Objective:To study the relationship between serum adiponectin level and early vascular aging (EVA).Methods:The cross-sectional study method was used. Six hundred and seventy-two subjects who completed health checkup from June to December 2023 in the Affiliated Hospital of Qingdao University were selected. The subjects were divided into the EVA group (237 cases) and the control group (435 cases) based on brachial-ankle pulse wave velocity (baPWV). According to the adiponectin tertiles method, the subjects were divided into low adiponectin subgroup (2.4 to 6.6 mg/L, 225 cases), medium adiponectin subgroup (6.7 to 9.1 mg/L, 227 cases) and high adiponectin subgroup (9.2 to 19.8 mg/L, 220 cases). The basic demographic information, past history and serological indexes were recorded. Univariate and multivariate binary Logistic regression analyses were used to analyze the risk factors for EVA, and multivariate Logistic regression was used to analyze the effect of adiponectin on EVA.Results:The male proportion, age, body mass index (BMI), systolic blood pressure, diastolic blood pressure, triglycerides (TG), fasting blood glucose (FBG), uric acid, glycated hemoglobin (HbA 1c), homocysteine, baPWV and alcohol history proportion in EVA group were significantly higher than those in control group: 64.98% (154/237) vs. 53.33% (232/435), 53 (47, 57) years old vs. 46 (39, 52) years old, (26.34 ± 3.37) kg/m 2 vs. (25.16 ± 3.91) kg/m 2, (132.27 ± 15.48) mmHg (1 mmHg = 0.133 kPa) vs. (117.30 ± 13.04) mmHg, (81.79 ± 11.04) mmHg vs. (71.93 ± 10.10) mmHg, 1.45 (1.03, 2.03) mmol/L vs. 1.08 (0.76, 1.65) mmol/L, 5.52 (5.03, 6.21) mmol/L vs. 5.14 (4.77, 5.56) mmol/L, (380.04 ± 96.43) μmol/L vs. (362.18 ± 94.94) μmol/L, 5.80 (5.50, 5.90)% vs. 5.70 (5.40, 5.82)%, 10.70 (9.01, 12.90) μmol/L vs. 9.96 (8.30, 12.20) μmol/L, 1 586 (1 511, 1 719) cm/s vs. 1 299 (1 215, 1 367) cm/s and 19.41% (46/237) vs. 13.56% (59/435), the adiponectin was significantly lower than that in control group: 7.00 (5.70, 8.75) mg/L vs. 8.40 (6.40, 10.60) mg/L, and there were statistical differences ( P<0.01 or <0.05). There were no statistical differences in total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), creatinine and smoking history proportion between two groups ( P>0.05). The male proportion, BMI, systolic blood pressure, diastolic blood pressure, TG, FBG, uric acid, creatinine, HbA 1c, homocysteine, EVA incidence, baPWV, smoking history proportion and alcohol history proportion in low adiponectin subgroup and medium adiponectin subgroup were significantly higher than those in high adiponectin subgroup, furthermore, the indexes except HbA 1c in low adiponectin subgroup were significantly higher than those in medium adiponectin subgroup, and there were statistical differences ( P<0.05); the HDL-C in low adiponectin subgroup and medium adiponectin subgroup was significantly lower than that in high adiponectin subgroup, furthermore, that in low adiponectin subgroup was significantly lower than that in medium adiponectin subgroup, and there were statistical differences ( P<0.05); there were no statistical differences in age, TC and LDL-C among the three subgroups ( P>0.05). Univariate binary Logistic regression analysis result showed that age, male, BMI, alcohol history, systolic blood pressure, diastolic blood pressure, TG, FBG, uric acid and HbA 1c were the risk factors for EVA ( P<0.01 or <0.05), while the adiponectin was a protective factor for EVA ( P<0.01). Multivariate binary Logistic regression analysis result showed that age, systolic blood pressure, TG and FBG were risk factors for EVA ( OR = 1.098, 1.066, 1.209 and 1.268; 95% CI 1.069 to 1.127, 1.050 to 1.082, 1.007 to 1.451 and 1.069 to 1.502; P<0.01 or <0.05), while adiponectin was a protective factor ( OR = 0.892, 95% CI 0.828 to 0.962, P<0.01). Multivariable Logistic regression analysis result showed that adiponectin consistently remained a protective factor for EVA across unadjusted, preliminary adjusted and fully adjusted covariate models ( OR = 0.553, 0.580 and 0.576; 95% CI 0.451 to 0.678, 0.440 to 0.764 and 0.435 to 0.763; P<0.01). Conclusions:The serum APN level is negatively correlated with the risk of EVA, which may be an independent protective factor for the EVA.

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 investigate the mechanism of prostaglandin E synthase 3(PTGES3)/heat shock protein 90(HSP90) in the medial prefrontal cortex regulating obesity-related cognitive dysfunction. Methods: This study consisted of clinical trials and animal experiments. In part one, obese patients scheduled for bariatric surgery, and healthy adults matching gender and age were recruited at the same time to reach 10 cases in each group. The cognitive level was assessed with trail making test part A(TMT-A) and victoria stroop tests(VST). Four-dimensional data-independent acquisition(4D-DIA) was used to screen the proteome changes in peripheral blood. In part two, forty SPF healthy male C57BL/6J mice were randomly divided into four groups: normal diet group(ND group), high fat diet induced obesity group(DIO group), DIO supplemented with the control virus group(DIO+Scramble group) and DIO supplemented with the interfering virus group(DIO+shPTGES3 group). The Morris water maze test was conducted to evaluate the cognitive behavior changes of the four groups of mice. The immunofluorescence staining was performed to detect the expression of PTGES3 and HSP90 in the medial prefrontal cortex and the activation of ionized calcium binding adapter molecule 1(IBA1)-labeled microglia. Results: In the case-control study, the cognitive function of obese patients significantly decreased, and the expression of PTGES3 in peripheral blood significantly increased, while the level of PTGES3 was negatively correlated with cognitive function. In animal experiments, compared with ND group, DIO group had significantly prolonged time reaching the target platform, otherwise, the residence time in the target quadrant was shortened in the Morris water maze test. Simultaneously, there were significant increase in the expression of PTGES3 and HSP90, and the activation of IBA1 in the medial prefrontal cortex. Compared with DIO+Scramble group, mice in the DIO+shPTGES3 group spent less time reaching the target platform, and stayed longer in the target quadrant. The expression and co-localization levels of PTGES3 and HSP90 in medial prefrontal cortex significantly decreased. The activation level of microglia cells was also attenuated by PTGES3 interference. Conclusion Obesity-related cognitive dysfunction may be attributed to PTGES3/HSP90 in the medial prefrontal cortex by mediating neural inflammation.

Objective To study the complexity of helical tomotherapy plans for different anatomical sites and explore its correlations with dosimetric verification.Methods A total of 71 complexity metrics of the helical tomotherapy plans executed by Accuray?planning system v.5.1.9.2 for nasopharyngeal carcinoma(n=52),breast cancer(n=19),cervical cancer(n=52)and central nervous system cancer(n=37)were analyzed.The correlations between metrics were analyzed,and principal component analysis was used for dimensionality reduction.The global complexity score(normalized plan complexity score,nPCS)was calculated.The top-contributing complexity metrics within the first 10 principal components were extracted,and a correlation analysis with the gamma passing rate was conducted.Results There was no significant correlation between most metrics.The global complexity score was different for different sites,with the highest median nPCS and the most complex plan in the head and neck cancer.The correlation analysis between the global complexity score and gamma passing rate revealed that in the central nervous system plans,CFNS90 and EPSTV1_0 had strong correlations with gamma passing rate for 3%/2 mm criterion,and a significant correlation was found between CFNS90 and gamma passing rate for 3%/3 mm criterion.Conclusion Complexity scores can quantize the complexity of helical tomotherapy plans for different sites,and certain metrics are correlated with gamma passing rate.

Objective:To construct a training program for epilepsy specialist nurses, so as to provide a theoretical basis for the training and development of epilepsy specialist nurses.Methods:A preliminary training program for epilepsy specialist nurses was developed based on literature review, semi-structured interview, focus group discussion and clinical practice experience. Delphi method was used to select 20 experts from several regions of China for two rounds of consultation.Results:In the two rounds of expert consultation, the effective recovery rate of the questionnaire was both 100%, the expert authority coefficient was 0.908 and 0.958, and the degree of coordination of expert opinions was 0.180 to 0.229 and 0.138 to 0.189, respectively ( P<0.05). The standardized training program for epilepsy specialist nurses was finally constructed, including 5 first-level indicators (admission conditions, general theory courses, specialty theory courses, specialty nursing practice courses, training modes and effectiveness evaluation), 15 second-level indicators and 61 third-level indicators. Conclusions:The training program constructed for epilepsy specialist nurses is scientific and reasonable, with certain reliability and practicality, which provides a theoretical basis for the training of epilepsy specialist nurses, and promotes the common development of China's epilepsy specialist nurse team.

Objective To study the complexity of helical tomotherapy plans for different anatomical sites and explore its correlations with dosimetric verification.Methods A total of 71 complexity metrics of the helical tomotherapy plans executed by Accuray?planning system v.5.1.9.2 for nasopharyngeal carcinoma(n=52),breast cancer(n=19),cervical cancer(n=52)and central nervous system cancer(n=37)were analyzed.The correlations between metrics were analyzed,and principal component analysis was used for dimensionality reduction.The global complexity score(normalized plan complexity score,nPCS)was calculated.The top-contributing complexity metrics within the first 10 principal components were extracted,and a correlation analysis with the gamma passing rate was conducted.Results There was no significant correlation between most metrics.The global complexity score was different for different sites,with the highest median nPCS and the most complex plan in the head and neck cancer.The correlation analysis between the global complexity score and gamma passing rate revealed that in the central nervous system plans,CFNS90 and EPSTV1_0 had strong correlations with gamma passing rate for 3%/2 mm criterion,and a significant correlation was found between CFNS90 and gamma passing rate for 3%/3 mm criterion.Conclusion Complexity scores can quantize the complexity of helical tomotherapy plans for different sites,and certain metrics are correlated with gamma passing rate.

Objective:To study the relationship between serum adiponectin level and early vascular aging (EVA).Methods:The cross-sectional study method was used. Six hundred and seventy-two subjects who completed health checkup from June to December 2023 in the Affiliated Hospital of Qingdao University were selected. The subjects were divided into the EVA group (237 cases) and the control group (435 cases) based on brachial-ankle pulse wave velocity (baPWV). According to the adiponectin tertiles method, the subjects were divided into low adiponectin subgroup (2.4 to 6.6 mg/L, 225 cases), medium adiponectin subgroup (6.7 to 9.1 mg/L, 227 cases) and high adiponectin subgroup (9.2 to 19.8 mg/L, 220 cases). The basic demographic information, past history and serological indexes were recorded. Univariate and multivariate binary Logistic regression analyses were used to analyze the risk factors for EVA, and multivariate Logistic regression was used to analyze the effect of adiponectin on EVA.Results:The male proportion, age, body mass index (BMI), systolic blood pressure, diastolic blood pressure, triglycerides (TG), fasting blood glucose (FBG), uric acid, glycated hemoglobin (HbA 1c), homocysteine, baPWV and alcohol history proportion in EVA group were significantly higher than those in control group: 64.98% (154/237) vs. 53.33% (232/435), 53 (47, 57) years old vs. 46 (39, 52) years old, (26.34 ± 3.37) kg/m 2 vs. (25.16 ± 3.91) kg/m 2, (132.27 ± 15.48) mmHg (1 mmHg = 0.133 kPa) vs. (117.30 ± 13.04) mmHg, (81.79 ± 11.04) mmHg vs. (71.93 ± 10.10) mmHg, 1.45 (1.03, 2.03) mmol/L vs. 1.08 (0.76, 1.65) mmol/L, 5.52 (5.03, 6.21) mmol/L vs. 5.14 (4.77, 5.56) mmol/L, (380.04 ± 96.43) μmol/L vs. (362.18 ± 94.94) μmol/L, 5.80 (5.50, 5.90)% vs. 5.70 (5.40, 5.82)%, 10.70 (9.01, 12.90) μmol/L vs. 9.96 (8.30, 12.20) μmol/L, 1 586 (1 511, 1 719) cm/s vs. 1 299 (1 215, 1 367) cm/s and 19.41% (46/237) vs. 13.56% (59/435), the adiponectin was significantly lower than that in control group: 7.00 (5.70, 8.75) mg/L vs. 8.40 (6.40, 10.60) mg/L, and there were statistical differences ( P<0.01 or <0.05). There were no statistical differences in total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), creatinine and smoking history proportion between two groups ( P>0.05). The male proportion, BMI, systolic blood pressure, diastolic blood pressure, TG, FBG, uric acid, creatinine, HbA 1c, homocysteine, EVA incidence, baPWV, smoking history proportion and alcohol history proportion in low adiponectin subgroup and medium adiponectin subgroup were significantly higher than those in high adiponectin subgroup, furthermore, the indexes except HbA 1c in low adiponectin subgroup were significantly higher than those in medium adiponectin subgroup, and there were statistical differences ( P<0.05); the HDL-C in low adiponectin subgroup and medium adiponectin subgroup was significantly lower than that in high adiponectin subgroup, furthermore, that in low adiponectin subgroup was significantly lower than that in medium adiponectin subgroup, and there were statistical differences ( P<0.05); there were no statistical differences in age, TC and LDL-C among the three subgroups ( P>0.05). Univariate binary Logistic regression analysis result showed that age, male, BMI, alcohol history, systolic blood pressure, diastolic blood pressure, TG, FBG, uric acid and HbA 1c were the risk factors for EVA ( P<0.01 or <0.05), while the adiponectin was a protective factor for EVA ( P<0.01). Multivariate binary Logistic regression analysis result showed that age, systolic blood pressure, TG and FBG were risk factors for EVA ( OR = 1.098, 1.066, 1.209 and 1.268; 95% CI 1.069 to 1.127, 1.050 to 1.082, 1.007 to 1.451 and 1.069 to 1.502; P<0.01 or <0.05), while adiponectin was a protective factor ( OR = 0.892, 95% CI 0.828 to 0.962, P<0.01). Multivariable Logistic regression analysis result showed that adiponectin consistently remained a protective factor for EVA across unadjusted, preliminary adjusted and fully adjusted covariate models ( OR = 0.553, 0.580 and 0.576; 95% CI 0.451 to 0.678, 0.440 to 0.764 and 0.435 to 0.763; P<0.01). Conclusions:The serum APN level is negatively correlated with the risk of EVA, which may be an independent protective factor for the EVA.

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 construct a training program for epilepsy specialist nurses, so as to provide a theoretical basis for the training and development of epilepsy specialist nurses.Methods:A preliminary training program for epilepsy specialist nurses was developed based on literature review, semi-structured interview, focus group discussion and clinical practice experience. Delphi method was used to select 20 experts from several regions of China for two rounds of consultation.Results:In the two rounds of expert consultation, the effective recovery rate of the questionnaire was both 100%, the expert authority coefficient was 0.908 and 0.958, and the degree of coordination of expert opinions was 0.180 to 0.229 and 0.138 to 0.189, respectively ( P<0.05). The standardized training program for epilepsy specialist nurses was finally constructed, including 5 first-level indicators (admission conditions, general theory courses, specialty theory courses, specialty nursing practice courses, training modes and effectiveness evaluation), 15 second-level indicators and 61 third-level indicators. Conclusions:The training program constructed for epilepsy specialist nurses is scientific and reasonable, with certain reliability and practicality, which provides a theoretical basis for the training of epilepsy specialist nurses, and promotes the common development of China's epilepsy specialist nurse team.

As a malignant hematological disease mainly originating from T or B lymphoid progenitor cell, acute lymphocytic leukemia (ALL) is more prevalent in children. With the gradual revelation of the autophagy mechanism, the role of autophagy in ALL has gained increasing attention. Autophagy can not only stimulate the survival of leukemia cells as an adaptive response, but also inhibit cell proliferation in some other cases. Therefore, how to regulate autophagy in promoting death or survival and reduce the drug resistance in ALL has become a hotspot. This article reviews the role of autophagy in ALL.