Aim To investigate the role and regulatory mechanism of CREB regulated transcription coactivator 2(CRTC2)in cardiomyocyte hypertrophy.Methods A pathological cardiomyocyte hypertrophy model was established in C57BL/6 mice by intraperitoneal injection of isoproterenol(ISO),the expression of CRTC2 in cardiac tissue was detec-ted by Western blot.The CRTC2 knockout mice model was constructed,the cardiac function of mice was detected by small animal echocardiography,the collagen fiber content in mice cardiac tissue was detected by Masson staining,the car-diomyocyte hypertrophy related proteins:skeletal muscle α1-actin(ACTA1)and brain natriuretic peptide(BNP),as well as ferroptosis related proteins:acyl-CoA synthetase long chain family member 4(ACSL4),solute carrier family 7 member 11(SLC7A11)and glutathione peroxidase 4(GPX4)in mice cardiac tissue were detected by Western blot,the iron ion content in mice cardiac tissue was detected by iron ion kit,to evaluate the correlation between CRTC2 and cardiomyocyte hypertrophy and ferroptosis.H9c2 cells were induced by ISO to construct an in vitro model of cardiomyocyte hypertrophy,the protein expressions of CRTC2,ACTA1,BNP,ACSL4,SLC7A11 and GPX4 were detected after intervention with fer-roptosis inhibitor ferrostatin-1(Fer-1).H9c2 cells with CRTC2 overexpression induced by ISO were used to construct an in vitro model of cardiomyocyte hypertrophy,the related indicators of cardiomyocyte hypertrophy and ferroptosis were detec-ted to explore the mechanism of CRTC2 in cardiomyocyte hypertrophy.Results Compared with the control group,the expression of CRTC2 protein in the cardiac tissue of ISO induced cardiomyocyte hypertrophy mice was increased(P＜0.05).Compared with wild-type mice,CRTC2-/-mice showed worsened cardiac function,manifested as increased left ventricular end-diastolic diameter(LVEDD),left ventricular end-systolic diameter(LVESD),left ventricular posterior wall thickness(LVPWT),heart weight/tibia length(HW/TL)and heart weight/body weight(HW/BW),decreased short axis shortening(FS)and ejection fraction(EF),increased collagen fiber content in cardiac tissue,upregulated ex-pression of cardiomyocyte hypertrophy-related proteins ACTA1 and BNP,increased mRNA and protein expression of ferrop-tosis-related protein ACSL4,decreased mRNA and protein expression of SLC7A11 and GPX4,and elevated iron ion content in cardiac tissue(P＜0.05 or P＜0.01).In vitro experiments showed that compared with ISO group,the ISO+Fer-1 group had no significant change in CRTC2 protein expression(P＞0.05),the expression of ACTA1 and BNP protein decreased,the surface area of cardiomyocyte reduced,the expression of ACSL4 protein decreased,and the expression of SLC7A11 and GPX4 proteins increased(P＜0.05 or P＜0.01).Compared with the ISO group,the LV-CRTC2+ISO group showed a decrease in surface area of cardiomyocytes(P＜0.01),a decrease in ACTA1,BNP and ACSL4 protein ex-pression,an increase in SLC7A11 and GPX4 protein expression,and a decrease in ROS and iron ion content(P＜0.05 or P＜0.01).Conclusion CRTC2 alleviates cardiomyocyte hypertrophy and protect cardiac function by suppressing fer-roptosis in cardiomyocytes.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Aim To investigate the role and regulatory mechanism of CREB regulated transcription coactivator 2(CRTC2)in cardiomyocyte hypertrophy.Methods A pathological cardiomyocyte hypertrophy model was established in C57BL/6 mice by intraperitoneal injection of isoproterenol(ISO),the expression of CRTC2 in cardiac tissue was detec-ted by Western blot.The CRTC2 knockout mice model was constructed,the cardiac function of mice was detected by small animal echocardiography,the collagen fiber content in mice cardiac tissue was detected by Masson staining,the car-diomyocyte hypertrophy related proteins:skeletal muscle α1-actin(ACTA1)and brain natriuretic peptide(BNP),as well as ferroptosis related proteins:acyl-CoA synthetase long chain family member 4(ACSL4),solute carrier family 7 member 11(SLC7A11)and glutathione peroxidase 4(GPX4)in mice cardiac tissue were detected by Western blot,the iron ion content in mice cardiac tissue was detected by iron ion kit,to evaluate the correlation between CRTC2 and cardiomyocyte hypertrophy and ferroptosis.H9c2 cells were induced by ISO to construct an in vitro model of cardiomyocyte hypertrophy,the protein expressions of CRTC2,ACTA1,BNP,ACSL4,SLC7A11 and GPX4 were detected after intervention with fer-roptosis inhibitor ferrostatin-1(Fer-1).H9c2 cells with CRTC2 overexpression induced by ISO were used to construct an in vitro model of cardiomyocyte hypertrophy,the related indicators of cardiomyocyte hypertrophy and ferroptosis were detec-ted to explore the mechanism of CRTC2 in cardiomyocyte hypertrophy.Results Compared with the control group,the expression of CRTC2 protein in the cardiac tissue of ISO induced cardiomyocyte hypertrophy mice was increased(P＜0.05).Compared with wild-type mice,CRTC2-/-mice showed worsened cardiac function,manifested as increased left ventricular end-diastolic diameter(LVEDD),left ventricular end-systolic diameter(LVESD),left ventricular posterior wall thickness(LVPWT),heart weight/tibia length(HW/TL)and heart weight/body weight(HW/BW),decreased short axis shortening(FS)and ejection fraction(EF),increased collagen fiber content in cardiac tissue,upregulated ex-pression of cardiomyocyte hypertrophy-related proteins ACTA1 and BNP,increased mRNA and protein expression of ferrop-tosis-related protein ACSL4,decreased mRNA and protein expression of SLC7A11 and GPX4,and elevated iron ion content in cardiac tissue(P＜0.05 or P＜0.01).In vitro experiments showed that compared with ISO group,the ISO+Fer-1 group had no significant change in CRTC2 protein expression(P＞0.05),the expression of ACTA1 and BNP protein decreased,the surface area of cardiomyocyte reduced,the expression of ACSL4 protein decreased,and the expression of SLC7A11 and GPX4 proteins increased(P＜0.05 or P＜0.01).Compared with the ISO group,the LV-CRTC2+ISO group showed a decrease in surface area of cardiomyocytes(P＜0.01),a decrease in ACTA1,BNP and ACSL4 protein ex-pression,an increase in SLC7A11 and GPX4 protein expression,and a decrease in ROS and iron ion content(P＜0.05 or P＜0.01).Conclusion CRTC2 alleviates cardiomyocyte hypertrophy and protect cardiac function by suppressing fer-roptosis in cardiomyocytes.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To explore the effect of health management based on constitution identification in perimenopausal women.Methods:From November 2020 to April 2021, 244 perimenopausal women from Doumen Street, Yuecheng District, Shaoxing were selected as the research subject by convenience sampling method, and divided into the control group (121 cases) and the observation group (123 cases) according to their respective communities. The control group adopted routine health management, while the observation group implemented health management based on constitution identification on the basis of routine health management. Three months after intervention, the perimenopausal symptom scores between the two groups were compared. One year after intervention, the number of individuals with biased constitution and quality of life scores between the two groups were compared.Results:Three months after intervention, the scores of hot flashes, sweating, insomnia, dizziness, fatigue, muscle and joint pain, headache, palpitations, and total score of perimenopausal symptom of the observation group were lower than those of the control group, with statistically significant differences ( P<0.05). Three months after intervention, there were no significant differences in scores of paraesthesia, mood swings, depressive suspicion, skin ant sensation, sexual difficulties, urinary tract infection between the observation group and the control group ( P>0.05). One year after intervention, the number of individuals with biased constitution of the observation group was less than that of the control group, and the difference was statistically significant ( P<0.05). One year after intervention, the scores of physical health, mental health, social relationship, and total score of quality of life of the observation group were higher than those of the control group, and the differences were statistically significant ( P<0.05). One year after intervention, there was no statistically significant difference in the score of the surrounding environment between the observation group and the control group ( P>0.05) . Conclusions:Health management based on constitution identification can improve the perimenopausal symptoms of perimenopausal women, gradually change their biased constitution towards a calm constitution, and improve their quality of life.

Objective To study the relationship between X-linked inhibitor of apoptosis protein (XIAP) expression and transitional cell carcinoma(TCC) development. Methods Forty-three TCC tissues and 12 normal transitional epithelial tissues were applied to detect XIAP expression by semi-quantitative RT-PCR, immunohistochemistry and western blot. The data were statistically analyzed by using SPSS11.5 according to the 2 groups (TCC and normal transitional epithelial) as well as the dif-ferent subgroups (tumor stage, grade, single or multiple tumor, primary or recurrence tumor). Results XIAP expression in TCC tissues was higher than in normal transitional epithelial tissues(im-munohistochemistry: 22±5 and 16±2, Western blot:1.21±0. 15 and 0. 61±0.24, mRNA: 1.17± 0. 30 and 0. 75±0. 17, P<0. 05). In the bladder tumors group, XIAP expression in recurrence tumors was higher than in primary tumors(immunohistochemistry: 24±3 and 20±3, Western blot: 1.66±0.28 and 1.10±0. 23, mRNA: 1.44±0. 27 and 1.05±0. 23, P<0. 05). However, there were no significant differences according to the tumor stage and tumor grade as well as tumor multi-plicity or not. Conclusion XIAP expression might serve as a biomarker in TCC diagnosis and recur-rence prediction.