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 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 To explore the effect of the holistic care on the quality of life assessment systemin patients with postoperative infection after kidney transplantation. Methods The clinical data of 92 patients with infection after renal transplantation in our hospital from January 2017 to December 2017, were analysed retrospectively. The patients were divided into nursing group（observation group）and routine nursing（controlgroup），according to different nursing plans，each group has 46 patients. The nursing quality were compared between the two groups，mainly on the scores of the nursing quality，survival quality score，anxiety score，compliance evaluation，physical comfort，environmental comfort ratings，and patient satisfaction. Results The comprehensive nursing quality（education，nursing attitude，nursing skills，ward management）in the observation group were significantly higher than that in the control group，and the difference were statistically significant（χ2 ＝ 14.286，P ＝ 0.003 ；χ2 ＝ 16.331，P ＝ 0.003 ；χ2 ＝ 19.247，P ＝ 0.002 ；χ2 ＝ 18.295，P ＝ 0.003）. The survival quality score in the observation group was significantly higher than that in thecontrol group，and the difference was statistically significant（t ＝ 4.672，P ＝ 0.002）. Anxiety scores in the observation group were significantly lower than that in the control group，and the difference was statistically significant（t ＝ -4.855，P ＝ 0.003）.The compliance score，physiological comfort score and environmental comfortscore in the observation group were significantly higher than those in the control group，and the difference werestatistically significant（t ＝ -19.218，P ＝ 0.002 ；t ＝ -14.821，P ＝ 0.003 ；t ＝ -16.115，P ＝ 0.002）.Nursing satisfaction in observation group was significantly higher than that in control group，and the difference was statistically significant（χ2 ＝ 4.873，P ＝ 0.001）Conclusion “The holistic care”intervention could reduce the psychologicalburden of patients and their families as well as relieve the pain on the body，mind and spirit in all aspects. This method could also improve the quality of patients' lives along with the quality of nursing work. It has important guidingsignificance for clinical management of infection after renal transplantation and related nursing work.

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 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.