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.

AIM:To investigate the reparative effect of extracellular vesicles(EVs)derived from H9 human embryonic stem cells(H9-hESCs)on endometrial injury.METHODS:EVs were isolated from the culture supernatant of H9-hESCs and characterized.A mouse model of endometrial injury was established,with bilateral uterine divisions into an EVs experimental group and a PBS control group.EVs and PBS were injected respectively.Histological changes in the endometrium were assessed using HE staining,and proliferating cell nuclear antigen(PCNA)expression was analyzed via immunohistochemistry.The impact of EVs on the proliferation of human endometrial stromal cells(hEndoSCs)was eva-luated using EdU staining and Western blot.RESULTS:H9-hESCs-EVs exhibited a membrane-structured nanobody with a particle size of(144.7±2.1)nm and expressed characteristic proteins CD63 and TSG101.Compared to the PBS control group,the EVs group showed increased endometrial tissue morphology,thickness,and gland numbers.The average opti-cal density of PCNA expression significantly increased in the EVs group compared to the PBS group(P<0.05).Results from EdU staining and Western blot demonstrated that H9-hESCs-EVs promoted hEndoSC proliferation,with a positive correlation observed between H9-hESCs-EVs and EVs protein concentration(P<0.05).CONCLUSION:H9-hESCs-EVs enhance the repair of endometrial injury by stimulating the proliferation of endometrial stromal cells.

Objective To estimate the value of nitrofuratoin in uncomplicated urinary tract infection by examining antimicrobial resistance of Escherichia coli (E.coli) strains isolated from the patients.Methods The antimicrobial susceptibility information was collected from two projects: an antimicrobial survey of clinical urine specimens in 20 hospitals in 15 provinces during 2004-2012 and CHINET Antimicrobial Resistance Surveillance Program during 2005-2014.Then the clinical consumption of nitrofuratoin was analyzed according to the data of sample hospitals from 6 cities of Yangtze River system between 2011 and 2015.Results The antimicrobial susceptibility of E.coli strains showed that the resistance rate to cephalosporins and fluoroquinolones were above 50% during the last decade.Meanwhile,the resistance rate to nitrofuratoin was below 10%.Conclusion Nitrofuratoin,which is an old drug in the treatment of urinary tract infection,is famous for its broad-spectrum antimicrobial activity and high sensitivity to ESBLs producing or non-ESBLs producing strains of E.coli.It is efficacious,safe and cost-effective in the treatment to uncomplicated urinary tract infection in women.Therefore,it is highly recommend that rational use of nitrofuratoin in the clinical practice.

Objective To investigate the effect of minitype titanium plate fixation through transoral approach in the treatment of unstable atlas fractures.Methods A retrospective case series study was made on 21 patients with unstable atlas fractures treated by minitype titanium plate fixation through transoral approach from June 2008 to June 2014.There were 15 males and 6 females,at age of (40.9 ± 10.6)years (range,21 to 57 years).Anterior 1/2 Jefferson fractures were seen in 12 patients and 1/2 ring Jefferson fractures in 9 patients.Preoperative visual analogue score (VAS) was 4-9 points [(7.6 ± 1.3) points].Before operation,degree of mobility of the cervical vertebra was (15.4 ± 3.9) °in bending,(10.8 ± 2.5) °in extending,(18.3 ± 3.1) ° in left-bending,(18.9 ± 2.7) ° in right-bending,(21.8 ± 5.8) °in left-rotation and (22.4 ± 4.6) ° in right-rotation.Operation time,intraoperative blood loss,VAS,cervical mobility and bone healing were detected after operation.Results Operation time was (86.3 ±25.3)m in,and intraoperative blood loss was (120.5 ± 33.3)ml.VAS was improved to 0-2 points [(1.6 ± 0.4) points] at postoperative 3 days (P ＜ 0.05).All patients were followed up for 12 to 48 months[(23.7 ±5.9) months].VAS was improved to 0-2 points[(0.6 ± 0.1) points] at postoperative 3 months (P ＜ 0.05).Degree of mobility of the cervical vertebra was improved significantly at postoperative 3 months,with the bending of(38.6 ± 4.5) °,extending of (39.3 ± 4.0) °,left-bending of (39.2 ± 4.0) °,right-bending of (39.2 ± 2.9) °,left-rotation of (66.8 ± 8.8) ° and right-rotation of (66.3 ± 9.2) ° (P ＜ 0.05).Postoperatively,there were no surgical wound incision infections and vertebral artery or spinal injuries,Bone union was found in all patients,without the occurrence of implant loosening or breakage and the dysfunction of the cervical vertebra.Conclusion Minitype titanium plate fixation through transoral approach is associated with less trauma,high healing rate and preservation of the activity of cervical vertebra in the treatment of unstable atlas fractures.

Objective To analyze whether routine prophyrlactic antibiotic administration is necessary for the patients undergoing coronary stent implantation. Methods The clinical data of 156 patients from January 2010 to December 2010 (prophylactic antibiotic therapy),and 466 patients from January 2014 to December 2014(no-prophylactic antibiotic therapy), who underwent coronary stent implantation, were retrospectively analyzed. The prophylactic antibiotics and the infection rates in two groups were compared. Results The rate of infections related to coronary stent implantation in no-prophylactic antibiotic therapy group and prophylactic antibiotic therapy group, such as surgical site infection (0.2% vs 1.3%,P>0.05) and catheter-related infection(0.6% vs 1.9%,P>0.05), was not significant different(P>0.05). Similarly, the unrelated to coronary stent implantation was not significant different, too ( P > 0. 05). Conclusion Routine prophylactic antibiotic administration is unnecessary for the patients undergoing coronary stent implantation.

Objective To investigate the role of expression in the differential diagnosis of thyroid follicular carcinoma and follicular variant of papillary carcinoma. Methods Seventy cases of thyroid lesions (including 15 cases of follicular adenomas, 15 cases of adinomatous goiters, 30 cases of papillary carcinomas and 10 cases of follicular carcinomas) were collected, and CD10 expression was detected by means of immunohistochemistry in above thyroid lesions. Results Seven of 9 cases of follicular variant of papillary carcinoma were CD10 positive (77.8), and 8 of 10 cases of follicular carcinoma were CD10 positive (80.0). However, CD10 was negative in all cases of non-follicular variant of papillary carcinoma, follicular adenoma, adinomatous goiter and normal thyroid tissue. Conclusion The detection of CD10 expression is useful to the differential diagnosis of thyroid follicular carcinoma and follicular variant of papillary carcinoma.