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. 

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 explore the expression of helper T cell 17（Th17）/regulatory T cell（Treg）levels in kidney transplant patients and their predictive value for the occurrence of pulmonary infections. Methods A total of 74 patients with kidney transplantation from January 2020 to December 2022 in People's Hospital ofXinjiang Uygur Autonomous Region were analysed in the study. The proportion of Th17 and Treg cells in peripheral blood was detected before and 2 months after surgery，and the Th17/Treg level was calculated. Patients werefollowed up for 1 year after surgery，and were divided into infected group and non infected group accordingto postoperative pulmonary infection. We focused on the ratio of Th17/Treg cells in renal transplant patients，and analyzed the predictive value of Th17/Treg ratio for postoperative pulmonary infection in renal transplant patients. Results There was a statistical significant difference in preoperative Th17/Treg ratio in kidney transplant patients with or without smoking history，diabetes，hypoproteinemia，and pulmonary infection history（P ＜ 0.05）. During the follow-up period of 74 kidney transplant patients，there were 21 cases of concurrent pulmonary infections，with an incidence rate of 28.38%（21/74）. The proportion of smoking history，diabetes，hypoproteinemia，and pulmonary infection history in the infected group was higher than that in the uninfected group，and the Tac blood concentration was higher than that in the uninfected group 2 months after surgery. The proportion of Treg cells in peripheral blood before and after surgery was lower than that in the non infected group，while the proportion of Th17 cells and Th17/Treg ratio were higher than those in the non infected group（P ＜ 0.05）. The results of plotting the receiver operating characteristic（ROC）curve showed that the AUC values of Th17，Treg cell ratio，and Th17/Treg level predicting postoperative pulmonary infection in kidney transplant patients were all ≥ 0.7 before surgery and 2 months after surgery，with a certain predictive value. Among them，the preoperative Th17/Treg ratio had a more ideal predictive value. Conclusion The elevated level ofTh17/Treg in renal transplant patients may participate in the process of pulmonary infection in renal transplant patients，and the high expression of Th17/Treg may be related to a history of smoking，diabetes and other risk factors for pulmonary infection；Preoperative examination of Th17/Treg ratio in patients can effectively predict the risk of postoperative pulmonary infection. 

Objective To explore the research status and hotspots of postoperative infection in lungtransplantation in the past decade. Methods The literature related to lung transplantation infection from 2014 to 2023 was searched based on the Web of Science core collection database，and CiteSpace6.2.R2 software was used to analyze the network of authors，countries，publishing institutions，as well as keyword co-occurrence，clustering，and emergence. Results A total of 1457 articles were included. The year of 2022 was the year with the highest number of publications. Husain Shahid’s team published the most papers. The United States was the country with the most papers，and the University of Toronto was the institution with the most papers. The keyword co-occurrence showed that the hotpots mainly focus on infection prevention and management，immune response and infection，assessment，treatment and research. Sixteen major sections were generated by keyword clustering with the value of the clustering module Q ＝ 0.76 and the average contour value of clustering S ＝ 0.89，and 26 keywords with citation explosion were obtained by keyword emergence. Conclusion At present，the research on lung transplantation infection is popular，and the research in Europe and the United States is in a leading position. The research hotspots mainly focuson the management of postoperative fungal and viral infections，immunity and rejection，and rapid diagnosis. Accurate assessment and rapid differentiation of immune rejection and infection，imaging-assisted diagnosis of infection，and management of viral infection in lung transplantation recipients are the research frontiers of lung transplantation infection. 

Objective To explore the molecular mechanisms and potential inflammation-related biomarkers of hepatic ischemia reperfusion injury（HIRI）process using bioinformatics techniques. Methods HIRI datasets GSE12720 and GSE14951 were obtained. Differential expressed genes（DEGs）were analyzed for functional enrichment and pathway enrichment. Hub genes were obtained using protein interactions analysis，and the correlationbetween Hub genes and inflammatory genes was calculated to finalize the key genes. The biological processes involved in these key genes were explored using GSEA analysis，and the expression of key genes was verified by mouse HIRI model. Finally，miRNAs of the key genes were predicted. Results Signaling pathways such as TNF，IL-17，MAPK and NF-κB were involved in inflammatory and immune processes during HIRI，and five key genes were closely related to inflammatory responses and their expressions are upregulated after HIRI. Conclusion This studyconfirms the central role of the inflammatory response in the process of HIRI and provides new perspectives for the discovery of the pathogenesis and potential therapeutic targets of HIRI. 

Objective To explore the effect of early attribution training on liver transplant recipients after surgery. Methods A retrospective analysis was conducted on 240 liver transplant recipients who underwent livertransplantation surgery at the Second Affiliated Hospital of Guangxi Medical University from January 1，2023 toDecember 31，2024. According to the random number method，the recipients were divided into two groups，with 120cases in each group. The control group received routine nursing care，while the intervention group received attributiontraining in addition to the routine nursing care. The changes in attribution style，sleep quality，and medical coping strategies between the two groups before and after 4 weeks of intervention were compared. Results After 4 weeks，the total attribution score，internal and external factors，prevalence，and stability of intervention groups were higherthan those of the control group（P ＜ 0.05）；The scores of surrender，avoidance，and facing in medical copingstrategies were higher than those in the control group（P ＜ 0.05）. Conclusion Early attribution training for liver transplant recipients can improve their attribution style，alleviate emotional management difficulties，improve sleep quality，and enhance their medical coping strategies.