Objectives:To preliminarily investigate the safety and efficacy of same-day discharge(SDD)following radiofrequency catheter ablation for arrhythmia.Methods:A total of 50 consecutive patients who underwent radiofrequency catheter ablation for arrhythmia in the SDD strategy at Fuwai Hospital from 8 July 2024 to 18 September 2024 were included in this analysis.The study evaluated the immediate success rate of the ablation,the rate of all-cause and arrhythmia-related readmission,outpatient or emergency visits and incidence of complications within 30 days post ablation,and recurrence rate of arrhythmias over a 3-month follow-up period.Results:The average age of the 50 patients was(47.2±16.1)years old,32 patients(64.0%)were male.Radiofrequency catheter ablation was performed in 47 patients(94.0%),including 18(36.0%)atrial fibrillation(AF)ablation.Three patients(6.0%)underwent electrophysiological study only.The immediate success rate for ablation patients was 100%(47/47).None of the patients developed vascular puncture-related or ablation-related complications.The average hospital stay and postoperative observation time were(6.84±1.13)hours and(3.40±1.12)hours,respectively.The all-cause and arrhythmia-related readmission,outpatient or emergency visits rates within 30 days were 12.0%(6/50)and 2.0%(1/50),respectively.Two patients(4.0%)post ablation experienced AF recurrence during the 3-months follow-up period.Conclusions:Radiofrequency catheter ablation for arrhythmias in SDD strategy is safe,effective,and feasible.

Objective: To investigate the effects of blood component transfusion on the prognosis of patients with varying severity of traumatic brain injury (TBI). Methods: A retrospective analysis was conducted on clinical data from 621 TBI patients admitted between January 2012 and December 2022. The patients in the blood transfusion group were categorized into three groups based on Glasgow Coma Scale (GCS) scores: severe impairment (GCS 3-8, n=302), moderate impairment (GCS 9-12, n=186), and mild impairment (GCS 13-14, n=133). General clinical data and laboratory test indexes were analyzed. Patients were further divided into two subgroups based on in-hospital mortality: death group (n=72) vs survival group (n=549). Univariate and multivariate logistic regression analysis was used to analyze the effects of different blood component transfusion volumes on the prognosis of TBI patients. ROC curve was used to evaluate the prognostic value of red blood cell transfusion volume. Results: Patients with GCS scores 3-8 had significantly longer hospital stays (21.73±15.89 vs 20.83±11.54 vs 15.5±7.76) and higher RBC transfusion volumes (6.16±6.79 vs 4.67±2.81 vs 3.67±3.20) than the other two groups (P<0.05). NLR, PCT, CRP, PT, Fib, FDP and DDI after the last transfusion showed significant differences from pre-transfusion values (P<0.05). The death group exhibited higher transfusion volumes of RBCs, plasma, platelets, and cryoprecipitate compared with the survival group (P<0.05). Univariate (OR: 1.541, 95%CI: 1.412-1.682) and multivariate (OR: 1.522, 95%CI: 1.362-1.700) logistic regression analyses showed that the RBC transfusion volume was a risk factor affecting the prognostic factors of TBI patients after infusion of blood components. ROC curve analysis showed that RBC transfusion volume could serve as a prognostic marker (sensitivity: 0.708, specificity: 0.812). Conclusion: Blood component transfusion alters inflammatory and coagulation markers in patients with different degrees of TBI, and RBC transfusion volume is a viable prognostic indicator for TBI outcomes.

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.

Objectives:To preliminarily investigate the safety and efficacy of same-day discharge(SDD)following radiofrequency catheter ablation for arrhythmia.Methods:A total of 50 consecutive patients who underwent radiofrequency catheter ablation for arrhythmia in the SDD strategy at Fuwai Hospital from 8 July 2024 to 18 September 2024 were included in this analysis.The study evaluated the immediate success rate of the ablation,the rate of all-cause and arrhythmia-related readmission,outpatient or emergency visits and incidence of complications within 30 days post ablation,and recurrence rate of arrhythmias over a 3-month follow-up period.Results:The average age of the 50 patients was(47.2±16.1)years old,32 patients(64.0%)were male.Radiofrequency catheter ablation was performed in 47 patients(94.0%),including 18(36.0%)atrial fibrillation(AF)ablation.Three patients(6.0%)underwent electrophysiological study only.The immediate success rate for ablation patients was 100%(47/47).None of the patients developed vascular puncture-related or ablation-related complications.The average hospital stay and postoperative observation time were(6.84±1.13)hours and(3.40±1.12)hours,respectively.The all-cause and arrhythmia-related readmission,outpatient or emergency visits rates within 30 days were 12.0%(6/50)and 2.0%(1/50),respectively.Two patients(4.0%)post ablation experienced AF recurrence during the 3-months follow-up period.Conclusions:Radiofrequency catheter ablation for arrhythmias in SDD strategy is safe,effective,and feasible.

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.

ObjectiveTo compare the effect of toe-spread-out exercises (TSO) and modified TSO in females with hallux valgus. MethodsFrom September to December, 2023, a total of 45 females with hallux valgus were recruited in Capital University of Physical Education and Sports and randomly divided into blank control group (n = 15), TSO group (n = 15), and modified TSO group (n = 15). The blank control group received no intervention, the TSO group received routine TSO, and the modified TSO group received fibularis longus fascia release followed by TSO, for eight weeks. Changes in the hallux valgus angle (HVA) and the cross-sectional area (CSA) of the abductor hallucis muscle were measured before intervention, and four and eight weeks after intervention, respectively. ResultsOne case dropped out from the blank control group. The changes of HVA in the TSO and modified TSO groups were significantly greater than in the blank control group (F > 15.263, P < 0.05). After four weeks of intervention, the change of left HVA in the modified TSO group was significantly greater than in the TSO group (P < 0.05). The main effect of time was significant on the CSA of the abductor hallucis muscle (F > 13.245, P < 0.05). The main effect of group was significant on the left foot's CSA of the abductor hallucis (F = 3.798, P < 0.05). The interaction effect of time and group was also significant (F > 4.744, P < 0.05). The CSA of the abductor hallucis in both the TSO and modified TSO groups after four weeks and eight weeks of intervention was significantly greater than before intervention (P < 0.05). At eight weeks, the CSA of the right foot in the modified TSO group was significantly greater than in the blank control group (P < 0.05). ConclusionBoth TSO and modified TSO can improve HVA and the CSA of the abductor hallucis muscle in females with hallux valgus, and modified TSO is better.

OBJECTIVE To evaluate the cost-effectiveness of using Bacillus Calmette-Guérin （BCG） versus epirubicin for intravesical perfusion after transurethral resection of bladder tumor （TUR-BT） in patients with intermediate- to high-risk non-muscle- invasive bladder cancer （NMIBC）. METHODS From the perspective of China’s health system， a Markov cohort model was constructed based on the ChiCTR-IIR-16008357 study. Quality-adjusted life years （QALYs） were used as the health outcome measure， with the willingness-to-pay（WTP） threshold set at one time the per capita gross domestic product of China in 2023 （89 358 yuan/QALY）. A cost-utility analysis was used to compare the incremental cost-effectiveness ratio （ICER） of the BCG regimen relative to the epirubicin regimen for intravesical perfusion after TUR-BT in patients with intermediate- to high-risk NMIBC in China. In addition， sensitivity analysis was performed. RESULTS The incremental cost of the BCG regimen compared to the epirubicin regimen was 34 309.51 yuan， with an incremental utility of 0.800 QALYs， resulting in an ICER of 42 871.33 yuan/QALY， which is below the WTP threshold. When the WTP threshold was 89 358 yuan/QALY， the probability that the BCG regimen would be acceptable was 77.70% in the probabilistic sensitivity analysis， higher than that of the epirubicin regimen， and the acceptability of the BCG regimen increased with increasing in the WTP threshold. CONCLUSIONS When the WTP threshold was set at one time the per capita gross domestic product of China in 2023， compared to epirubicin， BCG used for intravesical perfusion after TUR-BT in patients with intermediate- to high-risk NMIBC demonstrated better cost-effectiveness.

With the continuous development of informatization, digitalization and artificial intelligence technology, the working mode of the pathology department has gradually changed from the traditional manual check, paper circulation and physical carrier storage to the informatization process and digital storage. The traditional pathology discipline has ushered in unprecedented opportunities and challenges. Digital pathology department also emerge as the times require. Simultaneously, with the full integration of artificial intelligence technology in pathology department, the concept of "department of digital and intelligentialized pathology" was proposed. Based on information and digital technology, the digital intelligent pathology department integrates intelligent management system, optimizes the previous cumbersome management and workflow of the pathology department, develops advanced technologies such as intelligent material extraction, unmanned organization processing, artificial intelligence quality control, artificial intelligence diagnosis, and promotes the intelligent construction of the pathology department.

Objective:To evaluate the mechanical performance of customized metal prosthesis with tibia stems of varying lengths for tibial bone defects reconstruction.Methods:Morphologically matched postoperative finite element models of bone defect revision were developed, with three gradients (15 mm, 30 mm, and 45 mm) according to the degree of bone defect and were reconstructed with 3D printed tantalum metal prosthesis using three tibial stem lengths (80 mm, 120 mm, and 150 mm), respectively. Conventional static and dynamic (walking gait) loading was performed to analyze the peak tibial stress distribution and accumulated sliding distance at the prosthetic interface, and to assess the effects of the three tibial stems of different lengths on the stability of the customized tibial defect restorations and the internal tibial stress state.Results:The peak accumulated sliding distance of the dynamically loaded morphologically matched restorations ranged from 17.94 to 21.31 mm with static loading, which were 68% to 84.3% higher than those of 10.26 to 11.69 mm with static loading. The peak tibial stresses in the dynamically loaded model were greater than those in the statically loaded model, with an increase of 28%-49.2%, including 132.94-143.88 MPa in the statically loaded model and 170.41-200.14 MPa in the dynamically loaded model. The overall accumulated sliding distance of the tibia prosthetic model gradually decreased from the tibial osteotomy surface, and the accumulated peak sliding distances ranged from 10.26 to 11.69 mm for static loading, and from 17.94 to 21.31 mm for dynamic loading. The bone tissue stresses in the anterolateral and medial-posterior tibia increased gradually from top to bottom, and the maximum stress value in each section was in the posterior medial tibia (the maximum value was 200.14 MPa). The highest bone tissue stress in the lateral tibia was affected by the tibial stem length, which resulted in a different location, and it was the area most affected by stress shielding (maximum value of 170.65 MPa).Conclusion:For stability assessment of morphologically matched tantalum customized prosthesis, physiological gait dynamic loading studies are more reliable than static loading; the choice of tibial stem length depends on a combination of accumulated peak sliding distances and tibial bone stress distribution factors.

Combined with teaching practice, this study summarizes the teaching contents, methods and effect evaluation of pathological technology for professional postgraduates majoring in pathology. According to the basic conditions of postgraduates, the pathological technology training program has been formulated, student-centered heuristic teaching is carried out by using diversified teaching methods such as flipped classroom, interactive theoretical teaching is carried out by using the intelligent teaching platform, and practical teaching is carried out by using the problem-based learning mode, aiming to improve the theoretical literacy and practical level of pathological technology of professional postgraduates majoring in pathology, improve their clinical research thinking, and lay a foundation for clinical pathological diagnosis and scientific research in the future.