OBJECTIVE:Neuromuscular exercise is a new comprehensive rehabilitation therapy in recent years,but its effect on knee osteoarthritis is still controversial.The purpose of this paper is to systematically evaluate the efficacy of neuromuscular exercise on knee osteoarthritis pain and function. METHODS:The randomized controlled trials addressing neuromuscular exercise in the treatment of knee osteoarthritis pain and function were retrieved from PubMed,Cochrane Library,Embase,EBSCO,CNKI,Web of Science,China Biomedical Database(CBM),VIP,and WanFang Database.The retrieval time ranged from database inception to October 2023.The neuromuscular training group(experimental group)was given neuromuscular training or neuromuscular training as the main intervention;the control group was a blank group or given conventional rehabilitation.Outcome indicators included the Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC)score,walking time,knee stability,and the maximum number of knee flexion in 30 seconds.The risk of bias was evaluated by the Cochrane Collaboration tool and the Physiotherapy Evidence Database.Meta-analysis was performed using RevMan 5.4 software. RESULTS:A total of 11 randomized controlled trials were included,and 628 samples were extracted.The results of Meta-analysis showed that the experimental group was superior to the control group in terms of WOMAC pain score[standardized mean difference(SMD)=0.38,95%confidence interval(CI):0.08-0.69,P=0.01],knee stability(SMD=0.57,95%CI:0.23-0.92,P=0.001),the maximum number of knee joint flexion in 30 seconds(SMD=0.35,95%CI:0.05-0.65,P=0.02),and WOMAC physical function score(SMD=-0.79,95%CI:-1.30 to-0.28,P=0.002).In both groups,walking speed was increased and walking ability was improved in patients with knee osteoarthritis,but there was no significant difference(walking time:SMD=-0.22,95%CI:-0.48-0.03,P=0.09). CONCLUSION:Neuromuscular exercise can effectively improve knee joint pain,enhance the stability of the knee joint,and promote functional recovery in patients with knee osteoarthritis.However,more high-quality randomized controlled trials are still needed to further confirm the research.

Objective To explore effective management methods for improving the efficiency of medical record quality control and enhancing medical record quality.Methods Based on a medical record quality control system,a full-cycle medical record quality management model was constructed,encompassing"pre-task reminders,real-time quality control during treatment,in-hospital process control,submission verification upon discharge,post-discharge terminal quality control,and re-verification af-ter archiving."Results Medical records achieved 100％quality control,and their quality significantly improved.The defect rate of medical record front pages and the delayed writing rate of surgical records in the hospital's discharged medical records continued to decline in the year before system implementation,the first year after implementation,and the second year after im-plementation.The 3-day retrieval rate of medical records increased significantly in the first year after implementation,with statis-tically significant differences in all three indicators(front page defect rate:x2=114.966,P＜0.001;delayed writing rate of sur-gical records:x2=8 056.614,P＜0.001;3-day retrieval rate:x2=798.201,P＜0.001).Conclusion The full-cycle medi-cal record management model based on a quality control system can effectively improve medical record quality and enhance man-agement efficiency.Future efforts should focus on integrating continuously optimized AI technology to strengthen system perform-ance.

Objective To analyze the quality of medical record home pages for discharged patients in a tertiary hospital in Guangzhou in 2023 based on the dual dimensions of Diagnosis-Related Groups(DRG)and Disease Intervention Program(DIP),and to provide a basis for improving the refinement of medical record home page quality management.Methods Dis-charge medical records from a tertiary general hospital in 2023 were selected.All medical records were grouped using DRG,while medical records under Guangzhou medical insurance and cross-province medical insurance implemented under DIP were simulated for DIP grouping.The writing defects in the discharge medical records were analyzed,as well as the DRG grouping,RW,and DIP standard scores of medical records with home page filling defects.Results Among 172 230 discharge medical records,171 786 were grouped into DRG,91 768 were grouped into DIP,and 91 604 were grouped into both DRG and DIP.A significant positive correlation was found between RW and DIP standard scores(r2=0.681,P＜0.001).Among the 91 604 medical records grouped into both DRG and DIP,1 942(2.12％)had writing defects,including 837(0.91％)with home page defects.DRG groups with more home page defect records included NC15,HK2,EJ13,EB15,HJ13,and RE19.Conclusion Quality defects in medical record home pages impact the accuracy of DRG and DIP grouping.Systematic measures such as strengthening medical record writing training and establishing scientific reward and punishment mechanisms should be implemented to provide data sup-port for the further advancement of DRG/DIP payment reform.

Objective To test the hypothesis that backward crawling and forward crawling share similar inter-joint coordination patterns,thus providing potential evidence for the application of backward crawling in rehabilitation training.Methods The acceleration signals in the X,Y,and Z directions for 9 joints(including bilateral wrists,elbows,shoulders,knees,and hips)in 9 volunteers during forward and backward crawling were collected using a custom signal acquisition system,and the pressure signals were also recorded when the palms contacted the ground.The collected acceleration signals were preprocessed,segmented into cycles,and vectorized.Based on the pressure signals,a single crawling cycle was divided into support phase and swing phase.In addition,principal component analysis was applied to extract inter-joint coordination in limbs at various scales(sagittal,coronal,and transverse planes).Pearson correlation coefficients of inter-joint coordination patterns were compared between forward and backward crawling in support period,swing period,and full cycle.Results The correlation coefficients for coordination patterns in the full cycle at the transverse plane scale were 0.813 5(PC1)and 0.837 5(PC2),and the correlation coefficient of the support period PC2 was 0.901 8.At the sagittal plane scale,the correlation coefficient of the support period PC1 was 0.948 5.Conclusion The study provides preliminary evidence that limb motion coordination patterns during backward crawling are similar to those observed during forward crawling.Future research will further explore the effects of backward crawling on functional rehabilitation in individuals with motor impairments.

Objective To explore effective management methods for improving the efficiency of medical record quality control and enhancing medical record quality.Methods Based on a medical record quality control system,a full-cycle medical record quality management model was constructed,encompassing"pre-task reminders,real-time quality control during treatment,in-hospital process control,submission verification upon discharge,post-discharge terminal quality control,and re-verification af-ter archiving."Results Medical records achieved 100％quality control,and their quality significantly improved.The defect rate of medical record front pages and the delayed writing rate of surgical records in the hospital's discharged medical records continued to decline in the year before system implementation,the first year after implementation,and the second year after im-plementation.The 3-day retrieval rate of medical records increased significantly in the first year after implementation,with statis-tically significant differences in all three indicators(front page defect rate:x2=114.966,P＜0.001;delayed writing rate of sur-gical records:x2=8 056.614,P＜0.001;3-day retrieval rate:x2=798.201,P＜0.001).Conclusion The full-cycle medi-cal record management model based on a quality control system can effectively improve medical record quality and enhance man-agement efficiency.Future efforts should focus on integrating continuously optimized AI technology to strengthen system perform-ance.

Objective To analyze the quality of medical record home pages for discharged patients in a tertiary hospital in Guangzhou in 2023 based on the dual dimensions of Diagnosis-Related Groups(DRG)and Disease Intervention Program(DIP),and to provide a basis for improving the refinement of medical record home page quality management.Methods Dis-charge medical records from a tertiary general hospital in 2023 were selected.All medical records were grouped using DRG,while medical records under Guangzhou medical insurance and cross-province medical insurance implemented under DIP were simulated for DIP grouping.The writing defects in the discharge medical records were analyzed,as well as the DRG grouping,RW,and DIP standard scores of medical records with home page filling defects.Results Among 172 230 discharge medical records,171 786 were grouped into DRG,91 768 were grouped into DIP,and 91 604 were grouped into both DRG and DIP.A significant positive correlation was found between RW and DIP standard scores(r2=0.681,P＜0.001).Among the 91 604 medical records grouped into both DRG and DIP,1 942(2.12％)had writing defects,including 837(0.91％)with home page defects.DRG groups with more home page defect records included NC15,HK2,EJ13,EB15,HJ13,and RE19.Conclusion Quality defects in medical record home pages impact the accuracy of DRG and DIP grouping.Systematic measures such as strengthening medical record writing training and establishing scientific reward and punishment mechanisms should be implemented to provide data sup-port for the further advancement of DRG/DIP payment reform.

How to effectively reduce the size of infant pose estimation network models is a key issue restricting the"home-use"of infant pose estimation technology. Therefore,a lightweight method for infant pose estimation in home scenarios is proposed. The method takes the lightweight network MobileNetV3 as the encoding backbone and utilizesa PixelShuffle up-sampling module in the decoder for reducing the quantity of model parameters. Meanwhile,coordinate attention mechanism is used to better capture location information and channel feature information,highlighting the feature information of small targets and occluded human keypoints. Besides,the parallel cross-correlation convolution is further modified to enhance the capability of feature information extraction. The method's performance is verified on the general pose estimation dataset (COCO) and the dedicated infant pose estimation dataset (SyRIP). The results show that,with a calculation volume (GFLOPs) of only 0.96,the method achieves average accuracies of 73.5％ and 91.0％ on COCO and SyRIP datasets,respectively,proving that it can significantly reduce the quantity of model parameters and calculation volume without sacrificing pose estimation accuracy. The proposed lightweight estimation model is expected to be deployed on home appliances such as smart terminals,thereby realizing intelligent estimation of abnormal infant poses in home scenarios.

Objective:To explore the spatial correlation between gray matter volume (GMV) changes and neurotransmitter receptors/transporters in patients with first-episode schizophrenia (FES) .Methods:Fifty-four FES patients(FES group) and fifty-nine healthy controls (HC group) were selected from June 2014 to May 2020 in the Psychiatry Department of Renmin Hospital of Wuhan University. Structural magnetic resonance imaging (sMRI) was conducted on all subjects. Differences of GMV were compared across 400 cortical regions and 32 subcortical regions. Based on the positron emission tomography(PET) data from Neuromaps, which provides the density of 19 different neurotransmitter receptors and transporters, Spearman correlation analysis was performed to evaluate the spatial correlation between GMV changes and neurotransmitter systems.Results:Compared to the HC group, FES group exhibited significant GMV reductions in widespread cortical (90/400) and subcortical (6/32) regions (all FDR-corrected P<0.05). The effect size of GMV reduction (Cohen’s d) showed significant positive correlations with the density of 5-hydroxytryptamine 1a(5HT1a) ( r=0.400, Pspin=0.002), γ-aminobutyric acid type A receptor(GABA A)( r=0.307, Pspin=0.002), and metabotropic glutamate receptor 5(mGluR5) ( r=0.275, Pspin=0.020) receptors (all FDR-corrected P<0.05). Conclusion:GMV reductions in a wide range of brain regions existed in patients with FES. There are significant correlations between 5HT1a, GABA A and mGluR5 receptors and gray matter reduction in patients with FES. The disorder of these neurotransmitter receptors may be the potential neurobiological mechanism of gray matter structural abnormalities in the early stage of schizophrenia.

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.

How to effectively reduce the size of infant pose estimation network models is a key issue restricting the"home-use"of infant pose estimation technology. Therefore,a lightweight method for infant pose estimation in home scenarios is proposed. The method takes the lightweight network MobileNetV3 as the encoding backbone and utilizesa PixelShuffle up-sampling module in the decoder for reducing the quantity of model parameters. Meanwhile,coordinate attention mechanism is used to better capture location information and channel feature information,highlighting the feature information of small targets and occluded human keypoints. Besides,the parallel cross-correlation convolution is further modified to enhance the capability of feature information extraction. The method's performance is verified on the general pose estimation dataset (COCO) and the dedicated infant pose estimation dataset (SyRIP). The results show that,with a calculation volume (GFLOPs) of only 0.96,the method achieves average accuracies of 73.5％ and 91.0％ on COCO and SyRIP datasets,respectively,proving that it can significantly reduce the quantity of model parameters and calculation volume without sacrificing pose estimation accuracy. The proposed lightweight estimation model is expected to be deployed on home appliances such as smart terminals,thereby realizing intelligent estimation of abnormal infant poses in home scenarios.