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 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.

Endovascular technology has become the first choice for the treatment of lower extremities arteriosclerotic obliterans. Bioresorbable vascular scaffolds have attracted more and more attention as a choice of endovascular technology. In the last decade, poly(L-lactic acid) bioresorbable scaffolds with or without drug coating have shown acceptable medium and long-term safety and efficacy in lower extremities arteriosclerotic obliterans, but the lesions of the subjects were relatively simple. Magnesium alloy bioresorbable scaffolds are safe but less effective in the treatment of lower extremities arteriosclerotic obliterans. Both iron and zinc alloy bioresorbable scaffolds have shown considerable results in animal experiments. In particular, the success of implantation of drug-coated iron alloy bioresorbable scaffolds in below-the-knee artery indicated that the iron alloy bioresorbable scaffolds have officially entered the clinical trial stage. Through the comprehensive summation of the previous clinical and experimental data of bioresorbable vascular scaffolds and the pathological characteristics of lower extremities arteriosclerotic obliterans, it is shown that the drug-coated poly(L-lactic acid) bioresorbable scaffolds and iron alloy bioresorbable scaffolds will have greater development potential in the treatment of lower extremities arteriosclerotic obliterans.

Endovascular technology has become the first choice for the treatment of lower extremities arteriosclerotic obliterans. Bioresorbable vascular scaffolds have attracted more and more attention as a choice of endovascular technology. In the last decade, poly(L-lactic acid) bioresorbable scaffolds with or without drug coating have shown acceptable medium and long-term safety and efficacy in lower extremities arteriosclerotic obliterans, but the lesions of the subjects were relatively simple. Magnesium alloy bioresorbable scaffolds are safe but less effective in the treatment of lower extremities arteriosclerotic obliterans. Both iron and zinc alloy bioresorbable scaffolds have shown considerable results in animal experiments. In particular, the success of implantation of drug-coated iron alloy bioresorbable scaffolds in below-the-knee artery indicated that the iron alloy bioresorbable scaffolds have officially entered the clinical trial stage. Through the comprehensive summation of the previous clinical and experimental data of bioresorbable vascular scaffolds and the pathological characteristics of lower extremities arteriosclerotic obliterans, it is shown that the drug-coated poly(L-lactic acid) bioresorbable scaffolds and iron alloy bioresorbable scaffolds will have greater development potential in the treatment of lower extremities arteriosclerotic obliterans.

Objective To evaluate the effect of donor risk index (DRI) on the early prognosis of liver transplantation for acute-on-chronic liver failure (ACLF). Methods Clinical data of 159 ACLF recipients undergoing liver transplantation were retrospectively analyzed. According to the calculation formula of DRI, all recipients were divided into DRI < 1.65 group (n=96) and DRI≥1.65 group (n=63). Based on the Chronic Liver Failure Consortium acute-on-chronic liver failure score (CLIF-C ACLFs), all recipients were divided into CLIF-C ACLFs < 48 group (n=78) and CLIF-C ACLFs≥48 group (n=81). The early prognosis indexes including the length of intensive care unit (ICU) stay and the length of postoperative hospital stay of the recipients in each group were observed after liver transplantation. The 90 dsurvival rate of the recipients after liver transplantation was analyzed by Kaplan-Meier survival curve. The risk factors affecting the early prognosis of ACLF recipients after liver transplantation were analyzed by Cox's hazards regression model. Results The length of ICU stay and the length of postoperative hospital stay did not significantly differ between the DRI < 1.65 group and DRI≥1.65 group (both P > 0.05). The length of postoperative hospital stay did not significantly differ between the CLIF-C ACLFs < 48 group and CLIF-C ACLFs≥48 group (P > 0.05). The length of ICU stay in the CLIF-C ACLFs < 48 group was 4 (3-14) d, significantly shorter than 7 (1-33) d in the CLIF-C ACLFs≥48 group (P < 0.05). The CLIF-C ACLFs was a risk factor of the early prognosis of ACLF recipients after liver transplantation (P < 0.05). The postoperative 90 d survival rate did not significantly differ between the DRI < 1.65 group and DRI≥1.65 group (P > 0.05). The postoperative 90 d survival rate in the CLIF-C ACLFs < 48 group was 94%, significantly higher than 79% in the CLIF-C ACLFs≥48 group (P < 0.05). Conclusions The early prognosis of ACLF recipients after liver transplantation is correlated with the severity of the disease rather than the DRI. Liver transplantation should be performed early and promptly.

In-stent restenosis severely reduces the long-term patency rate after stent implantation, but drug-eluting stent may be possible to solve this problem. This paper mainly from two aspects of the mechanism of in-stent restenosis and clinical trials to state progress in research of drug-eluting stent in the applications of peripheral artery disease.
Constriction, Pathologic ; Drug-Eluting Stents ; Humans ; Peripheral Arterial Disease

Objective To study the efficacy of emergency orthotopic liver transplantation(EOLT) for acute(hepatic) failure(ALT).Methods A retrospective review was undertaken on the clinical data of 8 patients undergoing emergency liver transplantation for ALT.Results The 8 patients completely regained consciousness in 12 to 72 hours after operation.No case developed central nervous complications.One case of severe(hepatitis) complicated by acute renal failure died of respiratory infection and ARDS on postoperative day 7.One case who refused to take medication died from chronic rejection 12 months after operation.One case was(complicated) by bile duct stricture and biliary sludge at 14 months postoperatively and survived for 18 months.Four of the other 5 cases were followed up for 17 months and 1 cases for 14 months,and thir quality of life was excellent.3 of them have returned to work.Conclusions Emergency orthotopic liver thansplantation is an effective means to treat ALF.Intensive care and effective treatment preoperatively are pre-requisite(conditions) to ensure the success of EOLT.

17.1?mol/L).

0.05).Anastomotic inflammatory reaction occurred in 2 patients(3.2%) in BAR group and 13 patients(20.0%) in manual group.The difference was statistically significant(P

Objective To investigate the arterial blood supply of the pancreas head and provide a theoretical basis for the gastroduodenal artery reconstruction in pancreatic transplantation(PT).Methods Photograms of digital subtraction artery(DSA)which performing on 300 patients were analyzed to recognize the aberrations of arterial blood supply of pancreatic head.Results In 300 DSA photograms,the gastroduodenal artery(GD.a)was identified in 131 cases,and the anterior superior pancreaicduodenal artery(ASPD.a)and posterior superior pancreaicduodenal artery(PSPD.a)in 79 cases.The rate of aberrant origin of pancreatic transverse artery(PT.a)from GD.a was 12.98℅.There are some minor sources of blood supply to the pancreas head from GD.a.The rate of absence of an ASPD.a-AIPD.a anastomosis and PSPD.a-PIPD.a anastomosis was 15.19℅and 24.05℅,respectively.Conclusions The reconstruction of gastroduodenal artery can ensure a complete blood supply to the pancreatic head and duodenum in PT.