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Background Acute respiratory distress syndrome (ARDS) is a prevalent complication among critically ill patients, constituting around 10% of intensive care unit (ICU) admissions and mortality rates ranging from 35 to 46%. Hence, early recognition and prediction of ARDS are crucial for the timely administration of targeted treatment. However, ARDS is frequently underdiagnosed or delayed, and its heterogeneity diminishes the clinical utility of ARDS biomarkers. This study aimed to observe the incidence of ARDS among high-risk patients and develop and validate an ARDS prediction model using machine learning (ML) techniques based on clinical parameters. Methods This prospective cohort study in China was conducted on critically ill patients to derivate and validate the prediction model. The derivation cohort, consisting of 400 patients admitted to the ICU of the Peking University Third Hospital(PUTH) between December 2020 and August 2023, was separated for training and internal validation, and an external data set of 160 patients at the FU YANG People's Hospital from August 2022 to August 2023 was employed for external validation. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to screen predictor variables. Multiple ML classification models were integrated to analyze and identify the best models. Several evaluation indexes were used to compare the model performance, including the area under the receiver-operating-characteristic curve (AUC) and decision curve analysis (DCA). SHapley Additive ex Planations (SHAP) is used to interpret ML models. Results 400 critically ill patients were included in the analysis, with 117 developing ARDS during follow-up. The final model included gender, Lung Injury Prediction Score (LIPS), Hepatic Disease, Shock, and combined Lung Contusion. Based on the AUC and DCA in the validation group, the logistic model demonstrated excellent performance, achieving an AUC of 0.836 (95% CI: 0.762–0.910). For external validation, comprising 160 patients, 44 of whom developed ARDS, the AUC was 0.799 (95% CI: 0.723–0.875), significantly outperforming the LIPS score alone. Conclusion Combining the LIPS score with other clinical parameters in a logistic regression model provides a more accurate, clinically applicable, and user-friendly ARDS prediction tool than the LIPS score alone.

Objective. To explore and validate the value of clinical parameters combined with plasma biomarkers for predicting acute respiratory distress syndrome (ARDS) in patients of high risks in the surgical intensive care unit (SICU). Materials and Methods. We conducted a prospective, observational study from January 2020 to December 2023, which enrolled 263 patients of high risks in the SICU of Peking University Third Hospital consecutively; they were classified as ARDS and non-ARDS according to whether ARDS occurred after enrollment. Collected clinical characteristics and blood samples within 24 hr of admission to SICU. Blood samples from the first day to the seventh day of SICU were collected from patients without ARDS, and patients with ARDS were collected until 1 day after ARDS onset, forming data based on time series. ELISA and CBA were used to measure plasma biomarkers. Endpoint of the study was the onset of ARDS. Cox proportional hazard regression analysis was used to find independent risk factors of the onset of ARDS, then constructed a nomogram and tested its goodness-of-fit. Results. About 84 of 263 patients ended with ARDS. Univariate analysis found 15 risk factors showed differences between ARDS and non-ARDS, namely, interleukin 6, interleukin 8 (IL-8), angiopoietin Ⅱ, LIPS, APACHEⅡ, SOFA, PaO2/FiO2, age, sex, shock, sepsis, acute abdomen, pulmonary contusion, pneumonia, hepatic dysfunction. We included factors with p <0.2 in multivariate analysis and showed LIPS, PaO2/FiO2, IL-8, and receptor for advanced glycation end-products (RAGE) of the first day were independent risk factors for ARDS in SICU, a model combining them was good in predicting ARDS (C-index was 0.864 in total patients of high risks). The median of the C-index was 0.865, showed by fivefold cross-validation in the train cohort or validation cohort. The calibration curve shows an agreement between the probability of predicting ARDS and the actual probability of occurrence. Decision curve analysis indicated that the model had clinical use value. We constructed a nomogram that had the ability to predict ARDS in patients of high risks in SICU. Conclusions. LIPS, PaO2/FiO2, plasma IL-8, and RAGE of the first day were independent risk factors of the onset of ARDS. The predictive ability for ARDS can be greatly improved when combining clinical parameters and plasma biomarkers.

The aim of the current study was to explore the association between urinary glycosaminoglycans and the onset of acute respiratory distress syndrome (ARDS) in sepsis patients. We conducted a prospective cohort study of 49 sepsis patients and collected clinical characteristics, urine, and blood samples within 24 h of admission to the Surgical Intensive Care Unit (SICU) of Peking University Third Hospital. Samples from patients who did not develop ARDS were collected from day 1 to day 7 after SICU admission, while samples from patients who developed ARDS were collected from day 1 to the day of onset, forming time-series data. A liquid chromatography-mass spectrometry (LC-MS) system was used to determine urinary dermatan sulfate (DS), chondroitin sulfate (CS), and heparan sulfate (HS) concentrations; enzyme-linked immunosorbent assay (ELISA) and cytometric bead array (CBA) were used to measure plasma inflammatory factors. The endpoint was the onset of ARDS. We used generalized estimating equations to identify associations between urinary glycosaminoglycans and ARDS onset in sepsis patients. Twenty-two of the 49 sepsis patients developed ARDS, and 132 plasma samples and 132 urine samples were obtained. Between the ARDS and non-ARDS groups, sex, pneumonia, Acute Physiology and Chronic Health Evaluation II (APACHE II), Lung Injury Prediction Score (LIPS), and PaO 2 /FiO 2 showed statistically significant differences. Generalized estimating equations showed that DS and tumor necrosis factor-α (TNF-α) were independently associated with ARDS onset in sepsis patients, but their generalizability across ARDS subtypes warrants further validation.

IntroductionCatecholamines such as norepinephrine or epinephrine have been reported to participate in the development of acute respiratory distress syndrome (ARDS) by activating adrenergic receptors (ARs). But the role of α1-AR in this process has yet to be elucidated.MethodsIn this study, ARDS mouse model was induced by intratracheal instillation of lipopolysaccharide. After treatment with α1-AR agonist phenylephrine or antagonist prazosin, lung pathological injury, alveolar barrier disruption and inflammation, and haemodynamic changes were evaluated. Cytokine levels and cell viability of alveolar macrophages were measured in vitro. Nuclear factor κB (NF-κB), mitogen-activated protein kinase, and Akt signalling pathways were analysed by western blot.ResultsIt showed that α1-AR activation alleviated lung injuries, including reduced histopathological damage, cytokine expression, and inflammatory cell infiltration, and improved alveolar capillary barrier integrity of ARDS mice without influencing cardiovascular haemodynamics. In vitro experiments suggested that α1-AR stimulation inhibited secretion of TNF-α, IL-6, CXCL2/MIP-2, and promoted IL-10 secretion, but did not affect cell viability. Moreover, α1-AR stimulation inhibited NF-κB and enhanced ERK1/2 activation without significantly influencing p38, JNK, or Akt activation.DiscussionOur studies reveal that α1-AR stimulation could ameliorate lipopolysaccharide-induced lung injury by inhibiting NF-κB and promoting ERK1/2 to suppress excessive inflammatory responses of alveolar macrophages.

Background Norepinephrine (NE) has been reported to not only regulate cardiovascular activity but also influence inflammatory responses. But its role in sepsis-associated acute kidney injury (SA-AKI) has yet to be elucidated. Methods SA-AKI mouse model was induced by intraperitoneally instillation of lipopolysaccharide. After treatment with NE, haemodynamic changes, kidney pathological injury, kidney funcion, systemic and kidney inflammatory responses, were evaluated. Cytokine levels and nuclear factor κB (NF-κB), MAPKs and Akt signalling pathways of macrophages were measured in vitro. Intercellular tight junction and cell viability were detected to evaluate the effect of macrophage inflammatory response on renal tubular epithelium. Results NE enhanced cardiovascular haemodynamics, alleviated kidney histopathological injury and inflammatory cell infiltration, lowered the levels of kidney cytokines, NGAL, and KIM-1, as well as serum cytokines in SA-AKI mice. In vitro experiments indicated that NE suppressed the secretion of TNF-α, IL-6, and enhanced IL-10 secretion via a mechanism that involves inhibiting NF-κB rather than MAPKs or Akt activation of macrophage, and further alleviated the damage of renal tubule epithelium. Conclusion NE ameliorated SA-AKI via a mechanism that involves inhibiting NF-κB activation to suppress excessive inflammatory responses of macrophages, and improve the injury of renal tubule epithelium, on its basis of improvement of hemodynamic changes in septic mice. Clinical trial number Not applicable.

Malignant hyperthermia (MH) is a hypermetabolic syndrome with high mortality rates. Early detection and prompt intravenous administration of dantrolene are crucial for effective management of MH. However, there is currently a lack of comprehensive nationwide surveys on the availability of dantrolene and anesthesiologists' understanding of MH in China. A nationwide survey was conducted between January 2022 and June 2022. Online questionnaires on the cognition of MH among anesthesiologists in China were sent through social platforms to anesthesiologists in mainland China. Data regarding participants' perception of MH-related knowledge, availability of domestic dantrolene, and reported MH cases were collected in this study. Responses were collected from a total of 11,354 anesthesiologists representing 31 provinces across the Chinese mainland. Among the 11 scoring questions, the highest accuracy rates were observed for the question regarding therapeutic drugs for MH (99.3%) and the characteristics of MH (98.0%). Conversely, the question pertaining to the earliest clinical signs of MH had the lowest accuracy rate (23.5%). Significant variations were observed in the scores among different professional titles ( =0.003), academic degree ( <0.001), hospital classification ( <0.001), and urban hierarchy ( <0.001). Of the respondents, 919 (8.1%) anesthesiologists reported dantrolene availability in their hospitals, and 631 (5.6%) indicated unclear. A total of 136 hospitals in this survey reported at least one previous case of MH. Mainland China faces challenges such as insufficient experience in diagnosing and treating MH, as well as difficulty in obtaining dantrolene. To improve the public awareness of MH, it is imperative to establish and promote a refined MH training system. Additionally, a streamlined and rapid dantrolene linkage emergency system should be implemented to ensure prompt access to the drug.

Acute lung injury (ALI) is characterized by exaggerated inflammatory reactions and high clinical mortality rates, but targeted therapeutic interventions are lacking. Warifteine, which is a traditional remedy known for its antiinflammatory properties, has been identified, but its potential effects on ALI remain unexplored. In this study, a murine model of ALI was established by injecting LPS into wild-type (WT) or neutrophil-specific Gsdmd (Gasdermin D)–deficient mice. Pulmonary function was evaluated, and lung samples were collected for immunofluorescence staining and RNA sequencing analysis. Additionally, live imaging of the lungs as well as histological, biochemical, and molecular investigations were performed to assess the progression of LPS-induced ALI in mice. Mouse bone marrow–derived neutrophils were isolated and cultured to investigate the effects of warifteine in vitro. Our findings indicate that warifteine effectively mitigates LPS-induced lung pathology and dysfunction in mice. Mechanistic studies revealed that warifteine protects against ALI by inhibiting neutrophil extracellular trap formation and the resulting cytokine storm. Subsequent research demonstrated that warifteine influences neutrophil extracellular trap formation by inhibiting GSDMD oligomerization through the regulation of reactive oxygen species production by neutrophils. Collectively, these findings reveal a novel role for warifteine in the protection against ALI and suggest that modulating GSDMD oligomerization may be an innovative therapeutic strategy.

Acute lung injury (ALI) is characterized by exaggerated inflammatory reactions and high clinical mortality rates, but targeted therapeutic interventions are lacking. Warifteine, which is a traditional remedy known for its anti-inflammatory properties, has been identified, but its potential effects on ALI remain unexplored. In this study, a murine model of ALI was established by injecting lipopolysaccharide (LPS) into wild-type or neutrophil-specific Gasdermin D (Gsdmd)-deficient mice. Pulmonary function was evaluated, and lung samples were collected for immunofluorescence staining and RNA sequencing analysis. Additionally, live imaging of the lungs as well as histological, biochemical, and molecular investigations were performed to assess the progression of LPS-induced ALI in mice. Mouse bone marrow-derived neutrophils were isolated and cultured to investigate the effects of warifteine in vitro. Our findings indicate that warifteine effectively mitigates LPS-induced lung pathology and dysfunction in mice. Mechanistic studies revealed that warifteine protects against ALI by inhibiting neutrophil extracellular trap (NET) formation and the resulting cytokine storm. Subsequent research demonstrated that warifteine influences NET formation by inhibiting GSDMD oligomerization through the regulation of reactive oxygen species (ROS) production by neutrophils. Collectively, these findings reveal a novel role for warifteine in the protection against ALI and suggest that modulating GSDMD oligomerization may be an innovative therapeutic strategy.Acute lung injury (ALI) is characterized by exaggerated inflammatory reactions and high clinical mortality rates, but targeted therapeutic interventions are lacking. Warifteine, which is a traditional remedy known for its anti-inflammatory properties, has been identified, but its potential effects on ALI remain unexplored. In this study, a murine model of ALI was established by injecting lipopolysaccharide (LPS) into wild-type or neutrophil-specific Gasdermin D (Gsdmd)-deficient mice. Pulmonary function was evaluated, and lung samples were collected for immunofluorescence staining and RNA sequencing analysis. Additionally, live imaging of the lungs as well as histological, biochemical, and molecular investigations were performed to assess the progression of LPS-induced ALI in mice. Mouse bone marrow-derived neutrophils were isolated and cultured to investigate the effects of warifteine in vitro. Our findings indicate that warifteine effectively mitigates LPS-induced lung pathology and dysfunction in mice. Mechanistic studies revealed that warifteine protects against ALI by inhibiting neutrophil extracellular trap (NET) formation and the resulting cytokine storm. Subsequent research demonstrated that warifteine influences NET formation by inhibiting GSDMD oligomerization through the regulation of reactive oxygen species (ROS) production by neutrophils. Collectively, these findings reveal a novel role for warifteine in the protection against ALI and suggest that modulating GSDMD oligomerization may be an innovative therapeutic strategy.

Objective. To explore and validate the value of clinical parameters combined with plasma biomarkers for predicting acute respiratory distress syndrome (ARDS) in patients of high risks in the surgical intensive care unit (SICU). Materials and Methods. We conducted a prospective, observational study from January 2020 to December 2023, which enrolled 263 patients of high risks in the SICU of Peking University Third Hospital consecutively; they were classified as ARDS and non-ARDS according to whether ARDS occurred after enrollment. Collected clinical characteristics and blood samples within 24hr of admission to SICU. Blood samples from the first day to the seventh day of SICU were collected from patients without ARDS, and patients with ARDS were collected until 1 day after ARDS onset, forming data based on time series. ELISA and CBA were used to measure plasma biomarkers. Endpoint of the study was the onset of ARDS. Cox proportional hazard regression analysis was used to find independent risk factors of the onset of ARDS, then constructed a nomogram and tested its goodness-of-fit. Results. About 84 of 263 patients ended with ARDS. Univariate analysis found 15 risk factors showed differences between ARDS and non-ARDS, namely, interleukin 6, interleukin 8 (IL-8), angiopoietin Ⅱ, LIPS, APACHEⅡ, SOFA, PaO[sub.2]/FiO[sub.2], age, sex, shock, sepsis, acute abdomen, pulmonary contusion, pneumonia, hepatic dysfunction. We included factors with p<0.2 in multivariate analysis and showed LIPS, PaO[sub.2]/FiO[sub.2], IL-8, and receptor for advanced glycation end-products (RAGE) of the first day were independent risk factors for ARDS in SICU, a model combining them was good in predicting ARDS (C-index was 0.864 in total patients of high risks). The median of the C-index was 0.865, showed by fivefold cross-validation in the train cohort or validation cohort. The calibration curve shows an agreement between the probability of predicting ARDS and the actual probability of occurrence. Decision curve analysis indicated that the model had clinical use value. We constructed a nomogram that had the ability to predict ARDS in patients of high risks in SICU. Conclusions. LIPS, PaO[sub.2]/FiO[sub.2], plasma IL-8, and RAGE of the first day were independent risk factors of the onset of ARDS. The predictive ability for ARDS can be greatly improved when combining clinical parameters and plasma biomarkers.

Objective . To explore and validate the value of clinical parameters combined with plasma biomarkers for predicting acute respiratory distress syndrome (ARDS) in patients of high risks in the surgical intensive care unit (SICU). Materials and Methods . We conducted a prospective, observational study from January 2020 to December 2023, which enrolled 263 patients of high risks in the SICU of Peking University Third Hospital consecutively; they were classified as ARDS and non‐ARDS according to whether ARDS occurred after enrollment. Collected clinical characteristics and blood samples within 24 hr of admission to SICU. Blood samples from the first day to the seventh day of SICU were collected from patients without ARDS, and patients with ARDS were collected until 1 day after ARDS onset, forming data based on time series. ELISA and CBA were used to measure plasma biomarkers. Endpoint of the study was the onset of ARDS. Cox proportional hazard regression analysis was used to find independent risk factors of the onset of ARDS, then constructed a nomogram and tested its goodness‐of‐fit. Results . About 84 of 263 patients ended with ARDS. Univariate analysis found 15 risk factors showed differences between ARDS and non‐ARDS, namely, interleukin 6, interleukin 8 (IL‐8), angiopoietin Ⅱ, LIPS, APACHEⅡ, SOFA, PaO 2 /FiO 2 , age, sex, shock, sepsis, acute abdomen, pulmonary contusion, pneumonia, hepatic dysfunction. We included factors with p   < 0.2 in multivariate analysis and showed LIPS, PaO 2 /FiO 2 , IL‐8, and receptor for advanced glycation end‐products (RAGE) of the first day were independent risk factors for ARDS in SICU, a model combining them was good in predicting ARDS (C‐index was 0.864 in total patients of high risks). The median of the C‐index was 0.865, showed by fivefold cross‐validation in the train cohort or validation cohort. The calibration curve shows an agreement between the probability of predicting ARDS and the actual probability of occurrence. Decision curve analysis indicated that the model had clinical use value. We constructed a nomogram that had the ability to predict ARDS in patients of high risks in SICU. Conclusions . LIPS, PaO 2 /FiO 2 , plasma IL‐8, and RAGE of the first day were independent risk factors of the onset of ARDS. The predictive ability for ARDS can be greatly improved when combining clinical parameters and plasma biomarkers.