment by DXM and CXCR4 + PMN percentages increased significantly. SDF-1 levels on admission were associated with the risk of mechanical ventilation. Levels of miR 15b-5p, miR 146a-5p, miR 155-5p and miR 30d-5p were significantly increased in COVID-19 patients. Levels of miR-hsa-122 on admission were found significantly associated with mortality and its variation with the need for mechanical ventilation. Conclusions Our study suggests a possible involvement of the SDF-1/CXCR4 axis in the physiopathogenesis of COVID-19. COVID-19 polymorphonuclear neutrophils Mesenchymal stromal cells chemokines miRNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Almost 4 years after the discovery of the SARS-CoV-2 pathogen and its spread around the world from Wuhan province, the mechanisms involved in the pathogenicity and virulence of this coronavirus remain poorly understood. Several studies have attempted to investigate the interactions between the virus and the immune system and to identify host-related factors that could have an impact on the progression of COVID-19 [ 1 – 3 ]. A growing body of evidence points the dysregulation of innate immune cells during severe SARS-CoV-2 infection [ 4 ]. In this context, recent studies have reported a significant rise in polymorphonuclear neutrophils (PMN) count among patients with COVID-19, contrasting with most viral infections [ 5 ]. In addition, increased PMN count has been associated with the disease severity and poor prognosis of COVID-19 [ 5 ]. Polymorphonuclear neutrophils, which are the most abundant immune cells in human blood, are major effectors of the innate immune system and usually the first cells recruited at the site of infection [ 6 ]. Chemokines and their receptors mediate the recruitment and activation of leukocytes at sites of inflammation and infection. Neutrophils express mainly receptors of the CXCR family, including CXCR1, CXCR2, and CXCR4, that bind to CXC chemokines, such as CXCL2 (Macrophage inflammatory protein 2-alpha or MIP-2α), CXCL8 (Interleukin-8 or IL-8), and CXCL12 (Stromal cell-derived factor-1 or SDF-1) [ 7 ]. Among those, CXCL12/Stromal cell-derived factor-1 (SDF-1) is a key mediator of PMN homeostasis. CXCL12/CXCR4 signaling system is known to play an important role in the regulation of PMN distribution and trafficking in homeostasis and disease [ 8 ]. CXCR4 is a master regulator of neutrophil storage in the bone marrow and facilitates homing of circulating aged PMN back to the bone marrow [ 9 ]. Changes in the expression of CXCL12 in peripheral tissues was reported to establish gradients that promote PMN migration from the bone marrow during pathological conditions such as sepsis [ 10 ]. In COVID-19 patients, a significant overexpression of a subpopulation of PMN CD10lowCD101-CXCR4 + and an increase in chemokines such as IL-6, calprotectin and CXCL8 in patients with severe COVID-19, in connection with emergency myelopoiesis [ 11 ]. However, authors have shown that miRNAs (miRs) closely associated with CXCR4 expression such as miR-146a or miR-155 were significantly modulated in severe forms of COVID-19 [ 12 , 13 ]. miRs are potent regulators of immune response and have received particular attention in the study of COVID-19 infection. Recent studies have shown aberrant expression of several miRNAs in COVID-19 in association with disease progression [ 14 , 15 ]. Differentially expressed miRNAs were found to be involved in the modulation of molecular pathways that regulate inflammatory and antiviral immune response [ 16 , 17 ]. For example, miR-155-5p and miR-15b-5p, known to be involved in the negative modulation of pro-inflammatory cytokines and host immune response, were found upregulated during COVID-19, downregulated in patients with severe COVID-19 (compared to mild to moderate forms) [ 18 – 22 ]. Considering this, miRNAs have also been used as potential therapeutic targets in COVID-19, especially for the management of pathological consequences of COVID-19 [ 15 ]. Immunosuppressive therapies, such as the glucocorticoid dexamethasone (DXM), significantly reduce mortality in hospitalized COVID-19 patients receiving respiratory support [ 23 ]. However, mechanisms underlying the beneficial effects of DXM during severe COVID-19 remain poorly understood. We questioned if DXM may itself work in-part by affecting neutrophil function or modulating miRNAs expression in severe COVID-19 infection. We hypothesized that SARS-CoV-2 virus may modulate innate immune inflammatory response contributing to PMN activation and recruitment through chemokines and miRNAs known to be synthesized and secreted by MSCs in a septic context [ 24 , 25 ]. We primary aimed to investigate the CXCL12(SDF-1α)/CXCR4 signaling pathway by studying SDF-1α plasmatic levels and the CXCR4 expression modulation on PMN surface in COVID-19 patients in comparison with controls, and according to disease severity and DXM use. The secondary objectives were to examine the circulating miRNA profile of hospitalized COVID-19 patients before and after DXM treatment, and to assess potential correlation between these biomarkers and clinical outcomes. Methods Study approval and ethical considerations This study was conducted during the COVID-19 epidemic in La Reunion Island. Serological testing (collected as part of the patient’s care) was performed on plasma samples. All patients with RT-PCR-confirmed COVID-19 or their legal surrogate gave informed consent prior to inclusion at the time of entry at the hospital for the testing (CHU de la Réunion). Samples were deposited at the local Bioresource Center (CRB, certified by Euro-Quality System, NF 996-900) and declared to the local bio-collection of infectious diseases. The study was approved by the Human Ethics Committee of University of Bordeaux (“Comité Consultatif de Protection de Personnes se prêtant à des Recherches Biomédicales”, Bordeaux France, ref. 2008-A00151-54). The design of this work conforms to ethical standards of the Helsinki declaration. The CNIL (Commission Nationale Informatique et Libertés), commission involved in the protection of digital data, has also given its agreement for the use of data within the framework of this study. Design of the study This study was conducted as a prospective observational cohort study, at the University Hospital of La Réunion Island. The study population included 2 separate groups of patients: patients with documented SARS-CoV-2 infection (positive nasopharyngeal RT-PCR) admitted to the emergency ward between March 2022 and April 2023 (SARS-CoV-2 group) and a group of patients admitted to the emergency ward between December 2022 and April 2023 for minor traumatic pathologies and not presenting systemic inflammatory response syndrome controlled on admission biology (control group). Among COVID-19 patients, a subgroup received DXM upon hospital admission (6 mg per day as recommended by scientific committees taking the Villar protocol as a reference and referring to the recommendations concerning the benefit of low doses of corticosteroids at this time) [26, 27]. Study inclusion required a minimum of age of 18 years old and the ability to provide consent. Exclusion criteria were represented for both groups by pregnancy/nursing condition, administration of immunosuppressive treatments, mental illness or impossibility for the subject to have a good comprehension of the study, therapeutic limitation before inclusion and lack of health insurance coverage. Data collection and biological variables At time of inclusion (D1), we collected the demographic characteristics, comorbidities and main COVID-19 associated features assessed during hospitalization. We also evaluated the critical illness severity with SAPS II and SOFA scores on admission, as well as the need for an intensive care unit (ICU) and ventilation support. The time and cause of death and the length of hospital-stay as well as vital status after discharge within 6 months were collected prospectively. This manuscript adheres to the applicable CONSORT guidelines [28]. Biological variables of interest were assessed at different time points of hospitalization: on admission (D1) then on day 3 (D3), day 7 (D7), day 14 (D14) and day 21 (D21) unless patient’s discharge. The biological parameters measured included a blood ionogram, a blood count with white blood cells and platelet counts, arterial gasometry with lactatemia in the event of signs of respiratory distress or oxygen requirement, a dosage of complement factors, an electrophoresis of serum proteins, a dosage of troponinemia, Brain natriuretic peptide (BNP), a dosage of C-Reactive Protein (CRP), Procalcitonin (PCT) and a haemostasis assessment with measurement of the D-dimer level. Sample collection Blood samples were collected from SARS-CoV-2 and control patients within a day (24 hours) of their admission (first day or D1) to the emergency department (ED). In addition, samples were collected from DXM treated COVID-19 patients 48h after hospitalization. After collection, whole blood was immediately processed for flow cytometry analysis. Remaining blood was then centrifuged at 2000 g for 20 minutes and the supernatant was aliquoted and stored at -80°C for further analysis. Flow cytometry and characterization of the PMN For all patients, 100 µL of whole blood was mixed with 5 µL of phycoerythrin (PE)-conjugated antibodies against cell various surface markers. : we used fluorochrome-conjugated monoclonal antibodies against FcyRIII (CD16-PE), C3aR (C3aR-PE), C5aR (CD88-PE), Histones H3 (H3-PE), CXCR4 (CD184-PE), FMLP-R (FMLP-R PE), HMGB-1 (HMGB-1-PE) and Integrin alpha M (ITGAM-PE or CD11b-PE). Appropriate isotype controls were used to define background staining levels. Samples were then incubated at room temperature in the dark for 30 minutes. The red blood cells were lysed with the Beckman ImmunoPrep™ reagent system (Beckman Coulter, catalog number: 7,546,999). Flow cytometry analysis was performed with the Becton Dickinson C6 Plus AccuriTM flow cytometer and data were extracted using BD AccuriTM C6 version 1.0 software. PMN expression of surface molecules was determined by using forward and side scatter to identify the granulocyte population and to gate out other cells and debris. PMN were gated as CD16+ cells conjugated with FITC. Levels and percentage of PMN expressing the different markers were then analyzed, and data were processed as appropriate using GraphPad Prism software. Enzyme-linked immune-sorbent assay (ELISA) Chemokine concentrations in serum samples were measured using commercially available ELISA kits for CCL2 (Peprotech: cat. no. 900-K31), CXCL8 (Peprotech; cat. no. 900-K18) and CXCL12 (Peprotech; cat. no. 900-KXX), according to the manufacturer’s instructions. Samples were analyzed from independent experiments. Serum samples were prepared from whole blood (the same used for flow cytometry analysis) following centrifugation (20 min at 2000 g) and stored at −80°C until ELISA analysis in a batch. miRNA extraction Total plasma RNA was isolated from 200 μL of plasma using the miRNeasy Serum/Plasma Advanced Kit (Qiagen; Ref.: 217204) as per the manufacturer’s instructions. miRNA Serum/plasma spike in control (Qiagen; Ref.: 219610) was used as an internal control and a standard for quantifying miR in housekeeping gene’s fashion (1,6.10 8 copies/μL). The miRNAs selected to be assayed in plasma samples are: miR-155, miR-26a-5p, miR-146a-5p, miR-29a-3p, miR-15b-5p, miR-hsa-122, miR-30d-5p, and miR-155-5p. The list of primers used for RT-PCRs for each miR is presented in Table 6 of the supplementary material. These miRs were chosen because of their close involvement in the pathophysiology of COVID-19 or their role in PMN or MSC function. Quantitative real-time RT-PCR (qRT-PCR) analyzes RT experiments were performed using the miScript II RT (Qiagen; Ref.: 218161). RT was performed in a final volume of 20 Μl containing 5 Μl of extracted total RNA per reaction and 15 Μl of enzyme mix. RT was carried out in the QuantStudio 5 PCR thermocycler (Thermo Fisher Scientific). Cdna was collected and kept at −20 ◦C until use. Q-PCR experiments were performed using the miScript SYBR Green PCR (Qiagen; Ref.: 218075). qPCR was performed in a final volume of 5 μL containing 1 μL of extracted cDNA per reaction, 3 μL of enzyme mix and 1 μL of primer mix, with a final primer concentration of 1.25 μM. qPCR was carried out in the QuantStudio 5 PCR thermocycler (Thermo Fisher Scientific). Relative gene expression was calculated using Ce39 as a reference gene. Experiments were performed in triplicate. Primer sequences related to the genes are listed in the supplemental material. miRNA profiling with microarrays In order to compare the different miRNA profiles, we used GeneChip® miRNA 4.0 Array (Affymetrix, Santa Clara, CA, USA) allowing the sequencing of a battery of human miRs. For analysis with Affymetrix GeneChip miRNA 4.0 Arrays, samples were labeled on ice with the FlashTag Biotin HSR labeling kit according to the manufacturer’s instructions. GeneChip miRNA 4.0 arrays contain 100% miRbase version 20 coverage of 203 organisms and contain probes for 4,574 human non-coding RNAs (ncRNAs), including 2,578 miRNAs and 1,996 other ncRNAs (including scaRNAs and snoRNAs). Arrays were then washed and stained using the Affymetrix kit and Fluidics Station 450 according to protocol FS450_0002 and scanned using Affymetrix Command Console (AGCC) software and an Affymetrix GCS 3000 7G scanner to generate CEL files. Regarding the CEL files, they were analyzed using Affymetrix® Transcriptome Analysis Console (TAC) 4.0 softwareTM. With this software, the probe intensities of the miRNAs were normalized, and the miRNA gene expression was calculated with the Robust Multi-chip Analysis (RMA) setting the value of Detected Above Background (DABG) to 0.05. The changes of gene expression were shown as mean Fold Change (FC), considering miRNAs over or down expressed with thresholds above >2 or below <2, respectively. Significant dysregulated miRNAs were those with p-value less than 0.05. Statistical analysis Statistical analyses were performed with GraphPad Prism software version 8.01 and R Statistical Softwareä. Categorical variables were presented as frequency and percentages and continuous variables were described using either means and standard deviations (mean ±SD) for normally distributed data, or medians and interquartile ranges (IQRs) for data that were not normally distributed. The normality of the data distribution was determined by the Kolmogorov-Smirnov test and reject the normality. Differences between the steps (at day 1, day 3, day 7, day 14 and day 21 when available) were tested for significance by repeated measures of one-way ANOVA followed by the Bonferroni’s test for multiple comparisons. The difference values between the steps were calculated with the value post-value before. Spearman test was applied to analyze correlation between the variables. p-values ≤ 0.05 were considered statistically significant. Significance was indicated in the figures as follow: p-values ≤ 0.05 (*), p-values ≤ 0.01 (**), p-values ≤ 0.001 (***) and p-values ≤ 0.0001 (****). Results are expressed as mean ±standard error “SEM” and as percentage. Results Demography, clinical characteristics and biological parameters of study subjects on the day of admission (D1) Over the study period, a total of 67 patients were enrolled. There were 47 COVID-19 patients and 20 control subjects. Forty-four out of 47 patients admitted in emergency department (ED) for a SARS-CoV-2 related infection confirmed by a standard RT-PCR with consecutive genotype sequencing were analyzed. Three patients were excluded: one withdrew consent, and 2 patients finally presented a positive COVID-19 antigen test but with a negative RT-PCR, invalidating the initial diagnosis. Nineteen patients were analyzed in the control group due to withdrawal of consent. The flow-chart of the study is presented on Figure 1 . In the COVID-19 group, patients were mostly female (n=23; 52%), hypertensive, overweighted and admitted in ED after a median of 3 days after COVID-19 onset. On admission to the ED, most patients presented with dyspnea (n=35; 80%) and almost a quarter of patients presented with signs of acute respiratory distress. Twenty-four (55%) patients required the use of oxygen therapy on admission and 20 (45%) patients were treated with dexamethasone. Four (9%) received non-invasive mechanical ventilation with facial mask and 2 (5%) received ventilatory support by high-flow oxygen therapy (Optiflow®). Identification of the viral genotype was assessed in 18 (41%) patients, among which 13 (30%) had the Omicron variant. The characteristics of the population of COVID-19 patients are presented in Table 1 . Increased levels of CXCR4-positive peripheral blood PMN from patients with COVID-19 patients after 48h of Dexamethasone treatment Peripheral blood neutrophils from COVID-19 patients requiring oxygen therapy support were examined for the expression of chemoattractant receptors and activation/maturation markers at admission and 48h after DXM treatment using flow cytometry. We did not observe a significant difference in the median fluorescence intensity (MFI) of the CXCR4 receptor after 48 hours of dexamethasone administration in SARS-CoV-2 infected patients requiring oxygen therapy support (vs 48 hours of treatment, p=0.8). However, we detected a significant increase (11,08 %) in the percentage of PMN expressing the CXCR4 receptor after DXM treatment (34,2% on D1 versus 45.3% after treatment, p=0.01). Furthermore, we did not observe a significant difference for percentages of HMGB-1+ neutrophils before and after treatment ( Figure 2 ). Concerning CD16, C5aR and CD11b receptors no significant difference was found before and after DXM administration, although a tendency towards reduced CD16 and CD11b MFI was observed after treatment with DXM. We also observed a tendency towards increased of C3aR, Histone H3 and FMLP-R MFI after 48 hours of DXM treatment ( Figure 6 in supplemental material ). CCL2, CXCL8 and CXCL12 serum levels in COVID-19 patients before and after Dexamethasone treatment We assessed the serum levels of CCL2, CXCL12 and CXCL8 at admission for controls and COVID-19 patients and 48h after DXM treatment. The serum concentrations of CCL2, CXCL12 and CXCL8 were significantly higher in patients with SARS-CoV-2 infection compared to the control group (Figure 3). The protein expression levels were 153 pg/ml for CCL2 vs 90,1 pg/ml for the control patients (p < 0.001), 12,62 pg/ml for CXCL12 vs 4,26 pg/ml for the control patients (p <0.001) and 25,8 pg/ml for CXCL8 vs 13,9 pg/ml in controls (p<0.0001). Notably, the serum levels of the 3 tested chemokines decreased significantly in COVID-19 patients sampled after treatment with dexamethasone for 48 hours. The plasma levels of CCL2, CXCL12 and CXCL8 were respectively 99,96 pg/ml (p<0.001), 7,47pg/ml (p<0.01) and 17,56 pg/ml (0.1) in comparison with the levels observed on admission for these patients. These results are available on Figure 3 . Evolution of serum miRNA levels in COVID-19 patients before and after Dexamethasone treatment We measured serum levels of miR 15b-5p, miR 26a-5p, miR 155-5p, miR 30d-5p, miR 146a-5p, and miR hsa-122 by qRT PCR. We found that levels of the following miRs were significantly increased on admission in the group of SARS-CoV-2 infected patients compared to the control group: 15b-5p (2,54 against 0,33 for control patients, p < 0.001), miR 146a-5p (26,4 against 0,001 for controls, p < 0.001), miR 155-5p (0,13 against 0,007 for controls, p < 0.01) and miR 30d-5p (15,36 against 0,8 for controls, p 0.05). There was no significant difference in the expression levels of miR-15b-5p, miR-155-5p, miR-30d-5p and miR-hsa-122 before and after treatment with dexamethasone for 48h in COVID-19 patients (p >0.05). There was also a significant increase in the levels measured in serum of miR-26a-5p (0,40 against 0,10 for non-treated patients with COVID-19, p <0.001) and a significant decrease in the levels of miR-146a-5p (3,49 against 26,39 for non-treated patients with COVID-19, p <0.0001) in COVID-19 patients treated with DXM for 48 hours. We can also note trends towards a decrease in the levels of miR-15b-5p, miR-155-5p and miR-30d-5p in the group of COVID-19 patients after the same treatment but this was not found statistically significant. The results of these analyzes are presented in Figure 4 panel A and B. Chemokines, neutrophil surface receptors, miRNA and mortality Univariate analysis was used to study the relationship between the different biological parameters of interest and patient mortality at 6 months in the COVID-19 group. Twelve patients died within 6 months after prospective data collection. We found a significant association between the CXCL8 level on day 1 (19 [14; 29] pg/ml in the group of patients alive at M6 versus 28 [23; 43] pg/ml in the group of patients who died; p =0.027) and its variation between D1 and D3 in patients treated with DXM with a greater drop in CXCL8 levels in deceased patients than in patients alive at M6 (1 [-3; 12] against -8 [- 17; -7] pg/ml; p=0.045). The expression of the receptors of interest on the surface of the PMN (MFI or percentage of labeled PMN) was not found associated with mortality at M6. Among the parameters relating to miR, only the initial level of miR-122 was found significantly associated with mortality (p=0.039). The results of the univariate analysis are presented in Table 2 . Association between biological parameters and mechanical ventilation The results of the univariate analysis concerning the association between the use of mechanical ventilation (invasive or not) in COVID-19 patients showed an association between the need for mechanical ventilation and the following parameters : the ASA score (p=0.029), the NLR (p=0.043), the variation in the level of CXCL8 before and after DXM (p=0.023), the level of CXCL12 on day 1 (p=0.049), the mean of fluorescence intensity of CXCR4 at day 1 (p=0.037), the variation before/after treatment by DXM of miR-hsa-122 (p=0.039). The results of the univariate analyzes concerning the different parameters measured and the use of mechanical ventilation are presented in table 3 . Association between standard biological markers of sepsis and microRNA The results of the correlation matrices regarding the association between standard biological markers and observed levels of circulating miRs in COVID-19 patients are shown in Figure 5 . Among the notable factors significantly associated with the variation in PCT between days 1 and 3, we found the MFI of CXCR4 measured on day 1 (p<0.001). The PCT and the PCT/PMN ratio on D1 were both significantly associated with the MFI of C3aR measured on D1 (p<0.001) Comparison of miRNA profiles using microarray chips We were thus able to compare the miRNA profiles and significant overexpression or underexpression between 3 COVID-19 patients on D1 and 3 control patients on the one hand and between COVID-19 patients between their admission on D1 and the second blood sample performed on D3 in case of treatment with DXM. The results of the miRNA screening analysis showed that there were 36 overexpressed and 9 underexpressed miRs in COVID-19 patients at the time of their admission compared to the group of control patients. Furthermore, when we looked at the 36 miRs showing overexpression upon admission of patients infected with SARS-CoV-2, we saw that 25 of these miRs see their expression downmodulated after 48 hours of treatment with corticosteroids. Analysis by miRNA chip thus confirmed that miR-15b-5p, miR-30d-5p and miR-146a-5p were indeed upregulated in COVID-19 patients compared to uninfected patients and downregulated in the event of treatment with DXM for 48 hours. The results of the expressions of the different miRNAs are presented in Table 4 in supplemental material. Discussion Main results and findings Our study showed significant modulation of the CXCL12(SDF-1)/CXCR4 signaling pathway in COVID-19 patients compared to a control patient population. This signaling axis also appeared to be modulated by corticosteroid treatment in patients with severe COVID-19 requiring ventilatory support. COVID-19 patients treated with DXM had a significant increase in PMNs expressing the CXCR4 receptor. We also detected significantly higher serum levels of CXCL12 in COVID-19 patients compared to control subjects. After DXM treatment, these serum CXCL12 levels decreased significantly. The result of univariate analyzes regarding mortality showed a significant association between baseline levels and change in CXCL8 levels before and after corticosteroid treatment suggesting a possible link between patient outcome and modulation of the relevant chemokines. The use of mechanical ventilation appeared to be associated with NLR, measured levels of CXCL12 and MFI CXCR4 at entry as well as change in CXCL8 levels. There was an association between the variation in PCT between D1 and D3 and the MFI CXCR4 on D1. CXCL12/CXCR4 pathway and PMN homing in COVID-19 patients Polymorphonuclear neutrophils are cells of the innate immune system that are particularly interesting due to their key role and their close interactions with mesenchymal stromal cells (MSC) during septic states, particularly of viral origin. [24, 25, 29, 30]. Indeed, it is now well established that PMNs are among the first immune effectors to arrive at the site of attack upon exposure to a pathogen in its early phase. [31, 32]. The variation in their levels and certain of their characteristics are also used as prognostic markers in numerous physiopathological situations and their deregulation is often associated with increased morbidity and mortality [33–36]. Several authors noted that SARS-CoV-2 infection was characterized, unlike most viral infections, by more pronounced recruitment and activation of PMN functions [33, 37, 38]. This exacerbated recruitment of PMN results, on a biological level, in an increase in the number of circulating neutrophils contrasting with the decrease in the lymphocyte count, giving the neutrophil/lymphocyte ratio or NLR predictive criteria for morbidity and mortality [33, 37, 38]. This cellular signature is accompanied by hypersecretion of cytokines, now well documented in the literature, up to levels described as "cytokine storms" associated with greater morbidity and mortality in COVDI-19 patients [39, 40]. No data in the literature has so far made it possible to provide a physiopathological explanation for these biological particularities [41]. The first objective of our study was therefore to evaluate the modulation of PMN recruitment by chemokine signaling pathways, in particular the CXCL12/CXCR4 pathway, and to argue the prognostic characteristics of this variation in pathological conditions by comparing the expression of this chemokine and its receptor in patients infected with SARS-CoV-2 with or without corticosteroid treatment and in subjects free of infection. In our study, we demonstrated a significant increase in circulating levels of CXCL12 in patients infected with SARS-CoV-2, their modulation after treatment with DXM, as well as a modulation of the CXCR4 receptor on the surface of PMNs under conditions of SARS-CoV-2 infection. The SDF-1/CXCL12 pathway and its receptors CXCR4 and CXCR7 have already been identified as a major signaling pathway during septic states and are involved in “homing” phenomena allowing the recruitment of PMNs to the site of tissue damage [42]. The 2 types of receptors, CXCR4 and CXCR7, are present on the surface of hematopoietic cells, particularly PMN [43–45]. It has previously been demonstrated that the secretion of CXCL12 by MSCs in the bone marrow is responsible for maintaining the PMN pool in the hematopoietic niche and it has been established that the relative increase in peripheral CXCL12 levels in response to the presence of a pathogen, contrasting with stable levels at the hematopoietic niche, induced mobilization of neutrophils towards the blood [10, 46]. The CXCL12 pathway recruits not only mature PMNs but also immature PMNs from the BM and also modulates PMN phagocytosis activity at the site of injury [10, 29]. CXCL12 may also be involved in maintaining tight intercellular junctions during sepsis by a complex mechanism dependent on the adenosine A2B receptor [47]. The modulation of the CXCL12/CXCR4 pathway in COVID-19 patients can explain the previously reported recruitment of PMN, particularly at the pulmonary level and gives an original interpretation to the protective capacities of dexamethasone in the most severe patients. One hypothesis would be that the most severe patients would have an up-regulation of their CXCL12/CXCR4 pathway which would lead to recruitment in the lung of mature then immature PMNs, whose dysfunction would lead to the genesis of cytotoxic tissue lesions accompanied by alveolar edema and the phenomenon of NETosis observed in numerous studies [38, 48]. If this hypothesis is confirmed, it would also explain hypoxia phenomena and could provide new tools and early biomarkers to predict the evolution of respiratory function in COVID-19 patients and associated mortality. This could also open a new therapeutic field using antibodies directed against the CXCR4 receptor, which have already shown their effectiveness in animal models of sepsis, to reduce the recruitment and hyperactivation of PMNs, deleterious in this case [49]. If CXCL12 has been traditionally identified as a homeostatic chemokine that plays an essential role in regulating leukocyte trafficking, the role of CXCL12 and its receptor CXCR4 during viral infection is very complex and it can act as a positive or negative regulator of leukocyte migration depending on the presence or absence of co-factors at the site of infection [50]. Indeed, in suboptimal concentrations CXCL12 is able to form a complex with the alarmin High Mobility Group Box 1 (HMGB1) which is a DAMP released by damaged tissues. HMGB1-CXCL12 complex was shown to bind exclusively to the chemokine receptor CXCR4 and increase inflammatory cell migration [51]. In other pathological conditions, at high concentrations and in the absence of cofactors, CXCL12 was found to have a repulsive effect on mature T cells and on LyT CD4+ and CD8+ [52]. One of the hypotheses that can explain the CXCL12 repulsive effect on immune cells is that high CXCL12 concentrations would prevent immune effector cells infiltration into the damaged tissues once the pathogen has been eliminated in order to carry out tissue repair [52]. The presence of circulating alarmins at high concentrations during SARS-CoV-2 infection, associated with high serum levels of CXCL12, could explain a synergistic agonist effect on leukocyte recruitment, particularly on PMNs and the observed peripheral lymphopenia. Unfortunately, we were not able to obtain data regarding the circulating levels of HMGB1 in COVID-19 patients and further investigations are required to assess HMGB1 circulating levels as well as the formation of heterodimeric complexes with CXCL12 that can lead to a decrease in these concentrations. Of note, the redox state of HMGB1 seems to modulate the heterocomplex formation with CXCL12 and it would be therefore interesting to determine the oxidoreductive state of the molecule [53]. In our study, we observed a tendency to an increase in the expression of HMGB-1 on the surface of the PMNs after treatment with DXM which could be linked to a reduced circulating levels of the free form of this protein. The CXCL12/HMGB1 complex formation was shown to be disrupted by pamoic acid which reduced PMN recruitment in the airways in a pulmonary infection model ( in vivo P.aeruginosa pneumonia model) [54]. Many substances “blocking” different cytokine pathways have been used in COVID-19 but no data regarding blockers of the CXCL12/CXCR4 pathway appear to be available. Role of CXCR4 in the modulation of innate immunity CXCR4 is known to be up-regulated on the surface of eosinophils by the IFN-gamma pathway and by corticosteroids [55]. Some data from the literature show that modulation of the CXCR4 receptor on the surface of PMNs represents an alternative pathway for controlling the CXCL12/CXCR4 pathway. Its expression is notably known to be down-modulated by IFN-I and by GM-CSF and G-CSF [56]. Corticosteroids, including dexamethasone, are among the rare drugs that have demonstrated a positive effect in the treatment of COVID-19. Although most authors agree on its immunomodulatory effect, its effects at the molecular level are not well elucidated. We showed that the corticosteroid analogue dexamethasone could be associated with a decrease in circulating levels of CXCL12 and an up-regulation of its receptor CXCR4 on human PMNs in COVID-19 patients. DXM may therefore act in part by modulating CXCR4 expression on the surface of PMNs. The ability of dexamethasone, whose anti-inflammatory functions are well documented, to increase CXCR4 expression on PMNs could be considered part of a crucial homeostatic process. A study published in Nature Medicine in 2022 showed that DXM had an action on circulating neutrophils, in particular the population of IFN-activated PMNs, downregulated interferon-stimulated genes (ISGs), and activated IL-1R2+ neutrophils. It also appears to participate in the increase in the population of immunosuppressive immature neutrophils [57]. Our study provides an additional explanation for the action of DXM, particularly at the cellular and molecular level, on PMN. miRNA profiles, PMN modulation and potential role of MSCs in SARS-CoV-2 infected patients Several studies have been able to highlight the central role of PMNs during COVID-19 with a strong link established between their cell recruitment and the severity of the infection and its morbidity and mortality [33, 37, 38]. If many pathways are involved during the recruitment of these immune effectors, the CXCL12 (SDF-1α)/CXCR4/CXCR7 pathway seems to represent a key pathway for the mobilization of these cells and their communication with the MSCs during septic states [29, 47]. SDF-1α as well as other chemokines and soluble factors capable of establishing communication between PMNs and MSCs can be released in free form in the tissues or contained inside specialized extracellular vesicles (EVs), giving them greater longevity and protecting them during their journey through the vascular sector [58–60]. Exosomes, a population of small EVs, are increasingly studied in physiopathology and transport both protein material and small specialized ribonucleic acids (RNAs) that seem to play a key role in the interactions between MSCs and immune effectors, notably during SARS-CoV-2 infections [61, 62]. MicroRNAs (miRNAs) as potent regulators of immune responses have received much attention in this regard. Recent studies have shown aberrant expression of miRNAs in COVID-19 in association with disease progression [14, 15]. Differentially expressed miRNAs were enriched in pathways related to inflammation and antiviral immune response [16, 17]. miRNAs have also been considered as potential therapeutic targets in COVID-19, especially for the management of pathological consequences of COVID-19. In this article, we have discussed avenues for miRNA dysregulation in COVID-19. Many studies have focused on the role played by miRNAs during sepsis and this area has naturally been investigated during COVID-19. Many studies are interested in the prognostic nature of miRs in these patients by proposing specific patterns of patient evolution. In our study, we chose to investigate the link between PMN modulation by the CXCL12/CXCR4 axis and specific miR profiles from MSCs. We focused on certain pro-inflammatory and anti-inflammatory miRs, known to have been isolated from extracellular vesicles secreted by MSCs and some of which have known functions in modulating immune effectors of innate immunity, particularly neutrophils. Literature data on the miRs studied in our work, in the context of sepsis and COVID-19, when these existed, are presented in Table 6 of the supplementary material. The results produced from miRNA microarrays, although our study was not designed for this purpose, confirm the modulation of the different miRNAs of interest observed and provide interesting leads in favor of a specific pattern of COVID-19 severity. Beyond the valuable pathophysiological information provided by such techniques, they could also in the future provide prognostic tools for this type of patients, as is currently being studied in sepsis [63]. Strengths and limitations of our study Our study population appeared to be representative of the populations of patients suffering from COVID-19, particularly the Omicron variant, observed in the literature [64, 65]. The delays observed between the first symptoms and admission to the emergency room were notably similar to those noted by other authors [66]. The characteristics of our population regarding its severity were also comparable to those found in the literature with 20 patients out of 44 analyzed having required treatment with dexamethasone due to oxygen dependence which, again, is close to the data from literature [67]. Our study provides new evidence for the modulation of PMN homing by SARS-CoV-2 infection and by the initiation of corticosteroid treatment in this context by exploring a previously unstudied signaling pathway, except during sepsis. If many authors have mentioned the particular cytokine profiles observed during COVID-19, few studies have focused on the link between this secretion and the modulation of the immune system and its consequences. This study thus opens perspectives for better understanding the role of cytokines and innate immunity dysfunction in these patients. This could also help explain the severity of patients, particularly on the respiratory level. Our work also raises questions regarding the potential role of MSCs in the antiviral immune response and the potential impact of their dysfunction in the development of severe SARS-CoV-2 infections. But our study also owns several limitations. First, this is a single-center study. Then, its implementation at La Réunion University Hospital can be accompanied by a response to COVID-19 specific to the island population. It would therefore be appropriate to confirm these results on a larger and more diverse cohort of patients. The first waves of COVID-19 did not directly affect the island of Reunion or in smaller proportions and the cases observed were mainly due to delta or omicron variants, corresponding to the forms observed in the following epidemic waves at the global level, of which we know that the virulence was less pronounced. Second, our study was designed to show a difference in expression of PMN surface receptors and chemokines of interest between infected and control patients as well as in COVID-19 patients after 48 hours of dexamethasone treatment in order to assess its possible impact. Its low power does not allow conclusions to be drawn on the association between the parameters of interest and the morbidity and mortality of patients. Only observations and avenues for future studies can arise from this work. Finally, one of the limitations is the absence of a study of CXCR4 expression in control patients. This would have required additional samples to be taken and a specific request to the ethics committee, regarding biological samples from patients free of COVID-19. Conclusion and perspectives In conclusion, our study demonstrated a significant modulation of the CXCL12/CXCR4 pathway during COVID-19 and in patients treated with corticosteroids. It also suggests that the CXCL12/CXCR4 pathway is involved in the recruitment of neutrophils during COVID-19 and may be associated with the ventilatory prognosis of patients, particularly those with the most severe pulmonary damage. We also identified differential serum miRNAs profiles in patients infected with SARS-CoV-2 and in response to dexamethasone treatment. Other in vitro and in vivo studies are needed to confirm the link that may exist between this CXCL12/CXCR4 pathway, neutrophil recruitment and corticosteroid therapy. Our study also opens perspectives for the use of already identified antagonists of the CXCL12/CXCR4 pathway and its cofactors in presumed severe cases of COVID-19. Declarations Funding: this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Competing interests: the authors have no relevant financial or non-financial interests to disclose. Author contributions: MD and FB: study design, data acquisition, quality assessment, data interpretation, statistical analysis and manuscript drafting. LR: Manuscript drafting. PG: study design, manuscript drafting and quality assessment. YB and FW: data acquisition, manuscript drafting and quality assessment. EK: statistical analysis, data interpretation and manuscript drafting. DV, MP, JV and EF: data acquisition. All authors provided critical reviews of the manuscript and approved the final version. MD is the guarantor of the content of this manuscript. Data availability: the datasets generated during and/or analyzed during the current study are not publicly available due to its storage in a secure computer with data encryption but are available from the corresponding author on reasonable request. Ethics approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Bordeaux under number 2008-A00151-54. Consent to participate: informed consent was obtained from all individual participants included in the study. Aknowledgements: We would like to thank our professor and friend Philippe Gasque, who died on July 11, 2024 and who supervised this research work as director of the EPI and LICE-OI laboratory. 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Am J Emerg Med 54:46–57. https://doi.org/10.1016/j.ajem.2022.01.028 Tables Table 1 . Characteristics of the population of COVID-19 patients on the day of admission. Characteristics Number of patients N=44 (100%) Male sex, n (%) 21 (48%) Age years, median (IQR) 72 (61-83) BMI kg/m 2 , median (IQR) 25 (21-28) Delay between the onset of symptoms and ICU admission, median (IQR) 3 (1-4) Smoking history, n (%) 15 (34%) Overweight (25<BMI30), n (%) 6 (14%) Diabetes mellitus, n (%) 19 (43%) Hypertension, n (%) 29 (66%) Ischemic heart disease, n (%) 10 (23%) Chronic kidney disease, n (%) 7 (16%) Ischemic stroke history, n (%) 6 (14%) Patients with identified virus genotype, n (%) 18 (41%) Omicron viral genotype, n (%) 13 (30%) Scanner performed on admission, n (%) 39 (89%) Percentage of lung parenchyma involvement <25%, n (%) 27 (61%) Percentage of lung parenchyma involvement 25-75%, n (%) 9 (20%) SOFA score 2 (0-3) IGS II score 32 (27-40) Heart Rate (HR) on admission, median (IQR) 93 (77-110) Mean Arterial Pressure (MAP) on admission, median (IQR) 95 (84-103) Admission Glasgow Coma Scale (GCS) 15 (14-15) Presence of dyspnea on admission, n (%) 35 (80%) Signs of acute respiratory failure on admission, n (%) 11 (25%) Need for oxygen therapy on admission, n (%) 24 (55%) Pa0 2 /Fi0 2 ratio calculated on the day of admission in ICU, median (IQR) 259 (296-400) Need for non-invasive ventilation (NIV) 6 (14%) Non-invasive mechanical ventilation with facial mask, n (%) 4 (9%) Nasal High Flow Oxygen therapy, n (%) 2 (5%) Death during the 6-months follow-up, n (%) 12 (27%) Corticosteroids (dexamethasone), n (%) 20 (45%) Biological parameters Natremia (mmol/L), median (IQR) 137 (134-138) Creatinine (µmol/L), median (IQR) 90 (63-129) AST (IU/L), median (IQR) 37 (28-45) ALT (IU/L), median (IQR) 21 (15-31) C-Reactive Protein (mg/L), median (IQR) 36 (14-90) Procalcitonin (ng/ml), median (IQR) 0.12 (0.07-0.60) Haemoglobin (g/dl), median (IQR) 13 (12-14) Platelets (G/L), median (IQR) 186 (138-251) Leukocytes (G/L), median (IQR) 7 (5-9) Neutrophils count (G/L), median (IQR) 5.2 (3.9-7.4) Lymphocytes count (G/L), median (IQR) 0.8 (0.7-1.3) D-Dimers (µg/L), median (IQR) 898 (580-1450) Neutrophils/Lymphocytes Ratio (NLR), median (IQR) 6.38 (3.06-9.50) CRP/PCT Ratio (CPR), median (IQR) 143 (53-443) PCT/Polymorphonuclear Neutrophils Ratio, median (IQR) 0.03 (0.01-0.11) Table 2 . Relationships between mortality at M6 and various demographic characteristics and biological parameters and their variations D1 and D3 (univariate analysis). Parameters Alive 1 n=32 (100%) Death at 6 months 1 n=12 (100%) p-value 2 Gender (male) 16 (50%) 7 (58%) 0.62 Age (years) 72 [62; 84] 72 [58; 80] 0.83 ASA score 3 [2,00; 3,00] 3 [2,75; 3,00] 0,15 SOFA on admission 1.5 [0.0; 3.0] 4.0 [0.75; 5.0] 0.06 SAPS-II on admission 32 [27; 40] 37 [29; 41] 0.41 Biological parameters at Day 1 PMN (G/L) 4.7 [2.8; 6.5] 6.5 [5.6; 11.8] 0.011* NLR 6 [3; 10] 6 [5; 9] 0.43 PCT (ng/ml) 0.11 [0.07; 0.46] 0.37 [0.22; 3.75] 0.079 CRP (mg/L) 32 [13; 75] 47 [20; 92] 0.86 CRP/PCT ratio 186 [71; 405] 55 [5; 443] 0.17 PCT/PMN ratio 0.03 [0.01; 0.10] 0.05 [0.02; 0.53] 0.30 Delta PCT (D1-D3) 0 [0; 1] 0 [0; 0] 0.48 CXCL8 at day 1 (pg/ml) 19 [14; 29] 28 [23; 43] 0.027* Delta CXCL8 (D3-D1) 1 [-3; 12] -8 [-17; -7] 0.045* CXCL12 at day 1 (pg/ml) 10 [7; 13] 12 [8; 17] 0.22 Delta CXCL12 (D3-D1) 1 [0; 4] 1 [-4; 4] 0.77 Expression of neutrophil surface receptors CXCR4+ (MFI) 1.918 [1.493; 2.218] 1.778 [1.518; 2.877] 0.95 Delta CXCR4+ (MFI D3-D1) -254 [-459; 243] 312 [247; 677] 0.10 Delta percentage PMN CXCR4+ (%) 13 [-2; 17] 15[14; 50] 0.25 Percentage PMN CXCR4+ (%) 31 [26; 44] 28 [25; 46] 0.95 C3aR+ (MFI) 3.009 [2.629; 3.547] 2.898 [2.644; 4.207] 0.62 C5aR+ (MFI) 30.655 [20.215; 41.814] 28.621 [20.313; 45.488] 0.92 HMGB-1+ (MFI) 9.961 [5.326 ; 28.402] 11.750 [6.792; 12.843] >0.99 Percentage PMN HMGB-1+ (%) 0.15 [0.06; 0.24] 0.04 [0.03; 0.07] 0.07 Delta HMGB-1+ (MFI D3-D1) 5.412 [-4.836; 11.499] 1.903 [1.633; 16.917] 0.66 Delta percentage HMGB-1+ (%) 0.16 [0.06; 0.51] 0.09 [-0.01; 0.28] 0.79 FMLP-R+ (MFI) 16.552 [13.359; 22.240] 18.069 [16.392; 21.732] 0.64 Plasmatic miR levels (relative expression) miR-155-5p 0.05 [0.01 ; 0.19] 0.08 [0.04; 0.15] 0.59 miR-26a-5p 0.04 [0.01; 0.22] 0.05 [0.05; 0.28] 0.24 miR-146a-5p 1 [0; 4] 8 [1; 17] 0.072 Delta miR-146a-5p 0 [0; 2] 8 [1; 18] 0.10 miR-15b-5p 0.73 [0.22; 3.95] 1.72 [1.08; 3.89] 0.24 miR-30d-5p 2 [0; 11] 1 [2; 18] 0.16 miR-hsa-122 0.29 [0.05; 0.71] 0.78 [0.49; 1.79] 0.039* Delta miR-hsa-122 0 [0; 0] -1 [-3; 0] 0.12 Data are expressed in mean ±SD or median and IQR of the different biological parameters unless otherwise indicated. Plasmatic miR levels are expressed in relative expression. *p-value <0.05. ASA: American Society of anesthesiology score. SOFA: Sepsis-related Organ Failure Assessment score. SAPS II: Simplified Acute Physiology Score II. COPD: Chronic Obstructive Pulmonary Disease. RAA inhibitors: Renin-Angiotensin-Aldosterone Inhibitors. PCT: Procalcitonin. PE: Protein expression. CRP: C-Reactive Protein. NLR: Neutrophils/Lymphocytes ratio. PE: Protein Expression. MFI: Mean of fluorescence. D: difference between measurement at day 1 and day 3. miR: microRNA. 1 Median (IQR) or Frequency (%). 2 Wilcoxon rank sum exact test; Pearson's Chi-squared test; Wilcoxon rank sum test; Fisher's exact test Table 3 . Relationships between the need for mechanical ventilation and various parameters and their evolution between D1 and D3 ventilation (univariate analysis). Parameters No need for ventilation 1 n=39 Need for ventilation 1 n=5 p-value 2 Gender (male) 18 (46%) 3 (60%) 0.66 Age 72 [62; 84] 68 [60; 79] 0.77 ASA score 3 [2; 3] 3 [3; 3] 0.029* SOFA 2 [0; 3] 4 [3; 5] 0.079 SAPS-II 32 [27; 41] 38 [31; 39] 0.60 Biological parameters at Day 1 PMN (G/L) 5 [3; 7] 12 [6.5; 20] 0.015* NLR 6 [3; 9] 12 [8; 15] 0.043* PCT (ng/ml) 0.11 [0.07; 0.56] 0.35 [0.04; 5.13] 0.90 CRP (mg/L) 50 [12; 101] 23 [22; 28] 0.34 CRP/PCT ratio 163 [56; 384] 65 [6; 543] 0.55 PCT/PMN ratio 0.03 [0.01; 0.10] 0.05 [0.01; 0.20] 0.89 Delta PCT (D1-D3) 0 [0; 0] 0 [0; 2] 0.76 CXCL8 at day 1 (PE) 23 [15; 30] 29 [18; 29] 0.60 Delta CXCL8 (D3-D1) -6 [-14; 1] 13 [8; 20] 0.023* CXCL12 at day 1 (PE) 11.0 [7.0; 14.0] 8 [7.0; 10.0] 0.049* Delta CXCL12 (D3-D1) 1 [0; 5] 0 [-3; 0] 0.25 Expression of neutrophil surface receptors CXCR4+ (MFI) 1.921 [1.640; 2.365] 1.487 [1.380; 1.491] 0.037* Delta CXCR4 (MFI D3-D1) 241 [-304; 499] 149 [91; 206] >0.99 C3aR+ (MFI) 3.016 [2.682; 3.709] 2.672 [2.644; 2.797] 0.25 C5aR+ (MFI) 28.218 [19.868; 41.767] 28.621 [25.297; 34.716] 0.83 HMGB-1+ (MFI) 10.795 [5.458; 24.206] 11.063 [7.617; 21.900] 0.87 FMLP-R+ (MFI) 17.610 [13.934; 22.856] 16.538 [14.104; 18.720] 0.56 Plasmatic miR levels (relative expression) miR-155-5p 0.07 [0.01; 0.17] 0.04 [0.01; 0.04] 0.47 miR-26a-5p 0.05 [0.01; 0.24] 0.04 [0.01; 0.05] 0.84 miR-146a-5p 2 [0; 9] 1 [1; 1] 0.84 Delta miR-146a-5p 0 [0; 3] 0 [-5; 4] 0.77 miR-15b-5p 0.97 [0.28; 3.73] 1.26 [0.24; 4.56] >0.99 miR-30d-5p 3 [1; 11] 6 [2; 24] 0.54 miR-hsa-122 0.36 [0.09; 0.77] 0.47 [0.05; 0.78] >0,99 Delta miR-hsa-122 0 [0; 0] -1 [-7; -1] 0.039* Data are expressed in mean ±SD or median (IQR) of the different biological parameters unless otherwise indicated. Plasmatic miR levels are expressed in relative expression. *p-value <0.05. ASA: American Society of anesthesiology score. SOFA: Sepsis-related Organ Failure Assessment score. SAPS II: Simplified Acute Physiology Score II. COPD: Chronic Obstructive Pulmonary Disease. OSA: Obstructive Sleep Apnea Syndrome. RAA inhibitors: Renin-Angiotensin-Aldosterone Inhibitors. PCT: Procalcitonin. CRP: C-Reactive Protein. NLR: Neutrophils/Lymphocytes ratio. PE: Protein Expression. MFI: Mean of fluorescence. D: difference between measurement at day 1 and day 3. miR: microRNA. 1 Median (IQR) or Frequency (%). 2 Wilcoxon rank sum exact test; Pearson's Chi-squared test; Wilcoxon rank sum test; Fisher's exact test. Supplementary Files Keymessages.docx SupplementaryMaterialFigure6.docx SupplementarymaterialTable4.docx SupplementarymaterialTable5.docx SupplementarymaterialTable6.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major Revisions Needed 03 Oct, 2025 Reviewers agreed at journal 07 Jul, 2025 Reviewers invited by journal 07 Jan, 2025 Editor assigned by journal 30 Dec, 2024 First submitted to journal 27 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5662811","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398787821,"identity":"c72d76ec-4e1f-442b-94a0-6ba7536adb70","order_by":0,"name":"Matthieu Daniel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABKElEQVRIiWNgGAWjYJACZgYDEJUAQhZyDBJQYTYQUYFVA2MzkhYJY1QtZ3BpYYBqYWCQSGyQQJbFokW3/fzxxwUFNokN7MnPHjyokEjvn91jwFxRcTiPj733AMPBPRhazM4kMzbPMEhLbOB5Zm6QcEYid8adMwaMZ84cLmbjOZfAcOAZppYDQC08BoeB7kkwk0hsk8jdIJFj/rOx7TCQnWPA/OEAppbzj0Fa/gO1pH+TSPwnkW4AVMnY+A+iheEAFi03wLYcAGrJMQP5PQGipQGflseGs3kMko3beN6USSQckzCccSOtgLHhWHpiG88ZgwPYtJxPfPCZ54+dbD97+jbJHzU28vwzkjcwNtRYJ85v7zF8gEULHLBhFcWjYRSMglEwCkYBHgAAbYptsgi6Wb4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5605-3131","institution":"CHU de La Réunion: Centre Hospitalier Universitaire de la Reunion","correspondingAuthor":true,"prefix":"","firstName":"Matthieu","middleName":"","lastName":"Daniel","suffix":""},{"id":398787822,"identity":"bf578cfc-7cb8-43a2-b276-f269dc537227","order_by":1,"name":"Faustine Bernardin","email":"","orcid":"","institution":"Reunion University: Universite de la Reunion","correspondingAuthor":false,"prefix":"","firstName":"Faustine","middleName":"","lastName":"Bernardin","suffix":""},{"id":398787823,"identity":"7c415e2b-707c-4750-ad5e-33b70453b4ea","order_by":2,"name":"Laetitia Sennsfelder","email":"","orcid":"","institution":"Reunion University: Universite de la Reunion","correspondingAuthor":false,"prefix":"","firstName":"Laetitia","middleName":"","lastName":"Sennsfelder","suffix":""},{"id":398787824,"identity":"7c041404-5110-43dd-813a-930cce317fb8","order_by":3,"name":"Melissa Payet","email":"","orcid":"","institution":"Reunion University: Universite de la Reunion","correspondingAuthor":false,"prefix":"","firstName":"Melissa","middleName":"","lastName":"Payet","suffix":""},{"id":398787825,"identity":"b4cafe28-7a62-4f43-8db7-089a3b29448b","order_by":4,"name":"Damien Vagner","email":"","orcid":"","institution":"Reunion University: Universite de la Reunion","correspondingAuthor":false,"prefix":"","firstName":"Damien","middleName":"","lastName":"Vagner","suffix":""},{"id":398787826,"identity":"01326b4e-fb8c-4919-8661-6c4bbb1ba162","order_by":5,"name":"Elie Kantor","email":"","orcid":"","institution":"Hôpital Bichat - Claude-Bernard Service d'Anesthésie réanimation et surveillance continue médico-chirurgicale: Hopital Bichat - Claude-Bernard Service d'Anesthesie reanimation et surveillance continue medico-chirurgicale","correspondingAuthor":false,"prefix":"","firstName":"Elie","middleName":"","lastName":"Kantor","suffix":""},{"id":398787827,"identity":"243edcc8-2f35-4b26-ab52-4ec4e09e08ec","order_by":6,"name":"Flore Weisse","email":"","orcid":"","institution":"CHU de La Réunion: Centre Hospitalier Universitaire de la Reunion","correspondingAuthor":false,"prefix":"","firstName":"Flore","middleName":"","lastName":"Weisse","suffix":""},{"id":398787828,"identity":"8f1b127a-6e1a-48ed-9e6e-4fc298da10cb","order_by":7,"name":"Juliette Verhille","email":"","orcid":"","institution":"CHU de La Réunion: Centre Hospitalier Universitaire de la Reunion","correspondingAuthor":false,"prefix":"","firstName":"Juliette","middleName":"","lastName":"Verhille","suffix":""},{"id":398787829,"identity":"bbb9564f-d4fc-4beb-9179-54342ad96719","order_by":8,"name":"Elisabeth Fernandes","email":"","orcid":"","institution":"CHU de La Réunion: Centre Hospitalier Universitaire de la Reunion","correspondingAuthor":false,"prefix":"","firstName":"Elisabeth","middleName":"","lastName":"Fernandes","suffix":""},{"id":398787830,"identity":"82c1bdee-640a-499a-b2fd-183c5da8359d","order_by":9,"name":"Bertrand Guihard","email":"","orcid":"","institution":"CHU Toulouse: Centre Hospitalier Universitaire de Toulouse","correspondingAuthor":false,"prefix":"","firstName":"Bertrand","middleName":"","lastName":"Guihard","suffix":""},{"id":398787831,"identity":"05684553-6f9c-49d3-8a1f-b6db522034f4","order_by":10,"name":"Bérénice Doray","email":"","orcid":"","institution":"CHU de La Réunion: Centre Hospitalier Universitaire de la Reunion","correspondingAuthor":false,"prefix":"","firstName":"Bérénice","middleName":"","lastName":"Doray","suffix":""},{"id":398787832,"identity":"68ae46dc-4da3-4d5f-9924-b93db19ddfa4","order_by":11,"name":"Yosra Bedoui","email":"","orcid":"","institution":"Reunion University: Universite de la Reunion","correspondingAuthor":false,"prefix":"","firstName":"Yosra","middleName":"","lastName":"Bedoui","suffix":""},{"id":398787833,"identity":"22be83f5-e89f-47c1-b5a9-23136be5a89a","order_by":12,"name":"Loïc Raffray","email":"","orcid":"","institution":"CHU de La Réunion: Centre Hospitalier Universitaire de la Reunion","correspondingAuthor":false,"prefix":"","firstName":"Loïc","middleName":"","lastName":"Raffray","suffix":""},{"id":398787834,"identity":"a024fc2f-1c84-495c-ab63-a0673189637a","order_by":13,"name":"Philippe Gasque","email":"","orcid":"","institution":"CHU de La Réunion: Centre Hospitalier Universitaire de la Reunion","correspondingAuthor":false,"prefix":"","firstName":"Philippe","middleName":"","lastName":"Gasque","suffix":""}],"badges":[],"createdAt":"2024-12-17 14:49:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5662811/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5662811/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73516565,"identity":"c205bb9f-2474-4d10-92eb-3ab39a3a96a3","added_by":"auto","created_at":"2025-01-10 17:47:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39525,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow-chart of the study. \u003c/strong\u003eED: Emergency Department\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5662811/v1/7e786cbdbc6a475fb47b61b3.png"},{"id":73516573,"identity":"92b89cf5-0867-4dfe-a7aa-611ed9fd21cb","added_by":"auto","created_at":"2025-01-10 17:47:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenotypical characterization of peripheral blood neutrophils from COVID-19 patients before (D1) and after Dexamethasone treatment (D3+DXM). \u003c/strong\u003eFlow cytometry was used to evaluate the surface expression of A) CXCR4 and B) HMGB1 on polymorphonuclear neutrophils from COVID-19 patients on admission (D1; n=44) then after 48 hours of treatment with dexamethasone (D3+DXM; n=20). Results are represented as percentages of positive neutrophils or median fluorescence intensity (MFI). Bars indicate the median value for each study group. Results were statistically analyzed by the Bonferonni’s comparison test. *p=0.01 *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001; ****P ≤ 0.0001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5662811/v1/4d5dbd97ba7361f8015b48d3.png"},{"id":73516564,"identity":"2c87c65b-c1db-461a-94c1-c8041a3c6342","added_by":"auto","created_at":"2025-01-10 17:47:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41161,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistograms showing serum CCL2, CXCL12 and CXCL8 levels in COVID-19 patients and controls.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eELISA tests were performed in COVID-19 patients on admission and in patients who received dexamethasone after 48 hours of treatment and compared to the concentrations observed in patients from the control group. All experiments were done in triplicates and results are expressed as mean ± standard error. ** p-values ≤ 0.01, *** p-values ≤ 0.001, and **** p-values ≤ 0.0001represent significant differences from controls by one-way ANOVA followed by the Bonferroni’s test analysis.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5662811/v1/d617306c9b27bbce63f3d44b.png"},{"id":73517536,"identity":"c4cd584e-0ae1-449a-969a-15a40583bac6","added_by":"auto","created_at":"2025-01-10 17:55:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":57492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistograms showing the results serum miRNAs levels in COVID-19 patients on admission and in patients who received dexamethasone after 48 hours of treatment compared to the concentrations observed in patients from the control group.\u003c/strong\u003e Pro-inflammatory (panel A) and anti-inflammatory (panel B) miRNAs. All experiments were done in triplicates and results are expressed as mean ± standard error. ** p-values ≤0.01, *** p-values ≤0.001, and **** p-values ≤0.0001 represent significant differences from controls by one-way ANOVA followed by the Bonferroni’s test analysis.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5662811/v1/247be3653eb933b0d98adcb7.png"},{"id":73516566,"identity":"659a104d-adb9-4bb3-8884-d94000d66b56","added_by":"auto","created_at":"2025-01-10 17:47:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":466866,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation matrice representing the association between standard biological parameters and biomarkers (chemokines and labeled neutrophil surface receptors) in patients in the COVID-19 group.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5662811/v1/b72dc1d957742c122ffe473b.png"},{"id":73519867,"identity":"519e5b41-13f7-4def-8da6-c78f88f00988","added_by":"auto","created_at":"2025-01-10 18:11:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2781967,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5662811/v1/e17342ff-b24a-4a58-92d6-aca6d3802528.pdf"},{"id":73516562,"identity":"8b512938-8e95-4e79-9e07-7762cce16e11","added_by":"auto","created_at":"2025-01-10 17:47:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15609,"visible":true,"origin":"","legend":"","description":"","filename":"Keymessages.docx","url":"https://assets-eu.researchsquare.com/files/rs-5662811/v1/01ac7e93acd212677d6b7621.docx"},{"id":73516567,"identity":"811710d2-a2b4-4d25-930f-a5c306f71832","added_by":"auto","created_at":"2025-01-10 17:47:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":47646,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialFigure6.docx","url":"https://assets-eu.researchsquare.com/files/rs-5662811/v1/7fd0fa235788ce9354c3803e.docx"},{"id":73517538,"identity":"98e71cbe-1a61-4550-a248-b9be605e15e3","added_by":"auto","created_at":"2025-01-10 17:55:18","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":29264,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialTable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-5662811/v1/5c4fcbca030077f446288812.docx"},{"id":73516577,"identity":"e859e6ae-237b-4857-a4ca-fe1d0709de80","added_by":"auto","created_at":"2025-01-10 17:47:19","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16560,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialTable5.docx","url":"https://assets-eu.researchsquare.com/files/rs-5662811/v1/d0c309ac4374527b3f2f5b16.docx"},{"id":73517539,"identity":"8ddf817d-30f6-4b08-91a5-d00fb9d238b4","added_by":"auto","created_at":"2025-01-10 17:55:19","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":24671,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialTable6.docx","url":"https://assets-eu.researchsquare.com/files/rs-5662811/v1/14bf6dfa6f4a3b72a80d18ba.docx"}],"financialInterests":"","formattedTitle":"Role of CXCL12/CXCR4 pathway and miRNA expression profiles on polymorphonuclear mobilization in COVID-19 patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlmost 4 years after the discovery of the SARS-CoV-2 pathogen and its spread around the world from Wuhan province, the mechanisms involved in the pathogenicity and virulence of this coronavirus remain poorly understood. Several studies have attempted to investigate the interactions between the virus and the immune system and to identify host-related factors that could have an impact on the progression of COVID-19 [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A growing body of evidence points the dysregulation of innate immune cells during severe SARS-CoV-2 infection [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In this context, recent studies have reported a significant rise in polymorphonuclear neutrophils (PMN) count among patients with COVID-19, contrasting with most viral infections [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, increased PMN count has been associated with the disease severity and poor prognosis of COVID-19 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Polymorphonuclear neutrophils, which are the most abundant immune cells in human blood, are major effectors of the innate immune system and usually the first cells recruited at the site of infection [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChemokines and their receptors mediate the recruitment and activation of leukocytes at sites of inflammation and infection. Neutrophils express mainly receptors of the CXCR family, including CXCR1, CXCR2, and CXCR4, that bind to CXC chemokines, such as CXCL2 (Macrophage inflammatory protein 2-alpha or MIP-2α), CXCL8 (Interleukin-8 or IL-8), and CXCL12 (Stromal cell-derived factor-1 or SDF-1) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among those, CXCL12/Stromal cell-derived factor-1 (SDF-1) is a key mediator of PMN homeostasis. CXCL12/CXCR4 signaling system is known to play an important role in the regulation of PMN distribution and trafficking in homeostasis and disease [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. CXCR4 is a master regulator of neutrophil storage in the bone marrow and facilitates homing of circulating aged PMN back to the bone marrow [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Changes in the expression of CXCL12 in peripheral tissues was reported to establish gradients that promote PMN migration from the bone marrow during pathological conditions such as sepsis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn COVID-19 patients, a significant overexpression of a subpopulation of PMN CD10lowCD101-CXCR4\u0026thinsp;+\u0026thinsp;and an increase in chemokines such as IL-6, calprotectin and CXCL8 in patients with severe COVID-19, in connection with emergency myelopoiesis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, authors have shown that miRNAs (miRs) closely associated with CXCR4 expression such as miR-146a or miR-155 were significantly modulated in severe forms of COVID-19 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. miRs are potent regulators of immune response and have received particular attention in the study of COVID-19 infection. Recent studies have shown aberrant expression of several miRNAs in COVID-19 in association with disease progression [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Differentially expressed miRNAs were found to be involved in the modulation of molecular pathways that regulate inflammatory and antiviral immune response [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For example, miR-155-5p and miR-15b-5p, known to be involved in the negative modulation of pro-inflammatory cytokines and host immune response, were found upregulated during COVID-19, downregulated in patients with severe COVID-19 (compared to mild to moderate forms) [\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Considering this, miRNAs have also been used as potential therapeutic targets in COVID-19, especially for the management of pathological consequences of COVID-19 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImmunosuppressive therapies, such as the glucocorticoid dexamethasone (DXM), significantly reduce mortality in hospitalized COVID-19 patients receiving respiratory support [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, mechanisms underlying the beneficial effects of DXM during severe COVID-19 remain poorly understood. We questioned if DXM may itself work in-part by affecting neutrophil function or modulating miRNAs expression in severe COVID-19 infection.\u003c/p\u003e \u003cp\u003eWe hypothesized that SARS-CoV-2 virus may modulate innate immune inflammatory response contributing to PMN activation and recruitment through chemokines and miRNAs known to be synthesized and secreted by MSCs in a septic context [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. We primary aimed to investigate the CXCL12(SDF-1α)/CXCR4 signaling pathway by studying SDF-1α plasmatic levels and the CXCR4 expression modulation on PMN surface in COVID-19 patients in comparison with controls, and according to disease severity and DXM use. The secondary objectives were to examine the circulating miRNA profile of hospitalized COVID-19 patients before and after DXM treatment, and to assess potential correlation between these biomarkers and clinical outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy approval and ethical considerations\u003c/p\u003e\n\u003cp\u003eThis study was conducted during the COVID-19 epidemic in La Reunion Island. Serological testing (collected as part of the patient\u0026rsquo;s care) was performed on plasma samples. All patients with RT-PCR-confirmed COVID-19 or their legal surrogate gave informed consent prior to inclusion at the time of entry at the hospital for the testing (CHU de la R\u0026eacute;union). Samples were deposited at the local Bioresource Center (CRB, certified by Euro-Quality System, NF 996-900) and declared to the local bio-collection of infectious diseases. The study was approved by the Human Ethics Committee of University of Bordeaux (\u0026ldquo;Comit\u0026eacute; Consultatif de Protection de Personnes se pr\u0026ecirc;tant \u0026agrave; des Recherches Biom\u0026eacute;dicales\u0026rdquo;, Bordeaux France, ref. 2008-A00151-54). The design of this work conforms to ethical standards of the Helsinki declaration. The CNIL (Commission Nationale Informatique et Libert\u0026eacute;s), commission involved in the protection of digital data, has also given its agreement for the use of data within the framework of this study.\u003c/p\u003e\n\u003cp\u003eDesign of the study\u003c/p\u003e\n\u003cp\u003eThis study was conducted as a prospective observational cohort study, at the University Hospital of La R\u0026eacute;union Island. The study population included 2 separate groups of patients: patients with documented SARS-CoV-2 infection (positive nasopharyngeal RT-PCR) admitted to the emergency ward between March 2022 and April 2023 (SARS-CoV-2 group) and a group of patients admitted to the emergency ward between December 2022 and April 2023 for minor traumatic pathologies and not presenting systemic inflammatory response syndrome controlled on admission biology (control group). Among COVID-19 patients, a subgroup received DXM upon hospital admission (6 mg per day as recommended by scientific committees taking the Villar protocol as a reference and referring to the recommendations concerning the benefit of low doses of corticosteroids at this time) [26, 27].\u003c/p\u003e\n\u003cp\u003eStudy inclusion required a minimum of age of 18 years old and the ability to provide consent. Exclusion criteria were represented for both groups by pregnancy/nursing condition, administration of immunosuppressive treatments, mental illness or impossibility for the subject to have a good comprehension of the study, therapeutic limitation before inclusion and lack of health insurance coverage.\u003c/p\u003e\n\u003cp\u003eData collection and biological variables\u003c/p\u003e\n\u003cp\u003eAt time of inclusion (D1), we collected the demographic characteristics, comorbidities and main COVID-19 associated features assessed during hospitalization. We also evaluated the critical illness severity with SAPS II and SOFA scores on admission, as well as the need for an intensive care unit (ICU) and ventilation support. The time and cause of death and the length of hospital-stay as well as vital status after discharge within 6 months were collected prospectively. This manuscript adheres to the applicable CONSORT guidelines [28]. Biological variables of interest were assessed at different time points of hospitalization: on admission (D1) then on day 3 (D3), day 7 (D7), day 14 (D14) and day 21 (D21) unless patient\u0026rsquo;s discharge. The biological parameters measured included a blood ionogram, a blood count with white blood cells and platelet counts, arterial gasometry with lactatemia in the event of signs of respiratory distress or oxygen requirement, a dosage of complement factors, an electrophoresis of serum proteins, a dosage of troponinemia, Brain natriuretic peptide (BNP), a dosage of C-Reactive Protein (CRP), Procalcitonin (PCT) and a haemostasis assessment with measurement of the D-dimer level.\u003c/p\u003e\n\u003cp\u003eSample collection\u003c/p\u003e\n\u003cp\u003eBlood samples were collected from SARS-CoV-2 and control patients within a day (24 hours) of their admission (first day or D1) to the emergency department (ED). In addition, samples were collected from DXM treated COVID-19 patients 48h after hospitalization. After collection, whole blood was immediately processed for flow cytometry analysis. Remaining blood was then centrifuged at 2000 g for 20 minutes and the supernatant was aliquoted and stored at -80\u0026deg;C for further analysis.\u003c/p\u003e\n\u003cp\u003eFlow cytometry and characterization of the PMN\u003c/p\u003e\n\u003cp\u003eFor all patients, 100 \u0026micro;L of whole blood was mixed with 5 \u0026micro;L of phycoerythrin (PE)-conjugated antibodies against cell various surface markers. : we used fluorochrome-conjugated monoclonal antibodies against FcyRIII (CD16-PE), C3aR (C3aR-PE), C5aR (CD88-PE), Histones H3 (H3-PE), CXCR4 (CD184-PE), FMLP-R (FMLP-R PE), HMGB-1 (HMGB-1-PE) and Integrin alpha M (ITGAM-PE or CD11b-PE). Appropriate isotype controls were used to define background staining levels. Samples were then incubated at room temperature in the dark for 30 minutes. The red blood cells were lysed with the Beckman ImmunoPrep\u0026trade; reagent system (Beckman Coulter, catalog number: 7,546,999). Flow cytometry analysis was performed with the Becton Dickinson C6 Plus AccuriTM flow cytometer and data were extracted using BD AccuriTM C6 version 1.0 software. PMN expression of surface molecules was determined by using forward and side scatter to identify the granulocyte population and to gate out other cells and debris. PMN were gated as CD16+ cells conjugated with FITC. Levels and percentage of PMN expressing the different markers were then analyzed, and data were processed as appropriate using GraphPad Prism software.\u003c/p\u003e\n\u003cp\u003eEnzyme-linked immune-sorbent assay (ELISA)\u003c/p\u003e\n\u003cp\u003eChemokine concentrations in serum samples were measured using commercially available ELISA kits for CCL2 (Peprotech: cat. no. 900-K31), CXCL8 (Peprotech; cat. no. 900-K18) and CXCL12 (Peprotech; cat. no. 900-KXX), according to the manufacturer\u0026rsquo;s instructions. Samples were analyzed from independent experiments. Serum samples were prepared from whole blood (the same used for flow cytometry analysis) following centrifugation (20 min at 2000 g) and stored at \u0026minus;80\u0026deg;C until ELISA analysis in a batch.\u003c/p\u003e\n\u003cp\u003emiRNA extraction\u003c/p\u003e\n\u003cp\u003eTotal plasma RNA was isolated from 200 \u0026mu;L of plasma using the miRNeasy Serum/Plasma Advanced Kit (Qiagen; Ref.: 217204) as per the manufacturer\u0026rsquo;s instructions. miRNA Serum/plasma spike in control (Qiagen; Ref.: 219610) was used as an internal control and a standard for quantifying miR in housekeeping gene\u0026rsquo;s fashion (1,6.10\u003csup\u003e8\u003c/sup\u003e copies/\u0026mu;L). The miRNAs selected to be assayed in plasma samples are: miR-155, miR-26a-5p, miR-146a-5p, miR-29a-3p, miR-15b-5p, miR-hsa-122, miR-30d-5p, and miR-155-5p. The list of primers used for RT-PCRs for each miR is presented in Table 6 of the supplementary material. These miRs were chosen because of their close involvement in the pathophysiology of COVID-19 or their role in PMN or MSC function.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eQuantitative real-time RT-PCR (qRT-PCR) analyzes\u003c/p\u003e\n\u003cp\u003eRT experiments were performed using the miScript II RT (Qiagen; Ref.: 218161). RT was performed in a final volume of 20 \u0026Mu;l containing 5 \u0026Mu;l of extracted total RNA per reaction and 15 \u0026Mu;l of enzyme mix. RT was carried out in the QuantStudio 5 PCR thermocycler (Thermo Fisher Scientific). Cdna was collected and kept at \u0026minus;20 ◦C until use. Q-PCR experiments were performed using the miScript SYBR Green PCR (Qiagen; Ref.: 218075). qPCR was performed in a final volume of 5 \u0026mu;L containing 1 \u0026mu;L of extracted cDNA per reaction, 3 \u0026mu;L of enzyme mix and 1 \u0026mu;L of primer mix, with a final primer concentration of 1.25 \u0026mu;M. qPCR was carried out in the QuantStudio 5 PCR thermocycler (Thermo Fisher Scientific). Relative gene expression was calculated using Ce39 as a reference gene. Experiments were performed in triplicate. Primer sequences related to the genes are listed in the supplemental material.\u003c/p\u003e\n\u003cp\u003emiRNA profiling with microarrays\u003c/p\u003e\n\u003cp\u003eIn order to compare the different miRNA profiles, we used GeneChip\u0026reg; miRNA 4.0 Array (Affymetrix, Santa Clara, CA, USA) allowing the sequencing of a battery of human miRs. For analysis with Affymetrix GeneChip miRNA 4.0 Arrays, samples were labeled on ice with the FlashTag Biotin HSR labeling kit according to the manufacturer\u0026rsquo;s instructions. GeneChip miRNA 4.0 arrays contain 100% miRbase version 20 coverage of 203 organisms and contain probes for 4,574 human non-coding RNAs (ncRNAs), including 2,578 miRNAs and 1,996 other ncRNAs (including scaRNAs and snoRNAs). Arrays were then washed and stained using the Affymetrix kit and Fluidics Station 450 according to protocol FS450_0002 and scanned using Affymetrix Command Console (AGCC) software and an Affymetrix GCS 3000 7G scanner to generate CEL files. Regarding the CEL files, they were analyzed using Affymetrix\u0026reg; Transcriptome Analysis Console (TAC) 4.0 softwareTM. With this software, the probe intensities of the miRNAs were normalized, and the miRNA gene expression was calculated with the Robust Multi-chip Analysis (RMA) setting the value of Detected Above Background (DABG) to 0.05. The changes of gene expression were shown as mean Fold Change (FC), considering miRNAs over or down expressed with thresholds above \u0026gt;2 or below \u0026lt;2, respectively. Significant dysregulated miRNAs were those with p-value less than 0.05.\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed with GraphPad Prism software version 8.01 and R Statistical Software\u0026auml;. Categorical variables were presented as frequency and percentages and continuous variables were described using either means and standard deviations (mean \u0026plusmn;SD) for normally distributed data, or medians and interquartile ranges (IQRs) for data that were not normally distributed. The normality of the data distribution was determined by the Kolmogorov-Smirnov test and reject the normality. Differences between the steps (at day 1, day 3, day 7, day 14 and day 21 when available) were tested for significance by repeated measures of one-way ANOVA followed by the Bonferroni\u0026rsquo;s test for multiple comparisons. The difference values between the steps were calculated with the value post-value before. Spearman test was applied to analyze correlation between the variables. p-values \u0026le; 0.05 were considered statistically significant. Significance was indicated in the figures as follow: p-values \u0026le; 0.05 (*), p-values \u0026le; 0.01 (**), p-values \u0026le; 0.001 (***) and p-values \u0026le; 0.0001 (****). Results are expressed as mean \u0026plusmn;standard error \u0026ldquo;SEM\u0026rdquo; and as percentage.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDemography, clinical characteristics and biological parameters of study subjects on the day of admission (D1)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOver the study period, a total of 67 patients were enrolled. There were 47 COVID-19 patients and 20 control subjects. Forty-four out of 47 patients admitted in emergency department (ED) for a SARS-CoV-2 related infection confirmed by a standard RT-PCR with consecutive genotype sequencing were analyzed. Three patients were excluded: one withdrew consent, and 2 patients finally presented a positive COVID-19 antigen test but with a negative RT-PCR, invalidating the initial diagnosis. Nineteen patients were analyzed in the control group due to withdrawal of consent. The flow-chart of the study is presented on \u003cstrong\u003eFigure 1\u003c/strong\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the COVID-19 group, patients were mostly female (n=23; 52%), hypertensive, overweighted and admitted in ED after a median of 3 days after COVID-19 onset. On admission to the ED, most patients presented with dyspnea (n=35; 80%) and almost a quarter of patients presented with signs of acute respiratory distress. Twenty-four (55%) patients required the use of oxygen therapy on admission and 20 (45%) patients were treated with dexamethasone. Four (9%) received non-invasive mechanical ventilation with facial mask and 2 (5%) received ventilatory support by high-flow oxygen therapy (Optiflow\u0026reg;). Identification of the viral genotype was assessed in 18 (41%) patients, among which 13 (30%) had the Omicron variant. The characteristics of the population of COVID-19 patients are presented in \u003cstrong\u003eTable 1\u003c/strong\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIncreased levels of CXCR4-positive peripheral blood PMN from patients with COVID-19 patients after 48h of Dexamethasone treatment\u003c/p\u003e\n\u003cp\u003ePeripheral blood neutrophils from COVID-19 patients requiring oxygen therapy support were examined for the expression of chemoattractant receptors and activation/maturation markers at admission and 48h after DXM treatment using flow cytometry. We did not observe a significant difference in the\u0026nbsp;median fluorescence intensity\u0026nbsp;(MFI) of the CXCR4 receptor after 48 hours of dexamethasone administration in SARS-CoV-2 infected patients requiring oxygen therapy support (vs 48 hours of treatment, p=0.8). However, we detected a significant increase (11,08 %) in the percentage of PMN expressing the CXCR4 receptor after DXM treatment (34,2% on D1 versus 45.3% after treatment, p=0.01). Furthermore, we did not observe a significant difference for percentages of HMGB-1+ neutrophils before and after treatment (\u003cstrong\u003eFigure 2\u003c/strong\u003e). Concerning CD16, C5aR and CD11b receptors no significant difference was found before and after DXM administration, although a tendency towards reduced CD16 and CD11b MFI was observed after treatment with DXM. We also observed a tendency towards increased of C3aR, Histone H3 and FMLP-R MFI after 48 hours of DXM treatment (\u003cstrong\u003eFigure 6 in supplemental material\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCCL2, CXCL8 and CXCL12 serum levels in COVID-19 patients before and after Dexamethasone treatment\u003c/p\u003e\n\u003cp\u003eWe assessed the serum levels of CCL2, CXCL12 and CXCL8 at admission for controls and COVID-19 patients and 48h after DXM treatment. The serum concentrations of CCL2, CXCL12 and CXCL8 were significantly higher in patients with SARS-CoV-2 infection compared to the control group (Figure 3). The protein expression levels were 153 pg/ml for CCL2 vs 90,1 pg/ml for the control patients (p \u0026lt; 0.001), 12,62 pg/ml for CXCL12 vs 4,26 pg/ml for the control patients (p \u0026lt;0.001) and 25,8 pg/ml for CXCL8 vs 13,9 pg/ml in controls (p\u0026lt;0.0001). Notably, the serum levels of the 3 tested chemokines decreased significantly in COVID-19 patients sampled after treatment with dexamethasone for 48 hours. The plasma levels of CCL2, CXCL12 and CXCL8 were respectively 99,96 pg/ml (p\u0026lt;0.001), 7,47pg/ml (p\u0026lt;0.01) and 17,56 pg/ml (0.1) in comparison with the levels observed on admission for these patients. These results are available on \u003cstrong\u003eFigure 3\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvolution of serum miRNA levels in COVID-19 patients before and after Dexamethasone treatment\u003c/p\u003e\n\u003cp\u003eWe measured serum levels of miR 15b-5p, miR 26a-5p, miR 155-5p, miR 30d-5p, miR 146a-5p, and miR hsa-122 by qRT PCR. We found that levels of the following miRs were significantly increased on admission in the group of SARS-CoV-2 infected patients compared to the control group: 15b-5p (2,54 against 0,33 for control patients, p \u0026lt; 0.001), miR 146a-5p (26,4 against 0,001 for controls, p \u0026lt; 0.001), miR 155-5p (0,13 against 0,007 for controls, p \u0026lt; 0.01) and miR 30d-5p (15,36 against 0,8 for controls, p \u0026lt; 0.01). The expression levels of miR hsa 122 and miR 26a-5p showed no significant difference with the levels observed in the control group (respectively 0,6 and 0,1; p\u0026gt;0.05). There was no significant difference in the expression levels of miR-15b-5p, miR-155-5p, miR-30d-5p and miR-hsa-122 before and after treatment with dexamethasone for 48h in COVID-19 patients (p \u0026gt;0.05). There was also a significant increase in the levels measured in serum of miR-26a-5p (0,40 against 0,10 for non-treated patients with COVID-19, p \u0026lt;0.001) and a significant decrease in the levels of miR-146a-5p (3,49 against 26,39 for non-treated patients with COVID-19, p \u0026lt;0.0001) in COVID-19 patients treated with DXM for 48 hours. We can also note trends towards a decrease in the levels of miR-15b-5p, miR-155-5p and miR-30d-5p in the group of COVID-19 patients after the same treatment but this was not found statistically significant. The results of these analyzes are presented in \u003cstrong\u003eFigure 4\u003c/strong\u003e panel A and B.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChemokines, neutrophil surface receptors, miRNA and mortality\u003c/p\u003e\n\u003cp\u003eUnivariate analysis was used to study the relationship between the different biological parameters of interest and patient mortality at 6 months in the COVID-19 group. Twelve patients died within 6 months after prospective data collection. We found a significant association between the CXCL8 level on day 1 (19 [14; 29] pg/ml in the group of patients alive at M6 versus 28 [23; 43] pg/ml in the group of patients who died; p =0.027) and its variation between D1 and D3 in patients treated with DXM with a greater drop in CXCL8 levels in deceased patients than in patients alive at M6 (1 [-3; 12] against -8 [- 17; -7] pg/ml; p=0.045). The expression of the receptors of interest on the surface of the PMN (MFI or percentage of labeled PMN) was not found associated with mortality at M6. Among the parameters relating to miR, only the initial level of miR-122 was found significantly associated with mortality (p=0.039). The results of the univariate analysis are presented in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAssociation between biological parameters and mechanical ventilation\u003c/p\u003e\n\u003cp\u003eThe results of the univariate analysis concerning the association between the use of mechanical ventilation (invasive or not) in COVID-19 patients showed an association between the need for mechanical ventilation and the following parameters : the ASA score (p=0.029), the NLR (p=0.043), the variation in the level of CXCL8 before and after DXM (p=0.023), the level of CXCL12 on day 1 (p=0.049), the mean of fluorescence intensity of CXCR4 at day 1 (p=0.037), the variation before/after treatment by DXM of miR-hsa-122 (p=0.039). The results of the univariate analyzes concerning the different parameters measured and the use of mechanical ventilation are presented in \u003cstrong\u003etable 3\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAssociation between standard biological markers of sepsis and microRNA\u003c/p\u003e\n\u003cp\u003eThe results of the correlation matrices regarding the association between standard biological markers and observed levels of circulating miRs in COVID-19 patients are shown in \u003cstrong\u003eFigure 5\u003c/strong\u003e. Among the notable factors significantly associated with the variation in PCT between days 1 and 3, we found the MFI of CXCR4 measured on day 1 (p\u0026lt;0.001). The PCT and the PCT/PMN ratio on D1 were both significantly associated with the MFI of C3aR measured on D1 (p\u0026lt;0.001)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparison of miRNA profiles using microarray chips\u003c/p\u003e\n\u003cp\u003eWe were thus able to compare the miRNA profiles and significant overexpression or underexpression between 3 COVID-19 patients on D1 and 3 control patients on the one hand and between COVID-19 patients between their admission on D1 and the second blood sample performed on D3 in case of treatment with DXM. The results of the miRNA screening analysis showed that there were 36 overexpressed and 9 underexpressed miRs in COVID-19 patients at the time of their admission compared to the group of control patients. Furthermore, when we looked at the 36 miRs showing overexpression upon admission of patients infected with SARS-CoV-2, we saw that 25 of these miRs see their expression downmodulated after 48 hours of treatment with corticosteroids. Analysis by miRNA chip thus confirmed that miR-15b-5p, miR-30d-5p and miR-146a-5p were indeed upregulated in COVID-19 patients compared to uninfected patients and downregulated in the event of treatment with DXM for 48 hours. The results of the expressions of the different miRNAs are presented in Table 4 in supplemental material.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMain results and findings\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study showed significant modulation of the CXCL12(SDF-1)/CXCR4 signaling pathway in COVID-19 patients compared to a control patient population. This signaling axis also appeared to be modulated by corticosteroid treatment in patients with severe COVID-19 requiring ventilatory support. COVID-19 patients treated with DXM had a significant increase in PMNs expressing the CXCR4 receptor. We also detected significantly higher serum levels of CXCL12 in COVID-19 patients compared to control subjects. After DXM treatment, these serum CXCL12 levels decreased significantly. The result of univariate analyzes regarding mortality showed a significant association between baseline levels and change in CXCL8 levels before and after corticosteroid treatment suggesting a possible link between patient outcome and modulation of the relevant chemokines. The use of mechanical ventilation appeared to be associated with NLR, measured levels of CXCL12 and MFI CXCR4 at entry as well as change in CXCL8 levels. There was an association between the variation in PCT between D1 and D3 and the MFI CXCR4 on D1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCXCL12/CXCR4 pathway and PMN homing in COVID-19 patients\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePolymorphonuclear neutrophils are cells of the innate immune system that are particularly interesting due to their key role and their close interactions with mesenchymal stromal cells (MSC) during septic states, particularly of viral origin. [24, 25, 29, 30]. Indeed, it is now well established that PMNs are among the first immune effectors to arrive at the site of attack upon exposure to a pathogen in its early phase. [31, 32]. The variation in their levels and certain of their characteristics are also used as prognostic markers in numerous physiopathological situations and their deregulation is often associated with increased morbidity and mortality [33\u0026ndash;36]. Several authors noted that SARS-CoV-2 infection was characterized, unlike most viral infections, by more pronounced recruitment and activation of PMN functions [33, 37, 38]. This exacerbated recruitment of PMN results, on a biological level, in an increase in the number of circulating neutrophils contrasting with the decrease in the lymphocyte count, giving the neutrophil/lymphocyte ratio or NLR predictive criteria for morbidity and mortality\u0026nbsp;[33, 37, 38].\u0026nbsp;This cellular signature is accompanied by hypersecretion of cytokines, now well documented in the literature, up to levels described as \u0026quot;cytokine storms\u0026quot; associated with greater morbidity and mortality in COVDI-19 patients\u0026nbsp;[39, 40]. No data in the literature has so far made it possible to provide a physiopathological explanation for these biological particularities\u0026nbsp;[41]. The first objective of our study was therefore to evaluate the modulation of PMN recruitment by chemokine signaling pathways, in particular the CXCL12/CXCR4 pathway, and to argue the prognostic characteristics of this variation in pathological conditions by comparing the expression of this chemokine and its receptor in patients infected with SARS-CoV-2 with or without corticosteroid treatment and in subjects free of infection. In our study, we demonstrated a significant increase in circulating levels of CXCL12 in patients infected with SARS-CoV-2, their modulation after treatment with DXM, as well as a modulation of the CXCR4 receptor on the surface of PMNs under conditions of SARS-CoV-2 infection. The SDF-1/CXCL12 pathway and its receptors CXCR4 and CXCR7 have already been identified as a major signaling pathway during septic states and are involved in \u0026ldquo;homing\u0026rdquo; phenomena allowing the recruitment of PMNs to the site of tissue damage\u0026nbsp;[42]. The 2 types of receptors, CXCR4 and CXCR7, are present on the surface of hematopoietic cells, particularly PMN\u0026nbsp;[43\u0026ndash;45]. It has previously been demonstrated that the secretion of CXCL12 by MSCs in the bone marrow is responsible for maintaining the PMN pool in the hematopoietic niche and it has been established that the relative increase in peripheral CXCL12 levels in response to the presence of a pathogen, contrasting with stable levels at the hematopoietic niche, induced mobilization of neutrophils towards the blood\u0026nbsp;[10, 46]. The CXCL12 pathway recruits not only mature PMNs but also immature PMNs from the BM and also modulates PMN phagocytosis activity at the site of injury\u0026nbsp;[10, 29]. CXCL12 may also be involved in maintaining tight intercellular junctions during sepsis by a complex mechanism dependent on the adenosine A2B receptor\u0026nbsp;[47]. The modulation of the CXCL12/CXCR4 pathway in COVID-19 patients can explain the previously reported recruitment of PMN, particularly at the pulmonary level and gives an original interpretation to the protective capacities of dexamethasone in the most severe patients. One hypothesis would be that the most severe patients would have an up-regulation of their CXCL12/CXCR4 pathway which would lead to recruitment in the lung of mature then immature PMNs, whose dysfunction would lead to the genesis of cytotoxic tissue lesions accompanied by alveolar edema and the phenomenon of NETosis observed in numerous studies\u0026nbsp;[38, 48]. If this hypothesis is confirmed, it would also explain hypoxia phenomena and could provide new tools and early biomarkers to predict the evolution of respiratory function in COVID-19 patients and associated mortality. This could also open a new therapeutic field using antibodies directed against the CXCR4 receptor, which have already shown their effectiveness in animal models of sepsis, to reduce the recruitment and hyperactivation of PMNs, deleterious in this case\u0026nbsp;[49]. If CXCL12 has been traditionally identified as a homeostatic chemokine that plays an essential role in regulating leukocyte trafficking, the role of CXCL12 and its receptor CXCR4 during viral infection is very complex and it can act as a positive or negative regulator of leukocyte migration depending on the presence or absence of co-factors at the site of infection\u0026nbsp;[50]. Indeed, in suboptimal concentrations CXCL12 is able to form a complex with the alarmin High Mobility Group Box 1 (HMGB1) which is a DAMP released by damaged tissues. HMGB1-CXCL12 complex was shown to bind exclusively to the chemokine receptor CXCR4 and increase inflammatory cell migration\u0026nbsp;[51]. In other pathological conditions, at high concentrations and in the absence of cofactors, CXCL12 was found to have a repulsive effect on mature T cells and on LyT CD4+ and CD8+\u0026nbsp;[52]. One of the hypotheses that can explain the CXCL12 repulsive effect on immune cells is that high CXCL12 concentrations would prevent immune effector cells infiltration into the damaged tissues once the pathogen has been eliminated in order to carry out tissue repair\u0026nbsp;[52]. The presence of circulating alarmins at high concentrations during SARS-CoV-2 infection, associated with high serum levels of CXCL12, could explain a synergistic agonist effect on leukocyte recruitment, particularly on PMNs and the observed peripheral lymphopenia. Unfortunately, we were not able to obtain data regarding the circulating levels of HMGB1 in COVID-19 patients and further investigations are required to assess HMGB1 circulating levels as well as the formation of heterodimeric complexes with CXCL12 that can lead to a decrease in these concentrations. Of note, the redox state of HMGB1 seems to modulate the heterocomplex formation with CXCL12 and it would be therefore interesting to determine the oxidoreductive state of the molecule\u0026nbsp;[53]. In our study, we observed a tendency to an increase in the expression of HMGB-1 on the surface of the PMNs after treatment with DXM which could be linked to a reduced circulating levels of the free form of this protein. The CXCL12/HMGB1 complex formation was shown to be disrupted by pamoic acid which reduced PMN recruitment in the airways in a pulmonary infection model (\u003cem\u003ein vivo\u003c/em\u003e P.aeruginosa pneumonia model) [54]. Many substances \u0026ldquo;blocking\u0026rdquo; different cytokine pathways have been used in COVID-19 but no data regarding blockers of the CXCL12/CXCR4 pathway appear to be available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRole of CXCR4 in the modulation of innate immunity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCXCR4 is known to be up-regulated on the surface of eosinophils by the IFN-gamma pathway and by corticosteroids [55]. Some data from the literature show that modulation of the CXCR4 receptor on the surface of PMNs represents an alternative pathway for controlling the CXCL12/CXCR4 pathway. Its expression is notably known to be down-modulated by IFN-I and by GM-CSF and G-CSF [56]. Corticosteroids, including dexamethasone, are among the rare drugs that have demonstrated a positive effect in the treatment of COVID-19. Although most authors agree on its immunomodulatory effect, its effects at the molecular level are not well elucidated. We showed that the corticosteroid analogue dexamethasone could be associated with a decrease in circulating levels of CXCL12 and an up-regulation of its receptor CXCR4 on human PMNs in COVID-19 patients. DXM may therefore act in part by modulating CXCR4 expression on the surface of PMNs. The ability of dexamethasone, whose anti-inflammatory functions are well documented, to increase CXCR4 expression on PMNs could be considered part of a crucial homeostatic process. A study published in Nature Medicine in 2022 showed that DXM had an action on circulating neutrophils, in particular the population of IFN-activated PMNs, downregulated interferon-stimulated genes (ISGs), and activated IL-1R2+ neutrophils. It also appears to participate in the increase in the population of immunosuppressive immature neutrophils [57]. Our study provides an additional explanation for the action of DXM, particularly at the cellular and molecular level, on PMN.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003emiRNA profiles, PMN modulation and potential role of MSCs in SARS-CoV-2 infected patients\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral studies have been able to highlight the central role of PMNs during COVID-19 with a strong link established between their cell recruitment and the severity of the infection and its morbidity and mortality [33, 37, 38]. If many pathways are involved during the recruitment of these immune effectors, the CXCL12 (SDF-1\u0026alpha;)/CXCR4/CXCR7 pathway seems to represent a key pathway for the mobilization of these cells and their communication with the MSCs during septic states [29, 47]. SDF-1\u0026alpha; as well as other chemokines and soluble factors capable of establishing communication between PMNs and MSCs can be released in free form in the tissues or contained inside specialized extracellular vesicles (EVs), giving them greater longevity and protecting them during their journey through the vascular sector [58\u0026ndash;60]. Exosomes, a population of small EVs, are increasingly studied in physiopathology and transport both protein material and small specialized ribonucleic acids (RNAs) that seem to play a key role in the interactions between MSCs and immune effectors, notably during SARS-CoV-2 infections [61, 62]. MicroRNAs (miRNAs) as potent regulators of immune responses have received much attention in this regard. Recent studies have shown aberrant expression of miRNAs in COVID-19 in association with disease progression [14, 15]. Differentially expressed miRNAs were enriched in pathways related to inflammation and antiviral immune response [16, 17]. miRNAs have also been considered as potential therapeutic targets in COVID-19, especially for the management of pathological consequences of COVID-19. In this article, we have discussed avenues for miRNA dysregulation in COVID-19. Many studies have focused on the role played by miRNAs during sepsis and this area has naturally been investigated during COVID-19. Many studies are interested in the prognostic nature of miRs in these patients by proposing specific patterns of patient evolution. In our study, we chose to investigate the link between PMN modulation by the CXCL12/CXCR4 axis and specific miR profiles from MSCs. We focused on certain pro-inflammatory and anti-inflammatory miRs, known to have been isolated from extracellular vesicles secreted by MSCs and some of which have known functions in modulating immune effectors of innate immunity, particularly neutrophils. Literature data on the miRs studied in our work, in the context of sepsis and COVID-19, when these existed, are presented in Table 6 of the supplementary material. The results produced from miRNA microarrays, although our study was not designed for this purpose, confirm the modulation of the different miRNAs of interest observed and provide interesting leads in favor of a specific pattern of COVID-19 severity. Beyond the valuable pathophysiological information provided by such techniques, they could also in the future provide prognostic tools for this type of patients, as is currently being studied in sepsis [63].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStrengths and limitations of our study\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study population appeared to be representative of the populations of patients suffering from COVID-19, particularly the Omicron variant, observed in the literature [64, 65]. The delays observed between the first symptoms and admission to the emergency room were notably similar to those noted by other authors [66]. The characteristics of our population regarding its severity were also comparable to those found in the literature with 20 patients out of 44 analyzed having required treatment with dexamethasone due to oxygen dependence which, again, is close to the data from literature [67].\u003c/p\u003e\n\u003cp\u003eOur study provides new evidence for the modulation of PMN homing by SARS-CoV-2 infection and by the initiation of corticosteroid treatment in this context by exploring a previously unstudied signaling pathway, except during sepsis. If many authors have mentioned the particular cytokine profiles observed during COVID-19, few studies have focused on the link between this secretion and the modulation of the immune system and its consequences. This study thus opens perspectives for better understanding the role of cytokines and innate immunity dysfunction in these patients. This could also help explain the severity of patients, particularly on the respiratory level. Our work also raises questions regarding the potential role of MSCs in the antiviral immune response and the potential impact of their dysfunction in the development of severe SARS-CoV-2 infections.\u003c/p\u003e\n\u003cp\u003eBut our study also owns several limitations. First, this is a single-center study. Then, its implementation at La R\u0026eacute;union University Hospital can be accompanied by a response to COVID-19 specific to the island population. It would therefore be appropriate to confirm these results on a larger and more diverse cohort of patients. The first waves of COVID-19 did not directly affect the island of Reunion or in smaller proportions and the cases observed were mainly due to delta or omicron variants, corresponding to the forms observed in the following epidemic waves at the global level, of which we know that the virulence was less pronounced. Second, our study was designed to show a difference in expression of PMN surface receptors and chemokines of interest between infected and control patients as well as in COVID-19 patients after 48 hours of dexamethasone treatment in order to assess its possible impact. Its low power does not allow conclusions to be drawn on the association between the parameters of interest and the morbidity and mortality of patients. Only observations and avenues for future studies can arise from this work. Finally, one of the limitations is the absence of a study of CXCR4 expression in control patients. This would have required additional samples to be taken and a specific request to the ethics committee, regarding biological samples from patients free of COVID-19.\u003c/p\u003e"},{"header":"Conclusion and perspectives","content":"\u003cp\u003eIn conclusion, our study demonstrated a significant modulation of the CXCL12/CXCR4 pathway during COVID-19 and in patients treated with corticosteroids. It also suggests that the CXCL12/CXCR4 pathway is involved in the recruitment of neutrophils during COVID-19 and may be associated with the ventilatory prognosis of patients, particularly those with the most severe pulmonary damage. We also identified differential serum miRNAs profiles in patients infected with SARS-CoV-2 and in response to dexamethasone treatment. Other \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003eand \u003cem\u003ein vivo\u003c/em\u003e studies are needed to confirm the link that may exist between this CXCL12/CXCR4 pathway, neutrophil recruitment and corticosteroid therapy. Our study also opens perspectives for the use of already identified antagonists of the CXCL12/CXCR4 pathway and its cofactors in presumed severe cases of COVID-19.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e the authors have no relevant financial or non-financial interests to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e MD and FB: study design, data acquisition, quality assessment, data interpretation, statistical analysis and manuscript drafting. LR: Manuscript drafting. PG: \u0026nbsp;study design, manuscript drafting and quality assessment. YB and FW: data acquisition, manuscript drafting and quality assessment. EK: statistical analysis, data interpretation and manuscript drafting. DV, MP, JV and EF: data acquisition. All authors provided critical reviews of the manuscript and approved the final version. \u0026nbsp;MD is the guarantor of the content of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ethe datasets generated during and/or analyzed during the current study are not publicly available due to its storage in a secure computer with data encryption but are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Bordeaux under number 2008-A00151-54.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e informed consent was obtained from all individual participants included in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAknowledgements:\u0026nbsp;\u003c/strong\u003eWe would like to thank our professor and friend Philippe Gasque, who died on July 11, 2024 and who supervised this research work as director of the EPI and LICE-OI laboratory.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWe would like to thank the medical and paramedical staff of the emergency department, departments of infectious diseases and pulmonary diseases of La R\u0026eacute;union University Hospital for their help in this work and their involvement in the care provided to patients during the COVID-19 crisis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChalmers JD, Chotirmall SH (2020) Rewiring the Immune Response in COVID-19. 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Cytokine 169:156248. https://doi.org/10.1016/j.cyto.2023.156248\u003c/li\u003e\n\u003cli\u003eSato A, Ogino Y, Tanuma S, Uchiumi F (2021) Human microRNA hsa-miR-15b-5p targets the RNA template component of the RNA-dependent RNA polymerase structure in severe acute respiratory syndrome coronavirus 2. Nucleosides, Nucleotides \u0026amp; Nucleic Acids 40:790\u0026ndash;797. https://doi.org/10.1080/15257770.2021.1950759\u003c/li\u003e\n\u003cli\u003eChow JT-S, Salmena L (2020) Prediction and Analysis of SARS-CoV-2-Targeting MicroRNA in Human Lung Epithelium. Genes (Basel) 11:1002. https://doi.org/10.3390/genes11091002\u003c/li\u003e\n\u003cli\u003eThe RECOVERY Collaborative Group (2021) Dexamethasone in Hospitalized Patients with Covid-19. N Engl J Med 384:693\u0026ndash;704. https://doi.org/10.1056/NEJMoa2021436\u003c/li\u003e\n\u003cli\u003eLebeau G, Ah-Pine F, Daniel M, et al (2022) Perivascular Mesenchymal Stem/Stromal Cells, an Immune Privileged Niche for Viruses? Int J Mol Sci 23:8038. https://doi.org/10.3390/ijms23148038\u003c/li\u003e\n\u003cli\u003eDaniel M, Bedoui Y, Vagner D, et al (2022) Pathophysiology of Sepsis and Genesis of Septic Shock: The Critical Role of Mesenchymal Stem Cells (MSCs). Int J Mol Sci 23:9274. https://doi.org/10.3390/ijms23169274\u003c/li\u003e\n\u003cli\u003eVillar J, Ferrando C, Mart\u0026iacute;nez D, et al (2020) Dexamethasone treatment for the acute respiratory distress syndrome: a multicentre, randomised controlled trial. The Lancet Respiratory Medicine 8:267\u0026ndash;276. https://doi.org/10.1016/S2213-2600(19)30417-5\u003c/li\u003e\n\u003cli\u003eLongobardo A, Snow TAC, Montanari C, et al (2021) COVID-19 and non-COVID ARDS patients demonstrate a distinct response to low dose steroids- A retrospective observational study. Journal of Critical Care 62:46\u0026ndash;48. https://doi.org/10.1016/j.jcrc.2020.11.012\u003c/li\u003e\n\u003cli\u003eCuschieri S (2019) The CONSORT statement. 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Monaldi Arch Chest Dis. https://doi.org/10.4081/monaldi.2021.2050\u003c/li\u003e\n\u003cli\u003eLi W, Ai X, Ni Y, et al (2019) The Association Between the Neutrophil-to-Lymphocyte Ratio and Mortality in Patients With Acute Respiratory Distress Syndrome: A Retrospective Cohort Study. Shock 51:161\u0026ndash;167. https://doi.org/10.1097/SHK.0000000000001136\u003c/li\u003e\n\u003cli\u003eK\u0026aring;sine T, Dyrhol‐Riise AM, Barratt‐Due A, et al (2022) Neutrophil count predicts clinical outcome in hospitalized COVID‐19 patients: Results from the NOR‐Solidarity trial. J Intern Med 291:241\u0026ndash;243. https://doi.org/10.1111/joim.13377\u003c/li\u003e\n\u003cli\u003eCesta MC, Zippoli M, Marsiglia C, et al (2023) Neutrophil activation and neutrophil extracellular traps (NETs) in COVID-19 ARDS and immunothrombosis. Eur J Immunol 53:e2250010. https://doi.org/10.1002/eji.202250010\u003c/li\u003e\n\u003cli\u003eLowery SA, Sariol A, Perlman S (2021) Innate immune and inflammatory responses to SARS-CoV-2: Implications for COVID-19. Cell Host \u0026amp; Microbe 29:1052\u0026ndash;1062. https://doi.org/10.1016/j.chom.2021.05.004\u003c/li\u003e\n\u003cli\u003eChevrier S, Zurbuchen Y, Cervia C, et al (2021) A distinct innate immune signature marks progression from mild to severe COVID-19. Cell Reports Medicine 2:100166. https://doi.org/10.1016/j.xcrm.2020.100166\u003c/li\u003e\n\u003cli\u003eBorczuk AC, Yantiss RK (2022) The pathogenesis of coronavirus-19 disease. J Biomed Sci 29:87. https://doi.org/10.1186/s12929-022-00872-5\u003c/li\u003e\n\u003cli\u003eRatajczak MZ, Zuba-Surma E, Kucia M, et al (2006) The pleiotropic effects of the SDF-1-CXCR4 axis in organogenesis, regeneration and tumorigenesis. Leukemia 20:1915\u0026ndash;1924. https://doi.org/10.1038/sj.leu.2404357\u003c/li\u003e\n\u003cli\u003eChatterjee M, von Ungern-Sternberg SNI, Seizer P, et al (2015) Platelet-derived CXCL12 regulates monocyte function, survival, differentiation into macrophages and foam cells through differential involvement of CXCR4-CXCR7. Cell Death Dis 6:e1989. https://doi.org/10.1038/cddis.2015.233\u003c/li\u003e\n\u003cli\u003eSuratt BT, Petty JM, Young SK, et al (2004) Role of the CXCR4/SDF-1 chemokine axis in circulating neutrophil homeostasis. Blood 104:565\u0026ndash;571. https://doi.org/10.1182/blood-2003-10-3638\u003c/li\u003e\n\u003cli\u003eEash KJ, Greenbaum AM, Gopalan PK, Link DC (2010) CXCR2 and CXCR4 antagonistically regulate neutrophil trafficking from murine bone marrow. J Clin Invest 120:2423\u0026ndash;2431. https://doi.org/10.1172/JCI41649\u003c/li\u003e\n\u003cli\u003eStrydom N, Rankin SM (2013) Regulation of circulating neutrophil numbers under homeostasis and in disease. J Innate Immun 5:304\u0026ndash;314. https://doi.org/10.1159/000350282\u003c/li\u003e\n\u003cli\u003eNgamsri K-C, M\u0026uuml;ller A, B\u0026ouml;sm\u0026uuml;ller H, et al (2017) The Pivotal Role of CXCR7 in Stabilization of the Pulmonary Epithelial Barrier in Acute Pulmonary Inflammation. J Immunol 198:2403\u0026ndash;2413. https://doi.org/10.4049/jimmunol.1601682\u003c/li\u003e\n\u003cli\u003eWang J, Li Q, Qiu Y, Lu H (2022) COVID-19: imbalanced cell-mediated immune response drives to immunopathology. Emerg Microbes Infect 11:2393\u0026ndash;2404. https://doi.org/10.1080/22221751.2022.2122579\u003c/li\u003e\n\u003cli\u003eRamonell KM, Zhang W, Hadley A, et al (2017) CXCR4 blockade decreases CD4+ T cell exhaustion and improves survival in a murine model of polymicrobial sepsis. PLoS One 12:e0188882. https://doi.org/10.1371/journal.pone.0188882\u003c/li\u003e\n\u003cli\u003eCecchinato V, Martini V, Pirani E, et al (2023) The chemokine landscape: one system multiple shades. Front Immunol 14:1176619. https://doi.org/10.3389/fimmu.2023.1176619\u003c/li\u003e\n\u003cli\u003eSchiraldi M, Raucci A, Mu\u0026ntilde;oz LM, et al (2012) HMGB1 promotes recruitment of inflammatory cells to damaged tissues by forming a complex with CXCL12 and signaling via CXCR4. Journal of Experimental Medicine 209:551\u0026ndash;563. https://doi.org/10.1084/jem.20111739\u003c/li\u003e\n\u003cli\u003ePoznansky MC, Olszak IT, Foxall R, et al (2000) Active movement of T cells away from a chemokine. Nat Med 6:543\u0026ndash;548. https://doi.org/10.1038/75022\u003c/li\u003e\n\u003cli\u003eFassi EMA, Sgrignani J, D\u0026rsquo;Agostino G, et al (2019) Oxidation State Dependent Conformational Changes of HMGB1 Regulate the Formation of the CXCL12/HMGB1 Heterocomplex. Computational and Structural Biotechnology Journal 17:886\u0026ndash;894. https://doi.org/10.1016/j.csbj.2019.06.020\u003c/li\u003e\n\u003cli\u003eDe Leo F, Rossi A, De Marchis F, et al (2022) Pamoic acid is an inhibitor of HMGB1\u0026middot;CXCL12 elicited chemotaxis and reduces inflammation in murine models of Pseudomonas aeruginosa pneumonia. Mol Med 28:108. https://doi.org/10.1186/s10020-022-00535-z\u003c/li\u003e\n\u003cli\u003eNagase H, Miyamasu M, Yamaguchi M, et al (2001) Regulation of Chemokine Receptor Expression in Eosinophils. Int Arch Allergy Immunol 125:29\u0026ndash;32. https://doi.org/10.1159/000053849\u003c/li\u003e\n\u003cli\u003eNagase H, Miyamasu M, Yamaguchi M, et al (2002) Cytokine-mediated regulation of CXCR4 expression in human neutrophils. J Leukoc Biol 71:711\u0026ndash;717\u003c/li\u003e\n\u003cli\u003eSinha S, Rosin NL, Arora R, et al (2022) Dexamethasone modulates immature neutrophils and interferon programming in severe COVID-19. Nat Med 28:201\u0026ndash;211. https://doi.org/10.1038/s41591-021-01576-3\u003c/li\u003e\n\u003cli\u003eZheng G, Huang R, Qiu G, et al (2018) Mesenchymal stromal cell-derived extracellular vesicles: regenerative and immunomodulatory effects and potential applications in sepsis. Cell Tissue Res 374:1\u0026ndash;15. https://doi.org/10.1007/s00441-018-2871-5\u003c/li\u003e\n\u003cli\u003eKonrad FM, Meichssner N, Bury A, et al (2017) Inhibition of SDF-1 receptors CXCR4 and CXCR7 attenuates acute pulmonary inflammation via the adenosine A2B-receptor on blood cells. Cell Death Dis 8:e2832. https://doi.org/10.1038/cddis.2016.482\u003c/li\u003e\n\u003cli\u003eLai RC, Lim SK (2019) Membrane lipids define small extracellular vesicle subtypes secreted by mesenchymal stromal cells. J Lipid Res 60:318\u0026ndash;322. https://doi.org/10.1194/jlr.R087411\u003c/li\u003e\n\u003cli\u003eKrishnan A, Muthusamy S, Fernandez FB, Kasoju N (2022) Mesenchymal Stem Cell-Derived Extracellular Vesicles in the Management of COVID19-Associated Lung Injury: A Review on Publications, Clinical Trials and Patent Landscape. Tissue Eng Regen Med 19:659\u0026ndash;673. https://doi.org/10.1007/s13770-022-00441-9\u003c/li\u003e\n\u003cli\u003eFavaro E, Carpanetto A, Caorsi C, et al (2016) Human mesenchymal stem cells and derived extracellular vesicles induce regulatory dendritic cells in type 1 diabetic patients. Diabetologia 59:325\u0026ndash;333. https://doi.org/10.1007/s00125-015-3808-0\u003c/li\u003e\n\u003cli\u003eAntonakos N, Gilbert C, Th\u0026eacute;roude C, et al (2022) Modes of action and diagnostic value of miRNAs in sepsis. Front Immunol 13:951798. https://doi.org/10.3389/fimmu.2022.951798\u003c/li\u003e\n\u003cli\u003eDel \u0026Aacute;guila-Mej\u0026iacute;a J, Wallmann R, Calvo-Montes J, et al (2022) Secondary Attack Rate, Transmission and Incubation Periods, and Serial Interval of SARS-CoV-2 Omicron Variant, Spain. Emerg Infect Dis 28:1224\u0026ndash;1228. https://doi.org/10.3201/eid2806.220158\u003c/li\u003e\n\u003cli\u003eLee JJ, Choe YJ, Jeong H, et al (2021) Importation and Transmission of SARS-CoV-2 B.1.1.529 (Omicron) Variant of Concern in Korea, November 2021. J Korean Med Sci 36:e346. https://doi.org/10.3346/jkms.2021.36.e346\u003c/li\u003e\n\u003cli\u003eBacker JA, Eggink D, Andeweg SP, et al (2022) Shorter serial intervals in SARS-CoV-2 cases with Omicron BA.1 variant compared with Delta variant, the Netherlands, 13 to 26 December 2021. Euro Surveill 27:2200042. https://doi.org/10.2807/1560-7917.ES.2022.27.6.2200042\u003c/li\u003e\n\u003cli\u003eLong B, Carius BM, Chavez S, et al (2022) Clinical update on COVID-19 for the emergency clinician: Presentation and evaluation. Am J Emerg Med 54:46\u0026ndash;57. https://doi.org/10.1016/j.ajem.2022.01.028\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e. Characteristics of the population of COVID-19 patients on the day of admission.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of patients N=44 (100%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale sex, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e21 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge years, median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e72 (61-83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI kg/m\u003csup\u003e2\u003c/sup\u003e, median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e25 (21-28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelay between the onset of symptoms and ICU admission, median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e3 (1-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking history, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e15 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverweight (25\u0026lt;BMI\u0026lt;30), n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e17 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObesity (BMI\u0026gt;30), n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e6 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes mellitus, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e19 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e29 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIschemic heart disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e10 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic kidney disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e7 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIschemic stroke history, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e6 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients with identified virus genotype, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e18 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOmicron viral genotype, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e13 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScanner performed on admission, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e39 (89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage of lung parenchyma involvement \u0026lt;25%, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e27 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage of lung parenchyma involvement 25-75%, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e9 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOFA score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e2 (0-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIGS II score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e32 (27-40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart Rate (HR) on admission, median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e93 (77-110)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Arterial Pressure (MAP) on admission, median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e95 (84-103)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmission Glasgow Coma Scale (GCS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e15 (14-15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresence of dyspnea on admission, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e35 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSigns of acute respiratory failure on admission, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e11 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeed for oxygen therapy on admission, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e24 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePa0\u003csub\u003e2\u003c/sub\u003e/Fi0\u003csub\u003e2\u003c/sub\u003e ratio calculated on the day of admission in ICU, median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e259 (296-400)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeed for non-invasive ventilation (NIV)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e6 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eNon-invasive mechanical ventilation with facial mask, n (%)\u003c/strong\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e4 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eNasal High Flow Oxygen therapy, n (%)\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e2 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath during the 6-months follow-up, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e12 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorticosteroids (dexamethasone), n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e20 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiological parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNatremia (mmol/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e137 (134-138)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCreatinine (\u0026micro;mol/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e90 (63-129)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST (IU/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e37 (28-45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT (IU/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e21 (15-31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-Reactive Protein (mg/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e36 (14-90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcalcitonin (ng/ml), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.12 (0.07-0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHaemoglobin (g/dl), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e13 (12-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelets (G/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e186 (138-251)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeukocytes (G/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e7 (5-9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eNeutrophils count (G/L), median (IQR)\u003c/strong\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e5.2 (3.9-7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eLymphocytes count (G/L), median (IQR)\u003c/strong\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.8 (0.7-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD-Dimers (\u0026micro;g/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e898 (580-1450)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophils/Lymphocytes Ratio (NLR), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e6.38 (3.06-9.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP/PCT Ratio (CPR), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e143 (53-443)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCT/Polymorphonuclear Neutrophils Ratio, median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.03 (0.01-0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e. Relationships between mortality at M6 and various demographic characteristics and biological parameters and their variations D1 and D3 (univariate analysis).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlive\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=32 (100%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath at 6 months\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=12 (100%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eGender (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e16 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e7 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e72 [62; 84]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e72 [58; 80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eASA score\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e3 [2,00; 3,00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3 [2,75; 3,00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0,15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eSOFA on admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e1.5 [0.0; 3.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e4.0 [0.75; 5.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eSAPS-II on admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e32 [27; 40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e37 [29; 41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003eBiological parameters at Day 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003ePMN (G/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e4.7 [2.8; 6.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6.5 [5.6; 11.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eNLR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e6 [3; 10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6 [5; 9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003ePCT (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.11 [0.07; 0.46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.37 [0.22; 3.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e32 [13; 75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e47 [20; 92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eCRP/PCT ratio\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e186 [71; 405]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e55 [5; 443]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003ePCT/PMN ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.03 [0.01; 0.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.05 [0.02; 0.53]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDelta PCT (D1-D3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0 [0; 1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0 [0; 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eCXCL8 at day 1 (pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e19 [14; 29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e28 [23; 43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDelta CXCL8 (D3-D1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e1 [-3; 12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e-8 [-17; -7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eCXCL12 at day 1 (pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e10 [7; 13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e12 [8; 17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDelta CXCL12 (D3-D1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e1 [0; 4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1 [-4; 4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003eExpression of neutrophil surface receptors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eCXCR4+ (MFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e1.918 [1.493; 2.218]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.778 [1.518; 2.877]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDelta CXCR4+ (MFI D3-D1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-254 [-459; 243]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e312 [247; 677]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDelta percentage PMN CXCR4+ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e13 [-2; 17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e15[14; 50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003ePercentage PMN CXCR4+ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e31 [26; 44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e28 [25; 46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eC3aR+ (MFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e3.009 [2.629; 3.547]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2.898 [2.644; 4.207]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eC5aR+ (MFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e30.655 [20.215; 41.814]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e28.621 [20.313; 45.488]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eHMGB-1+ (MFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e9.961 [5.326\u0026nbsp;; 28.402]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e11.750 [6.792; 12.843]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026gt;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003ePercentage PMN HMGB-1+ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.15 [0.06; 0.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.04 [0.03; 0.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDelta HMGB-1+ (MFI D3-D1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e5.412 [-4.836; 11.499]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.903 [1.633; 16.917]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDelta percentage HMGB-1+ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.16 [0.06; 0.51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.09 [-0.01; 0.28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eFMLP-R+ (MFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e16.552 [13.359; 22.240]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e18.069 [16.392; 21.732]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003ePlasmatic miR levels (relative expression)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003emiR-155-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.05 [0.01\u0026nbsp;; 0.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.08 [0.04; 0.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003emiR-26a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.04 [0.01; 0.22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.05 [0.05; 0.28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003emiR-146a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e1 [0; 4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e8 [1; 17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDelta miR-146a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0 [0; 2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e8 [1; 18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003emiR-15b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.73 [0.22; 3.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.72 [1.08; 3.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003emiR-30d-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e2 [0; 11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1 [2; 18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003emiR-hsa-122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.29 [0.05; 0.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.78 [0.49; 1.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.039*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eDelta miR-hsa-122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0 [0; 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e-1 [-3; 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are expressed in mean \u0026plusmn;SD or median and IQR of the different biological parameters unless otherwise indicated. Plasmatic miR levels are expressed in relative expression. *p-value \u0026lt;0.05. ASA: American Society of anesthesiology score. SOFA: Sepsis-related Organ Failure Assessment score. SAPS II: Simplified Acute Physiology Score II. COPD: Chronic Obstructive Pulmonary Disease. RAA inhibitors: Renin-Angiotensin-Aldosterone Inhibitors. PCT: Procalcitonin. PE: Protein expression. CRP: C-Reactive Protein. NLR: Neutrophils/Lymphocytes ratio. PE: Protein Expression. MFI: Mean of fluorescence. D: difference between measurement at day 1 and day 3. miR: microRNA. \u003csup\u003e1\u003c/sup\u003eMedian (IQR) or Frequency (%). \u003csup\u003e2\u003c/sup\u003eWilcoxon rank sum exact test; Pearson\u0026apos;s Chi-squared test; Wilcoxon rank sum test; Fisher\u0026apos;s exact test\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e. Relationships between the need for mechanical ventilation and various parameters and their evolution between D1 and D3 ventilation (univariate analysis).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo need for ventilation\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeed for ventilation\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eGender (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e18 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e3 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e72 [62; 84]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e68 [60; 79]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eASA score\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e3 [2; 3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e3 [3; 3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eSOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2 [0; 3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e4 [3; 5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eSAPS-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e32 [27; 41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e38 [31; 39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 614px;\"\u003e\n \u003cp\u003eBiological parameters at Day 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePMN (G/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e5 [3; 7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e12 [6.5; 20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e6 [3; 9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e12 [8; 15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePCT (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.11 [0.07; 0.56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.35 [0.04; 5.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e50 [12; 101]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e23 [22; 28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eCRP/PCT ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e163 [56; 384]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e65 [6; 543]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003ePCT/PMN ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.03 [0.01; 0.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.05 [0.01; 0.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eDelta PCT (D1-D3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0 [0; 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0 [0; 2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eCXCL8 at day 1 (PE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e23 [15; 30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e29 [18; 29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eDelta CXCL8 (D3-D1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e-6 [-14; 1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e13 [8; 20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eCXCL12 at day 1 (PE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e11.0 [7.0; 14.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e8 [7.0; 10.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eDelta CXCL12 (D3-D1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1 [0; 5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0 [-3; 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 614px;\"\u003e\n \u003cp\u003eExpression of neutrophil surface receptors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eCXCR4+ (MFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1.921 [1.640; 2.365]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.487 [1.380; 1.491]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eDelta CXCR4 (MFI D3-D1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e241 [-304; 499]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e149 [91; 206]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026gt;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eC3aR+ (MFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e3.016 [2.682; 3.709]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e2.672 [2.644; 2.797]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eC5aR+ (MFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e28.218 [19.868; 41.767]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e28.621 [25.297; 34.716]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHMGB-1+ (MFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e10.795 [5.458; 24.206]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e11.063 [7.617; 21.900]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eFMLP-R+ (MFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e17.610 [13.934; 22.856]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e16.538 [14.104; 18.720]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 614px;\"\u003e\n \u003cp\u003ePlasmatic miR levels (relative expression)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003emiR-155-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.07 [0.01; 0.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.04 [0.01; 0.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003emiR-26a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.05 [0.01; 0.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.04 [0.01; 0.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003emiR-146a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2 [0; 9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1 [1; 1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eDelta miR-146a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0 [0; 3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0 [-5; 4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003emiR-15b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.97 [0.28; 3.73]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.26 [0.24; 4.56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026gt;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003emiR-30d-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e3 [1; 11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e6 [2; 24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003emiR-hsa-122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.36 [0.09; 0.77]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.47 [0.05; 0.78]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026gt;0,99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eDelta miR-hsa-122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0 [0; 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-1 [-7; -1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.039*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are expressed in mean \u0026plusmn;SD or median (IQR) of the different biological parameters unless otherwise indicated. Plasmatic miR levels are expressed in relative expression. *p-value \u0026lt;0.05. ASA: American Society of anesthesiology score. SOFA: Sepsis-related Organ Failure Assessment score. SAPS II: Simplified Acute Physiology Score II. COPD: Chronic Obstructive Pulmonary Disease. OSA: Obstructive Sleep Apnea Syndrome. RAA inhibitors: Renin-Angiotensin-Aldosterone Inhibitors. PCT: Procalcitonin. CRP: C-Reactive Protein. NLR: Neutrophils/Lymphocytes ratio. PE: Protein Expression. MFI: Mean of fluorescence. D: difference between measurement at day 1 and day 3. miR: microRNA. \u003csup\u003e1\u003c/sup\u003eMedian (IQR) or Frequency (%). \u003csup\u003e2\u003c/sup\u003eWilcoxon rank sum exact test; Pearson\u0026apos;s Chi-squared test; Wilcoxon rank sum test; Fisher\u0026apos;s exact test.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmme","sideBox":"Learn more about [Journal of Molecular Medicine](https://www.springer.com/journal/109)","snPcode":"109","submissionUrl":"https://submission.nature.com/new-submission/109/3","title":"Journal of Molecular Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"COVID-19, polymorphonuclear neutrophils, Mesenchymal stromal cells, chemokines, miRNA","lastPublishedDoi":"10.21203/rs.3.rs-5662811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5662811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003ePolymorphonuclear neutrophils (PMN) are actively recruited during COVID-19 and yet dysfunctions are associated with its prognosis. The PMN receptor CXCR4 and its ligand SDF-1/CXCL12 are known to play a role in the recruitment of PMN. The primary objective was to evaluate the modulation of this pathway in COVID-19 patients and after treatment with dexamethasone (DXM). Secondary objectives were to evaluate miRNA expression profiles.\u003c/p\u003e\u003ch2\u003eMaterial and Methods\u003c/h2\u003e \u003cp\u003eWe conducted a prospective study comparing patients admitted to the emergency department from December 2022 to April 2023 for SARS-CoV-2 infection with a control population. We studied the PMN surface expression of the CXCR4 receptor, circulating levels of SDF-1 and miR levels. Patients treated with dexamethasone (DXM) were sampled again at H48.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eForty-four infected patients and 20 controls were analyzed. SDF-1 levels were significantly increased in COVID-19 patients and significantly decreased after treatment by DXM and CXCR4\u0026thinsp;+\u0026thinsp;PMN percentages increased significantly. SDF-1 levels on admission were associated with the risk of mechanical ventilation. Levels of miR 15b-5p, miR 146a-5p, miR 155-5p and miR 30d-5p were significantly increased in COVID-19 patients. Levels of miR-hsa-122 on admission were found significantly associated with mortality and its variation with the need for mechanical ventilation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study suggests a possible involvement of the SDF-1/CXCR4 axis in the physiopathogenesis of COVID-19.\u003c/p\u003e","manuscriptTitle":"Role of CXCL12/CXCR4 pathway and miRNA expression profiles on polymorphonuclear mobilization in COVID-19 patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 17:47:13","doi":"10.21203/rs.3.rs-5662811/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2025-10-03T13:16:32+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-07-07T13:17:39+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-07T10:03:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-30T05:03:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Molecular Medicine","date":"2024-12-27T09:58:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmme","sideBox":"Learn more about [Journal of Molecular Medicine](https://www.springer.com/journal/109)","snPcode":"109","submissionUrl":"https://submission.nature.com/new-submission/109/3","title":"Journal of Molecular Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a7ef8e4e-ce2a-435b-a0eb-339339f02010","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2025-10-03T17:17:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-10 17:47:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5662811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5662811","identity":"rs-5662811","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}