To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.

An implantable cardiac monitor(ICM)is a subcutaneous electronic device for recording and remotely transmitting electrocardiographic events.New-generation ICM,like the Reveal LINQ,have extended arrhythmia monitoring duration and improved diagnostic yield through miniaturization,large storage capacity,and easy implantation.They provide comprehensive monitoring data for cardiovascular disease diagnosis and treatment,optimize individualized treatment plans,and enhance patient prognosis.In recent years,research on ICM in cardiovascular fields has deepened,and their clinical application scope has expanded.They show significant advantages and unique value and are widely used in early cardiovascular disease diagnosis and prognostic management.In the future,ICM are expected to play a bigger role in boosting diagnostic and prognostic capabilities for cardiovascular disease patients,offering stronger support for early disease detection,accurate diagnosis,and effective management.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

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 investigate the risk factors and their diagnostic efficacy of perioperative lower limb deep vein thrombosis (DVT) in polytrauma patients with predominant severe limb trauma.Methods:A retrospective cohort study was conducted to analyze the clinical data of 155 polytrauma patients with predominant severe trauma who were admitted to Wuxi Ninth People′s Hospital from January 2021 to December 2024, including 64 males and 91 females, aged 13-95 years [(52.1±16.9)years]. Abbreviated injury scale (AIS) was 5-15 points [(7.4±2.1)points] and injury severity score (ISS) was 17-59 points [(21.3±6.5)points]. Based on the occurrence of DVT in the perioperative period, the patients were divided into preoperative DVT group with 17 patients (11.0%) and non-preoperative DVT group with 138 patients (89.0%) as well as postoperative DVT group with 24 patients (15.5%) and non-postoperative DVT group with 131 patients (84.5%). Basic clinical data were collected, including gender, age, body mass index (BMI), underlying diseases (hypertension, diabetes mellitus), hemoglobin level (Hb), platelet count (PLT), D-dimer, ISS, trauma site [cranial and brain trauma, thoracic and abdominal trauma, upper limb trauma, lower limb trauma (femoral fracture, patellar fracture, tibial or fibular fracture, foot fracture, vascular injury), and pelvic fracture], preoperative waiting time for surgery, surgical site (pelvis and lower limb, other areas), surgical protocols (pelvic and lower limb internal fixation, external fixation of lower limb, lower limb amputation), operation duration less or more than 2 hours, amount of intraoperative blood loss, intraoperative blood transfusion requirement, venous thromboembolism (VTE) prophylaxis (pharmacological and mechanical modalities) and length of hospital stay. Univariate analysis and multivariate binary Logistic regression analysis were conducted to investigate the correlation between the aforementioned indicators and incidence of perioperative lower limb DVT in polytrauma patients with predominant severe limb trauma and determine the independent risk factors. Receiver operating characteristic (ROC) curve and area under the curve (AUC) of the relevant risk factors were analyzed to evaluate and compare the diagnostic efficacy of the risk factors for perioperative lower limb DVT in polytrauma patients with predominant severe limb trauma.Results:Univariate analysis results showed that age, history of hypertension, D-dimer, thoracic and abdominal trauma, pelvic fracture, preoperative waiting time for surgery, and length of hospital stay were significantly correlated with preoperative of DVT of the lower limbs in the patients ( P<0.05). The results of multivariate binary Logistic regression analysis showed that age ( OR=1.05, 95% CI 1.00, 1.10, P<0.05), pelvic fracture ( OR=5.03, 95% CI 1.09, 23.20, P<0.05), preoperative waiting time for surgery ( OR=1.10, 95% CI 1.00, 1.22, P<0.05) and length of hospital stay ( OR=0.89,95% CI 0.81,0.98, P<0.05) were highly correlated with preoperative DVT of the lower limbs in the patients ( P<0.05). Univariate analysis results showed that age, D-dimer, ISS, foot fracture, and length of hospital stay were significantly correlated with postoperative DVT of the lower limbs in the patients ( P<0.05). The results of multivariate binary Logistic regression analysis showed that age ( OR=1.05, 95% CI 1.01, 1.08, P<0.01), D-dimer ( OR=1.05, 95% CI 1.00, 1.10, P<0.05), ISS ( OR=1.09, 95% CI 1.01, 1.17, P<0.05), and foot fracture ( OR=3.51 , 95% CI 1.25 , 9.87 , P<0.05) were significantly correlated with postoperative DVT of the lower limbs in the patients ( P<0.05). The results of the ROC curve analysis indicated that preoperative waiting time for surgery (AUC=0.83, 95% CI 0.75, 0.91) had the highest diagnostic efficacy for preoperative DVT of the lower limbs in the patients, with the diagnostic efficacies of pelvic fracture (AUC=0.75, 95% CI 0.65, 0.85) and age (AUC=0.70, 95% CI 0.59, 0.82) decreasing successively. For postoperative DVT of the lower limbs in the patients, D-dimer (AUC=0.71, 95% CI 0.61, 0.81) exhibited the highest diagnostic efficacy, followed by age (AUC=0.70, 95% CI 0.59, 0.81), ISS (AUC=0.64, 95% CI 0.51, 0.76) and foot fracture (AUC=0.62, 95% CI 0.49, 0.74), with diagnostic efficacy decreased successively. Conclusions:For polytrauma patients with predominant severe limb trauma, age, pelvic fracture and preoperative waiting time for surgery are independent risk factors for preoperative DVT, while age, D-dimer, ISS and foot fracture are independent risk factors for postoperative DVT. Additionally, preoperative waiting time for surgery has the best diagnostic efficacy for preoperative DVT, followed by pelvic fracture and age. D-dimer has the best diagnostic efficacy for postoperative DVT, followed by age, ISS and foot fracture.

Aim To investigate the effect of eplerenone(EPL)on the alleviation of rheumatoid arthritis(RA)based on voltage-gated potassium channel 1.3(Kv1.3)/B-cell lymphoma-2(Bcl-2)/nuclear factor-κB(NF-κB)to inhibit macrophage M1 polarization in mice.Methods Bioinformatics technology was used to screen disease pathways and targets,and the binding affinity and stability of EPL-Kv1.3 complex system were calculated.A mouse model of RA was established and treated with EPL by intragastric administration for 42 days.The indicators reflecting drug remission of RA were recorded and detected.RAW264.7 cells were treated with EPL to detect the indicators reflecting the effect of drugs on macrophage M1 polarization,and to verify the upstream and downstream key targets of re-lated signaling pathways mediated by drugs.Results Bioinformatics analysis showed that the disease targets were mainly involved in inflammatory response and NF-κB signaling pathway,and EPL-Kv1.3 had high affinity and stable binding.In animal experiments,the detec-tion of anti-cyclic citrullinated peptide antibody(CCP-Ab)and joint score indicated the successful establish-ment of the model.Compared with the model group,EPL could reduce the toe redness and swelling score,alleviate the plantar redness and swelling,synovial swelling,and reduce fibrosis and inflammatory cell in-filtration in mice.The medium-dose and high-dose EPL groups reduced the HE staining score(P＜0.05,P＜0.01),and the high-dose EPL group reduced the serum RF in mice(P＜0.01).CCK-8 results showed that low,medium and high doses of EPL had no effect on the activity of RAW264.7 macrophages(P＞0.05).Compared with the model group,EPL treatment significantly reduced the contents of IL-6,TNF-α and NO in supernatant of the cells(P＜0.01),reduced the nuclear translocation of NF-KB-p65 in the high-dose EPL group,reduced the M1 polarization and increased the proportion of M2 polarization in the medium and high-dose EPL groups(P＜0.01).The mRNA levels of MyD88,IκB-α,NF-κB-p65,NF-KB-p50,IL-1 β and iNOS were significantly reduced in each dose group of EPL(P＜0.01).EPL significantly increased the pro-tein expression of Bcl-2(P＜0.01)and decreased the protein expression of Kv1.3,MyD88,p-IκB-α/IκB-α,p-p65/p65,IL-1 β and iNOS(P＜0.05).Conclusion EPL may play an immunomodulatory role in relieving RA in mice by regulating Kv1.3/Bcl-2/NF-κB path-way,reducing macrophage M1 polarization and amelio-rating macrophage-associated inflammatory response.

To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

An implantable cardiac monitor(ICM)is a subcutaneous electronic device for recording and remotely transmitting electrocardiographic events.New-generation ICM,like the Reveal LINQ,have extended arrhythmia monitoring duration and improved diagnostic yield through miniaturization,large storage capacity,and easy implantation.They provide comprehensive monitoring data for cardiovascular disease diagnosis and treatment,optimize individualized treatment plans,and enhance patient prognosis.In recent years,research on ICM in cardiovascular fields has deepened,and their clinical application scope has expanded.They show significant advantages and unique value and are widely used in early cardiovascular disease diagnosis and prognostic management.In the future,ICM are expected to play a bigger role in boosting diagnostic and prognostic capabilities for cardiovascular disease patients,offering stronger support for early disease detection,accurate diagnosis,and effective management.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.