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.

Objective:To explore the feasibility of a stable pig-to-monkey orthotopic liver transplantation (LT) model and provide a favorable experimental tool for preclinical studies of xenotransplantation.Methods:In this retrospective analysis, the authors reviewed the perioperative conditions and outcomes of 7 pig-to-monkey orthotopic liver transplants performed by a xenotransplantation research team of Second Xiangya Hospital.Gene-edited Banna miniature pigs were selected as donors and rhesus monkeys with similar anatomical characteristics, physiology, biochemistry and immune mechanism to humans were selected as recipients for pig-monkey xenogeneic orthotopic LT.Based upon classic transplantation procedures, whole liver xenogeneic orthotopic transplantation was performed.Surgical processes were modified for minimizing intraoperative hemorrhage and shortening anhepatic period.A bile duct drainage tube was implanted for observing bile secretion.ATG + anti-CD20 + snake venom factor and FK-506 were utilized for immunoinduction pre-operation while tacrolimus, mycophenolate mofetil (MMF) and methyl prednisolone for postoperative immunomaintenance therapy.Antibiotics and antiviral agents were also applied and thrombin complex for improving coagulation functions.Results:All procedures were successfully completed.After the stability and maturity of our model, in case No.7, donor's acquisition operative duration was 42 min without heat ischemic time, donor's trimming time 87 min, donor cold retention time 128 min, recipient's operative duration 123 min and anhepatic phase 27 min.Subhepatic inferior vena cava was occluded for 38 min.Blood loss was around 10 ml.And 4/7 models survived post-operation and the longest survival time was 27 h. Among 3 non-survival cases, the causes were anesthesia accident (n=1) and immaturity of early operation (n=2). Model No.7 had a biliary secretion volume of 86 ml post-operation.Conclusions:Qualified donor acquisition, high-quality vascular anastomosis, intraoperative reduction of blood loss, shortening of anhepatic period, strict fluid replenishing and careful monitoring are essential for boosting the success rate of pig-monkey liver xenotransplantation model.Optimization of donor gene combination and advanced immunosuppression protocol help to further achieve the long-term survival of pig-monkey liver xenotransplantation model.

Liver transplantation is a curable therapy to save the life of patients with end-stage liver diseases of different causes. The serious shortage of liver donors led to the death of a large number of patients with end-stage liver disease. Genetic-engineered pigs used for xenotransplantation are one of the potential solutions to alleviate the liver donor shortage, but currently clinical application of such xenogeneic liver grafts is hindered by acute rejection, thrombocytopenia, and coagulation dysfunctions in patients. The development of genetic-engineering technology and the application of new immunosuppressants have controlled the acute rejection of liver xenotransplantation to some extent. However, acute humoral rejection may become another major issue for the long-term graft failure. This review summarized and discussed the potential and underlying mechanisms and preventive measures of acute humoral rejection in liver xenotransplantation.

Objective To investigate the efficacy and safety of programmed cell death protein-1 (PD-1) monoclonal antibody on the treatment of malignant tumor after solid organ transplantation (SOT). Methods The relevant literatures in 7 databases were searched. The data on 54 cases of recipients with malignant tumors treated with PD-1 monoclonal antibody after SOT were collected, and the clinical effects and rejection of SOT recipients treated with PD-1 monoclonal antibody were analyzed. Results Total 32 acceptable articles including 54 SOT recipients were incorporated, including 43 males and 11 females aged 14-79 years old. There are 29 renal transplant recipients, 19 liver transplant recipients and 6 heart transplant recipients. The types of PD-1 monoclonal antibody agent used by SOT recipients included pembrolizumab for 28 patients and nivolumab for 26 patients. The overall remission rate, disease progression rate and fatality rate of PD-1 monoclonal antibody for postoperative malignant tumors of SOT recipients were 32% (17/54), 44% (24/54) and 36% (19/54), respectively. After treatment with PD-1 monoclonal antibody for postoperative malignant tumors of SOT recipients, the incidence of rejection was 39% (21/54), indicating no significant correlation between rejection and type of PD-1 monoclonal antibody (P > 0.05). Conclusions PD-1 monoclonal antibody can effectively treat postoperative malignant tumors of SOT recipients, and may induce rejection during the treatment. But rejection is not the most common cause for death of recipients.

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.

Objective To evaluate the outcome of 1iver transplantation for acute-on-chronic liver failure (ACLF) patients .Methods We included 453 consecutive patients with previously cirrhosis who underwent liver transplantation between January 2013 and December 2017 .Patients were categorized as no ACLF (n=294) and ACLF(n=159) according to EASL-CLIF consortium criteria .Furthermore ,we used ACLF grades to categorize the ACLF patients .Their clinical data were reviewed and their 90-days survival outcomes were compared .Results Compared with the no ACLF group ,the length of stay in the ICU was significantly prolonged for all patients with ACLF ,and the 90-days survival rate after transplantation was significantly reduced in ACLF group .The length of stay in the ICU was shorter in Grade 1 and Grade 2 group when compared to Grade 3 group .The 90-days survival rate of no ACLF ,Grade 1 ,Grade 2 and Grade 3 group were 93 .20％ ,92 .59％ ,93 .33％ and 73 .68％ ,respectively .There were no statistically significant differences in 90-days survival rate among the no ACLF ,Grade 1 and Grade 2 group .However , the 90-days survive rate of Grade 3 group was lower than that of other groups .Conclusions Liver transplantation has been shown to be safe and effective with good outcome in patients with ACLF and should be offered in early course of ACLF before onset of multi-organ failure .

Objective To construct the orthotopic mouse liver transplantation model and cover troubleshooting,in order to provide experimental techniques support for organ transplantation pathology and immunology studies.Methods Male C57BL/6 mice,10-12 weeks,were selected as the allograft donors.Male C3H mice with same age were selected as the allograft recipients.The orthotopic mouse liver transplantation model consisted of 3 stages,including harvesting the donor liver,back-table preparation of the liver graft and transplantation of the donor liver into the recipient.The average time for harvesting the donor livers was (40 ± 8.8) min,(23 ± 4.7) min for preparing the donor livers and (75 ± 9.6) min for transplanting the donor livers into the recipient.Results Seventy pairs of mice were used for the preliminary experiments.For the formal experiments,the allograft transplantation was established on 220 pairs with 90.4％ successful rate.Conclusion It is the skillful and high quality microsurgical technique that is the guarantee of establishing the orthotopic mouse liver transplantation model successfully.

ObjectiveTo investigate the expression of T helper (Th) 17 cells and the related interleukin 17 (IL-17) in acute renal allograft rejection in mice and its significance.Methods We established a mouse renal allograft model,in which mice were randomly divided into a renal isograft group and an acute renal allograft rejection group.Three and 7 d after the transplantation,the serum interferon (IFN)-γand IL-17 levels in the mice were determined by enzyme-linked immunosorbent assay,the percentage of Th1 and Th17 cells in the total kidney-infiltrating lymphocytes was investigated by flow cytometry,and the transplanted kidney species were given routine pathological examination after fixation with 10％ formalin.ResultsCompared with the isograft group,the allograft mice showed a significantly higher content of IL-17 (P ＜0.05 ) but not IFN-γ in the serum 3 d after transplantation,and showed significantly higher serum IL-17 and IFN-γcontents 7 d after transplantation (P ＜ 0.05 ).Also,compared with the isograft group,the allograft mice exhibited significantly higher percentage of Th1 and Th17 cells on both day 3 and day 7 ( P ＜ 0.05 ).In the allograft group,the contents of serum IFN-γand IL-17 and the percentage of Th1 and Th17 cells were significantly higher on day 7 than on day 3 (P ＜ 0.05 ).Routine pathological examination indicated that,as time passed,the allograft mice showed gradually stronger rejection responses.ConclusionTh17 cells might play an important role in the development of acute renal allograft rejection,and IL-17 can be used as an early indicator of acute rejection.

OBJECTIVE: To investigate the role of T helper cell 17(Th17) cells and their cytokines IL-17 in heart allograft rejection in mice. METHODS: The heart transplantation models were randomly divided into 2 groups: a allograft group (n=12) and an isograft group (n=12).On the post-operative day (POD) 3 and 7, serum IL-17 level was tested by enzyme-linked immunosorbent assay, and Th17 cells in heart grafts were measured by flow cytometry. The heart grafts were harvested and saved in 10% formalin, and then embedded in paraffin. RESULTS: Compared with the iso-graft group, the allograft group had a higher serum level of IL-17 on POD3 and POD7 (P<0.05), and the level of IL-17 was higher on POD7 than that on POD3 (P<0.05). The allograft group had more Th17 cells infiltrating in grafts on POD3 and POD7 (P<0.05) and there were more Th17 cells infiltrating on POD7 than that on POD3 (P<0.05). Histological examination showed that the prolongation of post transplantation time resulted in more rejection pathological changes. CONCLUSION Th17 cells may play an important role in the development of heart transplant rejection. IL-17 may serve as a predictive parameter for allograft rejection in the future.
Animals ; Graft Rejection ; blood ; diagnosis ; immunology ; Heart Transplantation ; adverse effects ; immunology ; Interleukin-17 ; blood ; Male ; Mice ; Mice, Inbred BALB C ; Mice, Inbred C57BL ; Random Allocation ; Th17 Cells ; immunology