Objective:To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.Methods:The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-Ⅳ v2.0 (MIMIC-Ⅳ v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA).Results:A total of 3?295 patients with septic shock were included, with 2?164 surviving and 1?131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2?307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95% CI) was 0.795-0.849], 0.823 (95% CI was 0.796-0.849), and 0.810 (95% CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95% CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95% CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca 2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score Ⅲ (SAPSⅢ), the highest WBC, acute physiology score Ⅲ (APSⅢ), the lowest Na +, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95% CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APSⅢ score, SAPSⅢ score, and sequential organ failure assessment (SOFA) score] with AUC (95% CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. Conclusions:The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca 2+, Hb, WBC, SAPSⅢ score, APSⅢ score, Na +, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.

ObjectiveThis study aims to investigate the effects of Linggui Zhugantang （LGZGT） on ventricular remodeling （VR） in mice with myocardial infarction （MI） and its impact on the Ras homologgene A （RhoA）/Rho-associated coiled-coil forming protein kinase （ROCK） signaling pathway. MethodsThe MI model of mice was established by ligating the left anterior descending coronary artery （LAD）. They were divided into the sham-operated group， the model group， the low-dose， medium-dose， and high-dose groups of LGZGT （2.34， 4.68， 9.36 g·kg^-1）， and the captopril group （3.25 mg·kg^-1）， with 10 mice in each group. After four weeks of continuous drug administration by gavage， the level of cardiac function in each group of mice was examined using small animal Doppler ultrasound. Hematoxylin-eosin （HE） staining and Masson staining was used to assess the morphological changes of myocardial tissue and calculate the rate of collagen fiber deposition in mouse myocardial tissue. Wheat germ agglutinin （WGA） staining was employed to compare the cross-sectional area of cardiomyocytes in each group of mice. The expression levels of α-smooth muscle actin （α-SMA）， matrix metalloproteinase-2 （MMP-2）， type Ⅰcollagen （Col Ⅰ）， Col Ⅲ， tissue inhibitor of metalloproteinase 1（TIMP1）， B-cell lymphoma-2 （Bcl-2）-associated X protein （Bax）， Bcl-2， Caspase-3， and cleaved Caspase-3 were detected by Western blot. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to evaluate the mRNA levels of the pathway-related genes RhoA， ROCK1， and ROCK2. The protein expression levels of RhoA， ROCK1， and ROCK2 were tested by Western blot. ResultsThe level of cardiac function was markedly declined in the model group compared to the sham-operated group（P<0.01）. Myocardial tissue morphology changed significantly. The cross-sectional area of cardiomyocytes was significantly enlarged. The expression of α-SMA， MMP-2， Col Ⅰ， and Col Ⅲ was significantly upregulated（P<0.01）， and TIMP1 protein expression was significantly reduced（P<0.01）. The expressions of apoptosis-related proteins Bax were significantly up-regulated（P<0.01）， while the expression of Bcl-2 protein was significantly decreased（P<0.01）. The mRNA expression of RhoA， ROCK1， and ROCK2 were significantly upregulated （P<0.01）. Compared to the model group， the low-dose， medium-dose， and high-dose groups of LGZGT and the captopril group significantly reversed the experimental results of the model group in a dose-dependent manner （P<0.05， P<0.01）. ConclusionLGZGT significantly attenuated myocardial fibrosis， myocardial hypertrophy， and cardiomyocyte apoptosis after MI in mice and effectively reversed VR， the mechanism of which may be related to the modulation of the RhoA/ROCK signaling pathway.

ObjectiveThis study aims to investigate the effects of Linggui Zhugantang （LGZGT） on ventricular remodeling （VR） in mice with myocardial infarction （MI） and its impact on the Ras homologgene A （RhoA）/Rho-associated coiled-coil forming protein kinase （ROCK） signaling pathway. MethodsThe MI model of mice was established by ligating the left anterior descending coronary artery （LAD）. They were divided into the sham-operated group， the model group， the low-dose， medium-dose， and high-dose groups of LGZGT （2.34， 4.68， 9.36 g·kg^-1）， and the captopril group （3.25 mg·kg^-1）， with 10 mice in each group. After four weeks of continuous drug administration by gavage， the level of cardiac function in each group of mice was examined using small animal Doppler ultrasound. Hematoxylin-eosin （HE） staining and Masson staining was used to assess the morphological changes of myocardial tissue and calculate the rate of collagen fiber deposition in mouse myocardial tissue. Wheat germ agglutinin （WGA） staining was employed to compare the cross-sectional area of cardiomyocytes in each group of mice. The expression levels of α-smooth muscle actin （α-SMA）， matrix metalloproteinase-2 （MMP-2）， type Ⅰcollagen （Col Ⅰ）， Col Ⅲ， tissue inhibitor of metalloproteinase 1（TIMP1）， B-cell lymphoma-2 （Bcl-2）-associated X protein （Bax）， Bcl-2， Caspase-3， and cleaved Caspase-3 were detected by Western blot. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to evaluate the mRNA levels of the pathway-related genes RhoA， ROCK1， and ROCK2. The protein expression levels of RhoA， ROCK1， and ROCK2 were tested by Western blot. ResultsThe level of cardiac function was markedly declined in the model group compared to the sham-operated group（P<0.01）. Myocardial tissue morphology changed significantly. The cross-sectional area of cardiomyocytes was significantly enlarged. The expression of α-SMA， MMP-2， Col Ⅰ， and Col Ⅲ was significantly upregulated（P<0.01）， and TIMP1 protein expression was significantly reduced（P<0.01）. The expressions of apoptosis-related proteins Bax were significantly up-regulated（P<0.01）， while the expression of Bcl-2 protein was significantly decreased（P<0.01）. The mRNA expression of RhoA， ROCK1， and ROCK2 were significantly upregulated （P<0.01）. Compared to the model group， the low-dose， medium-dose， and high-dose groups of LGZGT and the captopril group significantly reversed the experimental results of the model group in a dose-dependent manner （P<0.05， P<0.01）. ConclusionLGZGT significantly attenuated myocardial fibrosis， myocardial hypertrophy， and cardiomyocyte apoptosis after MI in mice and effectively reversed VR， the mechanism of which may be related to the modulation of the RhoA/ROCK signaling pathway.

Hemodynamics plays a vital role in the development and progression of cardiovascular diseases, and is closely associated with changes in morphology and function. Reliable detection of hemodynamic changes is essential to improve treatment strategies and enhance patient prognosis. The combination of computational fluid dynamics with cardiovascular imaging technology has extended the accessibility of hemodynamics. This review provides a comprehensive summary of recent developments in the application of computational fluid dynamics for cardiovascular hemodynamic assessment and a succinct discussion for potential future development.