Objective To identify potential biomarkers for proliferative diabetic retinopathy(PDR)using proteomics and transcriptomics data.Methods In this study,the proteomics dataset(PXD046630)and two transcriptomics datasets(GSE60436 and GSE102485)were derived from the aqueous humor samples and fibrovascular membranes of PDR patients,respectively.Differentially expressed genes(DEGs)were identified via R software,specifically the limma and edgeR pack-ages.The shared DEGs between PXD046630 and GSE60436 were analyzed via protein-protein interaction(PPI),Gene On-tology(GO)enrichment,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses.The key DEGs were validated in GSE102485 via receiver operating characteristic(ROC)curve analysis.A quantitative polymerase chain reaction(qPCR)assay was used to confirm the mRNA of these candidate biomarkers in human retinal microvascular endothelial cells(HRMECs)cultured in high glucose and low oxygen conditions.Results A total of 59 shared DEGs and 26 hub genes were identified from the PXD046630 and GSE60436 datasets.KEGG analysis revealed that six pathways,inclu-ding extracellular matrix-receptor interaction,proteoglycans in cancer,and complement and coagulation cascades,were enriched in 12 key DEGs.Fibronectin 1(FN1),tissue inhibitor of metalloproteinase 3(TIMP3),complement factor H(CFH),decorin(DCN),and lipoprotein receptor-related protein-2(LRP2)were identified as potential biomarkers on the basis of their AUC values being greater than 0.900(CI≥95％).The mRNA expression levels of FN1,CFH,and LRP2 were significantly increased in HRMECs cultured in high glucose and low oxygen conditions.Conclusion FN1,CFH,and LRP2 are potential biomarkers for PDR,and further studies are needed to explore their roles and therapeutic potential in PDR.

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 identify potential biomarkers for proliferative diabetic retinopathy(PDR)using proteomics and transcriptomics data.Methods In this study,the proteomics dataset(PXD046630)and two transcriptomics datasets(GSE60436 and GSE102485)were derived from the aqueous humor samples and fibrovascular membranes of PDR patients,respectively.Differentially expressed genes(DEGs)were identified via R software,specifically the limma and edgeR pack-ages.The shared DEGs between PXD046630 and GSE60436 were analyzed via protein-protein interaction(PPI),Gene On-tology(GO)enrichment,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses.The key DEGs were validated in GSE102485 via receiver operating characteristic(ROC)curve analysis.A quantitative polymerase chain reaction(qPCR)assay was used to confirm the mRNA of these candidate biomarkers in human retinal microvascular endothelial cells(HRMECs)cultured in high glucose and low oxygen conditions.Results A total of 59 shared DEGs and 26 hub genes were identified from the PXD046630 and GSE60436 datasets.KEGG analysis revealed that six pathways,inclu-ding extracellular matrix-receptor interaction,proteoglycans in cancer,and complement and coagulation cascades,were enriched in 12 key DEGs.Fibronectin 1(FN1),tissue inhibitor of metalloproteinase 3(TIMP3),complement factor H(CFH),decorin(DCN),and lipoprotein receptor-related protein-2(LRP2)were identified as potential biomarkers on the basis of their AUC values being greater than 0.900(CI≥95％).The mRNA expression levels of FN1,CFH,and LRP2 were significantly increased in HRMECs cultured in high glucose and low oxygen conditions.Conclusion FN1,CFH,and LRP2 are potential biomarkers for PDR,and further studies are needed to explore their roles and therapeutic potential in PDR.

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 observe the effect of comprehensive intervention on the incidence of lactation galactostasis in communities from the perspective of preventive medicine， so as to prevent the occurrence of the mammary ducts obstruction disease. MethodsA total of 400 women in the early stage of pregnancy were selected from four communities. Those in two communities were set up as the intervention group. Comprehensive intervention for the prevention and treatment of mammary ducts obstruction disease during pregnancy and "Six-step recanalization manual therapy" practical guidance were conducted on the intervention group. The pregnant women from the other two communities were the control group， who received no intervention or guidance training. The two groups were followed up at 1 month， 3 months and 5 months postpartum， and the occurrence of mammary ducts obstruction disease was investigated and interventions were carried out. ResultsThe incidence of galactostasis in the intervention group was 26.0%， 10.6% and 5.1%， respectively at 1， 3 and 5 months after delivery， and the incidence of galactostasis in the control group was 38.0%， 22.2% and 8.3%， respectively. The incidence of galactostasis at 1 month and3 months after delivery was statistically different （P<0.05）， while the incidence of galactostasis 5 months after delivery was not statistically different （P>0.05）. The protection rates of comprehensive intervention on galactostasis were 31.58%， 52.25% and 38.55%， respectively. ConclusionPublicity and education work of prevention and control of the mammary ducts obstruction disease and "Six-step recanalization manual therapy" practical guidance can effectively reduce the occurrence of plugged mammary ducts， and therefore should be promoted.

Objective:To explore the establishment of a surgical complication monitoring mode based on data on the medical record homepage, and analyze its impact on the trend of changes in surgical complication incidence.Methods:A monitoring mode of surgical complication was developed based on the " structure-process-results" framework by using surgical complication rates derived from performance appraisal for a tertiary general hospital in Guangzhou. The number of surgical complications and the number of discharged surgical patients was collected from the hospital from January 2019 to June 2022 through the home page collection system for performance appraisal of national tertiary public hospitals. Descriptive analysis was used to analyze the incidence of surgical complications, and Joinpoint regression was used to analyze the trend of changes in the incidence of surgical complications. Monthly percentage change ( MPC) and average monthly percentage change ( AMPC) were calculated. Results:Since the hospital began implementing the surgical complication monitoring mode in May 2021, the incidence of surgical complications had decreased from 2.55% in June 2021 to 0.82% in June 2022, with an MPC of -5.58% ( P=0.024), which was better than the changes from January 2019 to May 2021 ( MPC=0.18%, P=0.755). Conclusions:The surgical complication monitoring mode constructed by the hospital can effectively reduce the incidence of surgical complications, providing reference for optimizing hospital′s medical quality management process and decision-making mode.

Objective:To investigate how medical staff recognize and understand the nursing quality evaluation in robot-assisted arthroplasty so as to provide reference and evidence for construction of a nursing quality evaluation system for robot-assisted arthroplasty.Methods:The descriptive phenomenological research method was used for this qualitative research. From May to October, 2021, 6 doctors and 9 nurses from Operating Room, Laoshan Campus, Hospital Affiliated to Qingdao University were interviewed in a semi-structured way about the nursing quality evaluation for robot-assisted arthroplasty. The data were sorted out by Nvivo12.0 qualitative analysis software, and the interview data were analyzed while the themes and topics refined according to the Colaizzi seven-step analysis of phenomenological data.Results:Three themes were extracted. ① The first theme was related to the quality evaluation of nursing structure, including 2 topics: nursing staff allocation and nursing quality management in operating room. ② The second theme was related to the quality evaluation of nursing process, including 4 topics: environment and facilities, nosocomial infection control, management of patients' operative safety, and specialized operative nursing. ③ The third theme was related to the quality evaluation of nursing outcomes, including 3 topics: satisfaction for operating room nursing, incidence of adverse events and patients' benefits.Conclusion:The themes and topics for nursing quality evaluation in robot-assisted arthroplasty extracted from the perspective of medical staff can provide reference for construction of a reasonable, scientific, efficient and comprehensive nursing quality evaluation index system.

Objective:To understand the current practice of preoperative bowel preparation in elective colorectal surgery in China.Methods:A cross-sectional questionnaire survey was conducted through wechat. The content of the questionnaire survey included professional title of the participants, the hospital class, dietary preparation and protocol, oral laxatives and specific types, oral antibiotics, gastric intubation, and mechanical enema before elective colorectal surgery. A stratified analysis based on hospital class was conducted to understand their current practice of preoperative bowel preparation in elective colorectal surgery.Result:A total of 600 questionnaires were issued, and 516 (86.00%) questionnaires of participants from different hospitals, engaged in colorectal surgery or general surgeons were recovered, of which 366 were from tertiary hospitals (70.93%) and 150 from secondary hospitals (29.07%). For diet preparation, the proportions of right hemicolic, left hemicolic and rectal surgery were 81.59% (421/516), 84.88% (438/516) and 84.88% (438/516) respectively. The average time of preoperative dietary preparation was 2.03 days. The study showed that 85.85% (443/516) of surgeons chose oral laxatives for bowel preparation in all colorectal surgery, while only 4.26% (22/516) of surgeons did not choose oral laxatives. For mechanical enema, the proportions of right hemicolic, left hemicolic and rectal surgery were 19.19% (99/516), 30.04% (155/516) and 32.75% (169/516) respectively. Preoperative oral antibiotics was used by 34.69% (179/516) of the respondents. 94.38% (487/516) of participants were satisfied with bowel preparation, and 55.43% (286/516) of participants believed that preoperative bowel preparation was well tolerated. In terms of preoperative oral laxatives, there was no statistically significant difference between different levels of hospitals [secondary hospitals vs. tertiary hospitals: 90.00% (135/150) vs. 84.15% (308/366), χ 2=2.995, P=0.084]. Compared with the tertiary hospitals, the surgeons in the secondary hospitals accounted for higher proportions in diet preparation [87.33% (131/150) vs. 76.78% (281/366), χ 2=7.369, P=0.007], gastric intubation [54.00% (81/150) vs. 36.33% (133/366), χ 2=13.672, P<0.001], preoperative oral antibiotics [58.67% (88/150) vs. 24.86% (91/366), χ 2=12.259, P<0.001] and enema [28.67% (43/150) vs. 15.30% (56/366), χ 2=53.661, P<0.001]. Conclusion:Although the preoperative bowel preparation practice in elective colorectal surgery for most of surgeons in China is basically the same as the current international protocol, the proportions of mechanical enema and gastric intubation before surgery are still relatively high.

Objective:To understand the current practice of preoperative bowel preparation in elective colorectal surgery in China.Methods:A cross-sectional questionnaire survey was conducted through wechat. The content of the questionnaire survey included professional title of the participants, the hospital class, dietary preparation and protocol, oral laxatives and specific types, oral antibiotics, gastric intubation, and mechanical enema before elective colorectal surgery. A stratified analysis based on hospital class was conducted to understand their current practice of preoperative bowel preparation in elective colorectal surgery.Result:A total of 600 questionnaires were issued, and 516 (86.00%) questionnaires of participants from different hospitals, engaged in colorectal surgery or general surgeons were recovered, of which 366 were from tertiary hospitals (70.93%) and 150 from secondary hospitals (29.07%). For diet preparation, the proportions of right hemicolic, left hemicolic and rectal surgery were 81.59% (421/516), 84.88% (438/516) and 84.88% (438/516) respectively. The average time of preoperative dietary preparation was 2.03 days. The study showed that 85.85% (443/516) of surgeons chose oral laxatives for bowel preparation in all colorectal surgery, while only 4.26% (22/516) of surgeons did not choose oral laxatives. For mechanical enema, the proportions of right hemicolic, left hemicolic and rectal surgery were 19.19% (99/516), 30.04% (155/516) and 32.75% (169/516) respectively. Preoperative oral antibiotics was used by 34.69% (179/516) of the respondents. 94.38% (487/516) of participants were satisfied with bowel preparation, and 55.43% (286/516) of participants believed that preoperative bowel preparation was well tolerated. In terms of preoperative oral laxatives, there was no statistically significant difference between different levels of hospitals [secondary hospitals vs. tertiary hospitals: 90.00% (135/150) vs. 84.15% (308/366), χ 2=2.995, P=0.084]. Compared with the tertiary hospitals, the surgeons in the secondary hospitals accounted for higher proportions in diet preparation [87.33% (131/150) vs. 76.78% (281/366), χ 2=7.369, P=0.007], gastric intubation [54.00% (81/150) vs. 36.33% (133/366), χ 2=13.672, P<0.001], preoperative oral antibiotics [58.67% (88/150) vs. 24.86% (91/366), χ 2=12.259, P<0.001] and enema [28.67% (43/150) vs. 15.30% (56/366), χ 2=53.661, P<0.001]. Conclusion:Although the preoperative bowel preparation practice in elective colorectal surgery for most of surgeons in China is basically the same as the current international protocol, the proportions of mechanical enema and gastric intubation before surgery are still relatively high.