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 explore the role of nuclear receptor-binding SET-domain protein 1(NSD1)in the pathogenesis of nonobstructive azoospermia(NOA)by regulating the expressions of relevant genes.Methods:We detected the expression of NSD1 in the testis tissue of 7 male patients with obstructive azoospermia(OA)and 18 with NOA by qPCR and immunofluorescence assay,and determined the modification level of H3K36me2 in the testes of two groups of patients by immunofluorescence staining,Western blot and immunoprecipitation(IP).We examined the difference in the enrichment of H3K36me2 in the testis tissue by chromatin IP-based sequencing(ChIP-Seq),analyzed the genomic distribution and target genes using bioinformatics,and verified the expression levels of the target genes in the testes of the two groups of patients by qPCR.Results:Compared with the patients with OA,those with NOA showed dramatically decreased mRNA and protein expressions of NSD1(P=0.000 8).The binding of NSD1 to H3K36me2 was observed in the testis tissue of both the two groups of patients,while the modification level of H3K36me2 was evidently reduced in the NOA males.H3K36me2 was distributed mainly in the intergenic region in the testes of the two groups of patients,but the enrich-ment of H3K36me2 was obviously decreased in the NOA group.The differentially H3K36me2-enriched genes were involved in various biological processes,including tissue development,and cell morphogenesis.Results of ChIP-Seq and qPCR showed significantly down-regulated expressions of the target genes KIT,SPO11 and ACRV1 in the testis tissue of the NOA males compared with those in the OA patients(P＜0.01).Conclusion:The levels of NSD1 and H3K36me2 are decreased in testis tissue of the NOA patient,H3K36me2 is highly enriched in the spermatogenesis-related key genes KIT,SPO11 and ACRV1,and the down-regulated expression of NSD1 impairs spermatogenesis.

Objective:The objective of this study was to search for targeted small molecule inhibitors of Sortase A(SrtA)of Streptococcus mutans(S.mutans),and to provide an experimental basis for the exploration of novel and effective anti-caries drugs.Methods:Using the crystal structure of SrtA enzyme of S.mutans UA159(PDB code:4TQX)as the receptor and amino acid Cys205 as the covalent binding site,we screened potential small molecule compounds from the natural small molecule drug-like molecule libraries of the compounds using molecular covalent docking technology and ADMET drug-forming filtration.The study screened small molecule compounds that inhibit adhesion without antibacterial effect,as determined by the minimum inhibitory concentration(MIC)and bacterial adhesion experiments.The binding modes and stabilities of the small molecule compounds were verified through molecular dynamics simulation.Results:A total of 20 small molecule compounds were selected by molecular co-valent docking technology and ADMET druggability filtration,among which AK-968/40369373 and AK-968/40385877 inhibited the adhesion of S.mutans better than trans-chalcone,and the difference was statistically significant(P＜0.05).Molecular dynam-ics simulations confirmed that AK-968/40369373 and AK-968/40385877 could form stable covalent binding to SrtA.Conclusion:Virtual screening and validation are effective methods for selecting potential targeting inhibitors of SrtA.The small-molecule com-pounds AK-968/40369373 and AK-968/40385877 obtained in this study are expected to be used as small-molecule inhibitors tar-geting S.mutans SrtA.

Excessive fat accumulation,viral infections and sustained inflammatory responses caused by non-alcoholic and alcoholic factors can contribute to liver inflammation,fibrosis and carcinogenesis,promoting the development of chronic liver disease.Gaining an in-depth understanding of the etiologic factors and underlying mechanisms that lead to chronic liver disease can help identify potential therapeutic targets for targeted therapy.Lactate,as an important substance in hepatic metabolism,has been found to be involved in the process of chronic liver disease through various pathways,and this review will provide a useful reference for the prevention and treatment of chronic liver disease.

Objective:To understand the etiological surveillance and drug resistance characteristics of Legionella pneumophila (LP) from the aqueous environment of public places in Shanghai, from 2011 to 2020, and provide evidence for surveillance of the disease. Methods:Environmental water samples were systematically collected from public venues in urban and suburban districts of Shanghai for LP surveillance. All the identified LP isolates underwent a series of testings including serotyping, pulsed field gel electrophoresis (PFGE), sequence-based typing, and antimicrobial susceptibility testing. χ2 test or Cochran-Armitage trend tests were used for statistical analysis and for temporal resistance patterns. Results:Among 6 263 water samples, the LP-positive rate was 20.93% (1 311/6 263). The positivity rate decreased from 24.98% (287/1 149) in 2011-2012 to 20.02% (1 024/5 114) in 2013-2020 ( χ2=13.92, P<0.001), with the highest monthly positivity observed from June to August (23.79%, 745/3 132). A total of 1 365 LP strains were isolated, of which 912 were further characterized, including 10 serotypes, 149 PFGE patterns, and 33 sequence types (ST). The predominant serotype was Lp1 (86.84%, 792/912), and the dominant ST was ST752 (29.50%, 269/912). ST clustering revealed two major clonal groups CG1 and CG2, accounting for 91.12% (831/912) of the isolates. The 190 LPs involved in the drug sensitivity test showed three resistance profiles: azithromycin resistance (31.05%, 59/190), ciprofloxacin resistance (0.53%, 1/190) and azithromycin+ciprofloxacin resistance (0.53%, 1/190). Azithromycin-resistant strains were predominantly ST1 (64.41%, 38/59). The antimicrobial resistance rate showed a significant decline, from 48.65% (18/37) in 2011-2012 to 28.10% (43/153) in 2013-2020 ( χ2=9.38, P=0.002). Conclusions:Compared to from 2011 to 2012, both the positivity rate and antimicrobial resistance prevalence of LP in public aqueous environments of Shanghai exhibited an overall decline from 2013 to 2020. The predominant types of LP were serotype Lp1 and sequence type ST752, with notable high-level resistance to azithromycin. Measures as enhancing the enforcement of water safety regulations and prioritizing surveillance of azithromycin resistance in LP were recommended to mitigate public health risks.

Haloacetic acids(HAAs),as a class of disinfection byproducts in drinking water,pose potential threats to human health,so the rapid,accurate and simultaneous detection of HAAs is of great significance for ensuring drinking water safety.Aiming at the challenges in HAAs detection and risk analysis,a novel method for synchronous rapid detection of seven kinds of HAAs in drinking water based on in situ derivatization technology and headspace gas chromatography was developed in this study.Through single-factor optimization experiments,the optimal reaction parameters for in situ derivatization were determined,including the type and dosage of salting-out agent,the acidity of reaction system,the amount of phase transfer catalyst,the dosage of derivatization agent,and the extraction solvent volume.Methodologic validation showed that the seven kinds of HAAs exhibited excellent linear relationships within their respective detection concentration ranges(R2>0.998).The method detection limits(MDLs)ranged from 0.04 to 0.33 μg/L,and the limits of quantification(LOQs)were between 0.14 and 1.34 μg/L.For real water samples,the average spiked recoveries of the seven HAAs ranged from 90.9%to 107.7%,with relative standard deviation(RSDs)between 1.55%and 6.49%,and the HAAs contents in all tested samples were below the limits specified in the Standards for Drinking Water Quality(GB 5749-2022)of China.This method was featured with simple operation,fast analysis speed,high sensitivity,and good accuracy,providing an efficient and reliable technical support for routine monitoring of HAAs contaminants in drinking water and showing promising application value for widespread promotion.

Perimenopausal generalized anxiety disorder belongs to the category of"depression syndrome"in traditional Chinese medicine.It is caused by disharmony of blood vessels,stagnation of qi,and disturbance of the mind and brain,leading to excessive or insufficient qi in the five zang organs.Based on different clinical manifestations,it can be divided into five types of depression.In clinical treatment,Professor Zhuang Lixing applies the mind-regulating acupuncture therapy,with the main acupoints being Sishenzhen,Shenting(GV24),Yintang(EX-HN3),Shenmen(HT7),and Sanyinjiao(SP6).Additional acupoints can be selected according to the patient's symptoms.During the manipulation,the"guiding qi and harmonizing essence"reinforcing and reducing technique is used to regulate emotions and guide qi to relieve depression.This method has shown excellent clinical efficacy and is suitable for clinical promotion.

Objective:To understand the etiological surveillance and drug resistance characteristics of Legionella pneumophila (LP) from the aqueous environment of public places in Shanghai, from 2011 to 2020, and provide evidence for surveillance of the disease. Methods:Environmental water samples were systematically collected from public venues in urban and suburban districts of Shanghai for LP surveillance. All the identified LP isolates underwent a series of testings including serotyping, pulsed field gel electrophoresis (PFGE), sequence-based typing, and antimicrobial susceptibility testing. χ2 test or Cochran-Armitage trend tests were used for statistical analysis and for temporal resistance patterns. Results:Among 6 263 water samples, the LP-positive rate was 20.93% (1 311/6 263). The positivity rate decreased from 24.98% (287/1 149) in 2011-2012 to 20.02% (1 024/5 114) in 2013-2020 ( χ2=13.92, P<0.001), with the highest monthly positivity observed from June to August (23.79%, 745/3 132). A total of 1 365 LP strains were isolated, of which 912 were further characterized, including 10 serotypes, 149 PFGE patterns, and 33 sequence types (ST). The predominant serotype was Lp1 (86.84%, 792/912), and the dominant ST was ST752 (29.50%, 269/912). ST clustering revealed two major clonal groups CG1 and CG2, accounting for 91.12% (831/912) of the isolates. The 190 LPs involved in the drug sensitivity test showed three resistance profiles: azithromycin resistance (31.05%, 59/190), ciprofloxacin resistance (0.53%, 1/190) and azithromycin+ciprofloxacin resistance (0.53%, 1/190). Azithromycin-resistant strains were predominantly ST1 (64.41%, 38/59). The antimicrobial resistance rate showed a significant decline, from 48.65% (18/37) in 2011-2012 to 28.10% (43/153) in 2013-2020 ( χ2=9.38, P=0.002). Conclusions:Compared to from 2011 to 2012, both the positivity rate and antimicrobial resistance prevalence of LP in public aqueous environments of Shanghai exhibited an overall decline from 2013 to 2020. The predominant types of LP were serotype Lp1 and sequence type ST752, with notable high-level resistance to azithromycin. Measures as enhancing the enforcement of water safety regulations and prioritizing surveillance of azithromycin resistance in LP were recommended to mitigate public health risks.

Objective To explore the factors influencing cognitive frailty in elderly inpatients with chronic heart failure(CHF)during hospitalisation,8 prediction models were developed with various machine learning algorithms to identify the best model as a guidance for medical staff on clinical interventions.Methods Convenience sampling method was used to select 650 elderly CHF inpatients who stayed in our hospital between September 2023 and June 2024 as the study objects in the cross-sectional investigation.A total of 607 patients had completed the study.The patients were divided into a cognitive frailty group and a non-cognitive frailty group according to the presence or absence of cognitive frailty.Variables were initially screened using univariate analysis and stepwise Logistic regression.The total sample was then randomly divided into a training set(n=424)and a testing set(n=183)of a 7:3 ratio.Eight predictive models were created using the algorithms of neural network(NN),k-nearest neighbour(KNN),linear discriminant analysis(LDA),support vector machine(SVM),naive Bayes(NB),logistic regression,decision tree(DT)and random forest(RF)on the training set.The predictive performance of the models was compared using the data of the testing set.Results The prevalence of cognitive frailty in elderly CHF inpatients was 48.3%.Results of Logistic regression showed that age,marital status,education,body mass index,multi-morbidity,nutritional status,medication,frequency of weekly exercise and the living conditions were the key factors(P<0.05).The overall accuracy in classification of the eight predictive models ranged from 0.803 to 0.847,with F1-values of 0.778 to 0.833,precision of 0.848 to 0.897,and recall rate of 0.700 to 0.778.The area under the receiver operating characteristic curve was 0.820 to 0.901.Conclusion Of the eight predictive models,the prediction model created with LDA shows the best performance and prediction in terms of comprehensive prediction metrics,while the prediction model created with NN shows the worst performance in comprehensive prediction.

Objective:To investigate the predictive value of refeeding syndrome (RFS) and its influencing factors for 30-day intensive care unit (ICU) readmission in critically ill patients.Methods:A prospective cohort study was conducted. Critically ill patients admitted to the department of critical care medicine, department of respiratory and critical care medicine, and department of neurology at Tianjin Medical University General Hospital from January to April in 2025 were enrolled. Patients were assessed for RFS according to the American Society for Parenteral and Enteral Nutrition (ASPEN) criteria. General information within 24 hours of ICU admission was collected via the electronic medical record system. Treatment details and 30-day ICU readmission status were dynamically recorded. Participants were divided into readmission and non-readmission groups based on whether ICU readmission occurred within 30 days. Intergroup comparisons were performed to identify differences. Multivariate Logistic regression was used to analyze the relationship between RFS and its influencing factors with 30-day ICU readmission. Receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of risk factors.Results:A total of 196 critically ill patients were enrolled, among whom 25 (12.76%) were readmitted to ICU within 30 days and 171 (87.24%) were not. Significant differences were observed in the readmission group compared with the non-readmission group, including significantly higher rates of nasogastric decompression, higher acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) score, a higher incidence of RFS, and a longer duration of nasogastric decompression. Multivariate Logistic regression analysis showed that RFS was an independent risk factor for 30-day ICU readmission [odds ratio ( OR) = 5.756, 95% confidence interval (95% CI) was 1.603-20.670, P = 0.007]. APACHEⅡ score showed a positive correlation trend with 30-day ICU readmission ( OR = 1.057, 95% CI was 0.991-1.127, P = 0.092). ROC curve analysis showed that the combined prediction model incorporating RFS and APACHEⅡ score had an area under the ROC curve (AUC) of 0.766 (95% CI was 0.668-0.864), with a sensitivity of 88.0% and a specificity of 62.0%, which was significantly superior to a single indicator (the AUC of RFS and APACHEⅡ score was 0.639 and 0.624, respectively). Conclusions:RFS significantly increases the risk of 30-day ICU readmission in critically ill patients. A combined model incorporating RFS and APACHEⅡ score demonstrates good predictive efficacy for 30-day ICU readmission in critically ill patients.