Clinical Value of Serum S100A12 in Identifying ARDS Development and Predicting Deterioration in Critically Ill Patients

Clinical Value of Serum S100A12 in Identifying ARDS Development and Predicting Deterioration in Critically Ill 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 Clinical Value of Serum S100A12 in Identifying ARDS Development and Predicting Deterioration in Critically Ill Patients Wei Liu, Dandan Ji, Xingping Zhan, Mengshi Lu, Hao Xu, Zigang Zhu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4517003/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study aimed to investigate the clinical value of serum S100A12 in identifying the development of acute respiratory distress syndrome (ARDS), its association with subsequent oxygenation deterioration, and its ability to predict 28-day mortality in patients in the intensive care unit (ICU). Methods Based on the inclusion and exclusion criteria, the demographic data, chronic diseases, and acute physiological indices of ICU patients were collected from two independent general ICUs in the Department of Critical Care Medicine, Jiangnan University Medical Center. Serum S100A12 levels were measured at different time points using an enzyme-linked immunosorbent assay. T S100A12 was derived from serum S100A12 levels and converted to an inverse tangent function in our study. Patients meeting the Berlin definition of ARDS within three days of admission were categorised into ARDS and non-ARDS groups. The ARDS group was further divided into two groups based on the PF (PaO 2 /FiO 2 ) value at the time of diagnosis: PF 150 mmHg groups. To verify the correlation between serum S100A12 levels and oxygenation deterioration, three grouping sets based on the decrease rate in the oxygenation index within 4 days after ARDS diagnosis were used for substantial analysis: PF decrease rate < 30% group vs. PF decrease rate ≥ 30% group, PF decrease rate < 35% group vs. PF decrease rate ≥ 35% group, and PF decrease rate < 40% group vs. PF decrease rate ≥ 40% group. Additionally, to verify the correlation between serum S100A12 levels and 28-day mortality in patients with ARDS, the ARDS group was divided into survival and non-survival groups. Spearman’s correlation analysis was used to assess the association between indicators, logistic regression analysis was used to determine the odds ratios, and receiver operating characteristic curve analysis was used to evaluate predictive efficacy. Results A total of 144 patients were enrolled in this study from 1 August 2022 to 15 December 2022. At the time of ARDS diagnosis, serum S100A12 levels were significantly higher than those in patients without ARDS, and T S100A12 was identified as a risk factor for the development of ARDS. At the time of ARDS diagnosis, the serum S100A12 levels were significantly higher in the PF 150 mmHg group. Additionally, after ARDS diagnosis, serum S100A12 levels were significantly higher in the group with a higher rate of PF decrease. The PF decrease rate within 4 days was greater with higher serum S100A12 levels at the time of ARDS diagnosis. Additionally, T S100A12 and age were independent risk factors of 28-day mortality, and the combination of serum S100A12 levels and age exhibited a high degree of predictive accuracy for 28-day mortality in patients with ARDS. Conclusion T S100A12 is a risk factor of ARDS and 28-day mortality. Serum S100A12 levels were associated with a decline in oxygenation within four days of ARDS diagnosis. Additionally, the combination of serum S100A12 levels and age exhibited high efficacy in predicting 28-day mortality. Acute respiratory syndrome distress S100A2 predicting deterioration 28-day mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Acute respiratory distress syndrome (ARDS) which can be caused by intrapulmonary or extrapulmonary insults, is a clinical syndrome characterised by acute hypoxaemia, noncardiogenic pulmonary oedema, and dyspnoea [ 1 – 4 ]. Despite the application of multiple techniques, such as lung-protective ventilation strategies, fluid management, and prone ventilation, there is a lack of specialised pharmacological treatments, and the mortality of patients with ARDS remains high [ 5 ]. The LUNG SAFE study reported that the 28-day mortality of patients with ARDS was 35%, and that patients with severe ARDS had a mortality rate exceeding 40% [ 6 ]. Early identification of ARDS and subsequent oxygenation deterioration in critically ill patients may create opportunities for more aggressive interventions, leading to a reduction in ARDS mortality. In 2012, the Berlin definition clarified the diagnostic criteria for ARDS, including the time of onset, changes in chest imaging findings, oxygenation index, and the exclusion of cardiogenic pulmonary oedema. In 2023, 32 experts revised the definition of ARDS and proposed a new global definition. The significance of the early identification of ARDS was highlighted by the inclusion of patients receiving high flow oxygenation in the updated definition of ARDS screening [ 7 ]. However, serum markers for the early identification of ARDS, subsequent oxygenation deterioration, and 28-day mortality in critically ill patients are currently lacking. In our previous analysis of proteomic differences between patients with ARDS and healthy individuals, we observed a significant increase in serum S100A12 protein levels in patients with ARDS compared to healthy controls. S100A12 is a protein belonging to the S100 family; it is primarily found in neutrophils and is expressed in small amounts in monocytes. It can be released into body fluids, such as serum and urine, in response to inflammation and is involved in the development of several diseases [ 8 , 9 ]. S100A12 can mediate intracellular signalling by binding to advanced glycosylation end products (RAGE) receptors on cell membranes, which initiates the intracellular inflammatory signalling pathway, induces the expression of pro-inflammatory cytokines, and participates in immune and inflammatory regulation [ 9 , 10 ]. It is important to note that RAGE is expressed at low levels in all cells but at higher levels in lung tissues [ 11 – 14 ], and the effects of S100A12 may be more pronounced in the lungs. Based on the above information, we aimed to investigate the clinical value of serum S100A12 levels in identifying the development of ARDS in patients in the intensive care unit (ICU), its association with the deterioration of oxygenation, and its clinical value in predicting 28-day mortality. Materials and Methods Study Population From 1 August 2022 to 15 December 2022, 241 critically ill patients were admitted to two independent general ICUs in the Department of Critical Care Medicine, Jiangnan University Medical Center. Patients who met the specified criteria were included in this study (Fig. 1 ). This study was approved by the Ethics Committee of Jiangnan University Medical Center (Wuxi No. 2 People's Hospital; approval number: 2016W-001). Each human participant signed an informed consent statement. Sample Collection Five millilitres of peripheral venous blood was collected from the patients at the time of ICU admission and 24 and 72 h after ICU admission. The serum was obtained and then stored at -80°C to detect the S100A12 levels. Clinical Data Collection Sex, age, and body mass index (BMI) were recorded upon ICU admission. Medical history was obtained from the Hospital Information System. Temperature, heart rate, respiratory rate, blood pressure, blood gas analysis, neutrophils, platelets, creatinine, and total bilirubin were collected at the time of ICU admission (D0), and on the first (D1), third (D3), fifth (D5), and seventh (D7) day in the ICU. Acute physiology and chronic health evaluation II (APACHE II) scores were calculated and recorded [ 15 ]. All patients were followed-up with for 28-days after ICU admission, and the survival status at 28 days was recorded. Data collection was completed by two researchers who were blinded to the serum S100A12 levels. Grouping Criteria Patients who met the Berlin definition [ 1 ] within three days after ICU admission were categorised into the ARDS group, whereas the remaining patients were included in the non-ARDS group. Using the binary classification method, the ARDS group was further divided into two groups based on the oxygenation index at the time of ARDS diagnosis: the PF 150 mmHg group. Considering more important value for investigating oxygenation deterioration in mild-moderate ARDS than severe ARDS, patients with mild-moderate ARDS (PF values range from 100 to 300) were divided into three grouping sets based on the decrease rate in the oxygenation index within 4 days after ARDS diagnosis: PF decrease rate < 30% group vs. PF decrease rate ≥ 30% group, PF decrease rate < 35% group vs. PF decrease rate ≥ 35% group, and PF decrease rate < 40% group vs. PF decrease rate ≥ 40% group. Additionally, to verify the correlation between serum S100A12 levels and 28-day mortality in patients with ARDS, the ARDS group was divided into two groups: survival and non-survival. Biomarker Measurements Serum S100A12 levels were measured using an enzyme-linked immunosorbent assay (ELISA) kit (Elabscience Biotechnology Co., Ltd., Wuhan, China). The assay was performed according to the manufacturer's instructions. A standard curve was plotted for each enzyme plate using standard samples. The average optical density values of the two wells were substituted into a standard curve to calculate the concentrations of the samples. This ensured the reliability of the experimental data. Statistical Analysis SPSS (version 26.0) was used for the data analysis. The data conforming to a normal distribution are expressed as \(\:\:\stackrel{-}{\text{x}}\pm\:\text{s}\) , and the independent-samples t -test was used for comparison. Data that did not conform to a normal distribution are expressed as the median and quartiles, and the Mann–Whitney U test was used for analysis. The Chi-square test or Fisher's exact test was used to compare dichotomous variables between groups. Survival curve analysis was used to describe the survival status. Spearman’s correlation analysis was used to determine the degree of association between indicators. T S100A12 is an arctangent function-transformed variable in our study and was calculated using the following formula: T S100A12 = 1000 × ArcTan (serum S100A12 levels). Odds ratios (ORs) were determined by logistic regression analysis. Receiver operating characteristic (ROC) curves were used to analyse the predictive validity of the variables. Differences were considered statistically significant at p < 0.05. Graphs were generated using R 4.3.2 and GraphPad Prism 8. Results Characteristics of the Study Population In total, 144 critically ill patients were enrolled in this study. All study participants were screened daily according to the Berlin definition [ 1 ] (Fig. 1 ). After 3 days of screening, 51 patients with ARDS were categorised into the ARDS group within three days of ICU admission, whereas the remaining 93 patients were categorised into the non-ARDS group. There were no statistically significant differences in demographics and medical history between the ARDS and non-ARDS groups ( p > 0.05) (Table 1 ). To explore the influence of severity in critically ill patients, the APACHE II scores were compared between patients with and without ARDS, and the results are shown in Table 2 . There were no statistically significant differences ( p > 0.05) in APACHE II scores between patients with and without ARDS at any of the three time points (D0, D1, and D3). Table 1 Baseline characteristics of patients with and without ARDS ARDS, acute respiratory distress syndrome; BMI, body mass index. Baseline Characteristics Patients with ARDS Patients without ARDS p -value Male 38(74.5%) 58(62.4%) 0.139 Age(years) 75(60–82) 70(59-80.5) 0.215 BMI(kg/m²) 22.5(20.8–24.4) 22.5(20.8–24.5) 0.843 Smoke 15(29.4%) 36(38.7%) 0.322 Diabetes 17(33.3%) 25(26.9%) 0.415 Hypertension 32(62.7%) 46(49.5%) 0.126 Table 2. Comparison of APACHEII scores between patients with and without ARDS Time-point Patients with ARDS Patients without ARDS p value N APACHE II scores N APACHE II scores D0 38 19.00（15.75-23.00） 106 17.00（14.00-21.00） 0.051 D1 45 19.00（14.00-23.25） 99 19.00（12.00-23.00） 0.495 D3 51 16.00（13.00-22.00） 79 17.00（12.00-21.00） 0.99 ARDS, acute respiratory distress syndrome; ICU, intensive care unit; APACHEII, Acute Physiology and Chronic Health Evaluation II. D0 refers to the time of ICU admission, while D1 and D3 refer to the first and third day after ICU admission, respectively. The Serum S100A12 Level was Increased in Patients with ARDS A comparison of S100A12 levels between patients with and without ARDS at three time points (D0, D1, and D3) revealed significant differences. S100A12 levels were higher in patients with ARDS that in those without ARDS at all three time points (Fig. 2 ). T S100A12 is a Risk Factor of the Development of ARDS In this study, we created a transition variable to amplify the efficiency of identifying the development of ARDS in ICU patients. T S100A12 showed good fit in binary logistic regression analysis. For every 1 unit increase in T S100A12 , the risk of ARDS development increased by 4.9%, 7.8%, 7.0% at D0, D1, and D3, respectively (Table 3). Table 3. Serum S100A12 and T S100A12 level for risk assessment of ARDS development in three time-points after ICU admission Time-point S100A12 T S100A12 Crude Odds ratio (95% Confidence Interval) p value Crude Odds ratio (95% Confidence Interval) p value D0 1.011(0.998-1.023) 0.095 1.049 (1.002-1.098) 0.041 D1 1.017(1.002-1.032) 0.027 1.078(1.027-1.131) 0.002 D3 1.021(1.003-1.038) 0.021 1.072(1.021-1.126) 0.005 ARDS, acute respiratory distress syndrome; ICU, intensive care unit. D0 refers to the time of ICU admission, while D1 and D3 refer to the first and third day after ICU admission, respectively. T S100A12 = 1000 × ArcTan (Serum S100A12 levels) . Serum S100A12 Levels were Up-regulated in Patients with ARDS with Poorer Oxygenation The serum S100A12 level in the PF 150 mmHg group, indicating that serum S100A12 levels were upregulated in patients with ARDS with poorer oxygenation (Fig. 3). Positive Correlation Between Serum S100A12 Levels and Oxygenation Deterioration in Patients with Mild-to-moderate ARDS over Four Days after ARDS Diagnosis According to the Berlin definition, PF values between 0 and 100 indicate severe ARDS, whereas PF values between 100 and 300 indicate mild-to-moderate ARDS. In this study, 44 patients had mild-to-moderate ARDS, accounting for 86.27% of the total number of patients with ARDS. Figure 4 shows a significant correlation between the serum S100A12 levels and the rate of decrease in PF after ARDS diagnosis ( p 0.05) in the initial PF values detected at the time of ARDS diagnosis (Table 4 ). However, comparison of the initial serum S100A12 levels showed a statistically significant difference ( p < 0.05). In other words, for mild-to-moderate ARDS, the initial PF value cannot be used to assess oxygenation deterioration during the subsequent four days after ARDS diagnosis; however, serum S100A12 levels can be used. Table 4 Comparison of the serum S100A12 level and PF value between two groups in three different group sets based on the PF value decrease rate PF value decrease rate PF value S100A12 (ng/ml) < 30%(n = 32) 169.32(141.0-204.5) 44.40(37.75–53.84) ≥ 30%(n = 12) 216.5(157.5-275.8) 58.17(50.86–77.73) p -value 0.065 0.033 < 35%(n = 35) 175.0(141.0-213.5) 44.57(37.75–53.84) ≥ 35%(n = 9) 202.0(159.0-277.5) 59.89(55.70-73.91) p -value 0.083 0.019 < 40%(n = 37) 184.0(142.0-220.0) 44.59(37.35–53.97) ≥ 40%(n = 7) 165.0(154.5-262.5) 59.89(56.37–68.16) p -value 0.312 0.011 The PF values and serum S100A12 levels were measured at the time of ARDS diagnosis. PF = PaO 2 /FiO 2 . ARDS, acute respiratory distress syndrome. T S100A12 is an Independent Risk Factor of 28-day Mortality in Patients with ARDS Table 5 shows that patients in the non-survival group were older than those in the survival group after 28-days of follow-up, and serum S100A12 levels in the non-survival group were higher than those in the survival group. Univariate analysis revealed a statistically significant difference in S100A12 and age between the two groups ( p < 0.05). Logistic regression analysis revealed that both age and the T S100A12 level were independent risk factors of the 28-day mortality in patients with ARDS (Table 6 ). Additionally, ROC curve analysis demonstrated that the S100A12 level and age could predict 28-day mortality in patients with ARDS. The figure depicts that the AUC for the S100A12 + Age model was significantly higher than that for the S100A12 or Age model, indicating that the S100A12 + Age model has a better predictive value for predicting 28-day mortality that the S100A12 and Age models. (Fig. 5 ). Table 5 Comparison of baseline characteristics in patients with ARDS between the survival and non-survival groups PF and serum S100A12 levels were measured at the time of ARDS diagnosis. BMI, body mass index; ARDS, acute respiratory distress syndrome. PF = PaO 2 /FiO 2 . Baseline Characteristics Non-survival Survival group p -value Male 9(69.2%) 29(76.3%) 0.613 Age(years) 78.92 ± 11.56 68.68 ± 14.74 0.028 BMI(kg/m²) 23.01 ± 2.90 22.82 ± 3.43 0.854 Smoke 5(38.5%) 10(26.3%) 0.487 Diabetes 6(46.2%) 11(28.9%) 0.315 Hypertension 9(69.2%) 23(63.2%) 0.743 PF value 140.0(103.2-234.3) 170.0(139.1-222.6) 0.226 S100A12(ng/ml) 56.29(46.82–86.29) 44.78(36.31–59.22) 0.027 Table 6 Logistic regression analysis of 28-day mortality in the ARDS group Index Crude OR (95% CI) p value Index Corrected OR (95% CI) p value Age 1.066(1.004–1.131) 0.037 Age 1.069(1.002–1.140) 0.043 S100A12 0.010(0.992–1.028) 0.283 - - - T S100A12 1.108(1.003–1.224) 0.043 T S100A12 1.118(1.001–1.250) 0.049 Based on the univariate analysis, age and T S100A12 scores were included in the logistic regression model. These results indicate that both age and the T S100A12 are risk factors for 28-day mortality in patients with ARDS. T S100A12 = 1000 × ArcTan (serum S100A12 level). ARDS, acute respiratory distress syndrome; OR, odds ratio; 95%CI, 95% confidence interval. Discussion Several studies have focused on identifying potential biomarkers to reveal the process of ARDS and prognosis. These are helpful in clinical decision making and treatment optimisation to decrease the mortality rate, especially for the development of ARDS and possible adverse consequences. However, few studies have reported these parameters, especially for early identification of ARDS development, subsequent oxygenation deterioration, and 28-day mortality in ICU patients. This retrospective analysis aimed to assess the clinical application of serum S100A12 in ARDS based on our previous proteomic findings (Supplement 1). Here, we found that T S100A12 was significantly higher in patients with ARDS compared with those without ARDS, and that T S100A12 can be used to identify the risk of ARDS in the early stages after ICU admission. Serum S100A12 levels were upregulated in patients with ARDS with poor oxygenation, and the degree of oxygenation deterioration was positively correlated with serum S100A12 levels in patients with mild-to-moderate ARDS. T S100A12 and age were risk factors of 28-day mortality. Various insults can induce the release of S100A12 into bodily fluids. S100A12 recognizes and binds to RAGE located on the cell membrane [ 16 – 19 ]. Ligand-receptor binding activates downstream pathways [ 20 ]. In a clinical trial involving 14 patients with ARDS, the concentrations of both S100A12 and RAGE in the alveolar lavage fluid significantly increased [ 21 ]. Similarly, serum levels of S100A12 and soluble late glycosylation end-product receptors (sRAGE) were significantly elevated in both mouse models and patients with ARDS [ 22 ]. Two previous experiments corroborated the discovery of elevated serum S100A12 levels in patients with ARDS, as evidenced in our experiment. Recent animal studies have demonstrated that S100A12 plays a role in the progression of ARDS by activating the NLRP3 inflammatory pathway by binding to RAGE [ 22 , 23 ]. Similarly, targeted blockade of the S100A12/NLRP3 axis significantly attenuated lung injury in septic rats [ 24 ]. Thus, S100A12 may play a crucial role in the progression of ARDS. Several studies have reported elevated S100A12 levels in inflammatory bowel disease, juvenile rheumatoid arthritis, asthma, leukodystrophy, Kawasaki disease, and adult Still's disease [ 25 – 28 ]. However, few studies have reported its application in the assessment of ARDS development. A previous study measuring serum S100A12 levels in 102 healthy controls and 102 patients with traumatic brain injury (TBI) found that serum S100A12 levels predicted the development of ARDS [ 29 ]. In our study, we found that serum S100A12 levels were significantly higher in patients with ARDS compared with those without ARDS, and that T S100A12 can be used to identify patients admitted to the ICU who are at risk of developing ARDS. Leukocytes are significantly increased in the blood of patients with chronic hypoxic diseases [ 30 ]. Animal studies have shown that hypoxia leads to a significant increase in leukocytes in the lung tissues and blood of rats [ 31 ]. Similarly, significant neutrophil infiltration was observed in lung tissue samples from patients [ 4 ]. Because S100A12 is mainly distributed in neutrophils, S100A12 is associated with respiratory failure in patients with ARDS. In addition, increased leukocyte infiltration depletes the available O 2 in tissues, leading to further tissue hypoxia [ 32 ]. Neutrophil aggregation in the lung tissue also causes further damage to the lung tissue, leading to further respiratory failure, confirming the experimental conclusion of this study that S100A12 levels are positively correlated with the percentage decrease in oxygenation in patients with ARDS within 4 days. Both the Berlin definition in 2012 and the new global ARDS definition in 2023 advocate using the PF value to assess ARDS severity, which is also associated with mortality [ 1 , 7 , 33 ]. A multicentre clinical trial reported that mortality was significantly higher in patients with ARDS with a PF 150 mmHg at the time of diagnosis [ 34 ]. In this study, patients with ARDS with a PF ≤ 150 mmHg had higher serum S100A12 levels those with a PF > 150 mmHg. Moreover, patients with higher serum S100A12 levels experienced a greater decline in oxygenation within 4 days, whereas no significant difference was found in the comparison of PF values at the time of ARDS diagnosis among the three groups. The degree of oxygenation deterioration was positively correlated with serum S100A12 levels in patients with mild-to-moderate ARDS. Furthermore, serum S100A12 levels were significantly higher in the non-survival group than in the survival group in patients with ARDS. The combination of S100A12 and age demonstrated good predictive efficacy in the ROC curve analysis. To the best of our knowledge, this study is the first to describe T S100A12 as an independent risk factor for both ARDS development and 28-days mortality, Daily monitoring of serum T S100A12 levels can provide a better assessment of the risk of ARDS development. The newly introduced variable, after its conversion through an inverse tangent function, constitutes the mapping relationship with the original variable. Transformation of the arctangent function results in the mapping of the original variable to a specific range of values. This process resulted in data exhibiting a normal distribution and reduced dispersion. Moreover, the newly introduced and transformed variables are positively correlated. Consequently, the variables can be transformed using the inverse tangent function, which fulfils the specific data analysis requirements. To the best of our knowledge, this variable transformation method is not currently employed in ARDS research. This study presents a novel approach to data analysis. The present study has several limitations. First, to avoid overfitting, only a limited number of clinical variables were included in the logistic regression models. Consequently, potentially relevant variables may not have been evaluated. However, our focus was on study variables that have been previously associated with poor outcomes in ARDS and other critical illnesses. Additionally, the small sample size of patients with ARDS may have limited the identification of prognostic factors in this study. Based on previous animal and clinical studies on the S100A12 protein, we do not believe that the sample size affected our research conclusions. This is because the magnitude of the effect, rather than the sample size, was statistically significant. However, large-scale clinical trials are required to confirm these findings. Owing to the retrospective nature of this study, we could not establish causality between S100A12 protein levels and ARDS. Further studies are required to determine the mechanisms underlying these observations. Given the heterogeneous nature of ARDS, future studies should stratify cohorts according to etiological factors to identify additional confounders. Additionally, the findings cannot be generalised to patients with cardiogenic pulmonary oedema, atelectasis, or pleural effusion because these patients were excluded from the study. Conclusions The serum level of T S100A12 can be used to identify ARDS development at an early stage. In our analysis, T S100A12 was identified as a risk factor for ARDS development and mortality within 28 days. The combination of S100A12 and age exhibited high efficacy in predicting the 28-day mortality. Additionally, the changes in oxygenation observed in patients within 4 days post-ARDS diagnosis can be understood based on the levels of S100A12. Declarations Ethics approval and consent to participate This study adhered to the ethical principles of the 1964 Declaration of Helsinki. The protocol has received approval from the Ethics Committee of Wuxi No. 2 People's Hospital (approval number 2016W-001). Additionally, approvals for the protocol and informed consent documents are obtained from the Institutional Review Board of each participating institution prior to enrolling study participants. Written informed consent is required and obtained from legally authorized representatives at each participating site. Each human participant signed an informed consent statement. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This study received funding from the Major Research Project Fund of the Wuxi Municipal Health and Family Planning Commission (z201601); Wuxi Medical Innovation Team Project (CXTD2021018); and the Top Talent Support Program for young and middle-aged people, provided by the Wuxi Health Committee (HB2023036). Author Contribution W. L. and DD. J. collected and interpreted the data, drafted and revised the manuscript. XP. Z. and MS. L. performed material preparation, data collection, and analysis. H. X., ZG. Z and HY. C. provided critical comments to help revise the manuscript. L.L. and JW. M. chaired the group and conceptualized and designed the study. All authors read and approved the final manuscript. Acknowledgments Not applicable. Availability of data and materials Any data collected during this study can be acquired from the corresponding author upon a reasonable request. References RUBENFELD G D RANIERIVM, THOMPSON B T, et al. Acute respiratory distress syndrome: the Berlin Definition. JAMA. 2012;307(23):2526–33. FERGUSON ND. The Berlin definition of ARDS: an expanded rationale, justification, and supplementary material. Intensive Care Med. 2012;38(10):1573–82. MATTHAY M A, ZEMANS R L, ZIMMERMAN G A, et al. Acute respiratory distress syndrome. Nat Rev Dis Primers. 2019;5(1):18. MATTHAY M A, WARE L B, ZIMMERMAN G A. The acute respiratory distress syndrome. J Clin Invest. 2012;122(8):2731–40. GORMAN E A, O'KANE C M, MCAULEY D F. 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Serum S100A12 as a prognostic biomarker of severe traumatic brain injury. Clin Chim Acta. 2018;480:84–91. PéREZ-MéNDEZ VILLARJ, BLANCO L. A universal definition of ARDS: the PaO2/FiO2 ratio under a standard ventilatory setting–a prospective, multicenter validation study. Intensive Care Med. 2013;39(4):583–92. VILLAR J, FERNáNDEZ C, GONZáLEZ-MARTíN JM et al. Respiratory Subsets in Patients with Moderate to Severe Acute Respiratory Distress Syndrome for Early Prediction of Death. J Clin Med, 2022, 11(19). Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818–29. Florentin J et al. May. VEGF Receptor 1 Promotes Hypoxia-Induced Hematopoietic Progenitor Proliferation and Differentiation. Front Immunol 13 882484. 12 2022. Florentin J, et al. Interleukin-6 mediates neutrophil mobilization from bone marrow in pulmonary hypertension. Cell Mol Immunol vol. 2021;18(2):374–84. Sanderlin EJ, et al. GPR4 deficiency alleviates intestinal inflammation in a mouse model of acute experimental colitis. Biochimica et biophysica acta. Mol basis disease vol. 2017;1863(2):569–84. Additional Declarations No competing interests reported. Supplementary Files supplement.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4517003","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344556602,"identity":"400f936e-4c98-4e3e-bc87-730a3776e9e8","order_by":0,"name":"Wei Liu","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Liu","suffix":""},{"id":344556604,"identity":"9472e6e2-95e0-40fd-b802-2e68509b139d","order_by":1,"name":"Dandan Ji","email":"","orcid":"","institution":"Wuxi No.2 People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Ji","suffix":""},{"id":344556605,"identity":"313c17f9-48c8-4b89-808b-f3d2fb05d2fa","order_by":2,"name":"Xingping Zhan","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xingping","middleName":"","lastName":"Zhan","suffix":""},{"id":344556606,"identity":"f27fc4e1-024c-4b1a-8868-e66686d5e05c","order_by":3,"name":"Mengshi Lu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengshi","middleName":"","lastName":"Lu","suffix":""},{"id":344556607,"identity":"d25fe886-c851-41b7-93b3-60a9fb6a62f2","order_by":4,"name":"Hao Xu","email":"","orcid":"","institution":"Wuxi No.2 People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Xu","suffix":""},{"id":344556608,"identity":"fe6062b8-e322-4902-a0bd-decc9b345139","order_by":5,"name":"Zigang Zhu","email":"","orcid":"","institution":"Wuxi No.2 People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zigang","middleName":"","lastName":"Zhu","suffix":""},{"id":344556609,"identity":"5b5ebfd4-f8b6-430e-bcc9-ea5554c62fe8","order_by":6,"name":"Hongyu Chen","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Chen","suffix":""},{"id":344556610,"identity":"7bf2dac0-0333-47a4-bdae-ebe4349be75e","order_by":7,"name":"Jiawei Ma","email":"","orcid":"","institution":"Wuxi No.2 People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiawei","middleName":"","lastName":"Ma","suffix":""},{"id":344556611,"identity":"7fe919ad-75c7-43e1-bd76-234da4a6f8e5","order_by":8,"name":"Liang Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABKUlEQVRIie2RMUvDQBSALxxcl6dxvGJxdH7BUWn+yoUMLkGcikOGSiEuqXPEP+EmblcOkuWarhk6ZOrUIZNksXhZpEMaOwrmg3s8Hu+De+8R0tPzB7EHs0UpHkIwiUTxU2eHlWGc+ljpdGQScZyCRXA5fInojUlwr96laE3OgTEgWlf3Zbgevw+WklQTRezXafvHsrm8AhiB9TR/Q5Fu/I/4TlhJrghfy1blVOfCB86AwtIoTPkoA6QnkSLIRatCzAgKkALjQYliZ5TVFulXh3JWBM5jIigADwh6kRo3e6BWh9IsmVQyBQ4povesBBZbXMT5LfCiXTEXzGpvF7puNts49adycRU4ZT25vrCTA7PswZrDeFMTpHnwa7+Blia4x3T29PT0/C++AcXGabmCHuxGAAAAAElFTkSuQmCC","orcid":"","institution":"Wuxi No.2 People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Liang","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-06-02 12:39:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4517003/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4517003/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63886961,"identity":"7988d6e2-ec83-4809-bc63-d1afe57c5f60","added_by":"auto","created_at":"2024-09-03 11:34:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80267,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study population.\u003c/p\u003e\n\u003cp\u003eICU, intensive care unit; ARDS, acute respiratory distress syndrome. D0 refers to the time of ICU admission, while D1 and D3 refer to the first and third day after ICU admission, respectively.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517003/v1/af35d4e1eeef094d207ca208.jpg"},{"id":63889405,"identity":"fb1c0410-a60d-4e11-b2b1-d7366a010814","added_by":"auto","created_at":"2024-09-03 11:50:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69137,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the S100A12 levels in patients with and without ARDS at different time points. (A) Comparison of the serum S100A12 levels between patients with and without ARDS. (B) The serum levels of S100A12 in 38 patients with ARDS (red box) were higher than those of 106 patients without ARDS (blue box) at the D0 time point. (C) The serum levels of S100A12 in 45 patients with ARDS (red box) were higher than those of 99 patients without ARDS (blue box) at the D1 time point. (D) The serum levels of S100A12 in 51 patients with ARDS (red box) were higher than those of 79 patients without ARDS (blue box) at the D3 time point. ARDS, acute respiratory distress syndrome; ICU, intensive care unit. D0 refers to the time of ICU admission, while D1 and D3 refer to the first and third day after ICU admission, respectively.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517003/v1/e6010525d490dc205de1a76a.jpg"},{"id":63888129,"identity":"4dfc59c6-ae39-41ce-a54b-24b02c8a0db3","added_by":"auto","created_at":"2024-09-03 11:42:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44924,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of serum S100A12 levels between the PF ≤ 150 mmHg group and PF \u0026gt; 150 mmHg group. The serum S100A12 level and PF value were obtained at the time of patient diagnosis. PF = PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517003/v1/493244be041b2880016b6395.jpg"},{"id":63886963,"identity":"ca906b05-c4e6-485a-a88f-e84f5ac29436","added_by":"auto","created_at":"2024-09-03 11:34:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32672,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant correlation between serum S100A12 levels and the decrease rate in PF value after ARDS diagnosis. PF = PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e. ARDS, acute respiratory distress syndrome.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517003/v1/5f02dd495f4dfe308995d1fe.jpg"},{"id":63886958,"identity":"ab668bf3-333e-4f1b-ad97-9221fa0276b2","added_by":"auto","created_at":"2024-09-03 11:34:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":28084,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve analysis of\u003csub\u003e \u003c/sub\u003e28-day mortality in patients with acute respiratory distress syndrome. ROC of S100A12 model: Cutoff value: 44.396 ng/ml, sensitivity：92.30%; specificity: 50%, AUC：0.706, p-value：0.027. ROC of Age model: Cutoff value: 78.5 years, sensitivity: 61.5%, specificity: 78.9%, AUC: 0.706, p-value: 0.027. ROC of Age+S100A12 model: Sensitivity: 61.5%, specificity: 81.6%, AUC: 0.753, p-value: 0.007. The AUC for the S100A12+Age model was higher than that for the S100A12 or Age model, indicating that the S100A12+Age model has a better predictive value for 28-day mortality. AUC, area under the ROC curve.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517003/v1/afdd5250e957d0c4e41cc6d7.jpg"},{"id":75518301,"identity":"e2c198f0-93af-4c91-a70f-c02d85979a7f","added_by":"auto","created_at":"2025-02-05 11:47:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1226207,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4517003/v1/07e68df9-49a3-464d-acea-8997ac921605.pdf"},{"id":63886957,"identity":"2b01eb4b-6795-4d50-9267-bc8fcc9421a6","added_by":"auto","created_at":"2024-09-03 11:34:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":48556,"visible":true,"origin":"","legend":"","description":"","filename":"supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-4517003/v1/42d91bd94092d0723cfa9260.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical Value of Serum S100A12 in Identifying ARDS Development and Predicting Deterioration in Critically Ill Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute respiratory distress syndrome (ARDS) which can be caused by intrapulmonary or extrapulmonary insults, is a clinical syndrome characterised by acute hypoxaemia, noncardiogenic pulmonary oedema, and dyspnoea [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite the application of multiple techniques, such as lung-protective ventilation strategies, fluid management, and prone ventilation, there is a lack of specialised pharmacological treatments, and the mortality of patients with ARDS remains high [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The LUNG SAFE study reported that the 28-day mortality of patients with ARDS was 35%, and that patients with severe ARDS had a mortality rate exceeding 40% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Early identification of ARDS and subsequent oxygenation deterioration in critically ill patients may create opportunities for more aggressive interventions, leading to a reduction in ARDS mortality.\u003c/p\u003e \u003cp\u003eIn 2012, the Berlin definition clarified the diagnostic criteria for ARDS, including the time of onset, changes in chest imaging findings, oxygenation index, and the exclusion of cardiogenic pulmonary oedema. In 2023, 32 experts revised the definition of ARDS and proposed a new global definition. The significance of the early identification of ARDS was highlighted by the inclusion of patients receiving high flow oxygenation in the updated definition of ARDS screening [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, serum markers for the early identification of ARDS, subsequent oxygenation deterioration, and 28-day mortality in critically ill patients are currently lacking.\u003c/p\u003e \u003cp\u003eIn our previous analysis of proteomic differences between patients with ARDS and healthy individuals, we observed a significant increase in serum S100A12 protein levels in patients with ARDS compared to healthy controls. S100A12 is a protein belonging to the S100 family; it is primarily found in neutrophils and is expressed in small amounts in monocytes. It can be released into body fluids, such as serum and urine, in response to inflammation and is involved in the development of several diseases [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. S100A12 can mediate intracellular signalling by binding to advanced glycosylation end products (RAGE) receptors on cell membranes, which initiates the intracellular inflammatory signalling pathway, induces the expression of pro-inflammatory cytokines, and participates in immune and inflammatory regulation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It is important to note that RAGE is expressed at low levels in all cells but at higher levels in lung tissues [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and the effects of S100A12 may be more pronounced in the lungs.\u003c/p\u003e \u003cp\u003eBased on the above information, we aimed to investigate the clinical value of serum S100A12 levels in identifying the development of ARDS in patients in the intensive care unit (ICU), its association with the deterioration of oxygenation, and its clinical value in predicting 28-day mortality.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eFrom 1 August 2022 to 15 December 2022, 241 critically ill patients were admitted to two independent general ICUs in the Department of Critical Care Medicine, Jiangnan University Medical Center. Patients who met the specified criteria were included in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study was approved by the Ethics Committee of Jiangnan University Medical Center (Wuxi No. 2 People's Hospital; approval number: 2016W-001). Each human participant signed an informed consent statement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample Collection\u003c/h2\u003e \u003cp\u003eFive millilitres of peripheral venous blood was collected from the patients at the time of ICU admission and 24 and 72 h after ICU admission. The serum was obtained and then stored at -80\u0026deg;C to detect the S100A12 levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eClinical Data Collection\u003c/h2\u003e \u003cp\u003eSex, age, and body mass index (BMI) were recorded upon ICU admission. Medical history was obtained from the Hospital Information System. Temperature, heart rate, respiratory rate, blood pressure, blood gas analysis, neutrophils, platelets, creatinine, and total bilirubin were collected at the time of ICU admission (D0), and on the first (D1), third (D3), fifth (D5), and seventh (D7) day in the ICU. Acute physiology and chronic health evaluation II (APACHE II) scores were calculated and recorded [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. All patients were followed-up with for 28-days after ICU admission, and the survival status at 28 days was recorded. Data collection was completed by two researchers who were blinded to the serum S100A12 levels.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eGrouping Criteria\u003c/h2\u003e \u003cp\u003ePatients who met the Berlin definition [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] within three days after ICU admission were categorised into the ARDS group, whereas the remaining patients were included in the non-ARDS group. Using the binary classification method, the ARDS group was further divided into two groups based on the oxygenation index at the time of ARDS diagnosis: the PF\u0026thinsp;\u0026lt;\u0026thinsp;150 mmHg group and the PF\u0026thinsp;\u0026gt;\u0026thinsp;150 mmHg group. Considering more important value for investigating oxygenation deterioration in mild-moderate ARDS than severe ARDS, patients with mild-moderate ARDS (PF values range from 100 to 300) were divided into three grouping sets based on the decrease rate in the oxygenation index within 4 days after ARDS diagnosis: PF decrease rate\u0026thinsp;\u0026lt;\u0026thinsp;30% group \u003cem\u003evs.\u003c/em\u003e PF decrease rate\u0026thinsp;\u0026ge;\u0026thinsp;30% group, PF decrease rate\u0026thinsp;\u0026lt;\u0026thinsp;35% group \u003cem\u003evs.\u003c/em\u003e PF decrease rate\u0026thinsp;\u0026ge;\u0026thinsp;35% group, and PF decrease rate\u0026thinsp;\u0026lt;\u0026thinsp;40% group \u003cem\u003evs.\u003c/em\u003e PF decrease rate\u0026thinsp;\u0026ge;\u0026thinsp;40% group. Additionally, to verify the correlation between serum S100A12 levels and 28-day mortality in patients with ARDS, the ARDS group was divided into two groups: survival and non-survival.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBiomarker Measurements\u003c/h2\u003e \u003cp\u003eSerum S100A12 levels were measured using an enzyme-linked immunosorbent assay (ELISA) kit (Elabscience Biotechnology Co., Ltd., Wuhan, China). The assay was performed according to the manufacturer's instructions. A standard curve was plotted for each enzyme plate using standard samples. The average optical density values of the two wells were substituted into a standard curve to calculate the concentrations of the samples. This ensured the reliability of the experimental data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSPSS (version 26.0) was used for the data analysis. The data conforming to a normal distribution are expressed as\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\stackrel{-}{\\text{x}}\\pm\\:\\text{s}\\)\u003c/span\u003e\u003c/span\u003e, and the independent-samples \u003cem\u003et\u003c/em\u003e-test was used for comparison. Data that did not conform to a normal distribution are expressed as the median and quartiles, and the Mann\u0026ndash;Whitney U test was used for analysis. The Chi-square test or Fisher's exact test was used to compare dichotomous variables between groups. Survival curve analysis was used to describe the survival status. Spearman\u0026rsquo;s correlation analysis was used to determine the degree of association between indicators. T\u003csub\u003eS100A12\u003c/sub\u003e is an arctangent function-transformed variable in our study and was calculated using the following formula: T\u003csub\u003eS100A12\u003c/sub\u003e = 1000 \u0026times; ArcTan (serum S100A12 levels). Odds ratios (ORs) were determined by logistic regression analysis. Receiver operating characteristic (ROC) curves were used to analyse the predictive validity of the variables. Differences were considered statistically significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Graphs were generated using R 4.3.2 and GraphPad Prism 8.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the Study Population\u003c/h2\u003e \u003cp\u003eIn total, 144 critically ill patients were enrolled in this study. All study participants were screened daily according to the Berlin definition [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After 3 days of screening, 51 patients with ARDS were categorised into the ARDS group within three days of ICU admission, whereas the remaining 93 patients were categorised into the non-ARDS group. There were no statistically significant differences in demographics and medical history between the ARDS and non-ARDS groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo explore the influence of severity in critically ill patients, the APACHE II scores were compared between patients with and without ARDS, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. There were no statistically significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in APACHE II scores between patients with and without ARDS at any of the three time points (D0, D1, and D3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients with and without ARDS ARDS, acute respiratory distress syndrome; BMI, body mass index.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients with ARDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients without ARDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(74.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58(62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75(60\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70(59-80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.5(20.8\u0026ndash;24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.5(20.8\u0026ndash;24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36(38.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25(26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(62.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46(49.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"11\"\u003e\n \u003cp\u003eTable 2. Comparison of APACHEII scores between patients with and without ARDS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.3265306122449%\" rowspan=\"2\"\u003e\n \u003cp\u003eTime-point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" colspan=\"4\" style=\"width: 36.8025%;\"\u003e\n \u003cp\u003ePatients with ARDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.734693877551024%\" colspan=\"4\" style=\"width: 37.2328%;\"\u003e\n \u003cp\u003ePatients without ARDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" rowspan=\"2\" style=\"width: 10.4484%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.450704225352112%\" colspan=\"3\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.84507042253521%\" style=\"width: 29.7011%;\"\u003e\n \u003cp\u003eAPACHE II\u0026nbsp;scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" colspan=\"3\" style=\"width: 15.6612%;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.84507042253521%\" colspan=\"3\" style=\"width: 27.3777%;\"\u003e\n \u003cp\u003eAPACHE II\u0026nbsp;scores\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" colspan=\"2\"\u003e\n \u003cp\u003eD0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" colspan=\"2\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.804123711340207%\" style=\"width: 29.7011%;\"\u003e\n \u003cp\u003e19.00（15.75-23.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" colspan=\"3\" style=\"width: 15.6612%;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\" style=\"width: 22.0154%;\"\u003e\n \u003cp\u003e17.00（14.00-21.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\" style=\"width: 10.4484%;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" colspan=\"2\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" colspan=\"2\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.804123711340207%\" style=\"width: 29.7011%;\"\u003e\n \u003cp\u003e19.00（14.00-23.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" colspan=\"3\" style=\"width: 15.6612%;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\" style=\"width: 22.0154%;\"\u003e\n \u003cp\u003e19.00（12.00-23.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\" style=\"width: 10.4484%;\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\" colspan=\"2\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" colspan=\"2\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.804123711340207%\" style=\"width: 29.7011%;\"\u003e\n \u003cp\u003e16.00（13.00-22.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" colspan=\"3\" style=\"width: 15.6612%;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\" style=\"width: 22.0154%;\"\u003e\n \u003cp\u003e17.00（12.00-21.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" colspan=\"2\" style=\"width: 10.4484%;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eARDS, acute respiratory distress syndrome; ICU, intensive care unit; APACHEII, Acute Physiology and Chronic Health Evaluation II. D0 refers to the time of ICU admission, while D1 and D3 refer to the first and third day after ICU admission, respectively.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe Serum S100A12 Level was Increased in Patients with ARDS\u003c/h2\u003e \u003cp\u003eA comparison of S100A12 levels between patients with and without ARDS at three time points (D0, D1, and D3) revealed significant differences. S100A12 levels were higher in patients with ARDS that in those without ARDS at all three time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eT\u003csub\u003eS100A12\u003c/sub\u003e is a Risk Factor of the Development of ARDS\u003c/h2\u003e \u003cp\u003eIn this study, we created a transition variable to amplify the efficiency of identifying the development of ARDS in ICU patients. T\u003csub\u003eS100A12\u003c/sub\u003e showed good fit in binary logistic regression analysis. For every 1 unit increase in T\u003csub\u003eS100A12\u003c/sub\u003e, the risk of ARDS development increased by 4.9%, 7.8%, 7.0% at D0, D1, and D3, respectively (Table\u0026nbsp;3).\u003c/p\u003e\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 47.2266%;\"\u003e\n \u003cp\u003eTable 3. Serum S100A12 and\u0026nbsp;T\u003csub\u003eS100A12\u0026nbsp;\u003c/sub\u003elevel for risk assessment of ARDS development in three time-points after ICU admission\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6.538%;\"\u003e\n \u003cp\u003eTime-point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20.3095%;\"\u003e\n \u003cp\u003eS100A12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20.3095%;\"\u003e\n \u003cp\u003eT\u003csub\u003eS100A12\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.1626%;\"\u003e\n \u003cp\u003eCrude Odds ratio\u003c/p\u003e\n \u003cp\u003e(95% Confidence Interval)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.2165%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1626%;\"\u003e\n \u003cp\u003eCrude Odds ratio\u003c/p\u003e\n \u003cp\u003e(95% Confidence Interval)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.2165%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.538%;\"\u003e\n \u003cp\u003eD0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1626%;\"\u003e\n \u003cp\u003e1.011(0.998-1.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.2165%;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1626%;\"\u003e\n \u003cp\u003e1.049 (1.002-1.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.2165%;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.538%;\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1626%;\"\u003e\n \u003cp\u003e1.017(1.002-1.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.2165%;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1626%;\"\u003e\n \u003cp\u003e1.078(1.027-1.131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.2165%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6.538%;\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1626%;\"\u003e\n \u003cp\u003e1.021(1.003-1.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.2165%;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1626%;\"\u003e\n \u003cp\u003e1.072(1.021-1.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.2165%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 47.9221%;\"\u003e\n \u003cp\u003eARDS, acute respiratory distress syndrome; ICU, intensive care unit. D0 refers to the time of ICU admission, while D1 and D3 refer to the first and third day after ICU admission, respectively.\u0026nbsp;T\u003csub\u003eS100A12\u003c/sub\u003e =\u003csub\u003e\u0026nbsp;\u003c/sub\u003e1000\u0026nbsp;\u0026times;\u0026nbsp;ArcTan (Serum S100A12 levels)\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSerum S100A12 Levels were Up-regulated in Patients with ARDS with Poorer Oxygenation\u003c/h2\u003e \u003cp\u003eThe serum S100A12 level in the PF\u0026thinsp;\u0026lt;\u0026thinsp;150 mmHg group was significantly higher than that in the PF\u0026thinsp;\u0026gt;\u0026thinsp;150 mmHg group, indicating that serum S100A12 levels were upregulated in patients with ARDS with poorer oxygenation (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePositive Correlation Between Serum S100A12 Levels and Oxygenation Deterioration in Patients with Mild-to-moderate ARDS over Four Days after ARDS Diagnosis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAccording to the Berlin definition, PF values between 0 and 100 indicate severe ARDS, whereas PF values between 100 and 300 indicate mild-to-moderate ARDS. In this study, 44 patients had mild-to-moderate ARDS, accounting for 86.27% of the total number of patients with ARDS. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows a significant correlation between the serum S100A12 levels and the rate of decrease in PF after ARDS diagnosis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Interestingly, among the different PF decrease rate groups, we did not find any statistically significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the initial PF values detected at the time of ARDS diagnosis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, comparison of the initial serum S100A12 levels showed a statistically significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In other words, for mild-to-moderate ARDS, the initial PF value cannot be used to assess oxygenation deterioration during the subsequent four days after ARDS diagnosis; however, serum S100A12 levels can be used.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the serum S100A12 level and PF value between two groups in three different group sets based on the PF value decrease rate\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePF value decrease rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS100A12 (ng/ml)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30%(n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169.32(141.0-204.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.40(37.75\u0026ndash;53.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30%(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216.5(157.5-275.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.17(50.86\u0026ndash;77.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;35%(n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175.0(141.0-213.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.57(37.75\u0026ndash;53.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35%(n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202.0(159.0-277.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.89(55.70-73.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40%(n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184.0(142.0-220.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.59(37.35\u0026ndash;53.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40%(n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165.0(154.5-262.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.89(56.37\u0026ndash;68.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe PF values and serum S100A12 levels were measured at the time of ARDS diagnosis. PF\u0026thinsp;=\u0026thinsp;PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e. ARDS, acute respiratory distress syndrome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eT\u003csub\u003eS100A12\u003c/sub\u003e is an Independent Risk Factor of 28-day Mortality in Patients with ARDS\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that patients in the non-survival group were older than those in the survival group after 28-days of follow-up, and serum S100A12 levels in the non-survival group were higher than those in the survival group. Univariate analysis revealed a statistically significant difference in S100A12 and age between the two groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Logistic regression analysis revealed that both age and the T\u003csub\u003eS100A12\u003c/sub\u003e level were independent risk factors of the 28-day mortality in patients with ARDS (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Additionally, ROC curve analysis demonstrated that the S100A12 level and age could predict 28-day mortality in patients with ARDS. The figure depicts that the AUC for the S100A12\u0026thinsp;+\u0026thinsp;Age model was significantly higher than that for the S100A12 or Age model, indicating that the S100A12\u0026thinsp;+\u0026thinsp;Age model has a better predictive value for predicting 28-day mortality that the S100A12 and Age models. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of baseline characteristics in patients with ARDS between the survival and non-survival groups PF and serum S100A12 levels were measured at the time of ARDS diagnosis. BMI, body mass index; ARDS, acute respiratory distress syndrome. PF\u0026thinsp;=\u0026thinsp;PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-survival\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvival group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(69.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(76.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.92\u0026thinsp;\u0026plusmn;\u0026thinsp;11.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.68\u0026thinsp;\u0026plusmn;\u0026thinsp;14.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(69.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePF value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.0(103.2-234.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170.0(139.1-222.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS100A12(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.29(46.82\u0026ndash;86.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.78(36.31\u0026ndash;59.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of 28-day mortality in the ARDS group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCorrected OR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.066(1.004\u0026ndash;1.131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.069(1.002\u0026ndash;1.140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS100A12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.010(0.992\u0026ndash;1.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003eS100A12\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.108(1.003\u0026ndash;1.224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003csub\u003eS100A12\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.118(1.001\u0026ndash;1.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the univariate analysis, age and T\u003csub\u003eS100A12\u003c/sub\u003e scores were included in the logistic regression model. These results indicate that both age and the T\u003csub\u003eS100A12\u003c/sub\u003e are risk factors for 28-day mortality in patients with ARDS. T\u003csub\u003eS100A12\u003c/sub\u003e = 1000 \u0026times; ArcTan (serum S100A12 level). ARDS, acute respiratory distress syndrome; OR, odds ratio; 95%CI, 95% confidence interval.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSeveral studies have focused on identifying potential biomarkers to reveal the process of ARDS and prognosis. These are helpful in clinical decision making and treatment optimisation to decrease the mortality rate, especially for the development of ARDS and possible adverse consequences. However, few studies have reported these parameters, especially for early identification of ARDS development, subsequent oxygenation deterioration, and 28-day mortality in ICU patients. This retrospective analysis aimed to assess the clinical application of serum S100A12 in ARDS based on our previous proteomic findings (Supplement 1). Here, we found that T\u003csub\u003eS100A12\u003c/sub\u003e was significantly higher in patients with ARDS compared with those without ARDS, and that T\u003csub\u003eS100A12\u003c/sub\u003e can be used to identify the risk of ARDS in the early stages after ICU admission. Serum S100A12 levels were upregulated in patients with ARDS with poor oxygenation, and the degree of oxygenation deterioration was positively correlated with serum S100A12 levels in patients with mild-to-moderate ARDS. T\u003csub\u003eS100A12\u003c/sub\u003e and age were risk factors of 28-day mortality.\u003c/p\u003e \u003cp\u003eVarious insults can induce the release of S100A12 into bodily fluids. S100A12 recognizes and binds to RAGE located on the cell membrane [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Ligand-receptor binding activates downstream pathways [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In a clinical trial involving 14 patients with ARDS, the concentrations of both S100A12 and RAGE in the alveolar lavage fluid significantly increased [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly, serum levels of S100A12 and soluble late glycosylation end-product receptors (sRAGE) were significantly elevated in both mouse models and patients with ARDS [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Two previous experiments corroborated the discovery of elevated serum S100A12 levels in patients with ARDS, as evidenced in our experiment.\u003c/p\u003e \u003cp\u003eRecent animal studies have demonstrated that S100A12 plays a role in the progression of ARDS by activating the NLRP3 inflammatory pathway by binding to RAGE [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similarly, targeted blockade of the S100A12/NLRP3 axis significantly attenuated lung injury in septic rats [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Thus, S100A12 may play a crucial role in the progression of ARDS. Several studies have reported elevated S100A12 levels in inflammatory bowel disease, juvenile rheumatoid arthritis, asthma, leukodystrophy, Kawasaki disease, and adult Still's disease [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, few studies have reported its application in the assessment of ARDS development. A previous study measuring serum S100A12 levels in 102 healthy controls and 102 patients with traumatic brain injury (TBI) found that serum S100A12 levels predicted the development of ARDS [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In our study, we found that serum S100A12 levels were significantly higher in patients with ARDS compared with those without ARDS, and that T\u003csub\u003eS100A12\u003c/sub\u003e can be used to identify patients admitted to the ICU who are at risk of developing ARDS.\u003c/p\u003e \u003cp\u003eLeukocytes are significantly increased in the blood of patients with chronic hypoxic diseases [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Animal studies have shown that hypoxia leads to a significant increase in leukocytes in the lung tissues and blood of rats [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Similarly, significant neutrophil infiltration was observed in lung tissue samples from patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Because S100A12 is mainly distributed in neutrophils, S100A12 is associated with respiratory failure in patients with ARDS. In addition, increased leukocyte infiltration depletes the available O\u003csub\u003e2\u003c/sub\u003e in tissues, leading to further tissue hypoxia [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Neutrophil aggregation in the lung tissue also causes further damage to the lung tissue, leading to further respiratory failure, confirming the experimental conclusion of this study that S100A12 levels are positively correlated with the percentage decrease in oxygenation in patients with ARDS within 4 days.\u003c/p\u003e \u003cp\u003eBoth the Berlin definition in 2012 and the new global ARDS definition in 2023 advocate using the PF value to assess ARDS severity, which is also associated with mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A multicentre clinical trial reported that mortality was significantly higher in patients with ARDS with a PF\u0026thinsp;\u0026lt;\u0026thinsp;150 mmHg than in those with a PF\u0026thinsp;\u0026gt;\u0026thinsp;150 mmHg at the time of diagnosis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this study, patients with ARDS with a PF\u0026thinsp;\u0026le;\u0026thinsp;150 mmHg had higher serum S100A12 levels those with a PF\u0026thinsp;\u0026gt;\u0026thinsp;150 mmHg. Moreover, patients with higher serum S100A12 levels experienced a greater decline in oxygenation within 4 days, whereas no significant difference was found in the comparison of PF values at the time of ARDS diagnosis among the three groups. The degree of oxygenation deterioration was positively correlated with serum S100A12 levels in patients with mild-to-moderate ARDS. Furthermore, serum S100A12 levels were significantly higher in the non-survival group than in the survival group in patients with ARDS. The combination of S100A12 and age demonstrated good predictive efficacy in the ROC curve analysis. To the best of our knowledge, this study is the first to describe T\u003csub\u003eS100A12\u003c/sub\u003e as an independent risk factor for both ARDS development and 28-days mortality, Daily monitoring of serum T\u003csub\u003eS100A12\u003c/sub\u003e levels can provide a better assessment of the risk of ARDS development.\u003c/p\u003e \u003cp\u003eThe newly introduced variable, after its conversion through an inverse tangent function, constitutes the mapping relationship with the original variable. Transformation of the arctangent function results in the mapping of the original variable to a specific range of values. This process resulted in data exhibiting a normal distribution and reduced dispersion. Moreover, the newly introduced and transformed variables are positively correlated. Consequently, the variables can be transformed using the inverse tangent function, which fulfils the specific data analysis requirements. To the best of our knowledge, this variable transformation method is not currently employed in ARDS research. This study presents a novel approach to data analysis.\u003c/p\u003e \u003cp\u003eThe present study has several limitations. First, to avoid overfitting, only a limited number of clinical variables were included in the logistic regression models. Consequently, potentially relevant variables may not have been evaluated. However, our focus was on study variables that have been previously associated with poor outcomes in ARDS and other critical illnesses. Additionally, the small sample size of patients with ARDS may have limited the identification of prognostic factors in this study. Based on previous animal and clinical studies on the S100A12 protein, we do not believe that the sample size affected our research conclusions. This is because the magnitude of the effect, rather than the sample size, was statistically significant. However, large-scale clinical trials are required to confirm these findings. Owing to the retrospective nature of this study, we could not establish causality between S100A12 protein levels and ARDS. Further studies are required to determine the mechanisms underlying these observations. Given the heterogeneous nature of ARDS, future studies should stratify cohorts according to etiological factors to identify additional confounders. Additionally, the findings cannot be generalised to patients with cardiogenic pulmonary oedema, atelectasis, or pleural effusion because these patients were excluded from the study.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe serum level of T\u003csub\u003eS100A12\u003c/sub\u003e can be used to identify ARDS development at an early stage. In our analysis, T\u003csub\u003eS100A12\u003c/sub\u003e was identified as a risk factor for ARDS development and mortality within 28 days. The combination of S100A12 and age exhibited high efficacy in predicting the 28-day mortality. Additionally, the changes in oxygenation observed in patients within 4 days post-ARDS diagnosis can be understood based on the levels of S100A12.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study adhered to the ethical principles of the 1964 Declaration of Helsinki. The protocol has received approval from the Ethics Committee of Wuxi No. 2 People's Hospital (approval number 2016W-001). Additionally, approvals for the protocol and informed consent documents are obtained from the Institutional Review Board of each participating institution prior to enrolling study participants. Written informed consent is required and obtained from legally authorized representatives at each participating site. Each human participant signed an informed consent statement.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study received funding from the Major Research Project Fund of the Wuxi Municipal Health and Family Planning Commission (z201601); Wuxi Medical Innovation Team Project (CXTD2021018); and the Top Talent Support Program for young and middle-aged people, provided by the Wuxi Health Committee (HB2023036).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW. L. and DD. J. collected and interpreted the data, drafted and revised the manuscript. XP. Z. and MS. L. performed material preparation, data collection, and analysis. H. X., ZG. Z and HY. C. provided critical comments to help revise the manuscript. L.L. and JW. M. chaired the group and conceptualized and designed the study. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eAny data collected during this study can be acquired from the corresponding author upon a reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRUBENFELD G D RANIERIVM, THOMPSON B T, et al. Acute respiratory distress syndrome: the Berlin Definition. JAMA. 2012;307(23):2526\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFERGUSON ND. The Berlin definition of ARDS: an expanded rationale, justification, and supplementary material. Intensive Care Med. 2012;38(10):1573\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMATTHAY M A, ZEMANS R L, ZIMMERMAN G A, et al. Acute respiratory distress syndrome. Nat Rev Dis Primers. 2019;5(1):18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMATTHAY M A, WARE L B, ZIMMERMAN G A. The acute respiratory distress syndrome. J Clin Invest. 2012;122(8):2731\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGORMAN E A, O'KANE C M, MCAULEY D F. Acute respiratory distress syndrome in adults: diagnosis, outcomes, long-term sequelae, and management. Lancet. 2022;400(10358):1157\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBELLANI G, LAFFEY J G, PHAM T, et al. Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. JAMA. 2016;315(8):788\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMATTHAY MA, ARABI Y, ARROLIGA A C, et al. A New Global Definition of Acute Respiratory Distress Syndrome. Am J Respir Crit Care Med. 2024;209(1):37\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMEIJER B, GEARRY R B, DAY A S. The role of S100A12 as a systemic marker of inflammation. Int J Inflam, 2012, 2012: 907078.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXIA P, JI X. Roles of S100A8, S100A9 and S100A12 in infection, inflammation and immunity. Immunology. 2024;171(3):365\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSORCI G, RIUZZI F, GIAMBANCO I, et al. RAGE in tissue homeostasis, repair and regeneration. Biochim Biophys Acta. 2013;1833(1):101\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBRETT J, SCHMIDT A M, YAN SD, et al. Survey of the distribution of a newly characterized receptor for advanced glycation end products in tissues. Am J Pathol. 1993;143(6):1699\u0026ndash;712.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eENGLERT JM, HANFORD L E, KAMINSKI N, et al. A role for the receptor for advanced glycation end products in idiopathic pulmonary fibrosis. Am J Pathol. 2008;172(3):583\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHANFORD L E ENGHILDJJ, VALNICKOVA Z, et al. Purification and characterization of mouse soluble receptor for advanced glycation end products (sRAGE). J Biol Chem. 2004;279(48):50019\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHANFORD L E, FATTMAN C L, SHAEFER L M, et al. Regulation of receptor for advanced glycation end products during bleomycin-induced lung injury. Am J Respir Cell Mol Biol. 2003;29(3 Suppl):S77\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSHIRASAWA M, FUJIWARA N. Receptor for advanced glycation end-products is a marker of type I lung alveolar cells. Genes Cells. 2004;9(2):165\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMCELROY M C KASPERM. The use of alveolar epithelial type I cell-selective markers to investigate lung injury and repair. Eur Respir J. 2004;24(4):664\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHENG C, TSUNEYAMA K, KOMINAMI R, et al. Expression profiling of endogenous secretory receptor for advanced glycation end products in human organs. Mod Pathol. 2005;18(10):1385\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMORBINI P, VILLA C, CAMPO I, et al. The receptor for advanced glycation end products and its ligands: a new inflammatory pathway in lung disease? Mod Pathol. 2006;19(11):1437\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGUO W A, KNIGHT P R, RAGHAVENDRAN K. The receptor for advanced glycation end products and acute lung injury/acute respiratory distress syndrome. Intensive Care Med. 2012;38(10):1588\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWITTKOWSKI H, STURROCK A, VAN ZOELEN M A, et al. Neutrophil-derived S100A12 in acute lung injury and respiratory distress syndrome. Crit Care Med. 2007;35(5):1369\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG Z, HAN N, SHEN Y. S100A12 promotes inflammation and cell apoptosis in sepsis-induced ARDS via activation of NLRP3 inflammasome signalling. Mol Immunol. 2020;122:38\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLENGA MA BONDA W, FOURNET M, ZHAI R et al. Receptor for Advanced Glycation End-Products Promotes Activation of Alveolar Macrophages through the NLRP3 Inflammasome/TXNIP Axis in Acute Lung Injury. Int J Mol Sci, 2022, 23(19).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHU J, LIN X. Tocilizumab attenuates acute lung injury in rats with sepsis by regulating S100A12/NLRP3. Am J Transl Res. 2023;15(1):99\u0026ndash;113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCARVALHO A, LU J, FRANCIS JD et al. S100A12 in Digestive Diseases and Health: A Scoping Review. Gastroenterol Res Pract, 2020, 2020: 2868373.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSHANK JM, KELLEY B R JACKSONJW et al. The Host Antimicrobial Protein Calgranulin C Participates in the Control of Campylobacter jejuni Growth via Zinc Sequestration. Infect Immun, 2018, 86(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eALBERT C, NISHABEN P et al. REBECCA A,. Sa1799 - Fecal S100A12 Levels in Children with Inflammatory Bowel Disease. Gastroenterology, 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBAE C B, SUH C H, AN JM, et al. Serum S100A12 may be a useful biomarker of disease activity in adult-onset Still's disease. J Rheumatol. 2014;41(12):2403\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFENG MJ, NING W B, WANG W, et al. Serum S100A12 as a prognostic biomarker of severe traumatic brain injury. Clin Chim Acta. 2018;480:84\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026eacute;REZ-M\u0026eacute;NDEZ VILLARJ, BLANCO L. A universal definition of ARDS: the PaO2/FiO2 ratio under a standard ventilatory setting\u0026ndash;a prospective, multicenter validation study. Intensive Care Med. 2013;39(4):583\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVILLAR J, FERN\u0026aacute;NDEZ C, GONZ\u0026aacute;LEZ-MART\u0026iacute;N JM et al. Respiratory Subsets in Patients with Moderate to Severe Acute Respiratory Distress Syndrome for Early Prediction of Death. J Clin Med, 2022, 11(19).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlorentin J et al. May. VEGF Receptor 1 Promotes Hypoxia-Induced Hematopoietic Progenitor Proliferation and Differentiation. Front Immunol 13 882484. 12 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlorentin J, et al. Interleukin-6 mediates neutrophil mobilization from bone marrow in pulmonary hypertension. Cell Mol Immunol vol. 2021;18(2):374\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanderlin EJ, et al. GPR4 deficiency alleviates intestinal inflammation in a mouse model of acute experimental colitis. Biochimica et biophysica acta. Mol basis disease vol. 2017;1863(2):569\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute respiratory syndrome distress, S100A2, predicting deterioration, 28-day mortality","lastPublishedDoi":"10.21203/rs.3.rs-4517003/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4517003/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the clinical value of serum S100A12 in identifying the development of acute respiratory distress syndrome (ARDS), its association with subsequent oxygenation deterioration, and its ability to predict 28-day mortality in patients in the intensive care unit (ICU).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBased on the inclusion and exclusion criteria, the demographic data, chronic diseases, and acute physiological indices of ICU patients were collected from two independent general ICUs in the Department of Critical Care Medicine, Jiangnan University Medical Center. Serum S100A12 levels were measured at different time points using an enzyme-linked immunosorbent assay. T\u003csub\u003eS100A12\u003c/sub\u003e was derived from serum S100A12 levels and converted to an inverse tangent function in our study. Patients meeting the Berlin definition of ARDS within three days of admission were categorised into ARDS and non-ARDS groups. The ARDS group was further divided into two groups based on the PF (PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e) value at the time of diagnosis: PF\u0026thinsp;\u0026lt;\u0026thinsp;150 mmHg and PF\u0026thinsp;\u0026gt;\u0026thinsp;150 mmHg groups. To verify the correlation between serum S100A12 levels and oxygenation deterioration, three grouping sets based on the decrease rate in the oxygenation index within 4 days after ARDS diagnosis were used for substantial analysis: PF decrease rate\u0026thinsp;\u0026lt;\u0026thinsp;30% group \u003cem\u003evs.\u003c/em\u003e PF decrease rate\u0026thinsp;\u0026ge;\u0026thinsp;30% group, PF decrease rate\u0026thinsp;\u0026lt;\u0026thinsp;35% group \u003cem\u003evs.\u003c/em\u003e PF decrease rate\u0026thinsp;\u0026ge;\u0026thinsp;35% group, and PF decrease rate\u0026thinsp;\u0026lt;\u0026thinsp;40% group \u003cem\u003evs.\u003c/em\u003e PF decrease rate\u0026thinsp;\u0026ge;\u0026thinsp;40% group. Additionally, to verify the correlation between serum S100A12 levels and 28-day mortality in patients with ARDS, the ARDS group was divided into survival and non-survival groups. Spearman\u0026rsquo;s correlation analysis was used to assess the association between indicators, logistic regression analysis was used to determine the odds ratios, and receiver operating characteristic curve analysis was used to evaluate predictive efficacy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 144 patients were enrolled in this study from 1 August 2022 to 15 December 2022. At the time of ARDS diagnosis, serum S100A12 levels were significantly higher than those in patients without ARDS, and T\u003csub\u003eS100A12\u003c/sub\u003e was identified as a risk factor for the development of ARDS. At the time of ARDS diagnosis, the serum S100A12 levels were significantly higher in the PF\u0026thinsp;\u0026lt;\u0026thinsp;150 mmHg group than in the PF\u0026thinsp;\u0026gt;\u0026thinsp;150 mmHg group. Additionally, after ARDS diagnosis, serum S100A12 levels were significantly higher in the group with a higher rate of PF decrease. The PF decrease rate within 4 days was greater with higher serum S100A12 levels at the time of ARDS diagnosis. Additionally, T\u003csub\u003eS100A12\u003c/sub\u003e and age were independent risk factors of 28-day mortality, and the combination of serum S100A12 levels and age exhibited a high degree of predictive accuracy for 28-day mortality in patients with ARDS.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eT\u003csub\u003eS100A12\u003c/sub\u003e is a risk factor of ARDS and 28-day mortality. Serum S100A12 levels were associated with a decline in oxygenation within four days of ARDS diagnosis. Additionally, the combination of serum S100A12 levels and age exhibited high efficacy in predicting 28-day mortality.\u003c/p\u003e","manuscriptTitle":"Clinical Value of Serum S100A12 in Identifying ARDS Development and Predicting Deterioration in Critically Ill Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-03 11:34:37","doi":"10.21203/rs.3.rs-4517003/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6cf174f9-8156-46d4-84aa-d2373919b890","owner":[],"postedDate":"September 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-05T11:39:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-03 11:34:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4517003","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4517003","identity":"rs-4517003","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}