Temporomandibular disorders （TMD） encompass a prevalent group of conditions affecting the masticatory muscles， temporomandibular joint， and associated structures. Currently， it is widely recognized by scholars both domestically and internationally that TMD has a complex etiology and pathogenesis. Most patients present clinically with pain， yet effective treatment options remain limited. In recent years， the role of ion channels in the pathological mechanisms and therapeutic approaches for TMD has garnered increasing attention. This review systematically summarizes the function of ion channels in the management of temporomandibular joint diseases. First， the multifaceted roles of ion channels in treating TMD are examined， including their regulatory effects on TMJ muscle dysfunction， joint structural abnormalities， and TMJ-related pain. Finally， the potential value of ion channel research in TMD therapy is discussed. With advancing research in this field， ion channel-targeted therapies for temporomandibular disorders are expected to achieve new breakthroughs， thereby providing an important theoretical foundation and practical guidance for developing novel clinical treatment strategies.

Objective To explore the independent risk factors for complicated coronary heart disease (CHD) in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) and to construct a risk prediction model. Methods A retrospective study was performed to analyze the clinical data of 308 OSAHS patients who visited Jiangsu Provincial People's Hospital from January 2021 to June 2025. The patients were divided into the CHD group (n=80) and non-CHD group (n=228) according to the presence or absence of complicated CHD. Independent risk factors were identified using univariate analysis and multivariate logistic regression, and a nomogram prediction model was constructed. The model was evaluated using the calibration curve, Hosmer-Lemeshow test, Decision Curve Analysis (DCA) curve, and Receiver Operating Characteristic (ROC) curve. Results The incidence of CHD in OSAHS patients was 25.97%. Age, history of hypertension, oxygen desaturation index (ODI), severe polysomnography (PSG) grade, decreased high-density lipoprotein cholesterol, elevated homocysteine, and elevated troponin I were independent risk factors for OSAHS complicated with CHD (P<0.05). The nomogram model demonstrated good calibration (Hosmer-Lemeshow test, P=0.995), and the DCA curve showed high clinical net benefit. The area under the ROC curve was 0.940 (95% CI: 0.908~0.972), with a sensitivity of 0.842 and a specificity of 0.900. Conclusion Age, history of hypertension, ODI, PSG grade, high-density lipoprotein cholesterol, homocysteine, and troponin I are independent risk factors for CHD in OSAHS patients. The prediction model constructed based on these factors can provide a reliable tool for clinical nurses to early identify high-risk individuals and formulate targeted intervention strategies.

ObjectiveTo evaluate the corrective effects of a three-stage structured Tai Chi training program on abnormal neck and shoulder posture from a biomechanical perspective. MethodsEighty-two participants with abnormal neck and shoulder posture were randomly assigned to a treatment group and a control group， with 41 participants in each group. The treatment group received three-stage structured Tai Chi training six times per week， while the control group received online health education once per week. Both interventions lasted for 12 weeks. The primary outcome cervical postural deviation angle， and the secondary outcomes including cervical loading and bilateral shoulder horizontal deviation angle， were assessed before intervention， after 6 weeks and 12 weeks of intervention. Safety was also evaluated. ResultsUltimately， 40 participants from each group were included in the analysis. In the treatment group， cervical postural deviation angle and cervical spine loading were significantly reduced after 12 weeks of intervention compared with baseline and after 6 weeks of intervention. The bilateral shoulder horizontal deviation angle decreased significantly after both 6 and 12 weeks of intervention compared with baseline （P＜0.05）. In the control group， cervical spine loading was reduced after 6 and 12 weeks of intervention compared with baseline （P＜ 0.05）. After 12 weeks of intervention， the treatment group showed lower cervical postural deviation angle and bilateral shoulder horizontal deviation angle than the control group， whereas cervical loading was higher in the treatment group after 6 weeks of intervention （P＜0.05）. Significant differences were observed between groups in the changes from baseline to week 12 for cervical postural deviation angle and bilateral shoulder horizontal deviation angle， as well as in the changes from baseline to week 6 for cervical loading and bilateral shoulder horizontal deviation angle （P＜ 0.05）. No significant adverse events occurred in either group during the intervention period. ConclusionThree-stage structured Tai Chi can effectively improve abnormal neck and shoulder posture， demonstrating good safety and feasibility.

ObjectiveTo analyze the matching relationship between postgraduate training system for rehabilitation therapy and real-world workforce requirements, and develop a demand-oriented postgraduate training pathway based on the World Health Organization rehabilitation competency framework (RCF). MethodsPolicy document analysis, statistical analysis of publicly available recruitment data and trend analysis of rehabilitation service system development were adopted. Drawing on the RCF and its contextualized application, workforce demands for rehabilitation therapy professionals were sorted out in terms of demand scale, job hierarchy, service settings and specialty orientations. Against five competency domains defined by RCF, namely practice, professionalism, learning and development, management and leadership, and research, this study identified deficiencies within current postgraduate training and their impacts on job competency, and put forward optimized training strategies accordingly. ResultsThe demand for rehabilitation therapy professionals was featured by stratified job requirements, diversified service scenarios and interdisciplinary knowledge integration. Public hospitals constituted a major employer for master-level rehabilitation therapists. Core competencies for such posts included functional assessment and clinical reasoning, communication and collaboration, professionalism, continuous rehabilitation care, as well as evidence-based practice and research. It was needed to improve in aligning training objectives with real-world professional activities, matching curricula to multidisciplinary job demands, ensuring continuity of practical training, and building competency assessment and professional support systems. ConclusionJob adaptability of master graduates in rehabilitation therapy relies on the integration of institutional employment needs, professional practice activities and progressive competency development. Four RCF-based modules, such as foundational core competencies, contextualized specialty tracks, continuous multi-setting practice and learning-research-quality improvement, can provide a structured solution for clinically-oriented postgraduate education.

Objective:To establish criteria for diagnosing lymph node metastasis (LNM) in newly diagnosed papillary thyroid cancer (PTC) patients based on 18F-FDG PET/MR and evaluate its diagnostic efficiency. Methods:The data of 14 patients with PTC (all females, age (38.8±13.5) years) who underwent 18F-FDG PET/MR and ultrasound sequentially 2 weeks before surgery at the First Affiliated Hospital of the Air Force Medical University from May 2021 to August 2023 were retrospectively analyzed. Visual and semi-quantitative assessments were performed on all patients step by step (Ⅱ-Ⅵ area) and neck by neck (left, right, and central area). The dimensions of all suspected lymph nodes were measured on T 2 weighted imaging (WI)-MRI and SUV max was measured on PET. Taking postoperative pathology as the reference standard, the independent risk factors for predicting LNM were determined by multivariate logistic regression analysis, and the diagnostic efficiency of each model was evaluated by ROC curve analysis. Results:A total of 21 macroscopic regions of lymph nodes(15 were malignant, 6 were benign) and 178 lymph nodes (120 were malignant, 58 were benign) were cleared by surgery. Multivariate logistic regression analysis showed that SUV max (odds ratio ( OR)=1.865, 95% CI: 1.323-2.630, P<0.001) and short diameter on MRI (SD-MRI) ( OR=1.752, 95% CI: 1.189-2.580, P=0.005) were independent predictors of LNM. The cut-off value of SD-MRI in predicting LNM was 5.7mm (AUC=0.812, Youden index (YI)=0.463). For the SD-MRI cut-off values ≥5.7 or <5.7mm, the corresponding SUV max cut-off values were 1.6 and 1.8, respectively. When " dual threshold" quantitative criteria (SD-MRI≥5.7mm + SUV max≥1.6 or SD-MRI<5.7mm + SUV max≥1.8) was used as the diagnostic criteria of 18F-FDG PET/MR, the AUC and YI could be improved to 0.909 and 0.818. Based on the regional level analysis, sensitivity, specificity, and accuracy of LNM diagnosis by ultrasound, MRI, and 18F-FDG PET/MR " dual threshold" criteria were 11/15 vs 12/15 vs 13/15, 5/6 vs 3/6 vs 5/6, 76.2%(16/21) vs 71.4%(15/21) vs 85.7%(18/21), respectively. Conclusion:Compared with the ultrasound and MRI, the 18F-FDG PET/MR " dual threshold" criteria exhibits higher sensitivity and accuracy in determining the scope of LNM clearance for PTC patients.

Purpose To evaluate the diagnostic value of an abbreviated protocol MRI(AP-MRI)based on first post-contrast subtracted(FAST)images for breast cancer detection.Materials and Methods This study included imaging data from 160 female patients with solid breast lesions who underwent breast MRI in the First Affiliated Hospital of Henan University of Chinese Medicine from April 2021 to January 2024.Two AP-MRI protocols were extracted from the full diagnostic protocol(FDP),including:dynamic contrast-enhanced MRI(DCE-MRI)A protocol:FAST and maximum-intensity projection(MIP)images,and DCE-MRI B protocol:FAST+MIP+diffusion-weighted imaging(DWI).Lesions categorized as breast imaging reporting and data system(BI-RADS)1-3 were classified as negative,and those categorized as BI-RADS 4-5 were classified as positive.Pathological findings served as the diagnostic gold standard.Two radiologists independently evaluated the lesions as negative/positive and compared with the gold standard.The sensitivity,specificity and accuracy of the three protocols were compared.Receiver operating characteristic curves were generated for each protocol,and the area under the curve(AUC)was compared.Results The accuracy of the three protocols showed statistically significant differences(Cochran's Qreader1=6.000,P=0.050;Cochran's Qreader2=10.909,P=0.012).The accuracy of the DCE-MRI A protocol was significantly lower than that of the FDP protocol(Z=2.449,Preader1=0.043;Z=2.858,Preader2=0.013).There were no statistically significant differences in sensitivity(Cochran's Qreader1=3.000,P=0.223;Cochran's Qreader2=2.667,P=0.264)or specificity(Cochran's Qreader1=3.000,P=0.223;Cochran's Qreader2=2.800,P=0.247)between the two AP-MRI protocols and the FDP protocol.There were no statistically significant differences in AUC between the DCE-MRI B protocol and the FDP protocol(Z=1.390-1.719,all P>0.05),while the AUC of the DCE-MRI A protocol had lower AUCs than the FDP protocol(Z=1.980,2.441;both P<0.05).Conclusion The AP-MRI protocol combining FAST,MIP and DWI shows diagnostic accuracy comparable to that of the FDP and greatly saves time and cost,suggesting its potential as an alternative imaging strategy for women with dense breasts and as a new diagnostic approach for high-risk populations.

With rapid development of technology,generative artificial intelligence has emerged as a research hotspot in field of artificial intelligence,and demonstrates immense potential to enhance quality of medical education,reduce educational costs,and optimize allocation of educational resources in medical education.Medical Immunology is a complex and continuously evolving disci-pline that not only encompasses a vast amount of basic knowledge but also involves interdisciplinary intersections,which involves extensive foundational knowledge,cross-disciplinary integration and cutting-edge research.Furthermore,rapid updates in research progress,coupled with abstract and complex nature of content,pose significant challenges for teaching.Therefore,analyzing teaching advantages of generative artificial intelligence in empowering Medical Immunology,exploring its practical applications in Medical Immunology education,and innovating theoretical education systems and methodologies are of great significance for improving quality of Medical Immunology education.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective:To investigate the association of skeletal muscle function(including muscle strength and physical performance) and insulin resistance as well as type 2 diabetes mellitus(T2DM).Methods:The retrospective study included 942 patients who visited the Department of Geriatrics at Renji Hospital, Shanghai Jiao Tong University School of Medicine, between October 2020 and July 2024. Low skeletal muscle function was defined as either reduced muscle strength or impaired physical performance. Muscle strength was assessed by grip strength, while physical performance was evaluated using the 5-time chair stand test. The associations between muscle function, insulin resistance, and T2DM were analyzed.Results:A significant association was observed between decreased skeletal muscle function and a higher prevalence of T2DM( P=0.001). Further analysis revealed that decreased physical performance was significantly associated with increased T2DM prevalence( P<0.001), whereas reduced muscle strength showed no significant association with T2DM prevalence( P=0.331). Linear regression analysis indicated that both the homeostasis model assessment of insulin resistance(HOMA2-IR) and fasting blood glucose levels increased significantly with longer chair stand times( P<0.05). Restrictive cubic spline(RCS) analysis demonstrated a nonlinear relationship between chair-rising time and HOMA2-IR. Notably, when the cumulative chair-rising time exceed 8.1 s, HOMA2-IR increased significantly with prolonged chair stand time. Logistic regression analysis showed that compared with patients with normal physical performance, those with decreased physical performance had significantly higher odds of T2DM( OR=2.64, P<0.001) and insulin resistance( OR=2.34, P=0.002). Conclusion:Decline in physical performance is significantly positively associated with insulin resistance and the risk of T2DM. Morever, when the cumulative chair stand time exceed 8.1 s, HOMA2-IR increases progressively with further prolongation of chair stand time.

Sarcopenia is characterized by a decline in both skeletal muscle mass and function. Since skeletal muscle is the primary organ responsible for insulin-stimulated glucose uptake, sarcopenia, which occurs with aging, is believed to promote insulin resistance in skeletal muscle, leading to disruptions in glucose metabolism. Growing clinical evidence indicates that decreased muscle function may contribute to the development of type 2 diabetes, while a reduction in muscle mass alone does not appear to be significantly associated with its onset. This review, based on the latest research, summarizes the clinical evidence linking sarcopenia to glucose metabolism abnormalities and potential mechanisms through which impaired muscle function affects glucose metabolism. The goal is to inspire early prevention, intervention, and management of type 2 diabetes by targeting skeletal muscle.