Objective To evaluate the effect of glucocorticoid(GC)treatment on the prognosis of patients with severe viral pneumonia,and to screen for related influencing factors and optimal beneficiary groups,providing reference for clinical medication decisions.Methods Based on the MIMIC-Ⅳ database,eligible patients with severe viral pneumonia were screened and divided into GC group and non GC group.Baseline differences were balanced using propensity score matching(PSM).Kaplan-Meier survival curves were used to analyze the cumulative survival rate of two groups of patients at 30 d,and Cox regression models were used to evaluate the association between GC use and the 30 d mortality risk in patients.Results A total of 518 severe viral pneumonia patients were included,including 43 in the GC group and 475 in the non-GC group.After PSM,there were 43 cases in the GC group and 86 cases in the non-GC group.The Kaplan-Meier survival curves showed that the 30 d cumulative survival rate of patients in the GC group was significantly higher than that in the non-GC group(P＜0.05).The results of multivariate Cox regression analysis showed that GC treatment significantly reduced the 30 d mortality risk[HR=0.35,95％CI(0.154,0.793),P=0.012],especially for patients older than 54 years,receiving mechanical ventilation,and with acute kidney injury.GC use,age＞54 years,and acute kidney injury were independent predictors of patient mortality risk(C-index=0.718 1).Subgroup analysis showed that for specific indications(age＞54 years,mechanical ventilation,no myocardial infarction,no hypertension,no hyperlipidemia,no heart failure,complicated by acute kidney failure),GC use could effectively reduce the 30 d mortality risk.Conclusion GC could effectively improve the prognosis of severe viral pneumonia patients,but individualized patient characteristics and treatment risks need to be considered comprehensively to optimize the medication regimen.

Objective To aim at using machine learning algorithms to predict the risk of neonatal intensive care unit(ICU)mortality,providing clinicians with an early diagnosis and risk assessment tool to assist in decision-making.Methods Clinical data of preterm infants from the paediatric intensive care database retrospectively were collected.By using least absolute shrinkage and selection operator(LASSO)regression analysis and multivariate Logistic regression analysis,key clinical characteristics affecting preterm infant prognosis were screened.The study was balanced the data by using the synthetic minority oversampling technique,combined seven machine learning models to build a predictive model and evaluate its performance.The Shapley additive explanations(SHAP)was used for model interpretation.Results A total of 923 preterm infants were finally included,survival group comprised 886 infants,and death group comprised 37 infants.A total of 38 clinical characteristics were collected.LASSO screening identified 8 variables significantly associated with neonatal ICU mortality,including lactate,respiratory rate,chloride concentration,neutrophils,and red blood cell distribution width etc.Multivariate Logistic regression analysis revealed that lactate and respiratory rate were independent predictors of neonatal ICU outcomes.Internal testing and external validation showed that light gradient boosting machine model outperformed other models in terms of accuracy and precision etc.indicators.SHAP analysis indicated that respiratory rate and lactate levels had the largest predictive contribution to the risk of preterm infants mortality.Conclusion This study provides reliable tools for early identification and intervention in the prognosis of preterm infants,emphasizing the importance of key indicators.

Objective To explore the target and mechanism of Fengliao Changweikang in treating irritable bowel syndrome(IBS)based on network pharmacology and molecular docking technology.Methods The effective components of Fengliao Changweikang were screened by data mining,and the drug-target network was constructed to identify the key targets and their pathways related to IBS.The molecular docking verification of the main active ingredients and core target proteins was carried out,and the binding energy was evaluated and the results were visualized.Results Fengliao Changweikang contained 24 potential active ingredients,134 active ingredient targets,and 2353 disease-related targets.After intersecting these,70 potential target points were identified,involving 898 biological processes,96 molecular functions,and 66 cellular components.Kyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that its mechanism of action in treating IBS may involve regulating key signaling pathways such as cancer pathways,PI3K/Akt signaling pathways,and phospholipase D pathways through interactions with core targets.Molecular docking analysis indicated that the core compounds in Fengliao Changweikang exhibit good binding activity with core receptor proteins.Conclusion Fengliao Changweikang works through multiple signaling pathways and multiple target mechanisms to treat IBS.

Objective To evaluate the effect of glucocorticoid(GC)treatment on the prognosis of patients with severe viral pneumonia,and to screen for related influencing factors and optimal beneficiary groups,providing reference for clinical medication decisions.Methods Based on the MIMIC-Ⅳ database,eligible patients with severe viral pneumonia were screened and divided into GC group and non GC group.Baseline differences were balanced using propensity score matching(PSM).Kaplan-Meier survival curves were used to analyze the cumulative survival rate of two groups of patients at 30 d,and Cox regression models were used to evaluate the association between GC use and the 30 d mortality risk in patients.Results A total of 518 severe viral pneumonia patients were included,including 43 in the GC group and 475 in the non-GC group.After PSM,there were 43 cases in the GC group and 86 cases in the non-GC group.The Kaplan-Meier survival curves showed that the 30 d cumulative survival rate of patients in the GC group was significantly higher than that in the non-GC group(P＜0.05).The results of multivariate Cox regression analysis showed that GC treatment significantly reduced the 30 d mortality risk[HR=0.35,95％CI(0.154,0.793),P=0.012],especially for patients older than 54 years,receiving mechanical ventilation,and with acute kidney injury.GC use,age＞54 years,and acute kidney injury were independent predictors of patient mortality risk(C-index=0.718 1).Subgroup analysis showed that for specific indications(age＞54 years,mechanical ventilation,no myocardial infarction,no hypertension,no hyperlipidemia,no heart failure,complicated by acute kidney failure),GC use could effectively reduce the 30 d mortality risk.Conclusion GC could effectively improve the prognosis of severe viral pneumonia patients,but individualized patient characteristics and treatment risks need to be considered comprehensively to optimize the medication regimen.

Objective To aim at using machine learning algorithms to predict the risk of neonatal intensive care unit(ICU)mortality,providing clinicians with an early diagnosis and risk assessment tool to assist in decision-making.Methods Clinical data of preterm infants from the paediatric intensive care database retrospectively were collected.By using least absolute shrinkage and selection operator(LASSO)regression analysis and multivariate Logistic regression analysis,key clinical characteristics affecting preterm infant prognosis were screened.The study was balanced the data by using the synthetic minority oversampling technique,combined seven machine learning models to build a predictive model and evaluate its performance.The Shapley additive explanations(SHAP)was used for model interpretation.Results A total of 923 preterm infants were finally included,survival group comprised 886 infants,and death group comprised 37 infants.A total of 38 clinical characteristics were collected.LASSO screening identified 8 variables significantly associated with neonatal ICU mortality,including lactate,respiratory rate,chloride concentration,neutrophils,and red blood cell distribution width etc.Multivariate Logistic regression analysis revealed that lactate and respiratory rate were independent predictors of neonatal ICU outcomes.Internal testing and external validation showed that light gradient boosting machine model outperformed other models in terms of accuracy and precision etc.indicators.SHAP analysis indicated that respiratory rate and lactate levels had the largest predictive contribution to the risk of preterm infants mortality.Conclusion This study provides reliable tools for early identification and intervention in the prognosis of preterm infants,emphasizing the importance of key indicators.

Objective To explore the target and mechanism of Fengliao Changweikang in treating irritable bowel syndrome(IBS)based on network pharmacology and molecular docking technology.Methods The effective components of Fengliao Changweikang were screened by data mining,and the drug-target network was constructed to identify the key targets and their pathways related to IBS.The molecular docking verification of the main active ingredients and core target proteins was carried out,and the binding energy was evaluated and the results were visualized.Results Fengliao Changweikang contained 24 potential active ingredients,134 active ingredient targets,and 2353 disease-related targets.After intersecting these,70 potential target points were identified,involving 898 biological processes,96 molecular functions,and 66 cellular components.Kyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that its mechanism of action in treating IBS may involve regulating key signaling pathways such as cancer pathways,PI3K/Akt signaling pathways,and phospholipase D pathways through interactions with core targets.Molecular docking analysis indicated that the core compounds in Fengliao Changweikang exhibit good binding activity with core receptor proteins.Conclusion Fengliao Changweikang works through multiple signaling pathways and multiple target mechanisms to treat IBS.