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 To investigate the causes of kidney graft loss in early stage （within 1 yearafter surgery） in recipients with donation after cardiac death （DCD） kidney transplantation. Methods A retrospective analysis of the clinical data of 50 recipients with early kidney graft loss after DCD donor kidneytransplantation was performed. Results 50 recipients lost transplanted kidneys. The causes of kidney loss included19 rejections（38.0%），17 deaths（34.0%）with normal transplanted kidney function，5 vascular complications of transplanted kidneys（10.0%），4 cases ofcarbapenem-resistant Klebsiella infection（8%），4 cases of primary non-functioning kidney（8.0%），and 1 case of recurrence of primary kidney disease（2.0%）. The main reason for the lossof the transplanted kidney in the recipient within 1 year after the DCD donor kidney transplantation is rejection，and the secondary cause is the death of the recipient with normal renal function，complications of the transplanted kidney，primary non-functional kidney，and drug-resistant infection. Recurrence of the primary disease of the diseased kidney was rare. Conclusion Summarizing the reasons for early graft loss after DCD donor kidney transplantationis to provides reference for organ transplant physicians to strengthen targeted prevention and treatment. Ultimately，it may increase the 1-year survival of DCD donor kidney transplantation and reduce the dissatisfaction or medicaldispute caused by patients and their families.