Methods: Cervical radiographs were analyzed. EOP hyperplasia was classified into three subtypes with standardized length measurements. Variables encompassed demographics, ONL-related indices, and sagittal parameters. Subtype comparisons and multivariate regression analyses (with EOP length as dependent variable) were conducted. Results: Analysis of 187 CSM patients (64.2% male) identified gender-specific patterns: males exhibited greater EOP length (9.4±6.8 mm vs. 4.6±3.4 mm, p<0.001). Type III EOP demonstrated male predominance (82.4% vs. type I 31.8%, type II 51.4%; p<0.001), with associated longer hyperplasia length (11.6±6.6 mm vs. type II 5.1±1.9 mm, p<0.001). Type III EOP was associated with higher ONL prevalence (type III 64.8% vs. type I 45.5%, type II 41.9%; p=0.010) and longer ONL osteophyte length (type III 18.8±9.8 mm vs. type I 14.2±8.1 mm, type II 14.2±9.4 mm; p=0.046). Multivariate regression confirmed male sex (β=–3.82, p=0.009), ONL osteophyte length (β=0.16, p=0.017), T1 slope (β=0.27, p=0.041), and spino-cranial angle (β=–0.19, p=0.009) as factors independently associated with EOP length (adjusted R²=0.382). Conclusions Severe EOP hyperplasia exhibits a male-predominant distribution pattern and demonstrates significant radiological associations with ONL and cervical sagittal imbalance in CSM patients. These findings advocate for EOP evaluation in clinical evaluations to identify high-risk biomechanical profiles.

Objective To construct a global model of cardiac surface motion based on average intensity projection(AIP)image.Methods The cine magnetic resonance imaging data were divided into training set and test set for model construction and validation.The datum points were obtained on the AIP surface point cloud,and the corresponding points of each cardiac phase surface point cloud were found based on surface features.Principal component analysis was used to extract feature information,establish the mapping relationship between the datum points and the corresponding points,and construct a model for predicting each temporal phase surface point cloud from the AIP surface point cloud.Results The RMSE of the model on test set for corresponding point prediction ranged from(0.209±0.020)mm to(0.841±0.074)mm,while the Euclidean distance for each time phase surface point cloud prediction ranged from(1.399±0.029)mm to(1.658±0.100)mm.Conclusion The proposed global model exhibit high accuracy and can provide a reference for image segmentation and clinical treatments.

Objective To construct a Nomogram risk prediction model for early recurrence of hepatocellular carcinoma(HCC)radical hepatectomy based on CT imaging omics and traditional Chinese medicine tongue imaging features.Methods 216 patients who underwent HCC radical hepatectomy in the First Affiliated Hospital of Henan University of Traditional Chinese Medicine from September 2018 to September 2022 were selected,the clinical dataes were retrospective analyzed.The patients were randomly divided into modeling set(n=152)and validation set(n=64)by 7:3 ratio,the recurrence situation one year after surgery of the two groups were recorded.A recurrence prediction model was constructed by the modeling set of patient dataes,all patients underwent postoperative plain scan combined with enhanced CT examination,and the Imaging omics feature indicators was extracted,the tongue imaging characteristics was recorded,the Cox multivariate model was used to analyze the factors related to postoperative recurrence in the modeling set of patients,and a risk prediction model was constructed,the model was validated by validation set dataes.Results There were 52 patients in the modeling set experienced recurrence within 1 year,the recurrence rate was 34.21%;there were 23 cases of recurrence in the validation set patients within 1 year,the recurrence rate was 35.94%.The Cox multivariate model analysis showed that the Rad-score,Tongue texture and tongue shape were independent influence factors for postoperative recurrence of HCC(P<0.05).Based on this,the Nomogram column chart was established.The ROC analysis showed that the AUC of the column chart model for predicting postoperative recurrence in the modeling set and validation set patients were 0.811 and 0.824,the sensitivities were 0.875 and 0.833 respectively,the specificities were 0.617 and 0.750 respectively.Conclusions The early postoperative recurrence of HCC is related to Rad score,tongue texture,and tongue shape.Based on this,the Nomogram prediction model constructed has high accuracy in predicting early postoperative recurrence.

AIM:To investigate the impact of recombinant human CC16 protein(rhCC16)on cigarette smoke extract(CSE)-induced senescence-associated secretory phenotype(SASP)in human bronchial epithelial cells(HBECs)and in the lung tissues of chronic obstructive pulmonary disease(COPD)mice,and to explore the underlying mechanism.METHODS:HBECs were induced into cellular senescence using 5%CSE.The senescent HBECs were treated with 250 ng/mL rhCC16,and the levels of reactive oxygen species(ROS)were assessed using the 2',7'-dichlorodihydrofluorescein diacetate(DCFH-DA)method.The levels of trimethylated histone H3 at lysine 9(H3K9me3),a marker of senescence-associated heterochromatic foci(SAHF),were detected using a Western blot assay.RT-qPCR and ELISA were utilized to measure the mRNA expression and protein levels of SASP components including interleukin-1 beta(IL-1β),IL-6,IL-8,chemokine(C-X-C motif)ligand-1(CXCL-1),matrix metalloproteinase 1(MMP1)and MMP3.Passive smoking was con-ducted for six months to induce COPD in mice.RhCC16(2.5 μg/g body weight)or an equal volume of PBS(20 μL)was intranasally administered from the 16th week of smoking in the COPD+rhCC16 group or COPD+PBS group,respectively,with administration 2 hours before smoking.ROS levels in lung tissue cells were investigated using DCFH-DA staining.H3K9me3 levels in lung tissues were tested using Western blot assay.RT-qPCR and ELISA were performed to examine the mRNA expression and protein levels of IL-1β,IL-6,IL-8,CXCL-1,MMP1 and MMP3.RESULTS:DCFH-DA staining results showed that CSE stimulation increased ROS levels in HBECs,while rhCC16 treatment reduced them(P＜0.01).Western blot results indicated that CSE stimulation elevated H3K9me3 levels in HBECs,which were decreased with rhCC16 treatment(P＜0.01).RT-qPCR and ELISA assays demonstrated that CSE stimulation upregulated the mRNA and protein levels of IL-1β,IL-6,IL-8,CXCL-1,MMP1 and MMP3 in HBECs,which were reduced with rhCC16 admin-istration(P＜0.05).DCFH-DA staining results showed an increase in ROS levels in the lung tissues of COPD mice,which were decreased with rhCC16 administration(P＜0.01).Western blot data revealed an increase in H3K9me3 levels in the lung tissues of COPD mice,which were reduced with rhCC16 treatment(P＜0.01).RT-qPCR and ELISA assays demon-strated an upregulation of the mRNA and protein levels of IL-1β,IL-6,IL-8,CXCL-1,MMP1 and MMP3 in the lung tis-sues of COPD mice,which were reduced with rhCC16 treatment(P＜0.05).No statistically significant differences were ob-served in the above-mentioned indicators between the lung tissues of COPD and COPD+PBS mice(P＞0.05).CONCLU-SION:rhCC16 can effectively inhibit CSE-induced SASP in HBECs and in the lung tissues of COPD mice,with its under-lying mechanism potentially related to the inhibition of the ROS-H3K9me3 signaling pathway.

Objective:To construct a machine learning classification prediction model using planning-omics (planomics) features to predict the γ pass rate of intensity-modulated radiotherapy (IMRT) plan dose verification in fixed-field thoracic tumors, and evaluate the application of planomics in radiotherapy quality assurance.Methods:The fixed-field IMRT plans of 240 patients with chest tumors admitted to Department of Radiotherapy, Henan Cancer Hospital from August 2022 to March 2023 were retrospectively analyzed. All plans underwent dose verification using the electronic portal imaging system detector on the Varian accelerator to collect field dose data. The dose verification results were analyzed through Portal Dosimetry in the treatment planning system of Eclipse. The γ pass rate standard was set at 2%/2 mm with a 10% dose threshold. From the planning documents, 48 conventional planning features, 2476 planomics features, and the combination of the previous two feature sets were extracted. Subsequently, an auto-encoder classification model was constructed. To evaluate the classification efficacy of various feature sets, 20 random train-test divisions were conducted by calculating the area under the receiver operating characteristic curve (AUC) values along with the accuracy rates.Results:After the feature selection, 2 conventional features and 16 planomics features were finally selected. In the testing set, the AUC values for the model using combined features, planomics features, and conventional planned features were 0.802±0.030, 0.740±0.069, and 0.673±0.083, respectively. In contrast, in the training set, these AUC values were 0.844±0.074, 0.816±0.047, and 0.687±0.036, respectively. The accuracy rates were 0.752±0.083, 0.703±0.110, and 0.648±0.081 in the testing set, and 0.753±0.098, 0.751±0.075, and 0.624±0.054 in the training set for the combined, planomics, and conventional planning feature sets, respectively.Conclusions:For thoracic fixed-field adjusted radiotherapy planning, the machine learning method based on planomics features can be utilized to build a classification model for predicting the γ pass rate. Combining planomics features with conventional planned features can enhance the predictive performance of the classification models.

Objective By analying the concentration degree of China's medical service market from 2017 to 2021,to analyze the current situation of competition in China's medical service market,and put forward relevant policy rec-ommendations to provide reference for optimizing resource allocation in China's medical service market.Methods The competition of medical service market in each province(region and city)was calculated by Herfindahl-Hirschman In-dex(HHI)and visualized by Stata18 software.Results From 2017 to 2021,the annual growth rate of the number of beds and health technicians in most regions of China was higher than 2.5％,but the absolute number was significant-ly different.In most provinces(regions and cities),the HHI calculated by the number of beds and the number of health technicians ranged from 0.07 to 0.14,and the HHI showed an upward trend but a small change in the five years.Conclusion In recent years,the degree of competition in China's medical service market is generally high,but with the expansion of the supply of material resources and human resources in each province(region and city),the medical service market in most areas is developing towards a monopoly trend.

AIM:To investigate the impact of recombinant human CC16 protein(rhCC16)on cigarette smoke extract(CSE)-induced senescence-associated secretory phenotype(SASP)in human bronchial epithelial cells(HBECs)and in the lung tissues of chronic obstructive pulmonary disease(COPD)mice,and to explore the underlying mechanism.METHODS:HBECs were induced into cellular senescence using 5%CSE.The senescent HBECs were treated with 250 ng/mL rhCC16,and the levels of reactive oxygen species(ROS)were assessed using the 2',7'-dichlorodihydrofluorescein diacetate(DCFH-DA)method.The levels of trimethylated histone H3 at lysine 9(H3K9me3),a marker of senescence-associated heterochromatic foci(SAHF),were detected using a Western blot assay.RT-qPCR and ELISA were utilized to measure the mRNA expression and protein levels of SASP components including interleukin-1 beta(IL-1β),IL-6,IL-8,chemokine(C-X-C motif)ligand-1(CXCL-1),matrix metalloproteinase 1(MMP1)and MMP3.Passive smoking was con-ducted for six months to induce COPD in mice.RhCC16(2.5 μg/g body weight)or an equal volume of PBS(20 μL)was intranasally administered from the 16th week of smoking in the COPD+rhCC16 group or COPD+PBS group,respectively,with administration 2 hours before smoking.ROS levels in lung tissue cells were investigated using DCFH-DA staining.H3K9me3 levels in lung tissues were tested using Western blot assay.RT-qPCR and ELISA were performed to examine the mRNA expression and protein levels of IL-1β,IL-6,IL-8,CXCL-1,MMP1 and MMP3.RESULTS:DCFH-DA staining results showed that CSE stimulation increased ROS levels in HBECs,while rhCC16 treatment reduced them(P＜0.01).Western blot results indicated that CSE stimulation elevated H3K9me3 levels in HBECs,which were decreased with rhCC16 treatment(P＜0.01).RT-qPCR and ELISA assays demonstrated that CSE stimulation upregulated the mRNA and protein levels of IL-1β,IL-6,IL-8,CXCL-1,MMP1 and MMP3 in HBECs,which were reduced with rhCC16 admin-istration(P＜0.05).DCFH-DA staining results showed an increase in ROS levels in the lung tissues of COPD mice,which were decreased with rhCC16 administration(P＜0.01).Western blot data revealed an increase in H3K9me3 levels in the lung tissues of COPD mice,which were reduced with rhCC16 treatment(P＜0.01).RT-qPCR and ELISA assays demon-strated an upregulation of the mRNA and protein levels of IL-1β,IL-6,IL-8,CXCL-1,MMP1 and MMP3 in the lung tis-sues of COPD mice,which were reduced with rhCC16 treatment(P＜0.05).No statistically significant differences were ob-served in the above-mentioned indicators between the lung tissues of COPD and COPD+PBS mice(P＞0.05).CONCLU-SION:rhCC16 can effectively inhibit CSE-induced SASP in HBECs and in the lung tissues of COPD mice,with its under-lying mechanism potentially related to the inhibition of the ROS-H3K9me3 signaling pathway.

Objective This study aimed to develop an automated method for syndrome element differenti-ation in Traditional Chinese Medicine(TCM).Methods We first constructed and trained an Instruction-tuned Multi-Task TCM text embedding model(Instr-MT-TCM)using four distinct TCM task datasets,including domain knowledge,synonymous terminology,syndrome differentiation and treatment,and TCM case labels.Subsequently,five TCM diagnostics experts holding master's degrees or higher were organized to screen a real-world TCM case dataset and annotate symptoms and signs.The purpose was to evaluate the F1-score of the proposed method—the combination of Instr-MT-TCM and a Large Language Model(LLM)—by comparing its performance against the manual annotation result on the syndrome element differentiation task.Finally,to validate its feasibility in real-world clinical settings,the method was applied to 48 prostate cancer cases to calculate the syndrome element scores.Results The Instr-MT-TCM model showed rapid performance improvement in its early training phase,achieving a Recall@1(R@1)of 0.848.Experts curated a dataset of 1,793 real-world clinical cases,covering 34 common diseases and 66 syndrome patterns.In the syndrome element differentiation task,the collaborative framework of LLM and Instr-MT-TCM achieved a mean F1-score of 0.927,outperforming the 0.512 from manual annota-tion.The syndrome element analysis revealed that the predominant elements of disease nature were fire(heat)and yin deficiency,while the main elements of disease location were bladder and kidney.Conclusion This study proposes and validates a novel method for automated TCM syndrome element dif-ferentiation based on the synergy between LLM and our custom Instr-MT-TCM model.Achieving a high F1-score(0.927)on real-world data,the method demonstrates excellent accuracy and generalization ability.Its application in prostate cancer analysis highlights its significant clinical potential,offering effective technical support,and a new research direction for intelligent TCM syndrome element differentiation.

The global burden of obesity and related chronic diseases is increasing,making weight management a core issue in the field of public health.This article systematically reviewed the latest developments in weight management from three dimensions-policy frameworks,technological innovations,and practical models-both domestically and internationally.It summarized existing experiences and key challenges,aiming to provide theoretical references and practical directions for constructing scientific,precise and sustainable weight management strategies.

Objective By analying the concentration degree of China's medical service market from 2017 to 2021,to analyze the current situation of competition in China's medical service market,and put forward relevant policy rec-ommendations to provide reference for optimizing resource allocation in China's medical service market.Methods The competition of medical service market in each province(region and city)was calculated by Herfindahl-Hirschman In-dex(HHI)and visualized by Stata18 software.Results From 2017 to 2021,the annual growth rate of the number of beds and health technicians in most regions of China was higher than 2.5％,but the absolute number was significant-ly different.In most provinces(regions and cities),the HHI calculated by the number of beds and the number of health technicians ranged from 0.07 to 0.14,and the HHI showed an upward trend but a small change in the five years.Conclusion In recent years,the degree of competition in China's medical service market is generally high,but with the expansion of the supply of material resources and human resources in each province(region and city),the medical service market in most areas is developing towards a monopoly trend.