Objective To investigate the effect and mechanisms of celecoxib on right heart function in mice with acute high-altitude hypoxia exposure.Methods Male C57BL/6J mice(7 weeks old)were housed in a hypobaric chamber simulating an altitude of 5 800 m for 2 d to establish an animal model of acute hypobaric hypoxia.①Eighteen mice were randomly assigned to plain+saline(P+S),high-altitude hypoxia exposure+saline(H+S),and high-altitude hypoxia exposure+celecoxib(H+Cel).Body weight and routine blood indicators were measured,and cardiac ultrasound examination were performed for heart rate(HR),pulmonary artery acceleration time to ejection time ratio(AT/ET),tricuspid annular plane systolic excursion(TAPSE),tricuspid annular systolic velocity(S'),and left ventricular ejection fraction(LVEF)and fractional shortening(FS).Targeted metabolomic profiling was applied to detect the cardiac arachidonic acid(AA)metabolite levels.The contents of 12,13-dihydroxy-9Z-octadecenoic acid(12,13-diHOME)in the heart,liver,brown adipose tissue,and plasma were quantified by ELISA.② Eighteen mice were randomly assigned into plain+saline(P+S),high-altitude hypoxia exposure+saline(H+S)and high-altitude hypoxia exposure+12,13-diHOME(H+di)groups.Body weight,routine blood tests,and echocardiography were performed as above.③ Thirty-two mice were randomly divided into high-altitude hypoxia exposure+saline(H+S),high-altitude hypoxia exposure+celecoxib(H+Cel),high-altitude hypoxia exposure+soluble epoxide hydrolase inhibitor(sEHI)(H+sEHI),and high-altitude hypoxia exposure+sEHI+celecoxib(H+sEHI+Cel)groups.Body weight,routine blood tests,and echocardiography were performed as above.Cardiac and plasma contents of 12,13-diHOME and epoxyeicosatrienoic acids(EETs)were measured by ELISA.Results ① Compared to the P+S group,the H+S group exhibited significantly reduction of cardiac 12,13-diHOME level(P<0.001),increased counts of white blood cells(WBC)and neutrophils(P<0.01)and decreased TAPSE,S'and AT/ET both at resting state and under stress(P<0.01,P<0.001).Compared to the H+S group,the H+Cel group exhibited significantly increase of cardiac 12,13-diHOME level(P<0.05),reduced WBC and lymphocyte counts(P<0.01,P<0.05)and improved TAPSE and S'levels at resting state and under stress(P<0.01,P<0.001).② Compared to the H+S group,the H+di group demonstrated significantly improvement of TAPSE at basal and under stress(P<0.001)and a trend towards improved TAPSE at resting state(P=0.0532),but no obvious differences was observed in WBC and neutrophil counts between the H+di group and the H+S group.③ Compared to the H+Cel group,both the H+sEHI and H+sEHI+Cel groups exhibited significantly reduction of cardiac 12,13-diHOME level(P<0.01,P<0.05)though no statistical changes in cardiac function indicators.Compared to the H+S group,WBC counts and lymphocyte were decreased,and serum EETs level was incrased in the H+Cel group,H+sEHI group and H+sEHI+Cel group(P<0.01,P<0.001).Conclusion Celecoxib can elevate cardiac level of 12,13-diHOME and improves right heart function in mice after acute high-altitude hypoxia exposure through the CYP450-sEH metabolic pathway.

Objective Heart failure(HF)complicated by acute kidney injury(AKI)significantly impacts patient outcomes,and it is crucial to make early predictions of short-term mortality.This study is focused on developing an interpretable machine learning model to enhance early prediction accuracy in such clinical scenarios.Methods This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care Ⅳ(MIMIC-Ⅳ,version 2.0)database.Data from the first 24 hours after admission to the ICU were extracted and divided into a training set(70％)and a validation set(30％).We utilized the SHapley Additive exPlanation(SHAP)method to interpret the workings of an extreme gradient boosting(XGBoost)model and identify key prognostic factors.The XGBoost model's predictive ability was evaluated against three other machine learning models using the area under the curve(AUC)metric,and its interpretation was enhanced using the SHAP method.Results The study included 8 028 patients with HF complicated by AKI.The XGBoost model outperformed the other models,achieving an AUC of 0.93(95％confidence interval[CI]:0.78-0.94;accuracy=0.89),while neural network model showed the worst performance(AUC=0.79,95％CI:0.77-0.82;accuracy=0.82).Decision curve analysis showed the superior net benefit of the XGBoost model within the 9％to 60％threshold probabilities.SHAP analysis was performed to identify the top 20 predictors,with age(mean SHAP value 1.29)and Glasgow Coma Scale score(mean SHAP value 1.24)emerging as significant factors.Conclusions Our interpretable model offers an enhanced ability to predict mortality risk in HF patients with AKI in ICUs.This model can be used to assist in formulating effective treatment plans and optimizing resource allocation.