Objective To construct a lung cancer surgery-oriented disease-specific database covering the entire perioperative care pathway, thereby improving the quality and usability of key surgical data elements. Methods Real-world clinical data were extracted from a single-center thoracic surgery department. A standardized data model was established based on the open electronic health record (openEHR) standard. Large language model (LLM), optical character recognition (OCR), and artificial intelligence (AI)-driven techniques were employed to extract, structure, and perform quality control on unstructured clinical narratives, imaging reports, and radiological data, with a focus on capturing surgically relevant perioperative indicator. Results A multimodal database comprising 19 917 patients was established, including 7 930 males and 11 987 females, with ages ranging from 15 to 97 (61.7±9.7) years. The database includes 582 structured data variables, textual report data corresponding to 69 clinical indicators, 13 000 pulmonary function test PDF reports, and chest CT imaging data from 16 884 patients. This database comprehensively covers major information relevant to surgical diagnosis and treatment of lung cancer, significantly improving the completeness and granularity of surgical detail data. Large language models (LLMs) and optical character recognition (OCR) technologies enhanced the efficiency of converting unstructured data into structured formats, while a multi-level manual verification process ensured data accuracy and traceability. The database supports real-world research including comparisons of surgical procedures, prediction of postoperative complications, prognosis assessment, and multimodal data association analyses.

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.

Due to patient compliance and convenience, oral medication is likely the most common and acceptable method of drug administration. However, traditional dosage forms such as tablets or capsules may lead to low drug bioavailability and poor therapeutic efficiency. Therefore, with advancements in material science and micro/nano manufacturing technology, various carriers have been developed to enhance drug absorption in the gastrointestinal tract. In this context, we initially discuss the key biological factors that hinder drug transport and absorption (including anatomical, physical, and biological factors). Building on this foundation, recent progress in both conventional and innovative oral drug delivery routes aimed at improving drug bioavailability and targeting is reviewed. Finally, we explore future prospects for oral drug delivery systems as well as potential challenges in clinical translation.

Objective To investigate and compare the epidemiological characteristics of common respiratory viruses among influenza-like illness(ILI)and severe acute respiratory infection(SARI)cases in Huzhou,Zhejiang Province before and after the COVID-19 pandemic,so as to provide a basis for formulating and adjusting the prevention and control strategies for viral respiratory infectious diseases.Methods ILI and SARI cases at two influenza surveillance sentinel hospitals in Huzhou and had throat swab samples collected during Nov 2017 to Feb 2020(pre-COVID-19 pandemic period)and Dec 2022 to Apr 2024(post-COVID-19 mitigation phase)were selected as the participants.Seven common viral respiratory pathogens were tested,including influenza A virus(H1N1 and H3N2 subtypes),influenza B virus(Victoria lineage,FluB),respiratory syncytial virus(RSV),rhinovirus(HRV),adenovirus(ADV),and severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).The positive rates of respiratory pathogens before and after the COVID-19 pandemic were compared across different age groups and different time.Results A total of 7 948 ILI samples and 2 294 SARI samples were included.The overall positive rate of ILI samples increased from 33.6%to 47.1%,primarily due to the increase in influenza and COVID-19 infections;the overall positive rate of SARI samples decreased from 31.4%to 24.8%,mainly due to the reduction in HRV and ADV infections.During the post-COVID-19 mitigation phase,SARS-CoV-2(22.1%),H3N2(12.7%),and FluB(6.0%)were the primary pathogens in ILI samples,while RSV(7.1%),H3N2(5.3%),and HRV(4.5%)dominated in SARI samples.During the post-COVID-19 mitigation phase,the influenza virus circulation period was shortened.Before the COVID-19 pandemic,RSV was mainly detected in autumn and winter,while during the post-COVID-19 mitigation phase,out-of-season RSV epidemics were observed in spring and summer.Co-infection rate in ILI cases increased significantly in the post-COVID-19 mitigation phase,predominantly consisting of co-infections of COVID-19 and influenza A virus,while co-infection rate in SARI cases showed a decline.Conclusion We found important epidemiological changes in respiratory viruses in Huzhou during the post-COVID-19 mitigation phase compared to pre-COVID-19 period,including increased positive rates of influenza and COVID-19,and disruptions to the seasonal patterns of influenza and RSV.The prevention and control strategies should be adjusted in a timely manner based on the monitoring data.

Objective:To analyze the gene chip related to dermatomyositis based on bioinformatics,to explore the immune in-filtration mechanism of key genes in dermatomyositis by CIBERSORT deconvolution algorithm,and to predict the therapeutic targets of dermatomyositis by network pharmacology.Methods:The gene microarray of dermatomyositis was searched in GEO database,and the differentially coexpressed genes were screened and analyzed.The differentially coexpressed genes were analyzed by GO analysis,KEGG analysis,protein interaction network construction(PPI)by R software package.Verify the expression levels of key genes,and the correlation of immune cell infiltration was analyzed by CIBERSORT deconvolution method.Through the medical ontology informa-tion retrieval platform Coremine medical database,the traditional Chinese medicine treatment targets of dermatomyositis were screened and summarized.Results:A total of 196 differentially expressed genes were screened.GO enrichment analysis showed that these differentially expressed genes were mainly concentrated in defense response to virus,blood particles,double-stranded RNA binding,polypeptide antigen binding,and so on.KEGG enrichment analysis showed that it was enriched in RIG-Ⅰ-like receptor sig-nal pathway,Toll-like receptor signal pathway and other signal pathways related to the pathogenesis of dermatomyositis.Finally,four key genes of dermatomyositis,STAT1,ISG15,IRF7 and IRF9 were obtained.Through CIBERSORT algorithm,M1 macrophages,M2 macrophages and CD8+T cells were the three kinds of cells with the highest average proportion and the most obvious immune infil-tration,and there was a significant positive correlation between activated natural killer cells and activated dendritic cells,while there was a significant negative correlation between resting mast cells and activated mast cells.The therapeutic targets of traditional Chinese medicine such as fish brain stone were predicted based on Coremine medical database;through channel analysis,it could be found that these traditional Chinese medicines are mainly attributed to liver meridian,lung meridian,spleen meridian;efficacy analysis is mainly focused on clearing heat,detoxification,promoting blood circulation and removing blood stasis,relieving cough and resolving phlegm and so on.Conclusion:Four key genes and some key signal pathways of dermatomyositis,STAT1,ISG15,IRF7 and IRF9 were obtained by bioinformatics method,the immune infiltration mechanism was analyzed by CIBERSORT algorithm,and the thera-peutic potential targets of traditional Chinese medicine were screened out to provide direction for the pathogenesis and treatment of der-matomyositis.

Objective:To explore the effects of stepped exercise rehabilitation training on cardiopulmonary function and quality of life in patients with ischemic heart disease.Methods:This randomized controlled study enrolled 110 patients with ischemic heart disease admitted to Jiangsu Province Hospital between January 2021 and December 2023.Patients were randomly divided into control group(n=53,routine intervention program)and intervention group(n=52,additional stepped exercise rehabilitation training).After 3-month intervention,cardiopulmonary function[stroke volume(SV),cardiac index(CI),forced expiratory volume in the first second(FEV1),maximum voluntary ventilation(MVV)],quality of life[China questionnaire of quality of life in patients with cardiovascular diseases(CQQC)score],exercise endurance[exercise duration(ED),peak oxygen intake(VO2peak),anaerobic threshold(AT)]and incidence of major adverse cardiovascular events(MACE)were compared between the two groups.Results:We included 53 patients[28 males(52.83％),age 40～69(56.25±7.76)years old]in control group compared to 52 patients[27 males(51.92％),age 40～71(55.25±2.40)years old]in the intervention group after 3-month intervention.Com-pared to patients in the control group,those in the intervention group had significantly higher SV[(65.46±3.58)ml vs.(61.69±3.78)ml],CI[(2.82±0.12)L·min-1·m-2 vs.(2.54±0.09)L·min-1·m-2],FEV1[(2.50±0.06)L vs.(2.31±0.06)L],MVV[(79.66±0.82)L/min vs.(77.16±1.09)L/min],CQQC score[(65.23±2.84)points vs.(47.98±3.25)points],ED[(382.62±31.19)s vs.(353.37±36.32)s],VQ2peak[(20.05±2.43)ml·kg-1·min-2 vs.(16.86±1.61)ml·kg-1·min-2]and AT[(13.34±0.83)ml·kg-1·min-2 vs.(11.46±0.89)ml·kg-1·min-2](P＜0.001 all),and significantly lower total incidence of MACE(11.54％vs.32.08％,P=0.011).Conclu-sion:Stepped exercise rehabilitation training may improve cardiopulmonary function,quality of life,and exercise endurance in patients with ischemic heart disease during the rehabilitation.

Aim To investigate the effect of ACSL4 on the malignant biological behavior of esophageal cancer cells and gefitinib resistance by regulating FASN,and to explore the related mechanism.Methods Thirty-five fresh esophageal cancer tissues and adjacent nor-mal tissues,and 30 esophageal cancer tissues with ge-fitinib resistance were collected.The expressions of ACSL4 and FASN were detected by qRT-PCR and im-munohistochemistry.The expression levels of ACSL4 and FASN in human normal esophageal cells HET-1 A,esophageal cancer cell lines ECA109,EC9706,TE-1 and TE-1/GR were detected by qRT-PCR.Cells in each group were constructed by liposome transfection technique,and the drug resistance and proliferation a-bility of cells were detected by cloning and CCK-8 as-say,cell apoptosis was detected by flow cytometry,cell invasion ability was detected by Transwell,and EMT pathway protein expression was detected by Western blot.Results Compared with adjacent normal tis-sues,the expression of ACSL4 and FASN genes in cancer tissues increased,and there was a positive corre-lation.The expression of ACSL4 significantly increased in ECA109,EC9706 and TE-1 cells compared with HET-1 A cells.With the increase of gefitinib concen-tration,the expression of ACSL4 in TE-1 cells gradually increased,and the expression of ACSL4 in TE-1/GR cells was higher than that of TE-1.Compared with the control group and the si-NC group,the cell proliferation and invasion ability of si-ACSL4 group decreased,the number of apoptosis increased,the expression of E-Cadherin increased,and the expression of N-Cadherin,Vimentin and β-catenin decreased.The response ex-periment showed that compared with the si-ACSL4 group and the si-ACSL4+oe-NC group,the cells in the si-ACSL4+oe-FASN group increased drug resistance,increased proliferation and invasion ability,decreased apoptosis number and decreased expression of E-Cad-herin.The expressions of N-Cadherin,Vimentin and β-catenin increased.Conclusions By down-regulating the expression of FASN,ACSL4 reverses the resistance of esophageal cancer TE-1/GR cells to gefitinib and in-hibits the proliferation,invasion and accelerates apopto-sis of TE-1/GR cells,which may be related to the regu-lation of EMT signaling pathway.

Since the concept of artificial intelligence(AI)was proposed in 1956,medicine has been one of its core application fields.At present,AI technology has run through the whole diagnosis and treatment process,and has been extended to innovative scenarios such as drug research and development,surgical robots,and clinical trial optimization.Scenario application is the backbone of the technical system.Multimodal data fusion integrates heterogeneous data such as images,medical records,and genes,and federated learning realizes cross-institutional privacy protection and sharing.Deep learning achieved more than 90%sensitivity in imaging diagnosis for lung nodule detection,while generative AI accelerates drug molecule design.The core applications cover four major areas field:AI is more accurate than professional doctors in breast cancer and diabetic retinopathy screening;robotics shortens hospital stays and improves spinal screw placement accuracy;AI shortens the discovery cycle of drug targets;machine learning improves the efficiency of subject screening and enables real-time data monitoring.The application of AI in the medical field is first constrained by data quality and algorithm bias,and the"black box"characteristics of AI models and the ambiguity of responsibility attribution are the core obstacles to clinical implementation.This paper analyzes key technological breakthroughs and typical cases,discusses the application scenarios and challenges of AI in medicine,and aims to provide a reference for the future development of medical intelligence.

The pathological mechanisms and related physiological indicators of acute mountain sickness(AMS)were introduced,and the present situation of wearable devices applied in AMS auxiliary detection and diagnosis were reviewed.The challenges and future development directions of wearable devices applied in auxiliary detection and diagnosis were pointed out.[Chinese Medical Equipment Journal,2025,46(9):100-107]

Objective To explore the feasibility and corresponding implementation methods of constructing a sample resource bank based on individual-level data of completed clinical trials and using it to construct external controls for drug/device clinical trials.Methods Taking the pre-marketing clinical trial of transcatheter active valve replacement(TAVR)for the treatment of aortic valve stenosis as an example,the individual-level databases of multiple trials were standardized to form a sample bank.The original data of any trial in the sample bank were selected as the experimental group,and the remaining samples were selected as the control group.The potential confounding was handled by using the propensity score matching and stratification methods to clarify the process of constructing external controls based on the sample bank of individual-level data of clinical trials.Results This study included individual-level data of single-group trials of 4 TAVR devices,with a total of 569 subjects(59.2%male).The number of subjects in Trials 1 to 4 was 120,120,163,and 166,respectively.Propensity score matching enabled the matching of 113,117,125,and 147 subjects with comparable or similar characteristics from individual-level data from other trials,respectively,demonstrating a high matching success rate.The PS score distribution plot after stratification showed that the proportions of subjects in the experimental and control groups in strata 1 to 5 in scheme 1 were 4/103,11/103,22/92,32/87,and 51/64,respectively.For all constructed external controlled trials,a certain number of control samples with similar baseline characteristics to the experimental groups were distributed within each propensity score stratum.The results of the simulation test also reflected the potential differences between different devices in the 12-month all-cause mortality rate.Conclusions The sample bank constructed with individual-level data from clinical trials,as a high-quality data source,can serve as a source of external control for single-arm trials in the same field,and as a useful supplement to the external control scenario of real-world evidence to support drug and device development.At the same time,targeted research on research methods and bias control measures in related fields is also needed.