ObjectiveTo develop and preliminarily validate a prediction model for brace treatment failure risk in patients with adolescent idiopathic scoliosis (AIS), based on information obtained before brace treatment and during initial brace fitting. MethodsA total of 191 patients with AIS who initiated thoracolumbosacral orthosis treatment at Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from March, 2015 to August, 2024 and had determinable outcomes were retrospectively included. Patients were divided according to a prespecified scheme into a training set (n = 133) and a validation set (n = 58). Brace treatment failure was defined as progression of the major-curve Cobb angle by ≥ 5° from baseline during follow-up or at brace weaning, or a final major-curve Cobb angle > 45°. Logistic regression, random forest, support vector machine with radial basis function kernel, and extreme gradient boosting (XGBoost) models were developed using routinely available clinical and radiographic variables before brace treatment and during initial brace fitting. Stratified five-fold cross-validation was used for hyperparameter tuning in the training set. Model performance was evaluated in the validation set using the area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AUPRC), Brier score, calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) was used to interpret the main explanatory model. ResultsIn cross-validation of the training set, both random forest and XGBoost showed good discriminative performance, with cross-validation-AUC values of 0.871 and 0.880, respectively. In the validation set, the AUC values of random forest and XGBoost were 0.888 and 0.899, the AUPRC values were 0.678 and 0.830, and the Brier scores were 0.086 and 0.076, respectively. Random forest was selected as the primary model for interpretation, with a calibration intercept of 0.091 and a calibration slope of 0.995. SHAP analysis indicated that the in-brace correction rate and the major-curve Cobb angle measured in the brace contributed most to the model predictions. ConclusionThe random forest model developed using routinely available pretreatment and initial-fitting variables shows preliminary predictive value for brace treatment failure risk in AIS, and may provide quantitative support for follow-up scheduling, brace-fit reassessment and early adherence-oriented management.

Objective To investigate the risk factors for ICU-acquired weakness (ICU-AW) in patients with sepsis, and to construct a nomogram prediction model. Methods A total of 360 patients with sepsis admitted from January 2020 to December 2025 were enrolled as study subjects. The risk factors for ICU-AW were identified by logistic regression analysis. A nomogram prediction model was constructed, and the model was validated. Results Multivariate logistic regression analysis indicated that age, APACHE II score, duration of mechanical ventilation, duration of immobilization, duration of sedation, and NLR were risk factors for the occurrence of ICU-AW (P<0.05), while ALB was a protective factor (P<0.05). A nomogram model was constructed based on these seven variables. After validation via Bootstrap resampling, ROC curve showed an AUC of 0.949 (0.929–0.969) and a corrected AUC of 0.942 (0.940–0.948), indicating good discrimination degree of the model. Hosmer-Lemeshow test for calibration curve revealed a chi-square value of 10.188 (P=0.252), suggesting that the predicted value of fitting was generally consistent with the actual value. Decision curve analysis demonstrated that when the threshold was in the range of 0.05–0.9, the model had a good net benefit. Conclusion The nomogram prediction model for sepsis ICU-AW based on seven indicators including age, APACHE II score, NLR, ALB, and the durations of mechanical ventilation, immobilization, and sedation exhibits good predictive efficiency and stability. This nomogram prediction model is helpful for the clinical rapid screening of ICU-AW high-risk groups in patients with sepsis, and meets clinical needs for early warning and precise prevention and control.

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 investigate the efficacy of an interpretable integrated learning model combining clinical indicators,CT image features and radiomics features for the differential diagnosis of lung squamous cell carcinoma and adenocarcinoma,so as to provide references for clincal treatment decisions.Methods A retrospective analysis was conducted on clinical and imaging data from 220 patients(231 lesions)with primary non-small cell lung cancer at Jiangjin Central Hospital of Chongqing(Center 1)and 83 patients(84 lesions)at Chongqing General Hospital(Center 2).In Center 1,the squamous cell carcinoma group consisted of 60 patients(60 lesions),while the adenocarcinoma group included 160 patients(171 lesions).In Center 2,the squamous cell carcinoma group comprised 18 patients(18 lesions),and the adenocarcinoma group involved 65 patients(66 lesions).The patients were categorized into squamous cell carcinoma and adenocarcinoma groups based on pathological findings.Center 1 was randomly partitioned into a training set and a validation set at a 7∶3 ratio,while Center 2 served as the independent test set.Firstly,a deep learning model,VB-Net,was used to automatically segment the tumor region on the lung window image;secondly,the SMOTE(synthetic minority oversampling technique)method was used to balance the categories in the training set and standardize the extracted features with Z-scores;thirdly,the least absolute shrinkage and selection operator(LASSO)were used to select the optimal radiomics features and calculate the radiomics score(Radscore),and univariate and multivariate logistic regression was used to screen clinical indicators and independent clinical factors for differentiating lung squamous cell carcinoma and adenocarcinoma in CT image features;finally,three ensemble learning algorithms(AdaBoost,Bagging decision tree and XGBoost)were used to combine independent clinical factors and Radscore to construct the model.The receiver operating characteristic(ROC)curve was used to evaluate the diagnostic performance of the models.SHAP technique was used to analyze the feature contribution and model decision-making process.Results Among the evaluated ensemble models,AdaBoost and Bagging decision trees demonstrated overfitting tendencies.In contrast,the XGBoost model showed the best performance,achieving AUC values of 0.939,0.887 and 0.853 in the training,validation and independent test sets,respectively.SHAP indicated that Radscore was the most important feature affecting the performance of the model.The decision diagram enabled the visualization of the diagnostic process of the model.Conclusion The interpretable integrated learning model based on clinical indicators,CT image and radiomics features is expected to non-invasively diagnose lung squamous cell carcinoma and adenocarcinoma before treatment and assist clinicians make treatment decisions as early as possible.[Chinese Medical Equipment Journal,2025,46(7):12-20]

Objective To explore the correlation of neutrophil/lymphocyte ratio(NLR),monocyte/high density lipoprotein cholesterol ratio(MHR)and mild cognitive impairment(MCI)in patients with type 2 diabetes mellitus(T2DM).Methods 159 T2DM patients hospitalized in the Department of Endocrinology,Affiliated Hospital of Xuzhou Medical University from February 2023 to April 2023 were selected.The subjects were divided into normal cognitive function(Con,n=72)group and MCI group(n=87)based on score of Montreal cognitive assessment scale(MoCA).NLR and MHR were calculated and compared between the two groups.Spearman correlation analysis was used to analyze the correlation between NLR,MHR and MoCA score.Logistic regression analysis of the factors influencing the association between T2DM with MCI.Receiver operator characteristic(ROC)curve was used to evaluate the predictive value of NLR and MHR for DM complicated with MCI.Results Compared with Con group,MCI group showed a significant increase in hemoglobin A1c(HbA1c),fasting plasma glucose,NLR,MHR,neutrophil count,monocyte count,serum creatinine,cystatin C,while high density lipoprotein cholesterol lymphocytes,visual space and executive function,naming,attention,language ability,abstract thinking,delayed memory,orientation and MoCA score decreased(P＜0.05).Spearman correlation analysis showed that there was a negative correlation between MHR,NLR and MoCA score,visuospatial and executive function,delayed recall and attention.Logistic regression analysis showed that NLR,MHR,HbA1c were all risk factors for MCI in T2DM.ROC curve analysis showed that the area under the curve of NLR combined with MHR for diagnosing T2DM with MCI was 0.872,with corresponding sensitivity of 81.6%and specificity of 87.5%.Conclusions NLR and MHR are closely related to MCI in T2DM patients.The combination of NLR and MHR have certain efficacy in prediction T2DM with MCI.

Objective To investigate the relationship between the expression of discoidin,CUB and LCCL domain containing protein 1(DCBLD1)and cytoskeleton associated protein 2(CKAP2)in cervical cancer(CC)tissues and epithelial mesenchymal transition(EMT)and clinical prognosis.Methods 94 CC patients diagnosed and treated in Shiyan Hospital of Traditional Chinese Medicine from February 2017 to February 2019 were selected.The expression of DCBLD1 messenger ribonucleic acid(mRNA),CKAP2 mRNA,N-cadherin(N-cad)mRNA,vimentin(Vim)mRNA and TWIST mRNA in tissues was detected by real-time fluorescence quantitative PCR(qRT-PCR).The expressions of DCBLD1 and CKAP2 were detected by immunohistochemistry(IHC).Pearson correlation analysis was used to analyze the relationship between DCBLD1 mRNA,CKAP2 mRNA and EMT-related indicators.Kaplan-Meier survival curve was drawn to compare the prognosis of CC patients with different DCBLD1 mRNA and CKAP2 mRNA expression.COX regression analysis was used to analyze the prognostic factors of CC patients.Results The expression of DCBLD1 mRNA,CKAP2 mRNA,N-cad mRNA,Vim mRNA and TWIST mRNA in CC cancer tissues was higher than that in adjacent tissues,and the differences were statistically significant(t=32.763～52.824,all P<0.05).The positive rates of DCBLD1 protein(89.36%)and CKAP2 protein(87.23%)in CC cancer tissues were higher than those in adjacent normal tissues(7.45%,6.38%),and the differences were statistically significant(χ2=126.278,123.396,all P<0.001).The expression of DCBLD1 mRNA and CKAP2 mRNA in CC cancer tissues were positively correlated with the expression of N-cad mRNA,Vim mRNA and TWIST mRNA(r=0.655～0.744,all P<0.001).The expression of DCBLD1 mRNA and CKAP2 mRNA in patients with FIGO stage IB2～IIB and lymph node metastasis were higher than those in patients with stage IA～IB1 and without lymph node metastasis(t=25.644～35.674,all P<0.05).The 5-year progression free survival rates of the high expression groups of DCBLD1 mRNA and CKAP2 mRNA were 63.04%and 62.22%,respectively,which were lower than those of the low expression groups of DCBLD1 mRNA and CKAP2 mRNA(91.67%and 91.84%),respectively,and the differences were statistically significant(Log-Rank χ2=7.181,6.527,all P<0.05).FIGO stage IB2～IIB,high DCBLD1 mRNA and high CKAP2 mRNA were risk factors affecting the prognosis of CC patients(Wald χ2=8.277,15.877,10.927,all P<0.05).Conclusion The expression of DCBLD1 and CKAP2 in CC cancer tissues is significantly increased,which is related to EMT related indicators and plays a promoting role in the progression of CC tumors.They are new prognostic markers for CC.

A mounting body of research suggests that circRNAs significantly contribute to the develop-ment of myocardial fibrosis.The microarray results of human circular RNA expression profile indicated that circHERC4_041 expression increased in the myocardium of patients with heart failure,RT-qPCR a-nalysis confirmed that the myocardial expression level of circHERC4_041 in individuals with heart failure were considerably elevated compared to that in healthy organ donors.Fluorescence in situ hybridization(FISH)confirmed that circHERC4_041 was abundant in the cytoplasm of human cardiomyocyte AC16.Overexpression of circHERC4_041 in mouse myocardial fibroblasts(mCFs)mediated by adenovirus in-hibited the expression of fibrosis-related proteins in mCFs.Experiments involving cell proliferation,wound healing,and Transwell assays demonstrated that overexpression of circHERC4_041 suppressed the growth and mobility of mCFs(P＜0.001).Sequence analysis results suggested that circHERC4_041 con-tains potential ribosome entry sequence(IRES)and open reading frame(ORF).Western blot confirmed that circHERC4_041 could translate the 516 amino acid HERC4-516aa protein,which was mainly located in the cytoplasm of the cell.Cell functional experiments confirmed that circHERC4_041 inhibited the fi-brotic phenotype of mCFs by specifically translating HERC4-516aa(P＜0.05).The specific interaction between HERC4-516aa and transglutaminase 2(TGM2)was confirmed by IP-MS screening and Co-IP i-dentification.Further results found that the degradation of TGM2 was promoted through proteasome path-way.The overexpression of TGM2 in mCFs facilitated by adenoviral vectors could counteract the suppres-sive effects of HERC4-516aa on the fibrotic phenotype of mCFs.Therefore,this study confirmed that the HERC4-516aa protein translated by circHERC4_041 can specifically bind to TGM2 to inhibit the fibrotic phenotype of myocardial fibroblasts.

Objective Based on the intervention map to develop a comprehensive nutrition education program for pancreaticoduodenectomy patients and to explore the effect of its clinical application,aiming at providing references for clinical nursing practice.Methods A convenience sampling method was used to select 76 patients who were to undergo pancreaticoduodenectomy in the department of hepatobiliary and pancreatic surgery of a tertiary hospital in Anhui Province as the study subjects.The 38 patients admitted from October 2021 to September 2022 were in a control group,and the 38 patients admitted from October 2022 to July 2023 were in an experimental group.The experimental group received the comprehensive nutritional education programme constructed in this study,and the control group used conventional nutrition health education measures,and the length of intervention for both groups was from pre-hospitalization to discharge for 6 months.Nutrition-related indicators,postoperative complications,hospitalisation time,hospitalisation costs and satisfaction were compared between the 2 groups.Results A total of 64 patients completed this study,with 33 in the experimental group and 31 in the control group.Repeated measurement analysis of variance showed that the interaction effects of BMI,total serum protein and serum preprotein were statistically significant(P＜0.05).The incidence of complications,hospitalization days and hospitalization costs of the experimental group were lower than those of the control group(P＜0.05).The scores of nutrition education-related satisfaction in the experimental group were higher than those in the control group(P＜0.001).Conclusion A comprehensive nutritional education program based on intervention map can improve the nutritional status of pancreaticoduodenectomy patients to a certain extent,reduce the occurrence of complications,and promote patients'recovery.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.