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 To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Objective:To analyze the predictive role of WT1 expression levels pre-and early post-transplantation on relapse and overall survival(OS)in patients with acute myeloid leukemia(AML)undergoing allogeneic hematopoietic stem cell transplantation(allo-HSCT)during their first complete remission(CR1).Methods:A retrospective analysis was conducted on the clinical data of 107 adult AML patients who underwent allo-HSCT during their CR1 at our center between May 2012 and December 2021.The predictive role of bone marrow WT1 expression levels before transplantation and at 3 and 6 months post-transplantation on relapse and OS was explored in combination with relevant clinical factors.Results:The median follow-up time for the 107 patients was 70(range:11-117)months.Among the patients,15 cases died.Kaplan-Meier survial analysis showed that the 3-year overall survival(OS)rate was 85.0％.20 patients experienced relapse,with a median time to relapse of 8(range:0.5-44)months and a l-year cumulative relapse rate of 13.1％.The overall median value of WT1 before transplantation,3 months after transplantation,and 6 months after transplantation was 0.26％(range:0％-23.64％),with an upper quartile value of 0.74％.No statistically significant differences in WT1 expression levels were observed among the pre-transplantation,3-month post-transplantation,and 6-month post-transplantation time points(P=0.227).Univariate analysis showed that patients with WT1 levels＞0.74％at 3 months post-transplantation had a higher 1-year relapse rate(P=0.029)and lower 3-year OS rate(P＜0.001)compared to patients with WT1 levels ≤0.74％.Other significant factors affecting 1-year relapse included stem cell source(P=0.041)and chronic graft-versus-host disease(cGVHD)(P=0.013).For 3-year OS,additional influencing factors were genetic high risk(P=0.048)and stem cell source(P=0.016).Multivariate analysis revealed that WT1 level＞0.74％at 3 months post-transplantation had a trend to affect 1-year relapse rate(HR=3.309,95％CI:0.958-11.431,P=0.058),while the absence of cGVHD was an independent risk factor for 1-year relapse(HR=3.473,95％CI:0.749-16.100,P=0.037).Only WT1 level＞0.74％at 3 months post-transplantation was an independent risk factor for 3-year OS(HR=6.886,95％CI:2.402-19.738,P＜0.001).Conclusion:High WT1 expression level at 3 months post-transplantation in AML patients undergoing allo-HSCT during CR1 affects the 1-year relapse rate and 3-year OS,and is an independent risk factor affecting 3-year OS.These findings suggest that dynamic monitoring of WT1 expression levels has certain value in prognostic assessment of AML patients who received allo-HSCT during CR1.

OBJECTIVE To investigate the clinical distribution and dynamic change of drug resistance of K.pneu-moniae and carbapenem-resistant Klebsiella pneumoniae(CRKP)isolated from a three-A hospital of Suzhou so as to provide scientific bases for prevention and control of hospital-associated infections and reasonable application of antibiotics.METHODS The K.pneumoniae and CRKP strains that were isolated from the submitted specimens were collected from the patients who treated in the Affiliated Suzhou Hospital of Nanjing Medical University from 2019 to 2023.The clinical characteristics of the patients with infection and the trend of drug resistance were statis-tically analyzed.RESULTS Totally 5631 strains of K.pneumoniae were isolated,1205(21.40％)of which were CRKP,and the isolation rate of CRKP showed an upward trend in the five years(x2=236.352,P＜0.001).Among the K.pneumoniae isolates,51.59％were isolated from sputum,13.51％from urine;19.43％were isolated from intensive care unit(ICU),7.64％from emergency department,and 7.19％from respiratory department.There were significant differences in gender,age and season between the patients detected with CRKP and the patients detected with non-CRKP(P＜0.05).The drug resistance rates of the K.pneumoniae strains to cephalosporins,quinolones and carbapenems con-tinuously increased from 2019 to 2023(P＜0.001),the drug resistance rate to imipenem increased from 11.69％to 34.24％,meropenem from 10.92％to 34.24％.CONCLUSIONS The K.pneumoniae isolates show severe drug re-sistance from 2019 to 2023,and the isolation rate of CRKP strains rises increasingly.It is necessary for the hospi-tal to focus on the continuous monitoring of key populations and departments and optimize the management of an-tibiotics and infection control strategies so as to provide guidance for reasonable clinical use of antibiotics,effective control of transmission of drug-resistant strains and cope with the increasingly severe drug resistance.

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.

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.

Aim To construct and analyze the genotype of G protein-coupled receptor kinase 2(GRK2)conditional knockout mice in synoviocytes,and to provide an animal model for stud-ying the function of GRK2 in synoviocytes.Methods Grk2flox/+mice were bred to generate Grk2flox/flox mice,Grk2flox/flox mice were bred to Col1a1-iCre+mice,Grk2flox/+Col1a1-iCre+mice were bred to Grk2flox/flox mice.Grk2flox/flox Col1a1-iCre+mice were ob-tained as target mice.DNA was extracted and amplified by PCR to identify the genotype.Western blot was used to verify the effect of Grk2 knockout in synovium,liver and kidney tissues.HE staining was used to detect the effects of Grk2 conditional knockout in synovial cells on ankle synovium,liver and kidney tissues.Multiple immunofluorescence was used to detect GRK2 expression in synovial cells.Results The results of gene iden-tification showed that Grk2flox/flox Col1a1-iCre+mice had both Flox and Col1a1-iCre genotypes.Western blot results showed that GRK2 expression decreased in synovial tissues of Grk2flox/flox Col1a1-iCre+mice,but there was no significant change in the expression of GRK2 in liver and kidney tissues.HE staining showed that Grk2flox/flox Col1a1-iCre+mice had no significant pathological changes in the ankle synovium,liver and kidney.The results of multiple immunofluorescence showed that GRK2 expression in synovial cells of Grk2flox/flox Col1a1-iCre+mice de-creased.Conclusion Grk2 conditional knockout mice in syno-viocytes are successfully constructed and identified,which pro-vides an animal model for further study of the role of GRK2 in synovial-related diseases.

OBJECTIVE To investigate the clinical distribution and dynamic change of drug resistance of K.pneu-moniae and carbapenem-resistant Klebsiella pneumoniae(CRKP)isolated from a three-A hospital of Suzhou so as to provide scientific bases for prevention and control of hospital-associated infections and reasonable application of antibiotics.METHODS The K.pneumoniae and CRKP strains that were isolated from the submitted specimens were collected from the patients who treated in the Affiliated Suzhou Hospital of Nanjing Medical University from 2019 to 2023.The clinical characteristics of the patients with infection and the trend of drug resistance were statis-tically analyzed.RESULTS Totally 5631 strains of K.pneumoniae were isolated,1205(21.40％)of which were CRKP,and the isolation rate of CRKP showed an upward trend in the five years(x2=236.352,P＜0.001).Among the K.pneumoniae isolates,51.59％were isolated from sputum,13.51％from urine;19.43％were isolated from intensive care unit(ICU),7.64％from emergency department,and 7.19％from respiratory department.There were significant differences in gender,age and season between the patients detected with CRKP and the patients detected with non-CRKP(P＜0.05).The drug resistance rates of the K.pneumoniae strains to cephalosporins,quinolones and carbapenems con-tinuously increased from 2019 to 2023(P＜0.001),the drug resistance rate to imipenem increased from 11.69％to 34.24％,meropenem from 10.92％to 34.24％.CONCLUSIONS The K.pneumoniae isolates show severe drug re-sistance from 2019 to 2023,and the isolation rate of CRKP strains rises increasingly.It is necessary for the hospi-tal to focus on the continuous monitoring of key populations and departments and optimize the management of an-tibiotics and infection control strategies so as to provide guidance for reasonable clinical use of antibiotics,effective control of transmission of drug-resistant strains and cope with the increasingly severe drug resistance.

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.

Aim To construct and analyze the genotype of G protein-coupled receptor kinase 2(GRK2)conditional knockout mice in synoviocytes,and to provide an animal model for stud-ying the function of GRK2 in synoviocytes.Methods Grk2flox/+mice were bred to generate Grk2flox/flox mice,Grk2flox/flox mice were bred to Col1a1-iCre+mice,Grk2flox/+Col1a1-iCre+mice were bred to Grk2flox/flox mice.Grk2flox/flox Col1a1-iCre+mice were ob-tained as target mice.DNA was extracted and amplified by PCR to identify the genotype.Western blot was used to verify the effect of Grk2 knockout in synovium,liver and kidney tissues.HE staining was used to detect the effects of Grk2 conditional knockout in synovial cells on ankle synovium,liver and kidney tissues.Multiple immunofluorescence was used to detect GRK2 expression in synovial cells.Results The results of gene iden-tification showed that Grk2flox/flox Col1a1-iCre+mice had both Flox and Col1a1-iCre genotypes.Western blot results showed that GRK2 expression decreased in synovial tissues of Grk2flox/flox Col1a1-iCre+mice,but there was no significant change in the expression of GRK2 in liver and kidney tissues.HE staining showed that Grk2flox/flox Col1a1-iCre+mice had no significant pathological changes in the ankle synovium,liver and kidney.The results of multiple immunofluorescence showed that GRK2 expression in synovial cells of Grk2flox/flox Col1a1-iCre+mice de-creased.Conclusion Grk2 conditional knockout mice in syno-viocytes are successfully constructed and identified,which pro-vides an animal model for further study of the role of GRK2 in synovial-related diseases.