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 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.

Excessive theta (θ) frequency oscillation and synchronization in the basal ganglia (BG) has been reported in elderly parkinsonian patients and animal models of levodopa (L-dopa)-induced dyskinesia (LID), particularly the θ oscillation recorded during periods when L-dopa is withdrawn (the off L-dopa state). To gain insight into processes underlying this activity, we explored the relationship between primary motor cortex (M1) oscillatory activity and BG output in LID. We recorded local field potentials in the substantia nigra pars reticulata (SNr) and M1 of awake, inattentive resting rats before and after L-dopa priming in Sham control, Parkinson disease model, and LID model groups. We found that chronic L-dopa increased θ synchronization and information flow between the SNr and M1 in off L-dopa state LID rats, with a SNr-to-M1 flow directionality. Compared with the on state, θ oscillational activity (θ synchronization and information flow) during the off state were more closely associated with abnormal involuntary movements. Our findings indicate that θ oscillation in M1 may be consequent to abnormal synchronous discharges in the BG and support the notion that M1 θ oscillation may participate in the induction of dyskinesia.

AIM To study lipid-lowering effects of gallic acid on glutamate-induced obesity mice.METHODS The obese model was established through subcutaneous injection of 3mg/(g · d)sodium glutamate into neonatal mice.After the model was established,the mice were divided into normal control group,model group,positive control group [simvastatin 30 mg/(kg · d)],high-,and low-dose group of gallic acid [400,200 mg/(kg · d)],and were intragastrically administered for ten weeks.Mice in each group after the last administration were fasted for 12 h except water.Blood was sampled from mouse eyes.The organs and adipose were obtained to determine the organ index and fat index.The levels of HDL-C,TG,LDL-C and TC in serum and liver were determined by using the corresponding reagent kit,and the serum leptin level was determined by ELISA kit and simultaneous determination of SOD,GSH-Px and MDA levels in liver.RESULTS Compared with the normal control group,the body weight and fat weight significantly increased in the model group;the levels of TC,TG and LDL-C in serum and liver significantly increased;the serum leptin level significantly reduced;the activity levels of SOD and GSH-Px in the liver significantly reduced;and the level of MDA significantly increased.Compared with the model control group,the body weight and fat weight significantly reduced in the gallic acid group mice and the levels of TC and TG significantly reduced in the serum and liver;SOD and GSH-Px levels significantly increased,MDA level significantly decreased in the liver.CONCLUSION Gallic acid can significantly reduce the blood lipid level of glutamate-induced obese mice.

Objective To analyze impact of diversity care on mental state of self-management ability of patients with gastric cancer.Methods A total of 85 patients with gastric cancer in our hospital were divided into two groups according to the random principle.The control group was taken routine care and the observation group accepted diversity nursing on the basis of traditional way.Psychological state,self-management skills,and satisfaction were observed and analyzed.Results The scores of depression,fear,and anxiety were significantly lower than the control group (P ＜ 0.05).Before nursing,no significant difference was seen in self-management ability in the observation group compared with the control group (P ＞ 0.05),while after treatment it was significantly higher than the control group (P ＜ 0.05).The satisfaction in the observation group was significantly higher than that in the control group,the difference was statistically significant (P ＜ 0.05).Conclusion Compared to traditional care measures,the diversity care can effectively improve emotions and quality of life of patients with gastric cancer,and enhance the patient's self-management skills,so it is worthy of widely clinical practice.

Objective To analyze impact of diversity care on mental state of self-management ability of patients with gastric cancer.Methods A total of 85 patients with gastric cancer in our hospital were divided into two groups according to the random principle.The control group was taken routine care and the observation group accepted diversity nursing on the basis of traditional way.Psychological state,self-management skills,and satisfaction were observed and analyzed.Results The scores of depression,fear,and anxiety were significantly lower than the control group (P ＜ 0.05).Before nursing,no significant difference was seen in self-management ability in the observation group compared with the control group (P ＞ 0.05),while after treatment it was significantly higher than the control group (P ＜ 0.05).The satisfaction in the observation group was significantly higher than that in the control group,the difference was statistically significant (P ＜ 0.05).Conclusion Compared to traditional care measures,the diversity care can effectively improve emotions and quality of life of patients with gastric cancer,and enhance the patient's self-management skills,so it is worthy of widely clinical practice.

Objective To investigate the efficacy and the course of desensitization treatment in bronchial asthma with positive specific IgE to dust mite in children. Methods A total of 105 children with bronchial asthma with positive specific IgE to dermatophagoides farinae allergens were randomized into the observation group and the control group. Children in the control group were treated to continue anti-asthma according to the routine of prevention and treatment children with asthma. Chinldren in the observation group were treated by dermatophagoides farinae drops in addition to the treatment of children in the control group. The recurrence of asthma was compared between two groups at 25 weeks post-treatment. At 25 weeks post-treatment , children in the observation group was randomly divided into the observation groupⅠand group Ⅱ. Children in the observation groupⅠreceived continuous treatment except for desensitization treatment. Children in the observation group II received the sublingual immunotherapy with dermatophagoides farinae drops (No.4) for 1 year in addition to the treatment in the observation groupⅠ. The recurrence of asthma was also compared between the two sub-groups. Results The rate and times of recurrence of asthma were lower in the observation group than those in the control group(P < 0.05), with no significant differences between the observation groupⅠand groupⅡ (P > 0.05). Conclusion The recurrent rate and frequency of asthma could be reduced by the sublingual immunotherapy with dermatophagoides farinae drops in children with asthma of positive specific IgE to dust mite. The course of treatment may be half year long.

Postweaning multisystemie wasting syndrome (PMWS) is an important swine disease that is closely associated with porcine circovirus type 2 (PCV2). The capsid protein (Cap protein) is a major structural protein that has at least three immunoreactive regions, and it can be a suitable candidate antigen for detecting the specific antibodies of a PCV2 infection. In the present study, an indirect enzyme-linked immunosorbent assay (TcELISA)based on a truncated soluble Cap protein produced in Escherichia coli (E.coli) was established and validated for the diagnostic PCV2 antibodies in swine. The TcELISA was validated by comparison with an indirect immunofluorescence assay (IIFA). The diagnostic sensitivity (DSN), specificity (DSP), and accuracy of the TcELISA were 88.6%, 90.7% and 89.4%, respectively. The agreement rate was 89.38% between results obtained with TcELISA and IIFA on 113 field sera. A cross-reactivity assay showed that the method was PCV2-specific by comparison with other sera of viral disease. Therefore ,the TcELISA will be helpful for the development of a reliable serology diagnostic test for large scale detection of PCV2 antibodies and for the evaluation of vaccine against PCV2 in swine.