arcopenia is a systemic skeletal muscle disorder characterized by a decrease in skeletal muscle mass and progressive decline in function， with multiple signaling pathways involved in its occurrence and development. Among them， the AMP-activated protein kinase （AMPK） signaling pathway， as a key pathway regulating cellular energy homeostasis， plays an important role in the regulation of skeletal muscle metabolism and functional maintenance by improving abnormalities in glucose and lipid metabolism， balancing skeletal muscle protein synthesis and degradation， improving mitochondrial function， promoting autophagy， and inhibiting inflammatory responses and oxidative stress. This article reviews the research progress on how various traditional Chinese medicine （TCM） monomers， including polyphenols， flavonoids， and terpenoids； various traditional Chinese medicine extracts， such as those from Lycium barbarum ， Asini Corii Colla， and Panax quinquefolium ， and TCM compounds， such as Guiqi zhuangjin decoction， Jianpi qiangji granules， and Qigu capsules， intervene in sarcopenia by regulating the AMPK signaling pathway to promote muscle protein synthesis， inhibit protein degradation， improve mitochondrial function， and alleviate inflammation and oxidative stress. Additionally， their molecular mechanisms are explored. The aim is to deeply elucidate the basis of TCM in the prevention and treatment of sarcopenia and to provide theoretical support for the development of related innovative drugs.

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.

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 cumulative live birth rate (CLBR) and cost-effectiveness of fixed versus adjusted follicle-stimulation hormone (FSH) dosages in infertile women with different ovarian responses during their first assisted reproductive technology (ART) cycle.Methods:A retrospective real-world cohort study was conducted on 5 419 infertile women who underwent their first ART treatment at the Department of Reproductive Medicine of the First Affiliated Hospital of Nanjing Medical University between January 2013 and December 2017. All patients received an individualized starting dosage of gonadotropin. Based on whether FSH dosages were adjusted during controlled ovarian stimulation (COS), patients were divided into fixed-dosage group ( n=2 061) and adjusted-dosage group ( n=3 358). Clinical outcomes and FSH cost-effectiveness were compared between the two groups across different ovarian response groups, with CLBR as the primary outcome. Propensity score matching (PSM) and multivariable logistic regression were used to adjust for potential confounders. Results:FSH dosage adjustments were found in 62.0% (3 358/5 419) of cycles during COS. After PSM, baseline characteristics were comparable between the two groups (all P>0.05). After adjusting for confounders using multivariable logistic regression, FSH dosage adjustment was not significantly associated with CLBR ( OR=1.06, 95% CI: 0.94-1.20, P=0.332). Compared with the adjusted-dosage group, the fixed-dosage group showed no significant differences in CLBR in poor-, normal-, and high-responder groups (all P>0.05). The incidence of ovarian hyperstimulation syndrome (OHSS) did not differ significantly between the two groups ( P>0.05). In poor-, normal-, and high-responder groups, the total FSH dosages in the fixed-dose group [1 350 (375, 1 825) U, 1 200 (375, 1 500) U and 525 (375, 1 128) U, respectively] were significantly lower than those in the adjusted-dose group [1 875 (1 425, 2 294) U, P=0.001; 1 425 (450, 1 875) U, P<0.001; 600 (375, 1 425) U, P=0.020]. Similarly, average FSH costs in different ovarian response groups in the fixed-dosage group [4 725.0 (1 312.5, 6 387.5) yuan, 4 200.0 (1 312.5, 5 250.0) yuan and 1 837.5 (1 312.5, 3 947.3) yuan, respectively] were significantly lower than those in the adjusted-dosage group [6 562.5 (4 987.5, 8 028.1) yuan, P=0.001; 4 987.5 (1 575.0, 6 562.5) yuan, P<0.001; 2 100.0 (1 312.5, 4 987.5) yuan, P=0.020]. For normal-responders, the FSH cost per high-quality embryo in the fixed-dosage group [1 365.0 (875.0, 2 537.5) yuan] was significantly lower than that in the adjusted-dosage group [2 056.3 (1 268.8, 3 412.5) yuan, P<0.001]. Conclusion:FSH dosage adjustment during COS is not associated with CLBR or the incidence of OHSS. However, the fixed-dose group exhibited lower total FSH dosages and costs across different ovarian response populations. In the context of ART being covered by medical insurance, fixed FSH dosage may represent a more cost-effective ovarian stimulation protocol.

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*

Objective To explore the influence of optimistic personality on the sport motivation of college students,and investigate the mediating role of exercise self-efficacy and the gender differences.Methods A total of 520 questionnaires were distributed to students of a university in Shandong by convenience sampling method.The revised life orientation test,self-efficacy for exercise scale and sport motivation scale were used for online survey.Pearson correlation analysis was used to explore the correlations among variables,independent samples t-test was employed to analyze the gender differences of variables,and the mediating effect analysis was used to test the mediating effect of exercise self-efficacy between optimistic personality and sport motivation and the gender differeces.Results Totally 482 valid questionnaires were collected,and the effective rate was 92.7％,including 253 males(52.5％)and 229 females(47.5％).Pearson correlation analysis revealed significant correlations between optimistic personality,exercise self-efficacy and sport motivation in male and female students(all P＜0.01).Independent sample t-test results indicated that there were significant gender differences in exercise self-efficacy(t=2.84,P=0.005)and sport motivation(t=3.40,P＜0.001)among college students,with male students having higher levels than female students.The mediating effect analysis showed that the effect of optimistic personality on sport motivation can be partially mediated by exercise self-efficacy.There were gender differences in the mediating mechanism of optimistic personality → exercise self-efficacy → sport motivation.In males,exercise self-efficacy played a partial mediating role;in females,it played a full mediating role.The mediating effect values were 0.17 and 0.22,accounting for 60.7％and 71.0％of the total effect,respectively.Conclusion Optimistic personality affects sports motivation of college students by exercise self-efficacy.In addition,male students' optimistic personality has dual effects,which can directly stimulate their sport motivation and indirectly enhance their sport motivation by improving their exercise self-efficacy.Female students can enhance their sports motivation mainly by improving their exercise self-efficacy.

Objective To observe the value of prenatal ultrasound evaluation on morphology of Sylvian fissure(SF)during the second and third trimesters for screening fetal abnormal cerebral cortical development.Methods Totally 876 fetuses in the second and third trimesters were retrospectively included.Prenatal ultrasound was performed to observe the morphology of fetal SF and whether there was complicated abnormalities of the development of cerebral cortical and other structures.Then prenatal ultrasound was regularly reexamined,and the pregnancy outcome,while the growth and development of newborns were followed up.Results Among 876 fetuses,normal SF morphology was observed in 861 fetuses(861/876,98.29％),while 11 fetuses(11/876,1.26％)had delayed SF development(normal SF morphology but inconsistent with gestational week)and 4 fetuses(4/876,0.46％)had abnormal SF morphology,all complicated with abnormal cerebral cortical development and/or intracranial and extracranial structural malformations,and reexamination of prenatal ultrasound showed that SF morphology was consistent with the gestational week in 4 fetuses,SF morphology still did not match the gestational week in 4 fetuses,and SF morphology was still abnormal in 2 fetuses.Among these 15 fetuses,6 were successfully born and grew well after followed up until 10-15 months,4 were induced labor,while the rest 4(3 with delayed SF development and 1 with abnormal SF morphology)complicated with other severe malformations and 1 with abnormal SF morphology were induced labor or lost to follow-up,hence not undergoing ultrasound re-examination.Conclusion Prenatal ultrasound evaluation on SF morphology during the second and third trimesters had important value for screening fetal abnormal cerebral cortical development.

Objective:To evaluate the cumulative live birth rate (CLBR) and cost-effectiveness of fixed versus adjusted follicle-stimulation hormone (FSH) dosages in infertile women with different ovarian responses during their first assisted reproductive technology (ART) cycle.Methods:A retrospective real-world cohort study was conducted on 5 419 infertile women who underwent their first ART treatment at the Department of Reproductive Medicine of the First Affiliated Hospital of Nanjing Medical University between January 2013 and December 2017. All patients received an individualized starting dosage of gonadotropin. Based on whether FSH dosages were adjusted during controlled ovarian stimulation (COS), patients were divided into fixed-dosage group ( n=2 061) and adjusted-dosage group ( n=3 358). Clinical outcomes and FSH cost-effectiveness were compared between the two groups across different ovarian response groups, with CLBR as the primary outcome. Propensity score matching (PSM) and multivariable logistic regression were used to adjust for potential confounders. Results:FSH dosage adjustments were found in 62.0% (3 358/5 419) of cycles during COS. After PSM, baseline characteristics were comparable between the two groups (all P>0.05). After adjusting for confounders using multivariable logistic regression, FSH dosage adjustment was not significantly associated with CLBR ( OR=1.06, 95% CI: 0.94-1.20, P=0.332). Compared with the adjusted-dosage group, the fixed-dosage group showed no significant differences in CLBR in poor-, normal-, and high-responder groups (all P>0.05). The incidence of ovarian hyperstimulation syndrome (OHSS) did not differ significantly between the two groups ( P>0.05). In poor-, normal-, and high-responder groups, the total FSH dosages in the fixed-dose group [1 350 (375, 1 825) U, 1 200 (375, 1 500) U and 525 (375, 1 128) U, respectively] were significantly lower than those in the adjusted-dose group [1 875 (1 425, 2 294) U, P=0.001; 1 425 (450, 1 875) U, P<0.001; 600 (375, 1 425) U, P=0.020]. Similarly, average FSH costs in different ovarian response groups in the fixed-dosage group [4 725.0 (1 312.5, 6 387.5) yuan, 4 200.0 (1 312.5, 5 250.0) yuan and 1 837.5 (1 312.5, 3 947.3) yuan, respectively] were significantly lower than those in the adjusted-dosage group [6 562.5 (4 987.5, 8 028.1) yuan, P=0.001; 4 987.5 (1 575.0, 6 562.5) yuan, P<0.001; 2 100.0 (1 312.5, 4 987.5) yuan, P=0.020]. For normal-responders, the FSH cost per high-quality embryo in the fixed-dosage group [1 365.0 (875.0, 2 537.5) yuan] was significantly lower than that in the adjusted-dosage group [2 056.3 (1 268.8, 3 412.5) yuan, P<0.001]. Conclusion:FSH dosage adjustment during COS is not associated with CLBR or the incidence of OHSS. However, the fixed-dose group exhibited lower total FSH dosages and costs across different ovarian response populations. In the context of ART being covered by medical insurance, fixed FSH dosage may represent a more cost-effective ovarian stimulation protocol.

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 observe the value of prenatal ultrasound evaluation on morphology of Sylvian fissure(SF)during the second and third trimesters for screening fetal abnormal cerebral cortical development.Methods Totally 876 fetuses in the second and third trimesters were retrospectively included.Prenatal ultrasound was performed to observe the morphology of fetal SF and whether there was complicated abnormalities of the development of cerebral cortical and other structures.Then prenatal ultrasound was regularly reexamined,and the pregnancy outcome,while the growth and development of newborns were followed up.Results Among 876 fetuses,normal SF morphology was observed in 861 fetuses(861/876,98.29％),while 11 fetuses(11/876,1.26％)had delayed SF development(normal SF morphology but inconsistent with gestational week)and 4 fetuses(4/876,0.46％)had abnormal SF morphology,all complicated with abnormal cerebral cortical development and/or intracranial and extracranial structural malformations,and reexamination of prenatal ultrasound showed that SF morphology was consistent with the gestational week in 4 fetuses,SF morphology still did not match the gestational week in 4 fetuses,and SF morphology was still abnormal in 2 fetuses.Among these 15 fetuses,6 were successfully born and grew well after followed up until 10-15 months,4 were induced labor,while the rest 4(3 with delayed SF development and 1 with abnormal SF morphology)complicated with other severe malformations and 1 with abnormal SF morphology were induced labor or lost to follow-up,hence not undergoing ultrasound re-examination.Conclusion Prenatal ultrasound evaluation on SF morphology during the second and third trimesters had important value for screening fetal abnormal cerebral cortical development.