Objective To systematically analyze the literature characteristics of Journal of Organ Transplantation since its inception. Methods Using the China National Knowledge Infrastructure (CNKI) academic journal full-text database as the data source, all articles published in the Journal of Organ Transplantation from January 2010 to August 2025 were retrieved. After excluding non-academic papers, a total of 1 568 research papers were included. R language 4.3.0, Bibliometrix package 3.2.1, and Citespace software were used to analyze the number of publications, publishing institutions, authors, keywords and other aspects. Results The number of publications in Journal of Organ Transplantation increased from an average of 82 articles per year in the early years after its inception to 113 articles per year in recent years, a growth of 37.8%. The geographical distribution of publishing institutions covers 32 provinces, cities and autonomous regions nationwide, mainly concentrated in the South China, East China and North China regions, and has now basically covered the central and western regions in recent years. The author collaboration network includes 45 authors distributed across 7 major collaboration clusters, forming a stable multi-level national research system centered on key university-affiliated hospitals. The high-frequency keywords are dominated by "liver transplantation" (425 times) and "kidney transplantation" (396 times). The theme evolution shows a clear three-stage characteristic: initially focusing on clinical technology application, deepening to immune mechanism exploration in the middle stage, and recently (since 2022) focusing on cutting-edge research areas such as xenotransplantation. Conclusions Journal of Organ Transplantation has witnessed the rapid development of China's organ transplantation cause, fully reflecting the research status and trends in China's organ transplantation field, and has provided an important platform for the future development and international cooperation in China's organ transplantation field.

Most cancers are currently incurable, partly due to abnormal post-translational modifications (PTMs). In this study, we initially used multiple myeloma (MM) as a working model and found that SUMOylation activating enzyme subunit 1 (SAE1) promotes the malignancy of MM. Through proteome microarray analysis, SAE1 was identified as a potential target for bioactive colcemid or its derivative colchicine. Elevated levels of SAE1 were associated with poor clinical survival and increased MM proliferation in vitro and in vivo. Additionally, SAE1 directly SUMOylated and upregulated the total protein expression of p27, leading to LLPS-mediated nuclear export of p27. Our study also demonstrated the involvement of SAE1 in other types of cancer cells, and provided the first monomer crystal structure of SAE1 and its key binding model with colchicine. Colchicine also showed promising results in the Patient-Derived Tumor Xenograft (PDX) model. Furthermore, a controlled clinical trial with 56 MM patients demonstrated the clinical efficacy of colchicine. Our findings reveal a novel mechanism by which tumor cells evade p27-induced cellular growth arrest through p27 SUMOylation-mediated nuclear export. SAE1 may serve as a promising therapeutic target, and colchicine may be a potential treatment option for multiple types of cancer in clinical settings.

Objective To establish an acute rejection model of cervical heart transplantation in mice and evaluate the survival and dynamic rejection process post-transplantation. Methods Mice were randomly divided into sham operation group (n=10), syngeneic transplantation group (n=21), and allogeneic transplantation group (n=65). Sham operation, syngeneic cervical heart transplantation, and allogeneic cervical heart transplantation were performed respectively. The survival of recipient mice and grafts, histopathological changes of graft tissues, subpopulations of splenic lymphocytes, and expression of inflammatory factors in serum and grafts were observed. Results The survival rate and graft survival rate of the sham operation group and syngeneic transplantation group were 100% at 7 days after surgery. In the allogeneic transplantation group, 5 cases failed and died on the first day after surgery. The survival rate at 7 days after surgery was 86%, and all surviving mice had grafts that stopped beating at 7 days after surgery. The allogeneic transplantation group showed significant rejection at 7 days after surgery, accompanied by tissue damage and CD8⁺ T cell infiltration. The proportion of CD8⁺ T cells in the spleen continued to rise post-operation, while the proportion of CD4⁺ T cells showed a downward trend. The expression of interferon-γ in serum and grafts peaked at 5 days after surgery, while the expression of tumor necrosis factor-α showed no statistical significance. Conclusions Acute rejection following heart transplantation in mice intensifies between 5 to 7 days after surgery, which may be a critical time window for immunological intervention.

Objective:To explore the typology and influencing factors of nursing undergraduates'career willingness to care for terminally ill elderly patients.Methods:Using a convenience sampling method,a survey was conducted among 488 nursing undergraduates from three universities in Hunan Province between May and June 2024.Latent profile analysis(LPA)was employed to classify career willingness to care for terminally ill elderly patients,and logistic regression analysis was used to analyze factors influencing the career willingness.Results:Heterogeneity was observed in nursing undergraduates'career willingness,which was categorized into three groups:low positive attitude-low care awareness group(24.8%),high positive attitude-low care awareness group(56.7%),and high positive attitude-high care awareness group(18.5%).Logistic regression analysis revealed that gender,cohabitation with terminally ill elderly patients,only-child status,experience in caring for terminally ill patients,geriatric nursing training,and hospice care education were statistically significant factors influencing career willingness(P＜0.05).Conclusions:Nursing undergraduates'career willingness to care for terminally ill elderly patients exhibits distinct categorical characteristics.Individualized educational strategies should be developed to enhance their professional identity and career intention in this field.

Objective:To develop CNN-Dinov2, a deep learning-based automatic classification model for Gram-stained images, enabling rapid diagnosis of three prevalent Gram-negative bacilli in bloodstream infections: Escherichia coli ( E.coli), Klebsiella pneumoniae ( K.pneumoniae), and Pseudomonas aeruginosa ( P.aeruginosa). Methods:This evaluation study analyzed 1 425 Gram-stained microscopic images from patients with bloodstream infections at Houjie Hospital, in Dongguan City, collected between January 2023 and January 2024. The images, all positive for blood culture and identified as target strains, were categorized into Escherichia coli (419 images), Klebsiella pneumoniae (411 images), Pseudomonas aeruginosa (413 images), and other Gram-negative bacilli (182 images). They were randomly split into a training set (1 141 images), a validation set (141 images), and a test set (143 images) in an 8∶1∶1 ratio. A hybrid CNN-Dinov2 model was developed by integrating ResNet′s local feature extraction with Dinov2′s global pre-trained features, followed by a fully connected layer. The model was optimized by inputting the preprocessed images and adjusting parameters through loss calculation and backpropagation. AlexNet, Dinov2, and ResNet18 served as control models. The models′ classification performance was assessed using accuracy, precision, weighted F1 score, and recall rate, derived from the confusion matrix. The PR curve and AP value further evaluated each model′s classification capability across the four image categories. Results:The CNN-Dinov2 model achieved a training accuracy of 99.74%, a validation accuracy of 98.12%, and a validation loss of 0.070 6, demonstrating robust generalization without overfitting. Validation metrics revealed superior performance with an accuracy of 98.60%, precision of 98.65%, a weighted F1 score of 98.60%, and a recall rate of 98.60%, outperforming other models. The confusion matrix confirmed its strong classification capability, with the highest sum of diagonal values for identifying four types of bacteria. The macro average precision (AP) values under the precision-recall (PR) curves were all 1, indicating excellent discrimination across all categories. Overall, the CNN-Dinov2 model exhibited the best performance among the four models evaluated.Conclusion:This study successfully developed CNN-Dinov2, an automated classification model for Gram staining images. It offers valuable support for the rapid diagnosis of bloodstream infections caused by Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, demonstrating practical utility.

China emerges as a major country in nuclear energy development and the application of nuclear and radiologic technology. The diagnosis and treatment of acute radiation syndrom (ARS) caused by external irradiation represent a core function in the country′s medical rescue of nuclear and radiological emergencies. Clinically, ARS manifests hematopoietic, gastrointestinal, cutaneous, and central nervous system syndromes, with specific clinical manifestations, signs, severity, and prognosis strongly correlated with radiation dose. China has established a number of national and provincial centers for treating radiation-induced damage. Nevertheless, most medical staff have limited experience in ARS treatment. This consensus presents a summary of recent experience in treating ARS of China. In combination with recommendations from international organizations such as the World Health Organization (WHO), this consensus proposes key evidence of critical clinical issues of ARS, covering all links in the rescue of external irradiation-induced ARS. Initially, clinical diagnosis, syndromes, and severe degrees should be determined based on clinical symptoms and dose estimates. It is necessary to normalize clinical treatment measures for hematopoietic recovery, gastrointestinal injury treatment, infection control, symptomatic treatment, and multi-organ function preservation. To this end, this consensus offers cautions. This consensus provides principles of treatment with traditional Chinese medicine, psychological intervention, and follow-up. Additionally, it highlights multidisciplinary collaboration. It is recommended that this consensus be applied in relevant treatment centers.

China emerges as a major country in nuclear energy development and the application of nuclear and radiologic technology. The diagnosis and treatment of acute radiation syndrom (ARS) caused by external irradiation represent a core function in the country′s medical rescue of nuclear and radiological emergencies. Clinically, ARS manifests hematopoietic, gastrointestinal, cutaneous, and central nervous system syndromes, with specific clinical manifestations, signs, severity, and prognosis strongly correlated with radiation dose. China has established a number of national and provincial centers for treating radiation-induced damage. Nevertheless, most medical staff have limited experience in ARS treatment. This consensus presents a summary of recent experience in treating ARS of China. In combination with recommendations from international organizations such as the World Health Organization (WHO), this consensus proposes key evidence of critical clinical issues of ARS, covering all links in the rescue of external irradiation-induced ARS. Initially, clinical diagnosis, syndromes, and severe degrees should be determined based on clinical symptoms and dose estimates. It is necessary to normalize clinical treatment measures for hematopoietic recovery, gastrointestinal injury treatment, infection control, symptomatic treatment, and multi-organ function preservation. To this end, this consensus offers cautions. This consensus provides principles of treatment with traditional Chinese medicine, psychological intervention, and follow-up. Additionally, it highlights multidisciplinary collaboration. It is recommended that this consensus be applied in relevant treatment centers.

Objective:To explore the typology and influencing factors of nursing undergraduates'career willingness to care for terminally ill elderly patients.Methods:Using a convenience sampling method,a survey was conducted among 488 nursing undergraduates from three universities in Hunan Province between May and June 2024.Latent profile analysis(LPA)was employed to classify career willingness to care for terminally ill elderly patients,and logistic regression analysis was used to analyze factors influencing the career willingness.Results:Heterogeneity was observed in nursing undergraduates'career willingness,which was categorized into three groups:low positive attitude-low care awareness group(24.8%),high positive attitude-low care awareness group(56.7%),and high positive attitude-high care awareness group(18.5%).Logistic regression analysis revealed that gender,cohabitation with terminally ill elderly patients,only-child status,experience in caring for terminally ill patients,geriatric nursing training,and hospice care education were statistically significant factors influencing career willingness(P＜0.05).Conclusions:Nursing undergraduates'career willingness to care for terminally ill elderly patients exhibits distinct categorical characteristics.Individualized educational strategies should be developed to enhance their professional identity and career intention in this field.

Objective:To develop CNN-Dinov2, a deep learning-based automatic classification model for Gram-stained images, enabling rapid diagnosis of three prevalent Gram-negative bacilli in bloodstream infections: Escherichia coli ( E.coli), Klebsiella pneumoniae ( K.pneumoniae), and Pseudomonas aeruginosa ( P.aeruginosa). Methods:This evaluation study analyzed 1 425 Gram-stained microscopic images from patients with bloodstream infections at Houjie Hospital, in Dongguan City, collected between January 2023 and January 2024. The images, all positive for blood culture and identified as target strains, were categorized into Escherichia coli (419 images), Klebsiella pneumoniae (411 images), Pseudomonas aeruginosa (413 images), and other Gram-negative bacilli (182 images). They were randomly split into a training set (1 141 images), a validation set (141 images), and a test set (143 images) in an 8∶1∶1 ratio. A hybrid CNN-Dinov2 model was developed by integrating ResNet′s local feature extraction with Dinov2′s global pre-trained features, followed by a fully connected layer. The model was optimized by inputting the preprocessed images and adjusting parameters through loss calculation and backpropagation. AlexNet, Dinov2, and ResNet18 served as control models. The models′ classification performance was assessed using accuracy, precision, weighted F1 score, and recall rate, derived from the confusion matrix. The PR curve and AP value further evaluated each model′s classification capability across the four image categories. Results:The CNN-Dinov2 model achieved a training accuracy of 99.74%, a validation accuracy of 98.12%, and a validation loss of 0.070 6, demonstrating robust generalization without overfitting. Validation metrics revealed superior performance with an accuracy of 98.60%, precision of 98.65%, a weighted F1 score of 98.60%, and a recall rate of 98.60%, outperforming other models. The confusion matrix confirmed its strong classification capability, with the highest sum of diagonal values for identifying four types of bacteria. The macro average precision (AP) values under the precision-recall (PR) curves were all 1, indicating excellent discrimination across all categories. Overall, the CNN-Dinov2 model exhibited the best performance among the four models evaluated.Conclusion:This study successfully developed CNN-Dinov2, an automated classification model for Gram staining images. It offers valuable support for the rapid diagnosis of bloodstream infections caused by Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, demonstrating practical utility.

Objective:To classify the etiology of secondary hemophagocytic syndrome (sHLH) and explore its clinical, laboratory and therapeutic characteristics in order to deepen the understanding of the disease.Method:A retrospective observational study was conducted on sHLH patients who were treated at Peking University People's Hospital from January 2016 to December 2021. Patients under the age of 18 and those with missing clinical data were excluded. The distribution of departments visited and etiologies of sHLH were analyzed. Baseline data, clinical characteristics, complications, laboratory data, treatment, and in-hospital outcomes of sHLH were collected. The sHLH patients were then divided into 3 groups including malignancy group, macrophage activation syndrome (MAS) group and other etiologies (mainly infection) group. Intergroup comparisons were performed using chi-square tests, analysis of variance, Mann-Whitney tests, and other statistical methods.Results:A total 169 patients were enrolled, among these patients, 27.8% were malignancy-related HLH, 47.9% were MAS, and 24.3% were other etiologies related HLH. Statistical analysis revealed that the clinical characteristics of other etiological group was highly consistent with the malignancy group, including more and severer peripheral blood cell reduction, higher sCD25 levels, more Epstein-Barr virus infection, and the prognosis was similar, both were with more than 50% in-hospital mortality. And the incidence of hemophagocytosis was highest in other etiological groups (65.9%). In contrast, MAS group was with an obviously lower mortality of 17.3% ( P<0.05). Meanwhile, treatments including methylprednisolone pulse, cyclosporine A and interleukin-2 were used frequently in MAS group. Conclusion:Malignancy related HLH and other etiologies related HLH exhibit more similar clinical characteristics and prognosis, while the MAS group, has a milder overall condition and better prognosis.