Objective Establish model of abdominal heterotopic heart transplantation in mice and summarize the experience to provide animal model support for further study of organ transplantation immunology. Methods Inbred BALB/c（n ＝ 30）and C57BL/6（n ＝ 30）mice were selected as donors，and inbred BALB/c（n ＝ 60）mice were used as recipients. The ascending aorta of the donor was anastomosed to the abdominal aorta of the recipient，and the pulmonary artery of the donor was anastomosed to the inferior vena cava of the recipient respectively to establish the heterotopic heart transplantation model. The survival time and the rejection of grafts were observed postoperatively. Results The successful rate of transplantation was 85%（51/60）. The donoroperation time was（7.0±1.0）min，and the recipient operation time was（60±10）min. The vascular anastomosis time was（25±3.0）min. After the transplantation，no immunosuppressive agent was used，and the survival time of the graft was（7.6±0.9）d. The graft on the fifth day，the seventh day showed typical rejection by histopathology. Conclusion Skilled microsurgical techniques and timely management of surgical complications are key to the successful establishment of abdominal heterotopic heart transplantation in mice. 

Objective Post-renal transplantation anemia（PTA）occurs frequently in kidney transplant recipients，significantly impacting their quality of life and graft loss. Currently，effective methods to predictthe risk of persistent PTA early post-transplantation are lacking. This study aimed to develop a nomogram prediction model for early persistent PTA specifically tailored to kidney transplant recipients. Methods Using the electronic medical record system of Southern Hospital of Southern Medical University，patient data from January 1，2020 to December 31，2022 were obtained，and 245 subjects were ultimately selected as the research subjects. Among these，85% were randomly selected as the training set for model development，and the remaining 15% constituted the testing set. Using the Least Absolute Shrinkage and Selection Operator（Lasso）regression model，variables potentially affecting early persistent PTA were screened to identify predictive factors.A logistic regression analysis was employed to establish the prediction model. Model performance was assessed using Receiver operating characteristic（ROC）curves，area under the curve（AUC），Calibration plots，and decision Curve Analysis（DCA）. Results Identified predictive factors after screening included recipient's preoperative body mass index，preoperative serum albumin level，preoperative hemoglobin level，preoperative mean corpuscular volume，perioperative use of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers，exogenous iron supplementation，and exogenous erythropoietin supplementation. The model demonstrated good discriminativeability with an AUC of 0.87 for the training set and 0.75 for the testing set，indicating robust predictive performance. Calibration and DCA further confirmed the accuracy and clinical utility of the model. Conclusion This nomogram prediction model utilizes early recipient information，including demographic characteristics，laboratory data，and medication regimens，to accurately predict individualized risk of early persistent PTA in kidney transplant recipients. This provides a basis for early clinical intervention，potentially improving patient prognosis and quality of life. 

Objective To investigate the role of fission protein 1 （FIS1） in affecting renal ischemiareperfusion injury by regulating mitochondrial division and apoptosis. Methods Probing FIS1 expression levels and apoptosis levels were measured at different times in the renal tubular epithelial cell（ HK-2）with hypoxiareoxygenation（ HR）model and mouse renal ischemia-reperfusion（ IR） model. Cell lines with FIS1 knockdown and overexpression were constructed，changes in the degree of mitochondrial division were observed using mitochondrial probes，and changes in the level of apoptosis were detected with flow cytometry. Results FIS1 knockdown/ overexpression had essentially no effect in normal cells. After HR， knockdown of FIS1 inhibited mitochondrial division and reduced apoptosis levels， and vice versa after overexpression of FIS1. Conclusion In IRI， Inhibition of FIS1 expression reduces mitochondrial division and reduces the level of apoptosis， which is expected to be a potential therapeutic target for IRI. 

 Objective To elucidate the relationship between preoperative immunotherapy，the abundance of tertiary lymphoid structures（TLS）in hepatocellular carcinoma（HCC）tissues，and to evaluate patient prognosis following liver transplantation. Methods The clinical data of 149 liver transplant patients with liver cancer at Huashan Hospital Affiliated to Fudan University from January 2018 to December 2023 were retrospectively analyzed. Pathological slides of each patient were scored for TLS. Patients were categorized into four groups based on downstaging treatment outcomes ：those initially meeting the Milan criteria（n ＝ 35），those exceeding the Milan criteria without downstaging treatment（n ＝ 38），successful downstaging cases（n ＝ 33），and unsuccessful downstaging cases （n ＝ 43）. Kaplan-Meier analysis and the log-rank test were employed for survival analysis. The correlation betweenimmunotherapy and TLS abundance was assessed using non-parametric statistical methods. Results Survival analysis of the overall cohort revealed that patients with high intratumoral TLS abundance had significantly higher recurrence-free survival（RFS）than those with low TLS abundance（P ＜ 0.05）. Among patients receiving downstaging treatment，the recurrence risk in the successful downstaging group was significantly lower than in the unsuccessful group（P ＜ 0.05）. Non-parametric testing of the successful downstaging group demonstrated that preoperative immunotherapy significantly increased intratumoral TLS abundance（P ＜ 0.05）. Similarly，nonparametric testing of all patients receiving immunotherapy showed a statistically significant increase in intratumoral TLS abundance in the successful downstaging group（P ＜ 0.05）. Conclusion Successful downstaging withpreoperative immunotherapy improves the prognosis of HCC patients undergoing liver transplantation，potentially by enhancing intratumoral TLS abundance. 

Objective To investigate the characteristics of major histocompatibility complex class Ⅰ associated chain A （MICA） antibody in recipients after renal transplantation and its relationship with the pathological status of the transplanted kidneys. Methods The data of 355 patients from the Department of Kidney Transplantation at the First Affiliated Hospital of Xi'an Jiaotong University from January 2016 to December 2023 were retrospectively analyzed. According to the pathological diagnosis results of the transplanted kidneys， they were divided into rejection group（ 76 cases）， non-rejection group（ 54 cases）， and stable renal function group （225 cases）. Statistical analysis was performed using GraphPad Prism 9.0 statistical software. Results Among 355recipients， 18.3%（ 65 cases） were positive for MICA antibodies， and there was no correlation with HLA antibodies （r ＝ -0.202 7，P ＝ 0.1980）. There were significant differences in common MICA antibodies between rejection group，non-rejection group and stable renal function group. The multiples of MFI and threshold of MICA antibody in the rejection group were generally higher than those in the non-rejection group and the renal function stable group， especially MICA*009， MICA*004 and MICA*002（ P ＜ 0.000 1）. Receiver operating characteristic curve（ ROC） analysis found that MICA antibody has high diagnostic value in predicting interstitial inflammation〔（area under curve）AUC ＝ 0.8201〕and angiitis（ AUC ＝ 0.814 1） in acute renal allograft lesions，interstitial fibrosis（AUC ＝ 0.819 7） and tubular atrophy（ AUC ＝ 0.839 9） in chronic renal allograft nephropathy. The ability to predict antibodymediated rejection（ ABMR） was stronger than that of T-cell-mediated rejection（ TCMR）（ overall rejection ： AUC ＝ 0.688 5 ；ABMR ： AUC ＝ 0.603 8 ；TCMR ： AUC ＝ 0.542 3）. Conclusion The MICA antibody was different in rejection， non-rejection and stable renal function recipients after renal transplantation. MICA antibody has high value in predicting tubulointerstitial disease in transplanted kidneys， and its value in predicting ABMR is higher than that in predicting TCMR. 

Objective To observe the efficacy and survival status of single donor kidney transplantation with high Remuzzi score at zero-point biopsy. Methods A retrospective analysis was conducted on 178 recipients of single donor kidney transplantation who received deceased organ donation at the Second People's Hospital of Shanxi Province from January 2018 to January 2021. The donor kidneys underwent zeropoint biopsies and were evaluated with pathological Remuzzi scoring. The recipients were divided into high scoring group（≥ 4 and ≤ 6）and low scoring group（≤ 3）. The occurrence of delayed graft function of transplanted kidneys，postoperative renal function，occurrence of proteinuria，and survival of recipients and transplantedkidneys in both groups were observed with a follow-up time of 36 months. Results There were no statisticallysignificant differences（P ＞ 0.05）in gender ratio，body mass index，human leukocyte antigen（HLA）mismatch number，and donor kidney cold ischemia time between the two groups of recipients; there was no statistically significant difference in baseline blood creatinine and glomerular filtration rate before surgery（P ＞ 0.05）. A total number of 21 cases（23.6%）in the high scoring group experienced delayed graft function of transplanted kidneys after surgery，while 6 cases（6.7%）in the low scoring group experienced delayed graft function. The difference between the two groups was statistically significant（P ＜ 0.05），24 cases（27%）in the high scoring group developed proteinuria after surgery，while 9 cases（10.1%）developed proteinuria in the low scoring group. Through multiple factor analysis，it was found that the occurrence of proteinuria after kidney transplantation and the addition of mTOR immunosuppressants after surgery （OR ＝ 4.52， P ＜ 0.05）were related to thepreoperative Remuzzi score（OR ＝ 1.46，P ＜ 0.05）. At a follow-up of 36 months，the high scoring group had a blood creatinine level of（131.3±5.53）μmol/L and an eGFR level of（62.9±2.02）ml/（min · 1.73 m2 ）， while the low scoring group had a blood creatinine level of（121.3±2.18）μmol/L and an eGFR level of（65.0± 1.24）ml/（min·1.73 m2 ）. There was no statistically significant difference between the two groups（P ＞ 0.05）. Thesurvival rate of recipients in the high scoring group 36 months after surgery was 95.5%（85 cases），and the survival rate of transplanted kidneys was 95.5%（85 cases）. The survival rate of recipients in the low scoring group was 95.5% （85 cases），and the survival rate of transplanted kidneys was 97.7%（87 cases），with no statistically significant difference（P ＞ 0.05）. Conclusion Single kidney transplantation with a pre-transplant renal biopsy score of 6 ≥ Remuzzi ≥ 4 can achieve good long-term kidney survival and is worthy of clinical implementation. 

Objective To retrospectively summarize the perioperative clinical data of patients with endstage pulmonary arterial hypertension undergoing lung transplantation， and to analyze the recovery of cardiac function after surgery. Methods A retrospective analysis was conducted on six patients with end-stage pulmonary arterial hypertension who underwent lung transplantation at the Department of Lung Transplantation of ChinaJapan Friendship Hospital from March 2017 to June 2024. The general information of the patients before surgery， preoperative cardiac function， perioperative use of ECMO and IABP， and the recovery of cardiac function after surgery were analyzed. Results Among the six patients， three were male， and the median age was 33.5 years old. Four patients were diagnosed with idiopathic pulmonary arterial hypertension， and two had Eisenmenger's syndrome. All patients underwent a detailed cardiac function assessment before surgery. All six patients underwent double lungtransplantation with VA-ECMO assistance. The duration of ECMO assistance was 2 ～ 7 d， with a median time of 4 d. Two patients used IABP. Three patients developed left heart dysfunction after the surgery. But after effective treatment， all patients' cardiac function recovered during the perioperative period. Postoperative echocardiographic results showed that the relevant indicators of cardiac function of all the patients returned to normal， with statistically significant differences compared to preoperative levels（ P ＜ 0.05）. Conclusion Double lung transplantation is the last treatment option for patients with end-stage pulmonary arterial hypertension. Perioperative circulatory support measures， such as VA-ECMO and IABP， are beneficial for the recovery of cardiac function in such patients. 

Objective To explore the physiological and psychological factors affecting pulmonary infection in kidney transplant recipients and to construct a risk prediction model. Methods The clinical and follow-updata of 327 recipients undergoing kidney transplantation in Shandong Provincial Qianfoshan Hospital from January2019 to January 2024 were retrospectively analyzed. The recipients were divided into pulmonary infection group（102 cases）and non-pulmonary infection group（225 cases）according to whether pulmonary infection occurred after kidney transplantation. The data of the two groups of recipients was analyzed with multivariate regression analyze，theprediction model of pulmonary infection of kidney transplant recipients was constructed，and the receiver operatingcharacteristic（ROC）curve was used to verify the predictive value. Results The rate of pulmonary infection within 1 years after kidney transplantation was 31.19%. Age，blood glucose，insomnia severe index，use of cyclosporine and mycophenolate mofetil were independent risk factors for pulmonary infection. While white blood cell count on postoperative day 5 and positive psychological capital were independent protective factors（P ＜ 0.05）.The areaunder curve（AUC）of the constructed model equation for predicting the risk of pulmonary infection was 88.7% with a Kappa value of 0.66（P ＜ 0.05）. Conclusion The prediction model of pulmonary infection in kidney transplant recipients constructed based on physiological and psychological factors has a good predictive value. 

Objective To analyze the research hotspots and development trends of artificial intelligencein the field of organ transplantation. Methods Literatures on the application of artificial intelligence inthe field of organ transplantation from January 2004 to June 2024 were retrieved from the Web of ScienceCore Collection and China National Knowledge Infrastructure database. CiteSpace 6.2.R6 software was used for visual analysis. Results A total of 728 relevant literatures were retrieved，559 English literatures and 143 Chinese literatures were included after screening. The number of literatures published in the early stage was small，while the number of literatures published in recent years has increased significantly. The country with the largest number of articles was the United States，followed by China. The top three institutions in terms of foreignpublications were Mayo Clinic，University of Toronto and Harvard University. Zhejiang University，Peking UnionMedical College and the First Affiliated Hospital of Sun Yat-sen University were the most active institutions in China. The authors who have published the most articles in English were Cheungpasitporn Wisit and Thongprayoon Charat，and the author who has published the most articles in Chinese was Ren Bin. The main high-frequency keywords in English were machine learning，survival，artificial intelligence，liver transplantation，kidney transplantation，mortality，risk，model，outcome and deep learning. The main high-frequency keywords in Chinese were machine learning， liver transplantation， kidney transplantation， deep learning， artificial intelligence， hepatocellular carcinoma，radiomics，liver cancer，prognosis and tacrolimus. The first emergent keywords in English were artificial neural networks，data mining and survival analysis，and the most recent were predictive model and kidney transplant. The first emergent keywords in Chinese were neural networks，cyclosporine A，and blood concentration，and the most recent were deep learning and machine learning. Conclusion The application of artificial intelligence in thefield of organ transplantation is increasing significantly. The use of machine learning and deep learning to construct prediction models and to analyze the survival of various organ transplantation patients is a current research hotspots. In the future，exchanges and cooperation between countries and disciplines can be strengthened to promote the study and application of advanced AI technologies，so as to further promote the development of this field. 

Objective To improve the accuracy and clinical interpretability of recurrence prediction after liver transplantation in patients with hepatocellular carcinoma（HCC），and to explore the potential application of explainable machine learning models in integrating multimodal data. Methods This study included data from 138 liver transplant patients with liver cancer at the Third Medical Center of the Chinese People's Liberation Army General Hospital from December 2018 to December 2021. Preoperative contrast-enhanced CT radiomics features and clinical variables were extracted. Predictive models were developed using four machine learning methods ：LASSO regression，random forest，support vector machine（SVM），and neural network. Importance of variate was employed to identify key predictive factors. Model performance was evaluated using area under the receiver operating characteristiccurve（AUC），Brier score，and calibration curves. A nomogram was ultimately constructed based on important variables. Results The AUCs for the random forest model in predicting recurrence at 1，2，and 3 years were 0.881，0.906，and 0.915，respectively，significantly outperforming other models. The importance analysis identified five key imaging features，and the nomogram model combining these with clinical variables demonstrated good consistency and predictive capability. Conclusion Explainable machine learning models based on multimodal data can effectively improve the accuracy and transparency of recurrence prediction following liver transplantation in HCC patients. These models have strong clinical applicability and provide valuable support for individualized preoperative risk assessmentand treatment decision-making. 

Objective Maintaining stable concentrations of anti-rejection drugs represents a critical facetof post-kidney transplantation patient care; however，achieving personalized and precise management for each patient remains challenging. This study leverages an artificial intelligence-based deep learning framework to develop a machine learning predictive model for tacrolimus concentration，with the objective of recommending optimal dosing regimens for individual kidney transplant recipients. Methods Fifty kidney transplant recipients who underwentsurgery at the Urology Department of Beijing Friendship Hospital，Capital Medical University，between January 2024 and April 2025，were enrolled in this study. Drawing on prior experience in tacrolimus dosing，we collected data on patients' gender，age，weight，comorbidities，CYP3A5 metabolic phenotypes，initial tacrolimus（Tac）doses，and FK506 levels measured on postoperative days 7，9，11，13，and 15，with subsequent dosage adjustments made according to each concentration measurement. A LightGBM regression model was employed to predict and optimize tacrolimus dosing regimens. Results Among the 50 kidney transplant recipients enrolled in this study，none developed severe complications，including delayed recovery of graft function，postoperative infections，or bleedingThe dataset was partitioned into training and validation sets using a five-fold cross-validation approach. The final model demonstrated robust predictive performance in the test set，with a mean absolute error（MAE）of 0.166，root mean square error（RMSE）of 0.227，mean absolute percentage error（MAPE）of 7.035%，P20 of 0.935，P30 of 0.97，and a coefficient of determination（R-squared）of 0.932. Conclusion The LightGBM regression model exhibited excellent performance，providing a novel and effective strategy for personalized tacrolimus dosage adjustment in kidney transplant recipients.