Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Mycobacterium tuberculosis ( Mtb) is the causative agent of tuberculosis in humans and animals. Mtb invades the host′s lungs via airborne transmission, infects macrophages and causes tuberculosis. In some cases, the infection can spread to other tissues and organs. Despite the availability of several drugs for the treatment of tuberculosis, the emergence of multi-drug-resistant tuberculosis has led to high morbidity and mortality rates worldwide. Therefore, there is an urgent need for researchers to develop new anti-tuberculosis drugs that can treat tuberculosis more efficiently. Recent studies have shown that the virulence factors of Mtb play a crucial role in its pathogenicity. These factors primarily include secreted proteins, transcription factors, proteases, stress response proteins, metabolism-associated proteins, and cell-surface components. By evading the host′s immune surveillance through mechanisms such as anti-oxidative stress, regulating nutrient synthesis and metabolism, and modulating host cells apoptosis, Mtb is able to achieve long-term survival and spread with in the host. Understanding the mechanisms of Mtb virulence factors can provide new directions for targeted tuberculosis therapy. Therefore, knowledge of these virulence factors is essential for the development of new vaccines and anti-tuberculosis drugs. In this review, we summarize the latest research progress in the virulence factors of Mtb to provide a reference for targeted treatment of tuberculosis.

After hepatitis B virus (HBV) infection, numerous patients will develop serious sequelae such as liver failure and liver cancer. Finding a way to eradicate HBV has emerged as a top priority for the treatment of HBV infection. Clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) gene editing system represents a potent tool for genome manipulation. Many studies have confirmed the capability of the CRISPR/Cas9 gene editing system to remove the covalently closed circular DNA of HBV in cells, which has great potential for application in the treatment of chronic HBV infection. This article summarizes the application status and research progress of CRISPR/Cas9 system in anti-HBV infection, and explores the challenges that may be encountered.

Objective To establish primary and simian virus 40(SV40)T antigen transformed human vascular en-dothelial cell models,and provide available resources for endothelial research.Methods Human umbilical vein endothelial cells(HUVEC),human umbilical artery endothelial cells(HUAEC),great saphenous vein endothelial cells(GSVEC)and endothelial cells form endometrium and liver tissue were isolated and cultured respectively.Then,the primary endothelial cells were transformed by lentivirus containing SV40 big T and small T antigens,and continuously subcultured in vitro.The expression of CD31 was detected by flow cytometry,species identification-and mycoplasma detection by PCR,and cell identity was identified by STR detection.The transformed ECs were checked for HLA types.Some of them were tested for RNA expression profile and infected by Cas9 lentivirus to es-tablish stable clones.Results Totally 187 cell lines of transformed HUVEC,1 of transformed HUAEC,5 of trans-formed GSVEC,1 of transformed endothelial cells from endometrium and 1 of transformed endothelial cells from liv-er tissue,and 9 monoclonal HUVEC cell lines stably expressing Cas9 protein were established.All the transformed umbilical endothelial cells were CD31 positive ranging from 20%-90%for 20 cases,while for the rest 168 cases the positive rate was more than 90%.RNA expression revealed stable activation of cell proliferation(cell cycle and DNA synthesis).Their species were identified as human origin.The STR results were consistent with those of the primary culture and unique,and there was no mycoplasma contamination.All these cells could be obtained with the sharing services of National Science and Technology Infrastructure,the National Biomedical Cell-line Resource cen-ters(NSTI-BMCR).Conclusions A series of primary and SV40 T antigen transformed human vascular endothelial cell models have been established,which provide a tool for the study of cardiovascular diseases,inflammation,tumors and immune-related diseases.

ObjectiveTo investigate the expression level of lysophosphatidyllecithin acyltransferase 4 （LPCAT4） in pancreatic cancer and its effect on the proliferation of pancreatic cancer cells. MethodsIn this study， the differentially expressed genes of patients with KRAS mutant and wild-type pancreatic cancer were analyzed by online database LinkedOmics. The LPCAT4 expression in pancreatic cancer tissues was analyzed online by the University of Alabama at Birmingham Cancer Data Analysis （UALCAN）， Sangerbox and gene expression profile interaction analysis 2 （GEPIA2）. Kaplan-Meier Plotter database was used to explore the correlation between LPCAT4 and the prognosis of patients with pancreatic cancer. The expression of LPCAT4 in human pancreatic cancer cells were detected by quantitative real-time PCR and Western blot analysis. LPCAT4 was knocked down in the high-expressing SW1990 cell line and overexpressed in the low-expressing MIA PaCa-2 cell line. The effects of LPCAT4 expression on cell proliferation were assessed using CCK-8 and EdU assays. STRING and GEPIA2 databases were used to obtain LPCAT4 binding and coexpressed genes in tumors， which were then analyzed by GO and KEGG. ResultsAnalysis of the LinkedOmics online database revealed a significant upregulation of LPCAT4 in patients with KRAS mutant pancreatic cancer compared to patients with KRAS wild-type pancreatic cancer. The online analysis of GEPIA2， UALCAN and Sangerbox 3.0 showed that the expression of LPCAT4 was higher in pancreatic cancer than in normal tissues. Analysis of the Kaplan-Meier Plotter database revealed that high LPCAT4 expression was associated with poorer prognosis in pancreatic cancer patients.Western blot and qPCR results showed that expression of LPCAT4 in pancreatic cancer cell lines was significantly higher than in normal pancreatic ductal epithelial cells. Knockdown of LPCAT4 in SW1990 cells inhibited proliferation， while overexpression in MIA PaCa-2 cells promoted proliferation. Enrichment analysis indicated that LPCAT4 was closely related to sulfur metabolism. ConclusionsLPCAT4 is highly expressed in pancreatic cancer and is associated with poor prognosis of patients. It plays a significant regulatory role in the proliferation of pancreatic cancer cells， with its expression level closely correlated with cell proliferation capacity. These findings reveal the critical role of LPCAT4 in the malignant progression of pancreatic cancer and provide important evidence for its potential as a therapeutic target.

ObjectiveTo investigate the epidemiological feature of Mpox infection and genetic characteristics of Mpox viruses (MPXVs), so as to understand the etiological evolution of the pathogen. MethodsThe cases infected with MPXVs were originated from Changning District, Shanghai from July 20 to August 24 in 2023. Epidemiological investigations were conducted, and throat swabs, anal swabs, or vesicle fluid were collected for MPXVs nucleic acid testing. High-throughput sequencing was performed using Miniseq of the Illumina sequencing platform, and thereafter the sequences were concatenated and analyzed using the online analysis tool Nextclade. An evolutionary tree was constructed using the MEGA 11 software. ResultsAll 8 cases were male, with an average age of (35.76±7.00) years. Among them, 6 cases were identified through active hospital visits, and 2 cases were discovered during contact tracing for Mpox cases. Within the 21 days preceding the disease onset, all cases had male-male sexual behaviors, and the incubation period ranged from 6 to 10 days. 3 cases had a history of sexually transmitted diseases (STDs). MPXVs nucleic acid testing indicated that the detection rate of MPXVs was found to be 25.00% for throat swabs, 87.50% for anal swabs, and 100.00% for vesicle fluid, with statistically significant differences (χ²=11.052, P=0.004). Sequencing analyses using the online tool Nextclade indicated that all 8 MPXVs belonged to the West African clade Ⅱb, 4 MPXVs were classified as C.1 sub-lineages, and 4 MPXVs were identified as C.1.1 sub-lineages. Phylogenetic analysis using MEGA 11 indicated that 5 MPXVs were classified as Lineage C.1.1, closely related to the prevalent strains in Portugal and other European regions. ConclusionThe MPXVs sequences from Changning District are clssified into clade Ⅱb, lineage C.1.1. The detection rates of vesicle fluid and anal swabs for MPXVs are significantly higher than that of throat swabs.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective To investigate the protective effect and mechanism of the antioxidant Lutein in a mouse model of dry age-related macular degeneration(AMD)induced by hydroquinone.Methods Twenty-four specific pathogen-free(SPF)grade male C57BL/6 mice,aged 6-8 weeks,were randomly divided into 3 groups(n=8 per group).The control group was fed a standard diet and water for 3 months.The model group and the drug intervention group received 8 g·L-1 hydroquinone in drinking water combined with a high-fat diet for 2 months to establish the dry AMD mouse model.After modeling,the model group resumed a standard diet and water for 1 month.The drug group received oral gavage daily at 8:00 AM,administered Lutein at 0.1 g per kg body weight(dissolved in distilled water),for 1 month.At the experimental endpoint,all mice were euthanized,and samples were collected for analysis.The ultrastructure of retinal pigment epithelial(RPE)cells was observed by electron microscopy.Serum activities of superoxide dismutase(SOD),catalase(CAT),and glutathione peroxidase(GSH-Px)were measured using a microplate reader.mRNA expression levels of nuclear factor ery-throid 2-related factor 2(Nrf2),heme oxygenase-1(HO-1),and glutamate-cysteine ligase(GCL)in retinal tissue were de-tected by real-time quantitative PCR.Protein expression levels of Nrf2,HO-1,and GCL in retinal tissue were determined by Western blot.Results In the model group,mitochondria and intracellular organelles in RPE cells exhibited vacuolar de-generation,with compromised structural integrity;simultaneously,microvilli were disorganized and significantly reduced in number.In the drug group,the mitochondrial structure of RPE cells was relatively well-preserved,with morphology largely normal;microvilli structure was clear,and density showed some recovery compared to the model group.Compared to the control group,serum activities of SOD,CAT,and GSH-Px were significantly decreased in the model group(all P＜0.01).Compared to the model group,serum activities of SOD,CAT,and GSH-Px were significantly increased in the drug group(all P＜0.05).Compared to the control group,retinal mRNA expression levels of Nrf2,HO-1,and GCL were significantly decreased in the model group(all P＜0.01).Compared to the model group,retinal mRNA expression levels of Nrf2,HO-1,and GCL were significantly increased in the drug group(all P＜0.01).Compared to the control group,retinal protein ex-pression levels of Nrf2,HO-1,and GCL were decreased in the model group(all P＜0.05).Compared to the model group,retinal protein expression levels of Nrf2,HO-1,and GCL were increased in the drug group(all P＜0.05).Conclusion The antioxidant Lutein promotes the expression of downstream target genes HO-1 and GCL by activating the Nrf2 signaling pathway,which not only enhances the biosynthesis levels of antioxidant enzymes(SOD,CAT,GSH Px),but also effec-tively inhibits oxidative stress response by enhancing autophagic activity.

Objective To investigate the protective effect and mechanism of the antioxidant Lutein in a mouse model of dry age-related macular degeneration(AMD)induced by hydroquinone.Methods Twenty-four specific pathogen-free(SPF)grade male C57BL/6 mice,aged 6-8 weeks,were randomly divided into 3 groups(n=8 per group).The control group was fed a standard diet and water for 3 months.The model group and the drug intervention group received 8 g·L-1 hydroquinone in drinking water combined with a high-fat diet for 2 months to establish the dry AMD mouse model.After modeling,the model group resumed a standard diet and water for 1 month.The drug group received oral gavage daily at 8:00 AM,administered Lutein at 0.1 g per kg body weight(dissolved in distilled water),for 1 month.At the experimental endpoint,all mice were euthanized,and samples were collected for analysis.The ultrastructure of retinal pigment epithelial(RPE)cells was observed by electron microscopy.Serum activities of superoxide dismutase(SOD),catalase(CAT),and glutathione peroxidase(GSH-Px)were measured using a microplate reader.mRNA expression levels of nuclear factor ery-throid 2-related factor 2(Nrf2),heme oxygenase-1(HO-1),and glutamate-cysteine ligase(GCL)in retinal tissue were de-tected by real-time quantitative PCR.Protein expression levels of Nrf2,HO-1,and GCL in retinal tissue were determined by Western blot.Results In the model group,mitochondria and intracellular organelles in RPE cells exhibited vacuolar de-generation,with compromised structural integrity;simultaneously,microvilli were disorganized and significantly reduced in number.In the drug group,the mitochondrial structure of RPE cells was relatively well-preserved,with morphology largely normal;microvilli structure was clear,and density showed some recovery compared to the model group.Compared to the control group,serum activities of SOD,CAT,and GSH-Px were significantly decreased in the model group(all P＜0.01).Compared to the model group,serum activities of SOD,CAT,and GSH-Px were significantly increased in the drug group(all P＜0.05).Compared to the control group,retinal mRNA expression levels of Nrf2,HO-1,and GCL were significantly decreased in the model group(all P＜0.01).Compared to the model group,retinal mRNA expression levels of Nrf2,HO-1,and GCL were significantly increased in the drug group(all P＜0.01).Compared to the control group,retinal protein ex-pression levels of Nrf2,HO-1,and GCL were decreased in the model group(all P＜0.05).Compared to the model group,retinal protein expression levels of Nrf2,HO-1,and GCL were increased in the drug group(all P＜0.05).Conclusion The antioxidant Lutein promotes the expression of downstream target genes HO-1 and GCL by activating the Nrf2 signaling pathway,which not only enhances the biosynthesis levels of antioxidant enzymes(SOD,CAT,GSH Px),but also effec-tively inhibits oxidative stress response by enhancing autophagic activity.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.