Objective:To evaluate the imaging stability of a portable boom-type ophthalmic ultra-high-resolution optical coherence tomography (OCT) device in multiple application scenarios.Methods:The boom-type mode and handheld mode of the portable boom-type OCT and the desktop OCT were used to perform three-dimensional imaging tests on three healthy adults undergoing physical examinations at the Zhongshan Ophthalmic Center, Sun Yat-sen University as well as on OEMI-7 model eyes in a sitting position.The same two modes of the portable boom-type OCT were used to perform three-dimensional imaging on four awake non-sedated infants, two sedated infants and four healthy adults in the supine position.The obtained 3D imaging data were processed using a correlation analysis method between adjacent B-scans, and the offset of B-scan in the axial (z-axis) and the fast axis transverse (x-axis) were calculated.The procedures for in vivo human eye experiments followed the Declaration of Helsinki and were approved by the Ethics Committee of Zhongshan Ophthalmic Center, Sun Yat-sen University (No.2020 KYPJ154).All subjects and infant guardians signed the informed consent form. Results:Compared with the handheld imaging mode, the axial and fast axis lateral motion offsets of the model eye were significantly reduced in the boom-type imaging mode from (124.00±12.49)μm to (48.00±15.87)μm and from (24.00±1.00)μm to (2.67±0.57)μm, respectively ( t=2.932, 4.337; both P<0.001).In both human and model eyes, the axial and fast axis lateral motion offsets of the boom-type mode were significantly lower than in the traditional handheld operation mode (both P<0.001).The axial and lateral motion offsets between the boom-type mode and desk-top OCT imaging were comparable, without significant differences (both P>0.05).In both sedated and awake, non-sedated infants in the supine position, the axial offset of the portable boom-type OCT system was similar to that of the healthy adults, without significant difference in the overall comparison ( P=0.385), and the lateral offsets were higher than those of healthy adults, with statistically significant differences (both P=0.013).There was no significant difference in axial deviation between sedated and non-sedated infants ( P>0.05).The lateral deviation of non-sedated infants was higher than that of sedated infants, though the difference was not statistically significant ( P=0.247). Conclusions:The portable boom-type OCT system can maintain high-speed, high-resolution imaging performance while achieving imaging stability comparable to traditional desktop OCT systems.It is more suitable for bedside imaging of supine subjects, especially uncooperative infants, and has good clinical application prospects.

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 evaluate the imaging stability of a portable boom-type ophthalmic ultra-high-resolution optical coherence tomography (OCT) device in multiple application scenarios.Methods:The boom-type mode and handheld mode of the portable boom-type OCT and the desktop OCT were used to perform three-dimensional imaging tests on three healthy adults undergoing physical examinations at the Zhongshan Ophthalmic Center, Sun Yat-sen University as well as on OEMI-7 model eyes in a sitting position.The same two modes of the portable boom-type OCT were used to perform three-dimensional imaging on four awake non-sedated infants, two sedated infants and four healthy adults in the supine position.The obtained 3D imaging data were processed using a correlation analysis method between adjacent B-scans, and the offset of B-scan in the axial (z-axis) and the fast axis transverse (x-axis) were calculated.The procedures for in vivo human eye experiments followed the Declaration of Helsinki and were approved by the Ethics Committee of Zhongshan Ophthalmic Center, Sun Yat-sen University (No.2020 KYPJ154).All subjects and infant guardians signed the informed consent form. Results:Compared with the handheld imaging mode, the axial and fast axis lateral motion offsets of the model eye were significantly reduced in the boom-type imaging mode from (124.00±12.49)μm to (48.00±15.87)μm and from (24.00±1.00)μm to (2.67±0.57)μm, respectively ( t=2.932, 4.337; both P<0.001).In both human and model eyes, the axial and fast axis lateral motion offsets of the boom-type mode were significantly lower than in the traditional handheld operation mode (both P<0.001).The axial and lateral motion offsets between the boom-type mode and desk-top OCT imaging were comparable, without significant differences (both P>0.05).In both sedated and awake, non-sedated infants in the supine position, the axial offset of the portable boom-type OCT system was similar to that of the healthy adults, without significant difference in the overall comparison ( P=0.385), and the lateral offsets were higher than those of healthy adults, with statistically significant differences (both P=0.013).There was no significant difference in axial deviation between sedated and non-sedated infants ( P>0.05).The lateral deviation of non-sedated infants was higher than that of sedated infants, though the difference was not statistically significant ( P=0.247). Conclusions:The portable boom-type OCT system can maintain high-speed, high-resolution imaging performance while achieving imaging stability comparable to traditional desktop OCT systems.It is more suitable for bedside imaging of supine subjects, especially uncooperative infants, and has good clinical application prospects.

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.

This paper summarized the clinical experience of Professor GE Linyi in treating ulcerative colitis （UC） by stages with the method of clearing. Professor GE believes that the core pathogenesis of UC is dampness and heat in the intestines， and by taking the method of clearing as the basis， he proposed four methods for treatment of UC including clearing and transforming， clearing and dispersing，clearing and moisterning， clearing and nourishing. The pathogenesis of UC in its active stage is dampness and heat in the intestines， congestion and stagnation of qi and blood， and accumulation of stasis toxins， for which the treatment method is to clear and transform， accompanied by clearing and dispersing method. In terms of the clearing and transforming method， Bai Tou Weng Decoction （白头翁汤） combined with Haungqin （Radix Scutellariae）， Machixian （Herba Portulacae） and Pugongying （Herba Taraxaci） is taken as the basic prescription to clear and transform dampness and heat， cool blood， resolve toxins and stop dysentery. For the clearing and dispersing method， medicinals to rectify qi such as Chaihu （Radix Bupleuri）， Cuxiangfu （Vingar Rhizoma Cyperi）， Muxiang （Radix Aucklandiae）， Zhiqiao （Fructus Aurantii）， and Binlang （Semen Arecae）， as well as those to regulate blood such as Danggui （Radix Angelicae Sinensis）， Cebaiye （Cacumen Platycladi） and Diyutan （Radix Sanguisorbae Carbonisatus） are recommended. The pathogenesis of the remission stage is healthy qi depletion and lingering pathogen of dampness and heat stasis toxin in the intestines， for which the method of clearing and nourishing， clearing and moistening can be used； the latter is mainly for people with yin fluids injury， and self-made Qingrun Yichang Decoction （清润益肠汤） is recommended， while the former is for those with spleen and stomach weakness， and self-made Qingyang Jianpi Decoction （清养健脾汤） can be used.

Objective To explore the diagnosis of clinically suspicious von Willebrand disease(vWD)in a family and its pathogene-sis.Methods The pedigree information and the biological specimen were collected from the clinically suspected VWD patient and her family members(4 persons in total)in Peking University First Hospital.The levels of platelet count(PLT),activated partial thrombo-plastin time(APTT),vWF antigen(vWF:Ag),vWF activity(vWF:Ac)and FⅧ activity(FⅧ:C)were detected,and vWF risto-cetin cofactor(vWF:RCo)assay,ristocetin-induced platelet aggregation assay(RIPA)and vWF collagen binding(vWF:CB)assay were performed for phenotype diagnosis.The peripheral blood genomic DNAs were extracted from the proband and her family members to perform whole-exome sequencing for identifying the mutation of vWF gene,The mutation site was analyzed by using bioinformation tools to explore the pathogenesis of the proband.Results The APTT of proband(m 1)was slightly prolonged and her vWF:Ag,vWF:Ac,vWF:RCo and vWF:CB were significantly decreased.There was no obvious aggregation in RIPA assay(1.0 mg/mL and 1.25 mg/mL).In her father(Ⅱ3),APTT,FⅧ:C,vWF:Ag,vWF:Ac and vWF:CB were normal,but vWF:RCo was slightly decreased.In her mother(Ⅱ4),APTT,FⅧ:C,vWF:Ag,vWF:RCo and vWF:CB were all normal,but vWF:Ac significantly decreased.In her brother(Ⅲ2),APTT and FⅧ:C were normal,but vWF:Ag,vWF:Ac,vWF:RCo and vWF:CB were reduced to varying degrees.In all the family members(father,mother and brpther),no apparent aggregation in RIPA(1.0 mg/mL)was shown.Genetic analysis showed that the proband(Ⅲ1)carried a compound heterozygous mutation of vWF gene c.7288-9T＞G and c.1117C＞T,her father(Ⅱ3)carried vWF gene c.7288-9T＞G heterozygous mutation,and vWF gene c.1117C＞T heterozygous mutation was presented in both mother(Ⅱ4)and brother(Ⅲ2).Conclusion According to the results of laboratory tests,the proband was diagnosed as type 2A vWD.The hetero-zygous mutation in vWF gene c.1117C＞T and c.7288-9T＞G may be the molecular mechanism leading to type 2A vWD in the proband.

Objective:To explore the feasibility of a stable pig-to-monkey orthotopic liver transplantation (LT) model and provide a favorable experimental tool for preclinical studies of xenotransplantation.Methods:In this retrospective analysis, the authors reviewed the perioperative conditions and outcomes of 7 pig-to-monkey orthotopic liver transplants performed by a xenotransplantation research team of Second Xiangya Hospital.Gene-edited Banna miniature pigs were selected as donors and rhesus monkeys with similar anatomical characteristics, physiology, biochemistry and immune mechanism to humans were selected as recipients for pig-monkey xenogeneic orthotopic LT.Based upon classic transplantation procedures, whole liver xenogeneic orthotopic transplantation was performed.Surgical processes were modified for minimizing intraoperative hemorrhage and shortening anhepatic period.A bile duct drainage tube was implanted for observing bile secretion.ATG + anti-CD20 + snake venom factor and FK-506 were utilized for immunoinduction pre-operation while tacrolimus, mycophenolate mofetil (MMF) and methyl prednisolone for postoperative immunomaintenance therapy.Antibiotics and antiviral agents were also applied and thrombin complex for improving coagulation functions.Results:All procedures were successfully completed.After the stability and maturity of our model, in case No.7, donor's acquisition operative duration was 42 min without heat ischemic time, donor's trimming time 87 min, donor cold retention time 128 min, recipient's operative duration 123 min and anhepatic phase 27 min.Subhepatic inferior vena cava was occluded for 38 min.Blood loss was around 10 ml.And 4/7 models survived post-operation and the longest survival time was 27 h. Among 3 non-survival cases, the causes were anesthesia accident (n=1) and immaturity of early operation (n=2). Model No.7 had a biliary secretion volume of 86 ml post-operation.Conclusions:Qualified donor acquisition, high-quality vascular anastomosis, intraoperative reduction of blood loss, shortening of anhepatic period, strict fluid replenishing and careful monitoring are essential for boosting the success rate of pig-monkey liver xenotransplantation model.Optimization of donor gene combination and advanced immunosuppression protocol help to further achieve the long-term survival of pig-monkey liver xenotransplantation model.

Objective:To explore the effect of health management based on constitution identification in perimenopausal women.Methods:From November 2020 to April 2021, 244 perimenopausal women from Doumen Street, Yuecheng District, Shaoxing were selected as the research subject by convenience sampling method, and divided into the control group (121 cases) and the observation group (123 cases) according to their respective communities. The control group adopted routine health management, while the observation group implemented health management based on constitution identification on the basis of routine health management. Three months after intervention, the perimenopausal symptom scores between the two groups were compared. One year after intervention, the number of individuals with biased constitution and quality of life scores between the two groups were compared.Results:Three months after intervention, the scores of hot flashes, sweating, insomnia, dizziness, fatigue, muscle and joint pain, headache, palpitations, and total score of perimenopausal symptom of the observation group were lower than those of the control group, with statistically significant differences ( P<0.05). Three months after intervention, there were no significant differences in scores of paraesthesia, mood swings, depressive suspicion, skin ant sensation, sexual difficulties, urinary tract infection between the observation group and the control group ( P>0.05). One year after intervention, the number of individuals with biased constitution of the observation group was less than that of the control group, and the difference was statistically significant ( P<0.05). One year after intervention, the scores of physical health, mental health, social relationship, and total score of quality of life of the observation group were higher than those of the control group, and the differences were statistically significant ( P<0.05). One year after intervention, there was no statistically significant difference in the score of the surrounding environment between the observation group and the control group ( P>0.05) . Conclusions:Health management based on constitution identification can improve the perimenopausal symptoms of perimenopausal women, gradually change their biased constitution towards a calm constitution, and improve their quality of life.

Objective To investigate the efficacy and safety of programmed cell death protein-1 (PD-1) monoclonal antibody on the treatment of malignant tumor after solid organ transplantation (SOT). Methods The relevant literatures in 7 databases were searched. The data on 54 cases of recipients with malignant tumors treated with PD-1 monoclonal antibody after SOT were collected, and the clinical effects and rejection of SOT recipients treated with PD-1 monoclonal antibody were analyzed. Results Total 32 acceptable articles including 54 SOT recipients were incorporated, including 43 males and 11 females aged 14-79 years old. There are 29 renal transplant recipients, 19 liver transplant recipients and 6 heart transplant recipients. The types of PD-1 monoclonal antibody agent used by SOT recipients included pembrolizumab for 28 patients and nivolumab for 26 patients. The overall remission rate, disease progression rate and fatality rate of PD-1 monoclonal antibody for postoperative malignant tumors of SOT recipients were 32% (17/54), 44% (24/54) and 36% (19/54), respectively. After treatment with PD-1 monoclonal antibody for postoperative malignant tumors of SOT recipients, the incidence of rejection was 39% (21/54), indicating no significant correlation between rejection and type of PD-1 monoclonal antibody (P > 0.05). Conclusions PD-1 monoclonal antibody can effectively treat postoperative malignant tumors of SOT recipients, and may induce rejection during the treatment. But rejection is not the most common cause for death of recipients.

Objective To evaluate the outcome of 1iver transplantation for acute-on-chronic liver failure (ACLF) patients .Methods We included 453 consecutive patients with previously cirrhosis who underwent liver transplantation between January 2013 and December 2017 .Patients were categorized as no ACLF (n=294) and ACLF(n=159) according to EASL-CLIF consortium criteria .Furthermore ,we used ACLF grades to categorize the ACLF patients .Their clinical data were reviewed and their 90-days survival outcomes were compared .Results Compared with the no ACLF group ,the length of stay in the ICU was significantly prolonged for all patients with ACLF ,and the 90-days survival rate after transplantation was significantly reduced in ACLF group .The length of stay in the ICU was shorter in Grade 1 and Grade 2 group when compared to Grade 3 group .The 90-days survival rate of no ACLF ,Grade 1 ,Grade 2 and Grade 3 group were 93 .20％ ,92 .59％ ,93 .33％ and 73 .68％ ,respectively .There were no statistically significant differences in 90-days survival rate among the no ACLF ,Grade 1 and Grade 2 group .However , the 90-days survive rate of Grade 3 group was lower than that of other groups .Conclusions Liver transplantation has been shown to be safe and effective with good outcome in patients with ACLF and should be offered in early course of ACLF before onset of multi-organ failure .