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.

Objective:To investigate the effects of blood pressure management on the prognosis of acute cerebral infarction after intravenous thrombolytic therapy.Methods:The clinical data of 60 patients with acute cerebral infarction who received intravenous thrombolytic therapy in The First People's Hospital of Fuyang District, Hangzhou, China between September 2017 and June 2019 were retrospectively analyzed. These 60 patients were divided into groups A and B according to different treatment methods ( n = 30/group). Patients in the group A received intravenous thrombolytic therapy with recombinant tissue plasminogen activator and those in the group B received intravenous thrombolysis with recombinant tissue plasminogen activator in combination with antihypertensive treatment. All patients were treated for 2 courses of treatment (14 days) and followed up for 3 months. Blood pressure, cerebral blood flow and prognosis were compared between groups A and B. Results:After 24 hours of treatment, there were no significant differences in blood pressure and cerebral blood flow between groups A and B (both P < 0.05). In the group B, modified Rankin Scale score ≥ 2 points was found in 1 (3.3%) patient and intracranial hemorrhage in 0 (0.0%) patient, which were significantly lower than 12 (40.0%) and 6 (20.0%) patients, respectively in the group A ( χ2 = 29.897, 19.573, both P < 0.05). Total effective rate in the group B was significantly higher than that in the group A [96.7% (29/30) vs. 86.67% (26/30), χ2 = 21.302, P < 0.05]. Conclusion:Blood pressure management highly affects the prognosis of cerebral infarction after intravenous thrombolytic therapy. The first 24 hours of intravenous thrombolytic therapy is the optimal time for blood pressure management.

Objective To evaluate the effect of doxepin on the expression of p38 mitogen-activated protein kinase (p38 MAPK) in the spinal cord of rats with neuropathic pain (NP).Methods Sixty clean-grade male Wistar rats in which intrathecal catheters were successfully implanted,weighing 200-250 g,were divided into 3 groups (n =20 each) by a random number table method:sham operation group (S group),NP group and doxepin group (D group).NP was induced by chronic constriction injury (CCI) to sciatic nerve.Doxepin 20 mmol/L (10 μl) was intrathecally injected at 3,7,14 and 21 days after CCI (T1-4) in group D.The mechanical paw withdrawal threshold (MWT) and thermal paw withdrawal latency (TWL) were measured at 1 day before CCI (T0) and at T1-4.The rats were sacrificed after measurement of pain threshold at T4,and L4-6 segments of the spinal cord were removed for determination of the expression of p38MAPK protein by Western blot.Results Compared with S group,MWT was significantly decreased and TWL was shortened at T2-4,and the expression of p38MAPK protein was up-regulated in NP and D groups (P＜0.05).Compared with NP group,MWT was significantly increased and TWL was prolonged at T2-4,and the expression of p38MAPK protein was down-regulated in D group (P＜0.05).Conclusion The mechanism by which doxepin mitigates NP is related to down-regulating p38MAPK expression in the spinal cord of rats.

Objective To investigate the effect of caspases-3 on doxepin-induced apoptosis in rat neurons.Methods The PC12 cells seeded in culture plates were randomly divided into 4 groups (n =10 each) using a random number table:normal control group (group C);doxepin group (group D);caspase-3 inhibitor Z-DEVD-FMK group (group Z);doxepin + Z-DEVD-FMK group (group DZ).In group C,the cells were continuously incubated for 24 h.In group D,doxepin was added with the final concentration of 120 μmol/L,and the cells were continuously incubated for 24 h.In group Z,Z-DEVD-FMK was added with the final concentration of 10 μmol/L,and the cells were continuously incubated for 24 h.In group DZ,doxepin and Z-DEVD-FMK with the final concentrations of 120 and 10 μmol/L,respectively,were added,and the cells were continuously incubated for 24 h.After 24 h of incubation,the cell viability was detected by methyl thiazolyl tetrazolium assay,the cell morphology was observed under inverted microscope,and the neuronal apoptosis was measured by flow cytometry.Apoptosis rate was calculated.Results Compared with group C,the cell viability was significantly decreased,and apoptosis rate was increased in D and DZ groups (P＜0.01),and no significant change was found in the parameters mentioned above in group Z (P ＞ 0.05).Compared with group D,the cell viability was significantly increased,and apoptosis rate was decreased in group DZ (P＜ 0.01).The morphological changes were significantly mitigated in group DZ as compared with group D.Conclusion Caspases-3 may mediate doxepin-induced apoptosis in rat neurons.

Objective To investigate the effects of supreme laryngeal mask airway (SLMA) and endotracheal intubation on the elderly hypertensive patients treated with knee arthroplasty. Methods Forty cases of elderly hypertensive patients ASA Ⅰ-Ⅲ treated with knee arthroplasty in our hospital were selected and randomly divided into laryngeal mask airway group (group LMA)and endotracheal intubation (group TT),20 cases for each group.The same protocol for induction and maintenance of general anesthesia was used.After the patients entering,the changes of SpO2 and ECG were performed continuous noninvasive monitoring and SBP,DBP and HR were performed con-tinuous invasive monitoring.SBP,DBP and HR of two groups were recorded at different time points:before anesthesia induction (T0 ,based value),at intubation immediate (T1 ),5 mins after intubation (T2 )and 1 5 mins after intubation (T3 ).At the same time,the content of cortisol (Cor),atrial natri-uretic peptide (ANP)and the concentration of epinephrine (E)and norepinephrine (NE)were meas-ured at the corresponding time points above.Results Compared with T0 ,SBP and DBP at T1-T3 in group LMA were decreased(P <0.05 or P <0.01);SBP and DBP at T1 in group TT were increased while decreased at T2 ,T3 ,HR at T1 were increased(P < 0.05 or P < 0.01 ).Compared with group LMA,SBP and DBP at T1-T3 and HR at T1 ,T2 in group TT were increased (P <0.05 or P <0.01). Compared with T0 and group LMA,the content of E,NE and Cor at T1-T3 increased(P <0.05 or P <0.01).The level of ANP in both groups at T1-T3 were higher than those at T0 ,and group TT were higher than group LMA(P <0.01).Conclusion Compared to endotracheal intubation,SLAM can ef-fectively reduce the stress reaction of elderly hypertensive patients treated with general anesthesia in knee arthroplasty.