Objective To explore the potential categories of exercise perception in elderly hip arthroplasty(HA)patients,analyze the prediction factors of different categories,and provide references for clinical nursing.Methods A convenience sampling method was used to select elderly HA patients treated in 6 orthopedic wards of 2 tertiary A hospitals in Guilin from October 2024 to February 2025.The general data questionnaire,Exercise Benefits and Barriers Perception Scale for Hip/Knee Throplasty Patients,Tampa Scale of Kinesiophobia,the 5-Factor Modified Frailty Index,and Self-Efficacy for Rehabilitation Outcome Scale were used for investigation.Latent profile analysis was used to identify the potential categories of exercise perception of elderly HA patients,and decision tree model was used to explore the core predictive factors of different profile.Results 222 valid questionnaires were collected,with an effective response rate of 96.52％.A total of 222 elderly HA patients were divided into 3 profiles:high benefit-low barrier group(19.82％),low benefit-high barrier group(22.07％),and mild barrier group(58.11％).Frailty,kinesophobia,rehabilitation self-efficacy,residence,educational level and daily exercise were significant predictors of latent profiles(P＜0.05),and the frailty was located in the root node of the decision tree model.Conclusion The level of exercise perception in elderly HA patients needs to be improved,and shows population heterogeneity.Medical staff should prioritize interventions for low benefit-high barrier and mild-barrier groups,implementing targeted strategies based on profile characteristics and predictive factors to improve their exercise perception.

G protein-coupled receptors(GPCRs)are the largest family of membrane proteins in eukaryotes,with nearly 800 genes coding for these proteins.They are involved in many physiological processes,such as light perception,taste and smell,neurotransmitter,metabolism,endocrine and exocrine,cell growth and migration.Importantly,GPCRs and their ligands are the targets of approximately one third of all mar-keted drugs.GPCRs are traditionally known for their role in transmitting signals from the extracellular environment to the cell's interior via the plasma membrane.However,emerging evidence suggests that GPCRs are also localized on mitochondria,where they play critical roles in modulating mitochondrial functions.These mitochondrial GPCRs(mGPCRs)can influence processes such as mitochondrial respi-ration,apoptosis,and reactive oxygen species(ROS)production.By interacting with mitochondrial signaling pathways,mGPCRs contribute to the regulation of energy metabolism and cell survival.Their presence on mitochondria adds a new layer of complexity to the understanding of cellular signaling,highlighting the organelle's role as not just an energy powerhouse but also a crucial hub for signal transduction.This expanding understanding of mGPCR function on mitochondria opens new avenues for research,particularly in the context of diseases where mitochondrial dysfunction plays a key role.Ab-normalities in the phase conductance pathway of GPCRs located on mitochondria are closely associated with the development of systemic diseases such as cardiovascular disease,diabetes,obesity and Alz-heimer's disease.In this review,we examined the various types of GPCRs identified on mitochondrial membranes and analyzed the complex relationships between mGPCRs and the pathogenesis of various diseases.We aim to provide a clearer understanding of the emerging significance of mGPCRs in health and disease,and to underscore their potential as therapeutic targets in the treatment of these conditions.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is a prevalent type of cancer with a high mortality rate in its late stages. One of the major challenges in OSCC treatment is the resistance to epidermal growth factor receptor (EGFR) inhibitors. Therefore, it is imperative to elucidate the mechanism underlying drug resistance and develop appropriate precision therapy strategies to enhance clinical efficacy. METHODS: To evaluate the efficacy of the combination of the Ca 2+ /calmodulin-dependent protein kinase II (CAMK2) inhibitor KN93 and EGFR inhibitors, we performed in vitro and in vivo experiments using two FAT atypical cadherin 1 ( FAT1 )-deficient (SCC9 and SCC25) and two FAT1 wild-type (SCC47 and HN12) OSCC cell lines. We assessed the effects of EGFR inhibitors (afatinib or cetuximab), KN93, or their combination on the malignant phenotype of OSCC in vivo and in vitro . The alterations in protein expression levels of members of the EGFR signaling pathway and SRY-box transcription factor 2 (SOX2) were analyzed. Changes in the yes-associated protein 1 (YAP1) protein were characterized. Moreover, we analyzed mitochondrial dysfunction. Besides, the effects of combination therapy on mitochondrial dynamics were also evaluated. RESULTS: OSCC with FAT1 mutations exhibited resistance to EGFR inhibitors treatment. The combination of KN93 and EGFR inhibitors significantly inhibited the proliferation, survival, and migration of FAT1 -mutated OSCC cells and suppressed tumor growth in vivo . Mechanistically, combination therapy enhanced the therapeutic sensitivity of FAT1 -mutated OSCC cells to EGFR inhibitors by modulating the EGFR pathway and downregulated tumor stemness-related proteins. Furthermore, combination therapy induced reactive oxygen species (ROS)-mediated mitochondrial dysfunction and disrupted mitochondrial dynamics, ultimately resulting in tumor suppression. CONCLUSION Combination therapy with EGFR inhibitors and KN93 could be a novel precision therapeutic strategy and a potential clinical solution for EGFR-resistant OSCC patients with FAT1 mutations.
Humans ; ErbB Receptors/metabolism* ; Mouth Neoplasms/metabolism* ; Cell Line, Tumor ; Animals ; Drug Resistance, Neoplasm/genetics* ; Cadherins/metabolism* ; Carcinoma, Squamous Cell/metabolism* ; Mice ; Mutation/genetics* ; Mice, Nude ; Protein Kinase Inhibitors/therapeutic use* ; Cetuximab/pharmacology* ; Afatinib/therapeutic use* ; Cell Proliferation/drug effects* ; Signal Transduction/drug effects*

To better reflect the current status and key research trends in oral medicine research of China and to enhance the overall research abilities in this field, this special issue on oral medicine research integrates the work of several young academic leaders from oral medicine research centers nationwide. Their work focuses on key areas such as artificial intelligence-assisted diagnosis, the pathogenic mechanisms of intracellular bacteria, the relations between oral mucosal diseases and systemic diseases, as well as the study and reporting of rare and unique cases, aligning with the forefront of biomedical research. Although it does not encompass all the high-level researches conducted in China, it presents a rich and diverse array of academic achievements. Those achievements of researches reflect that it is important to further advance interdisciplinary and cross-disciplinary research collaborations, particularly by strengthening cooperation with engineering and technological disciplines in the future. We also need to set sights on driving researches in novel pathogenesis mechanisms of oral mucosal diseases, exploring innovative diagnostic technologies, enhancing diagnostic accuracy and treatment efficacy, and developing new effective intervention strategies. Additionally, greater emphases will be placed on ensuring the accuracy, comprehensiveness, readability, and scientific rigor of case studies and reports to promote sustained progress in this field.

Objective:To investigate the efficacy of different treatment of oral lichen planus (OLP) patients with thyroid antibody positive and its correlation with thyroid antibody, providing more targeted treatment for OLP patients with thyroid antibody positive.Methods:Patients who were admitted to Department of Oral Medicine, Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine for OLP with serum thyroid peroxidase antibody (TPOAb) and thyroglobulin antibody (TGAb) from February 2020 to June 2024 were included. Demographic and clinical data were recorded, and qualitative status and quantitative titers of TPOAb/TGAb were collected. TPOAb and/or TGAb positive patients were included into the thyroid antibody positive group, while patients with both TPOAb and TGAb negative were included into the thyroid antibody negative group. According to the treatment methods, they were divided into topical treatment group, topical treatment combined with oral immunosuppressant group, and topical treatment combined with oral immunoenhancer group. After 2 weeks and 1 month of treatment, the clinical efficacy of thyroid antibody positive group and negative group in OLP patients under different treatment methods were compared, and the correlations of different clinical efficacy with age, sex and thyroid antibody titer were analyzed.Results:A total of 116 OLP patients were included in this study, of which 38 (32.8%) were thyroid antibody positive and 78 (67.2%) were thyroid antibody negative. Compared with untreated, the modified OLP score and pain degree in thyroid antibody negative group were significantly improved after 2 weeks and 1 month of topical treatment and topical treatment combined with oral immunosuppression ( P<0.01). Although the thyroid antibody positive group also showed a remission trend, there was no statistical significance before and after treatment ( P>0.05). For topical treatment combined with oral immunoenhancer, the modified OLP score and pain degree in both groups of antibody positive and negative were statistically significant different after 2 weeks and 1 month of treatment compared with before ( P<0.01), while there was no statistically significant difference between the two groups ( P>0.05). Multivariate Logistic regression analysis showed that the treatment efficacy of OLP in topical treatment group, topical treatment combined with oral immunosuppressant group, and topical treatment combined with oral immunoenhancer group had no significant correlation with age, sex, as well as the TPOAb and TGAb titer ( P>0.05). Conclusions:Thyroid antibody TPOAb and TGAb could be reference factors for the selection of OLP treatment plan. For patients of OLP with positive thyroid antibodies, topical therapy combined with oral immunoenhancers may be preferred.

Objective:To develop PixelSIFT-UNet, a novel semantic segmentation model that integrates deep learning with scale-invariant feature transform (SIFT) algorithm to improve the segmentation accuracy of oral mucosal lesions.Methods:This investigation utilized 838 standard clinical white light images of oral mucosal diseases acquired from January 2020 to December 2022 at the Stomatology Hospital Zhejiang University School of Medicine. Randomization was achieved through Python′s random.seed function implementation. The random sample function was subsequently applied for sampling distribution. The dataset was stratified into three subsets with a 6∶2∶2 ratio: training ( n=506), validation ( n=166), and testing ( n=166). Lesion boundaries were annotated using Labelme software, and a PixelSIFT-UNet-based deep learning model was developed with VGG-16 and ResNet-50 backbone networks. Model parameters were optimized using the validation set, and performance metrics [including Dice coefficient, mean intersection over union (mIoU), mean pixel accuracy (mPA), and Precision] were assessed on the test set. The model′s performance was benchmarked against conventional semantic segmentation frameworks (U-Net and PSPNet). Results:The developed PixelSIFT-UNet model could achieve precise segmentation of three common oral mucosal lesions: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. Utilizing VGG-16 as the backbone network, the model achieved Dice coefficient, mIoU, mPA, and Precision values of 0.642, 0.699, 0.836, and 0.792, respectively. Implementation with ResNet-50 backbone network yielded metrics of 0.668, 0.733, 0.872 and 0.817, demonstrating significant improvements across all performance indicators compared to conventional U-Net model (relevant metrics: 0.662, 0.717, 0.861 and 0.809) and PSPNet model (relevant metrics: 0.671, 0.721, 0.858 and 0.813).Conclusions:The proposed PixelSIFT-UNet architecture demonstrates superior performance in oral mucosal lesion segmentation tasks, surpassing conventional semantic segmentation models and providing robust quantitative improvements in segmentation accuracy.

Oral potentially malignant disorders (OPMD) refer to a group of diseases occurring on the oral mucosa that harbor the potential to progress into oral squamous cell carcinoma, including oral leukoplakia, oral erythroplakia, discoid lupus erythematosus of the oral mucosa, oral submucous fibrosis, oral lichen planus, actinic cheilitis, etc. Diabetes mellitus (DM) is one type of diseases characterized by chronic hyperglycemia, with a high incidence and mortality rate worldwide. Hyperglycemia is the characteristic metabolic change in DM patients and those in the pre-diabetic stage, playing a determinative role in many complications related to DM. A number of clinical studies had revealed an association between hyperglycemia and OPMD, as well as its malignant transformation. This article will review the potential regulatory effects and mechanisms of high glucose states, such as diabetes, on OPMD, and assess the correlation between hyperglycemia and the malignant transformation of OPMD.

To better reflect the current status and key research trends in oral medicine research of China and to enhance the overall research abilities in this field, this special issue on oral medicine research integrates the work of several young academic leaders from oral medicine research centers nationwide. Their work focuses on key areas such as artificial intelligence-assisted diagnosis, the pathogenic mechanisms of intracellular bacteria, the relations between oral mucosal diseases and systemic diseases, as well as the study and reporting of rare and unique cases, aligning with the forefront of biomedical research. Although it does not encompass all the high-level researches conducted in China, it presents a rich and diverse array of academic achievements. Those achievements of researches reflect that it is important to further advance interdisciplinary and cross-disciplinary research collaborations, particularly by strengthening cooperation with engineering and technological disciplines in the future. We also need to set sights on driving researches in novel pathogenesis mechanisms of oral mucosal diseases, exploring innovative diagnostic technologies, enhancing diagnostic accuracy and treatment efficacy, and developing new effective intervention strategies. Additionally, greater emphases will be placed on ensuring the accuracy, comprehensiveness, readability, and scientific rigor of case studies and reports to promote sustained progress in this field.

Objective:To investigate the efficacy of different treatment of oral lichen planus (OLP) patients with thyroid antibody positive and its correlation with thyroid antibody, providing more targeted treatment for OLP patients with thyroid antibody positive.Methods:Patients who were admitted to Department of Oral Medicine, Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine for OLP with serum thyroid peroxidase antibody (TPOAb) and thyroglobulin antibody (TGAb) from February 2020 to June 2024 were included. Demographic and clinical data were recorded, and qualitative status and quantitative titers of TPOAb/TGAb were collected. TPOAb and/or TGAb positive patients were included into the thyroid antibody positive group, while patients with both TPOAb and TGAb negative were included into the thyroid antibody negative group. According to the treatment methods, they were divided into topical treatment group, topical treatment combined with oral immunosuppressant group, and topical treatment combined with oral immunoenhancer group. After 2 weeks and 1 month of treatment, the clinical efficacy of thyroid antibody positive group and negative group in OLP patients under different treatment methods were compared, and the correlations of different clinical efficacy with age, sex and thyroid antibody titer were analyzed.Results:A total of 116 OLP patients were included in this study, of which 38 (32.8%) were thyroid antibody positive and 78 (67.2%) were thyroid antibody negative. Compared with untreated, the modified OLP score and pain degree in thyroid antibody negative group were significantly improved after 2 weeks and 1 month of topical treatment and topical treatment combined with oral immunosuppression ( P<0.01). Although the thyroid antibody positive group also showed a remission trend, there was no statistical significance before and after treatment ( P>0.05). For topical treatment combined with oral immunoenhancer, the modified OLP score and pain degree in both groups of antibody positive and negative were statistically significant different after 2 weeks and 1 month of treatment compared with before ( P<0.01), while there was no statistically significant difference between the two groups ( P>0.05). Multivariate Logistic regression analysis showed that the treatment efficacy of OLP in topical treatment group, topical treatment combined with oral immunosuppressant group, and topical treatment combined with oral immunoenhancer group had no significant correlation with age, sex, as well as the TPOAb and TGAb titer ( P>0.05). Conclusions:Thyroid antibody TPOAb and TGAb could be reference factors for the selection of OLP treatment plan. For patients of OLP with positive thyroid antibodies, topical therapy combined with oral immunoenhancers may be preferred.

Objective:To develop PixelSIFT-UNet, a novel semantic segmentation model that integrates deep learning with scale-invariant feature transform (SIFT) algorithm to improve the segmentation accuracy of oral mucosal lesions.Methods:This investigation utilized 838 standard clinical white light images of oral mucosal diseases acquired from January 2020 to December 2022 at the Stomatology Hospital Zhejiang University School of Medicine. Randomization was achieved through Python′s random.seed function implementation. The random sample function was subsequently applied for sampling distribution. The dataset was stratified into three subsets with a 6∶2∶2 ratio: training ( n=506), validation ( n=166), and testing ( n=166). Lesion boundaries were annotated using Labelme software, and a PixelSIFT-UNet-based deep learning model was developed with VGG-16 and ResNet-50 backbone networks. Model parameters were optimized using the validation set, and performance metrics [including Dice coefficient, mean intersection over union (mIoU), mean pixel accuracy (mPA), and Precision] were assessed on the test set. The model′s performance was benchmarked against conventional semantic segmentation frameworks (U-Net and PSPNet). Results:The developed PixelSIFT-UNet model could achieve precise segmentation of three common oral mucosal lesions: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. Utilizing VGG-16 as the backbone network, the model achieved Dice coefficient, mIoU, mPA, and Precision values of 0.642, 0.699, 0.836, and 0.792, respectively. Implementation with ResNet-50 backbone network yielded metrics of 0.668, 0.733, 0.872 and 0.817, demonstrating significant improvements across all performance indicators compared to conventional U-Net model (relevant metrics: 0.662, 0.717, 0.861 and 0.809) and PSPNet model (relevant metrics: 0.671, 0.721, 0.858 and 0.813).Conclusions:The proposed PixelSIFT-UNet architecture demonstrates superior performance in oral mucosal lesion segmentation tasks, surpassing conventional semantic segmentation models and providing robust quantitative improvements in segmentation accuracy.