Objective: To explore the latent classes of peer victimization among junior high school students, and to analyze the association between the characteristics of different classes and non suicidal self injury(NSSI) behavior,so as to provide scientific basis for the prevention and intervention of NSSI behavior among junior high school students. Methods: A census method was used to select 19 738 students from 16 junior high schools in a central urban area of Wuhan as research subjects from November to December 2024. The Multidimensional Peer Victimization Scale and a self designed NSSI questionnaire were used to assess peer victimization and NSSI behaviors. Latent profile analysis (LPA) was used to identify latent classes of peer victimization among students by sex. Binary Logistic regression model was used to analyze the association between latent classes of peer victimization and NSSI behavior among junior high school students of different genders. Results: Latent patterns of peer victimization among boys were classified into 4 groups:no victimization group (85.8%, n =8 874), low victimization-verbal type group (6.6%, n =682), low victimization-mixed type group (5.7%, n =587) and high victimization group (1.9%, n =195); girls were classified into 4 groups:no victimization group (87.0%, n =8 179), low victimization group (7.5%, n =702), high victimization-social type group (3.8%, n =360) and high victimization-physical type group (1.7%, n =159). After controlling for only child status, place of residence, parenting style, and self rated family socioeconomic status, binary Logistic regression analysis showed that the risk of NSSI behavior among boys increased sequentially in the low victimization-verbal type group( OR =4.14, 95% CI =3.22-5.31), low victimization-mixed type group ( OR =4.75, 95% CI =3.68-6.13), and high victimization group ( OR =8.26, 95% CI =5.83-11.71) compared to the no victimization group; the risks of NSSI behavior among girls increased sequentially in the low victimization group ( OR =3.06, 95% CI = 2.51-3.72), high victimization-social type group ( OR =4.01, 95% CI =3.13-5.15), and high victimization-physical type group ( OR =6.18, 95% CI =4.38-8.73) compared to the no victimization group (all P <0.05). Conclusions There are gender differences in the latent classes of peer victimization among junior high school students and the risk of NSSI behavior varies across different victimization classes. Targeted prevention and intervention should be implemented based on the gender of junior high school students and the characteristics of peer victimization students experience.

ObjectiveTo investigate the effects and underlying mechanisms of Shenqi Wenfei prescription （SQWF） on chronic obstructive pulmonary disease （COPD）. MethodsA rat model of COPD with lung Qi deficiency was established using lipopolysaccharide （LPS） combined with cigarette smoke. Forty-eight SD rats were randomly divided into a blank group， a model group， low-， medium-， and high-dose SQWF groups （2.835， 5.67， 11.34 g·kg^-1）， and a Yupingfeng group （1.35 g·kg^-1）. Drug administration began on day 29 after modeling and continued for 2 weeks. The general condition of the rats was observed， and the lung function in each group was assessed. Hematoxylin-eosin （HE） staining was used to observe pathological changes in lung tissue. The proportion of inflammatory cells in bronchoalveolar lavage fluid （BALF） was measured. Apoptosis in lung tissue was examined by terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling （TUNEL） staining. The release level of lactate dehydrogenase （LDH） in BALF was detected by a microplate assay. Reactive oxygen species （ROS） levels in lung tissue were detected using fluorescent probes. The levels of malondialdehyde （MDA）， total superoxide dismutase （SOD）， and reduced glutathione （GSH） in BALF were measured by biochemical methods. Ultrastructural changes in lung cells were observed via transmission electron microscopy. Double immunofluorescence staining was performed to detect the expression of thioredoxin-interacting protein （TXNIP） and nucleotide-binding oligomerization domain-like receptor protein 3 （NLRP3） in lung tissue. Western blot analysis was used to detect the protein expression of TXNIP， NLRP3， apoptosis-associated speck-like protein containing a CARD （ASC）， cysteinyl aspartate-specific protease-1 （Caspase-1）， Caspase-1 p20， gasdermin D （GSDMD）， GSDMD N-terminal active fragment （GSDMD-N）， interleukin-1β （IL-1β）， and IL-18 in lung tissue. Serum IL-1β and IL-18 levels were measured by ELISA. ResultsCompared with the blank group， the model group showed lassitude， fatigue， tachypnea， and audible phlegm sounds， and lung function significantly declined （P0.01）. Pulmonary emphysema and inflammatory cell infiltration were obvious. The level of inflammatory cells in BALF increased significantly （P0.05）. The number of TUNEL-positive cells increased （P0.01）. Levels of LDH， ROS， and MDA in BALF increased significantly （P0.01）， while GSH and SOD activities decreased significantly （P0.01）. Lung tissue cells showed irregular morphology， swollen mitochondria， disrupted cell membranes， and abundant vesicles， i.e.， pyroptotic bodies. Protein levels of TXNIP， NLRP3， ASC， Caspase-1， Caspase-1 p20， GSDMD， GSDMD-N， IL-1β， and IL-18 in lung tissue were significantly elevated （P0.01）， and serum IL-1β and IL-18 levels also increased significantly （P0.01）. Compared with the model group， each medication group showed alleviation of qi deficiency symptoms and improved lung function （P0.01）. Pulmonary emphysema and inflammatory cell infiltration were reduced. Inflammatory cell levels decreased （P0.05）. The number of TUNEL-positive cells decreased significantly （P0.01）. Levels of LDH， ROS， and MDA decreased significantly （P0.05）， while GSH and SOD activities significantly increased （P0.01）. Morphological and structural damage in lung tissue was improved to varying degrees. Protein levels of TXNIP， NLRP3， ASC， Caspase-1， Caspase-1 p20， GSDMD， GSDMD-N， IL-1β， and IL-18 in lung tissue significantly decreased （P0.01）， and serum IL-1β and IL-18 levels also decreased significantly （P0.05）. ConclusionSQWF can improve lung function and alleviate inflammatory responses in COPD rats. Its mechanism may be related to regulating the ROS/TXNIP/NLRP3 pathway and inhibiting pyroptosis.

ObjectiveTo investigate the effects and underlying mechanisms of Shenqi Wenfei prescription （SQWF） on chronic obstructive pulmonary disease （COPD）. MethodsA rat model of COPD with lung Qi deficiency was established using lipopolysaccharide （LPS） combined with cigarette smoke. Forty-eight SD rats were randomly divided into a blank group， a model group， low-， medium-， and high-dose SQWF groups （2.835， 5.67， 11.34 g·kg^-1）， and a Yupingfeng group （1.35 g·kg^-1）. Drug administration began on day 29 after modeling and continued for 2 weeks. The general condition of the rats was observed， and the lung function in each group was assessed. Hematoxylin-eosin （HE） staining was used to observe pathological changes in lung tissue. The proportion of inflammatory cells in bronchoalveolar lavage fluid （BALF） was measured. Apoptosis in lung tissue was examined by terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling （TUNEL） staining. The release level of lactate dehydrogenase （LDH） in BALF was detected by a microplate assay. Reactive oxygen species （ROS） levels in lung tissue were detected using fluorescent probes. The levels of malondialdehyde （MDA）， total superoxide dismutase （SOD）， and reduced glutathione （GSH） in BALF were measured by biochemical methods. Ultrastructural changes in lung cells were observed via transmission electron microscopy. Double immunofluorescence staining was performed to detect the expression of thioredoxin-interacting protein （TXNIP） and nucleotide-binding oligomerization domain-like receptor protein 3 （NLRP3） in lung tissue. Western blot analysis was used to detect the protein expression of TXNIP， NLRP3， apoptosis-associated speck-like protein containing a CARD （ASC）， cysteinyl aspartate-specific protease-1 （Caspase-1）， Caspase-1 p20， gasdermin D （GSDMD）， GSDMD N-terminal active fragment （GSDMD-N）， interleukin-1β （IL-1β）， and IL-18 in lung tissue. Serum IL-1β and IL-18 levels were measured by ELISA. ResultsCompared with the blank group， the model group showed lassitude， fatigue， tachypnea， and audible phlegm sounds， and lung function significantly declined （P0.01）. Pulmonary emphysema and inflammatory cell infiltration were obvious. The level of inflammatory cells in BALF increased significantly （P0.05）. The number of TUNEL-positive cells increased （P0.01）. Levels of LDH， ROS， and MDA in BALF increased significantly （P0.01）， while GSH and SOD activities decreased significantly （P0.01）. Lung tissue cells showed irregular morphology， swollen mitochondria， disrupted cell membranes， and abundant vesicles， i.e.， pyroptotic bodies. Protein levels of TXNIP， NLRP3， ASC， Caspase-1， Caspase-1 p20， GSDMD， GSDMD-N， IL-1β， and IL-18 in lung tissue were significantly elevated （P0.01）， and serum IL-1β and IL-18 levels also increased significantly （P0.01）. Compared with the model group， each medication group showed alleviation of qi deficiency symptoms and improved lung function （P0.01）. Pulmonary emphysema and inflammatory cell infiltration were reduced. Inflammatory cell levels decreased （P0.05）. The number of TUNEL-positive cells decreased significantly （P0.01）. Levels of LDH， ROS， and MDA decreased significantly （P0.05）， while GSH and SOD activities significantly increased （P0.01）. Morphological and structural damage in lung tissue was improved to varying degrees. Protein levels of TXNIP， NLRP3， ASC， Caspase-1， Caspase-1 p20， GSDMD， GSDMD-N， IL-1β， and IL-18 in lung tissue significantly decreased （P0.01）， and serum IL-1β and IL-18 levels also decreased significantly （P0.05）. ConclusionSQWF can improve lung function and alleviate inflammatory responses in COPD rats. Its mechanism may be related to regulating the ROS/TXNIP/NLRP3 pathway and inhibiting pyroptosis.

ObjectiveGastric hemorrhage is one of the most common and life-threatening emergencies of the upper digestive tract. Early identification and continuous monitoring are essential for reducing rebleeding rates and mortality, particularly within the critical early hours after onset. Although endoscopy and radiological imaging can accurately localize bleeding sites, these approaches are invasive, resource-intensive, and unsuitable for continuous bedside monitoring. Electrical impedance tomography (EIT), as a noninvasive and radiation-free functional imaging technique, offers real-time visualization of conductivity distribution and has the potential for detecting intragastric bleeding based on the electrical contrast between blood and surrounding gastric tissues. In this study, a three-dimensional gastric EIT (3D-gEIT) framework is proposed to achieve noninvasive, real-time, and dynamic monitoring of gastric hemorrhage, with emphasis on spatial localization and quantitative volume assessment. MethodsA three-dimensional upper-abdominal simulation model incorporating the stomach, gastric wall, gastric contents, and surrounding tissues was established. Three electrode configurations, namely the dual layer ring, the four layer staggered ring, and the opposed dual plane array, were designed and systematically compared to evaluate their influence on depth sensitivity and spatial resolution. Based on the Tikhonov-Noser hybrid regularization scheme, a region-clustering constraint was introduced to develop the TK-Noser-RCC algorithm. This approach aggregates spatially adjacent elements with similar conductivity variations, thereby enhancing structural continuity and suppressing isolated noise artifacts. To validate the proposed framework, an upper-abdominal physical phantom was constructed using agar to simulate background tissue conductivity. Hemispherical high-conductivity inclusions with volumes ranging from 10 ml to 50 ml were attached to the inner gastric wall to mimic localized bleeding under different gastric filling states. Boundary voltages were acquired under a 120 kHz excitation current and reconstructed using the TK-Noser-RCC algorithm. Furthermore, an in vivo animal experiment was performed using a porcine model with adult-scale abdominal dimensions. A total of 100 ml of autologous blood was injected incrementally into the stomach to simulate progressive gastric hemorrhage, and time-difference EIT reconstruction was conducted at each injection stage to assess the dynamic system response under physiological conditions. ResultsSimulation results demonstrated that the opposed dual-plane electrode array achieved superior depth sensitivity distribution and spatial resolution. For a 40 ml hemorrhage model, the average ICC and SSIM improved by 55.9% and 38.8% compared with the dual-layer ring configuration, and by 64.0% and 39.5% compared with the four-layer staggered configuration. The proposed region-clustering constraint significantly enhanced reconstruction stability. Under added Gaussian noise of 40 dB and 30 dB, ICC values remained approximately 0.85, indicating effective artifact suppression and preservation of boundary integrity. In physical phantom experiments, reconstructed hemorrhage volumes increased approximately linearly with the preset hemispherical volumes, and the reconstructed high-conductivity regions closely matched the actual bleeding locations. Both empty-stomach and full-stomach conditions were evaluated, demonstrating that the opposed dual-plane configuration maintained stable imaging performance across varying gastric contents. In the animal experiment, reconstructed low-impedance regions expanded progressively with increasing injected blood volume. The spatial localization of the hemorrhage remained stable throughout the procedure, and no significant artifacts were observed. Quantitative analysis showed that reconstructed volume and average conductivity variation exhibited an approximately linear growth trend with injected blood volume, confirming the sensitivity of the system to dynamic intragastric conductivity changes. ConclusionThe proposed 3D-gEIT framework enables quantitative reconstruction of gastric hemorrhage volume and spatial distribution with improved depth sensitivity, structural continuity, and noise robustness compared with conventional EIT approaches. By integrating optimized electrode configuration and a region-clustering-constrained reconstruction algorithm, the system provides stable dynamic monitoring under both controlled phantom conditions and in vivo physiological environments. This method offers a noninvasive, real-time, and low-cost imaging strategy for early diagnosis, postoperative monitoring, and bedside surveillance of gastric bleeding.

Objective:To investigate the survival outcomes and prognostic factors in patients undergoing radical resection for pancreatic body and tail cancer.Methods:A retrospective case series study was conducted on 992 patients who underwent radical resection for pancreatic body and tail cancer at the Pancreatic Center of the First Affiliated Hospital of Nanjing Medical University from January 2016 to June 2024. In this study, 577 (58.2%) were male and 415 (41.8%) were female,with an age of (65±9) years (range: 26 to 86 years). Follow-up continued until June 2024. Survival rates were estimated using the Kaplan-Meier method,and prognostic factors were identified using univariate and multivariate Cox proportional hazards models.Results:Among 992 patients,open surgery was the predominant approach (89.1%, 884/992), and radical antegrade modular pancreatosplenectomy (RAMPS) was performed in 317 patients (32.0%). Combined organ resection,venous resection,and arterial resection were performed in 23.5%, 9.3%,and 11.2% of patients,respectively. The rates of R0, R1-1 mm, and R1-direct resections were 49.8% (494/992),41.5% (412/992), and 8.7% (86/992),respectively. Stage ⅡB was the most common TNM stage (32.2%,319/992). A total of 801 patients (80.8%) received adjuvant chemotherapy. The median follow-up period was 32.0(8.8) months(range:3.2 to 105.3 months),during which 508 patients (51.2%) died. The overall median survival (OS) was 26.4 months,with 1-,3-, and 5-year survival rates of 79.0%,40.0%, and 29.0%, respectively. In the recent five years (from 2020 to 2024), the median OS improved significantly to 34.1 months compared to 20.0 months from 2016 to 2019 ( P<0.01). Histological subtype analysis showed that the median OS time was 26.7 months for pancreatic ductal adenocarcinoma (PDAC, n=855),58.9 months for invasive intraductal papillary mucinous carcinoma (IPMC, n=32),and 15.7 months for adenosquamous carcinoma of pancreas (ASCP, n=73) ( P=0.001). Among PDAC patients, adjuvant chemotherapy significantly improved survival (29.1 months vs. 14.4 months, P<0.01);in IPMC patients, adjuvant chemotherapy also extended survival (65.7 months vs. 58.9 months, P=0.047). Although ASCP patients receiving chemotherapy had a longer median OS time than those without (18.8 months vs. 8.9 months),the difference was not statistically significant ( P=0.151). Multivariate Cox regression analysis in PDAC patients indicated that adjuvant chemotherapy, R0 resection, T stage,N stage,and tumor differentiation were independent prognostic factors ( P<0.01). The median OS time by TNM stage was:not reached for stage ⅠA, 51.6 months for ⅠB, 25.5 months for ⅡA, 23.7 months for ⅡB, 23.0 months for Ⅲ, and 14.4 months for Ⅳ. The median OS time for R0,R1-1 mm,and R1-direct resections was 34.1,24.7,and 15.7 months,respectively ( P<0.01). Conclusion:Adjuvant chemotherapy,R0 resection,tumor stage,and differentiation are independent prognostic factors for pancreatic body and tail cancer.

Objective:To investigate the survival outcomes and prognostic factors in patients undergoing radical resection for pancreatic body and tail cancer.Methods:A retrospective case series study was conducted on 992 patients who underwent radical resection for pancreatic body and tail cancer at the Pancreatic Center of the First Affiliated Hospital of Nanjing Medical University from January 2016 to June 2024. In this study, 577 (58.2%) were male and 415 (41.8%) were female,with an age of (65±9) years (range: 26 to 86 years). Follow-up continued until June 2024. Survival rates were estimated using the Kaplan-Meier method,and prognostic factors were identified using univariate and multivariate Cox proportional hazards models.Results:Among 992 patients,open surgery was the predominant approach (89.1%, 884/992), and radical antegrade modular pancreatosplenectomy (RAMPS) was performed in 317 patients (32.0%). Combined organ resection,venous resection,and arterial resection were performed in 23.5%, 9.3%,and 11.2% of patients,respectively. The rates of R0, R1-1 mm, and R1-direct resections were 49.8% (494/992),41.5% (412/992), and 8.7% (86/992),respectively. Stage ⅡB was the most common TNM stage (32.2%,319/992). A total of 801 patients (80.8%) received adjuvant chemotherapy. The median follow-up period was 32.0(8.8) months(range:3.2 to 105.3 months),during which 508 patients (51.2%) died. The overall median survival (OS) was 26.4 months,with 1-,3-, and 5-year survival rates of 79.0%,40.0%, and 29.0%, respectively. In the recent five years (from 2020 to 2024), the median OS improved significantly to 34.1 months compared to 20.0 months from 2016 to 2019 ( P<0.01). Histological subtype analysis showed that the median OS time was 26.7 months for pancreatic ductal adenocarcinoma (PDAC, n=855),58.9 months for invasive intraductal papillary mucinous carcinoma (IPMC, n=32),and 15.7 months for adenosquamous carcinoma of pancreas (ASCP, n=73) ( P=0.001). Among PDAC patients, adjuvant chemotherapy significantly improved survival (29.1 months vs. 14.4 months, P<0.01);in IPMC patients, adjuvant chemotherapy also extended survival (65.7 months vs. 58.9 months, P=0.047). Although ASCP patients receiving chemotherapy had a longer median OS time than those without (18.8 months vs. 8.9 months),the difference was not statistically significant ( P=0.151). Multivariate Cox regression analysis in PDAC patients indicated that adjuvant chemotherapy, R0 resection, T stage,N stage,and tumor differentiation were independent prognostic factors ( P<0.01). The median OS time by TNM stage was:not reached for stage ⅠA, 51.6 months for ⅠB, 25.5 months for ⅡA, 23.7 months for ⅡB, 23.0 months for Ⅲ, and 14.4 months for Ⅳ. The median OS time for R0,R1-1 mm,and R1-direct resections was 34.1,24.7,and 15.7 months,respectively ( P<0.01). Conclusion:Adjuvant chemotherapy,R0 resection,tumor stage,and differentiation are independent prognostic factors for pancreatic body and tail cancer.

Background: and Purpose In patients with large vessel occlusion (LVO), intravenous thrombolysis (IVT) frequently alters thrombus location; however, the clinical impact of this phenomenon remains unclear. We aimed to compare post-IVT thrombus dynamics between tenecteplase and alteplase and to evaluate the association between thrombus dynamics and 3-month outcomes. Methods: This retrospective study analyzed prospectively collected, multicenter data from consecutive patients with LVO who underwent bridging therapy between January 2022 and December 2024. Thrombus dynamics were classified as resolution, migration, or stability. Analyses incorporated propensity score matching with weighting to balance baseline characteristics. Results: Of the 806 initially included patients, 746 were included after matching (373 treated with tenecteplase and 373 treated with alteplase). The incidence of thrombus migration was significantly higher in the tenecteplase group than in the alteplase group (19.3% vs. 11.3%; odds ratio [OR]: 1.92; 95% confidence interval [CI] 1.27–2.91). The advantage of tenecteplase over alteplase was restricted to patients with an IVT-to-puncture time of <60 minutes (18.6% vs. 6.2%; p=0.001) and was no longer significant when the interval ≥60 minutes (19.7% vs. 15.0%; p=0.204; pinteraction=0.043). Additionally, thrombus migration was associated with a better functional outcome (OR: 1.62; 95% CI 1.04–2.53). Finally, tenecteplase was associated with improved functional independence compared with alteplase (OR: 1.43; 95% CI 1.04–1.95). Conclusions Tenecteplase demonstrated superior efficacy in inducing thrombus migration compared with alteplase, particularly within 60 minutes of IVT administration. Thrombus migration independently predicted improved functional independence. These findings support the preferential use of tenecteplase for bridging therapy in patients with LVO.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) is higher among individuals with inflammatory bowel disease (IBD) than in the general population. Emerging evidence indicates that the development of MASLD in patients with IBD may occur independently of traditional metabolic risk factors, suggesting a unique pathophysiological mechanism distinct from conventional MASLD pathways. Bile acids (BAs), which act as critical signaling molecules in enterohepatic circulation, play an essential role in maintaining gastrointestinal homeostasis through bidirectional gut–liver axis communication. These molecules are increasingly recognized as pivotal regulators in the progression of both MASLD and IBD. While previous studies have characterized the dynamics of BA in blood or fecal samples under both conditions, a comprehensive understanding of their metabolic profiles and the associated interactions within the gut–liver axis is still lacking. This review synthesizes current evidence from studies employing BA metabolomics to investigate IBD and MASLD. By focusing on BA signatures, we summarize conserved alterations between these two conditions and their potential mechanisms of disease progression. Our review advances understanding of BA-mediated pathways in IBD and MASLD, providing a foundation for assessing their roles in contributing to the pathogenesis of MASLD in patients with IBD.

Objective To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.