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.

Aim To investigate the effect of long-chain non-coding RNA(lncRNA)GS1-124K5.4 targeting regulation of PRDX6 on proliferation,migration and in-vasion of lung squamous carcinoma(LUSC)cells and the underlying mechanism.Methods The expression level of lncRNA GS1-124K5.4 in lung cancer tissues and adjacent tissues of 60 patients with LUSC were de-termined by fluorescence in situ hybridization.The ex-pression level of lncRNA GS1-124K5.4 in human nor-mal lung cells and LUSC cells were determined by qRT-PCR.Two kinds of LUSC cells(NCI-H 1703,SK-MES-1)with highest expression level of lncRNA GS1-124K5.4 were selected for subsequent experi-ments.The distribution of lncRNA GS1-124K5.4 in cells was studied by fluorescence in situ hybridization and prokaryotic separation.The effect of knockdown of lncRNA GS1-124K5.4 on proliferation of NCI-H1703 and SK-MES-1 cells was studied by CCK-8 experiment and cell clone formation experiment;the effect of knockdown of lncRNA GS1-124K5.4 on migration of NCI-H1703 and SK-MES-1 cells was studied by cell scratch experiment and Transwell cell migration experi-ment;and the effect of knockdown of lncRNA GS1-124K5.4 on invasion of NCI-H1703 and SK-MES-1 cells was studied by Transwell invasion experiment.The protein to be bound by lncRNA GS1-124K5.4 was detected by RNA pull-down combined with mass spec-trometry and immune-precipitation.The effect of knockdown of lncRNA GS1-124K5.4 targeting PRDX6 on proliferation,migration and invasion of NCI-H1703 and SK-MES-1 cells was studied.Results(1)The fluorescence intensity of lncRNA GS1-124K5.4 in lung squamous cell carcinoma increased compared with that in adjacent tissues(P＜0.05),and the expression of lncRNA GS1-124K5.4 was related with lymph node metastasis and clinical stage(P＜0.05).(2)The ex-pression level of lncRNA GS1-124K5.4 in NCI-H1703,NCI-H520 and SK-MES-1 cells significantly increased(P＜0.05).(3)The result of fluorescence in situ hybridization experiment and nucleoplasm sepa-ration experiment showed that lncRNA GS1-124K5.4 was mainly distributed in cell nucleus.(4)The prolif-eration,migration and invasion ability of NCI-H1703 and SK-MES-1 cells with knockdown of lncRNA GS1-124K5.4 significantly decreased(P＜0.05).(5)PRDX6 protein to be bound to LncRNA GS1-124K5.4 was determined by RNA pull-down combined with mass spectrometry and immunoprecipitation.(6)The prolif-eration,migration and invasion ability of NCI-H1703 and SK-MES-1 cells with overexpression of lncRNA GS1-124K5.4 significantly increased(P＜0.05);the proliferation,migration and invasion ability of NCI-H1703 and SK-MES-1 cells with knockdown of PRDX6 significantly decreased(P＜0.05);the proliferation,migration and invasion ability of NCI-H1703 and SK-MES-1 cells with overexpression of lncRNAGS1-124K5.4 and knockdown of PRDX6 showed no signifi-cant change(P＞0.05).Conclusions LncRNA GS1-124K5.4 is highly expressed in lung squamous cell carcinoma,and it may promote the proliferation,migration and invasion of lung squamous carcinoma cells by targeting the expression of PRDX6 protein.

Objective:To investigate the association between adiponectin-related copy number variation (CNV) region (CNVR) and gestational diabetes mellitus (GDM).Methods:Pregnant women who had prenatal screening in Anqing Municipal Hospital, Anhui Province, from February 2018 to December 2020 were surveyed for baseline information collection, and blood samples were collected. The outcome information was obtained by post pregnancy follow-up. Latex-enhanced immunoturbidimetry and ASA-CHIA chip were used to detect serum adiponectin levels and CNV of pregnant women, respectively. After genotyping, CNV data were processed with software PennCNV 1.0.5 following standard quality control procedure. CNVR were identified and integrated by using software R 4.3.3. Then the associations between CNVR and adiponectin was evaluated, and gene annotation and over-representation analysis were conducted. The log-binomial regression model was used to adjust relevant covariates and analyze the association between adiponectin-related CNVR and GDM.Results:The detection rate of GDM was 9.54% (176/1 845) in the pregnant women. The genotyping information of 1 840 people (99.73%) passed quality evaluation. A total of 33 878 CNVs were identified, and 1 449 CNVRs were obtained after integration. After the false discovery rate method correction, CNVR_132 (CHR2: 47611743-47635062), CNVR_254 (CHR3: 10182703-10183872), CNVR_691 (CHR7: 150637053-150834539) and CNVR_1101 (CHR14: 104248431-104830620) were correlated with adiponectin levels (all P<0.05). Over- representation analysis showed that the molecular function of ribonucleotide binding [Gene Ontology (GO): 0032553] was significantly enriched based on the GO database. The log-binomial regression model, adjusting age, pre-pregnancy BMI, history of miscarriage, smoking history, and family history of diabetes, indicated that CNVR_132 (CHR2: 47611743-47635062) and CNVR_1101 (CHR14: 104248431-104830620) were not statistically associated with the risk for GDM (both P>0.05). However, CNVR_254 (CHR3: 10182703-10183872, a RR=1.83, 95% CI: 1.15-2.91) and CNVR_691 (CHR7: 150637053-150834539, a RR=1.73, 95% CI: 1.23-2.43) might be associated with an increased risk for GDM (all P<0.05). Conclusion:Adiponectin-related CNVR_254 (CHR3: 10182703-10183872) and CNVR_691 (CHR7: 150637053-150834539) might be risk factors for the incidence of GDM.

Objective:To investigate the association between adiponectin-related copy number variation (CNV) region (CNVR) and gestational diabetes mellitus (GDM).Methods:Pregnant women who had prenatal screening in Anqing Municipal Hospital, Anhui Province, from February 2018 to December 2020 were surveyed for baseline information collection, and blood samples were collected. The outcome information was obtained by post pregnancy follow-up. Latex-enhanced immunoturbidimetry and ASA-CHIA chip were used to detect serum adiponectin levels and CNV of pregnant women, respectively. After genotyping, CNV data were processed with software PennCNV 1.0.5 following standard quality control procedure. CNVR were identified and integrated by using software R 4.3.3. Then the associations between CNVR and adiponectin was evaluated, and gene annotation and over-representation analysis were conducted. The log-binomial regression model was used to adjust relevant covariates and analyze the association between adiponectin-related CNVR and GDM.Results:The detection rate of GDM was 9.54% (176/1 845) in the pregnant women. The genotyping information of 1 840 people (99.73%) passed quality evaluation. A total of 33 878 CNVs were identified, and 1 449 CNVRs were obtained after integration. After the false discovery rate method correction, CNVR_132 (CHR2: 47611743-47635062), CNVR_254 (CHR3: 10182703-10183872), CNVR_691 (CHR7: 150637053-150834539) and CNVR_1101 (CHR14: 104248431-104830620) were correlated with adiponectin levels (all P<0.05). Over- representation analysis showed that the molecular function of ribonucleotide binding [Gene Ontology (GO): 0032553] was significantly enriched based on the GO database. The log-binomial regression model, adjusting age, pre-pregnancy BMI, history of miscarriage, smoking history, and family history of diabetes, indicated that CNVR_132 (CHR2: 47611743-47635062) and CNVR_1101 (CHR14: 104248431-104830620) were not statistically associated with the risk for GDM (both P>0.05). However, CNVR_254 (CHR3: 10182703-10183872, a RR=1.83, 95% CI: 1.15-2.91) and CNVR_691 (CHR7: 150637053-150834539, a RR=1.73, 95% CI: 1.23-2.43) might be associated with an increased risk for GDM (all P<0.05). Conclusion:Adiponectin-related CNVR_254 (CHR3: 10182703-10183872) and CNVR_691 (CHR7: 150637053-150834539) might be risk factors for the incidence of GDM.

The chemical composition of Paeoniae Radix Alba(PRA) is complex, with primary secondary metabolites including monoterpenoids, tannins, triterpenoids, and flavonoids. In previous studies on the material basis of PRA, it was found that, in addition to the widely studied characteristic monoterpene glycosides, tannic acid components also play an important role in the efficacy of PRA. However, their pharmacological effects have not been thoroughly investigated. This paper reviews the tannic acid components in PRA, including pentagaloyl glucose(PGG), tetragaloyl glucose(TGG), trigaloyl glucose(TriGG), and gallic acid, along with their structures, properties, and characteristics to provide a detailed discussion of their pharmacological activities and related mechanisms, aiming to offer a theoretical basis for the material basis research and clinical application of PRA.
Paeonia/chemistry* ; Tannins/chemistry* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Animals ; Plant Extracts

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

Objective To construct a hybrid decision-making model that integrates knowledge-driven and data-driven approaches,and to apply it to the etiological diagnosis of ventricular tachycardia(VT).Methods Clinical practice guidelines,expert consensus documents,and medical literature in the field of ar-rhythmia diseases from 2018 to 2023 were retrieved as knowledge sources.Retrospective electronic medical re-cord data of VT patients from Fuwai Hospital,Chinese Academy of Medical Sciences & Peking Union Medical College,from 2013 to 2023 were collected as the dataset.A knowledge-driven model was constructed using a knowledge-rule-based approach to establish clinical pathways.A three-class machine learning model for VT eti-ology diagnosis was developed based on real-world data,and the best-performing model was selected as the rep-resentative of the data-driven approach.The machine learning model was embedded into the decision nodes of the clinical pathway in the form of custom operators,forming the hybrid model.The precision,recall,and F1 score of the three models were evaluated.Results Three clinical practice guidelines were included as knowl-edge sources for the knowledge-driven model.A total of 1305 patient records were collected as the dataset,and five machine learning models were constructed,with the XGBoost model performing the best.The hybrid model adopted a knowledge-driven decision-making framework,embedding the XGBoost model into the decision nodes of a two-level classification.The precision,recall,and F1 scores of the three models were as follows:the knowledge-driven model achieved 80.4％,79.1％,and 79.7％;the data-driven model achieved 88.4％,88.5％,and 88.4％;and the hybrid model achieved 90.4％,90.2％,and 90.3％.Conclusions The hybrid model integrating knowledge-driven and data-driven approaches demonstrated higher accuracy,and all its deci-sion outcomes were based on evidence-based practices,aligning more closely with the actual diagnostic reason-ing of clinicians.Further rigorous validation is needed to assess the feasibility of widely applying the hybrid model in the medical field.

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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.