Background Neck-related work-related musculoskeletal disorders (WMSDs) are a major type of musculoskeletal disorders with a relatively high proportion. Shanghai has a large number of occupational populations; however, the occurrence of WMSDs at neck among the occupational populations across industries in this city has not been reported, and needs to be addressed. Objective To understand the occurrence of neck-related WMSDs and their influencing factors among occupational populations in 8 industries in Shanghai, and to provide a scientific basis for the prevention and control of WMSDs in this population. Methods From February 2024 to February 2025, a cross-sectional survey employed stratified cluster sampling to select 7829 employed workers from 48 enterprises across 8 industries in Shanghai. The study investigated the occurrence of neck-related WMSDs among the participants over the preceding 12 months. Logistic regression analysis was employed to examine the relationships between WMSDs and various occupational factors. Results A total of 7770 questionnaires were collected, yielding an effective response rate of 99.2%. Overall, the positive rate of neck-related WMSDs among the participants was 11.89% (924/7770). Among different industries, the environmental sanitation industry (19.48%), pharmaceutical manufacturing industry (14.57%), and cigarette manufacturing industry (13.66%) had higher prevalence of neck-related WMSDs. Univariate analysis showed that the risk of neck-related WMSDs was higher in the following groups: those with a job tenure ≥5 years in the current position (OR=1.50–1.70), females (OR=1.77), those with higher education levels (master’s degree or above, OR=2.27), and those with worse self-rated health (very poor health, OR=4.38) (P＜0.05). Multivariate logistic regression analysis identified the following independent risk factors for neck WMSDs: environmental sanitation industry (OR=7.30, 95%CI: 4.72, 11.87), higher educational attainment (master’s degree or above: OR=2.16, 95%CI: 1.36, 3.35), worse self-rated health status (very poor: OR=1.95, 95%CI: 1.31, 2.85), work performed in a single workshop (OR=1.36, 95%CI: 1.13, 1.64), understaffing (OR=1.46, 95%CI: 1.25, 1.70), slight neck forward flexion (OR=1.64, 95%CI: 1.32, 2.06), severe neck forward flexion (OR=2.55, 95%CI: 1.97, 3.30), and maintaining a fixed neck posture for a prolonged period (OR=2.28, 95%CI: 1.93, 2.70). Conversely, work rotation (OR=0.75, 95%CI: 0.65, 0.87), adequate rest (OR=0.70, 95%CI: 0.60, 0.82), and severe back flexion (OR=0.63, 95%CI: 0.46, 0.85) were protective factors against neck-related WMSDs. For the nomogram, the area under the curve (AUC)=0.75 (95%CI: 0.74, 0.77), Hosmer-Lemeshow test P=0.764, and Brier score=0.094, indicating that the model had good discrimination and calibration. Conclusion Among the occupational populations in Shanghai, the environmental sanitation industry, pharmaceutical manufacturing industry, and cigarette manufacturing industry are identified as high-risk industries for neck-related WMSDs. Industry affiliation, educational level, work organization patterns, and awkward neck working postures are confirmed as the core influencing factors for the development of neck-related WMSDs. Targeted and comprehensive intervention measures should be formulated focusing on the aforementioned high-risk industries and key influencing factors. Multi-dimensional collaborative efforts, including ergonomic optimization, improvement of labor management systems, and strengthened health education and promotion, should be implemented to effectively control the risk of neck-related WMSDs in the occupational population, and to provide robust statistical evidence for the advancement of regional occupational health prevention and control strategies.

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

Objective:To explore the value of 18F-fluorodeoxyglucose ( 18F-FDG) PET-CT dual-modality habitat imaging technology in predicting the epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma. Methods:This study was designed as a cross-sectional study. Clinical and imaging data of 403 patients with lung adenocarcinoma who underwent 18F-FDG PET-CT imaging with definitive EGFR results from January 2018 to April 2022 at the Third Affiliated Hospital of Soochow University were retrospectively analyzed.The patients were divided into a development set (282 cases) and a validation set (121 cases) using a stratified random sampling method at a 7∶3 ratio. An adaptive clustering algorithm was used to segment the regions of interest, forming different habitats and obtaining derived parameters. Independent samples t-test or Mann-Whitney U test were used to compare clinical, imaging indicators, and habitat-derived parameters between EGFR mutant and wild-type patient. The clinical, imaging indicators, and habitat-derived parameters that showed statistically significant differences in univariate analysis were included in multivariate logistic regression to construct clinical and clinical-habitat combined models, respectively. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s ability to predict EGFR mutations in lung adenocarcinoma. Additionally, the net reclassification index (NRI) was employed to assess the model′s classification improvement capability. Results:There were 249 cases of EGFR mutation and 154 cases of wild type. The optimal number of habitats was two, namely Habitat 1 and Habitat 2. The parameters included in the clinical model were smoking history, bronchial sign, pleural indentation sign, and tumor diameter. The parameters incorporated into the clinical-habitat combined model were smoking history, bronchial sign, pleural indentation sign, Habitat 2, and Habitat 1 voxel count. In the development set, the AUCs for predicting EGFR mutations in lung adenocarcinoma using the clinical model and the clinical-habitat combined model were 0.723 and 0.733, respectively, with no statistically significant difference ( Z=0.60, P=0.549); In the validation set, the AUCs were 0.684 and 0.715, respectively, with no statistically significant difference ( Z=1.32, P=0.186). The accuracy (0.694) and specificity (0.609) of the clinical-habitat combined model in the validation set were slightly higher than those of the clinical model (0.686 and 0.565, respectively). NRI analysis confirmed that the clinical-habitat combined model improved the correct classification of EGFR wild-type lung adenocarcinoma by 10.9% compared to the clinical model ( P=0.018). Conclusion:18F-FDG PET-CT dual-modality habitat imaging technology can be used to analyze the microenvironment of lung adenocarcinoma and has the potential in non-invasively predicting EGFR mutation status, providing an important basis for personalized and accurate treatment of patients with lung adenocarcinoma.

BACKGROUND: Epidemiological data on chronic diarrhea in the Chinese population are lacking, and the association between obesity and chronic diarrhea in East Asian populations remains inconclusive. This study aimed to investigate the prevalence of chronic diarrhea and its association with obesity in a representative community-dwelling Chinese population. METHODS: This cross-sectional study was based on a multistage, randomized cluster sampling involving 3503 residents aged 20-69 years from representative urban and rural communities in Beijing. Chronic diarrhea was assessed using the Bristol Stool Form Scale (BSFS), and obesity was determined based on body mass index (BMI). Logistic regression analysis and restricted cubic splines were used to evaluate the relationship between obesity and chronic diarrhea. RESULTS: The standardized prevalence of chronic diarrhea in the study population was 12.88%. The average BMI was 24.67 kg/m 2 . Of all the participants, 35.17% (1232/3503) of participants were classified as overweight and 16.13% (565/3503) as obese. After adjustment for potential confounders, individuals with obesity had an increased risk of chronic diarrhea as compared to normal weight individuals (odds ratio = 1.58, 95% confidence interval: 1.20-2.06). A nonlinear association between BMI and the risk of chronic diarrhea was observed in community residents of males and the overall participant group ( P  = 0.026 and 0.017, respectively). CONCLUSIONS This study presents initial findings on the prevalence of chronic diarrhea among residents of Chinese communities while offering substantiated evidence regarding the significant association between obesity and chronic diarrhea. These findings offer a novel perspective on gastrointestinal health management.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; Body Mass Index ; China/epidemiology* ; Chronic Disease/epidemiology* ; Cross-Sectional Studies ; Diarrhea/epidemiology* ; Obesity/complications* ; Prevalence ; East Asian People/statistics & numerical data*

Objective:To investigate the role of WW domain containing E3 ubiquitin protein ligase 1 (WWP1) in enamel development of mice.Methods:Single-cell RNA sequencing data of incisor tissues of postnatal day 7 (P7) mice and mandibular first molar tooth germs of P3.5 mice were used to analyze the expression of Wwp1 in dental epithelial cells. Immunohistochemistry was performed to observe the distribution and expression levels of WWP1 in the epithelium of mouse incisors and mandibular first molar tooth germs. Wwp1 knockout (Wwp1 KO) mice were generated and collected with their control littermates at P1, P7, three mice per group, as well as at P14, P28, 2 months (2M), and 3M, six mice per group. The enamel volumes of molars and incisors were analyzed using micro-CT. Scanning electron microscopy was employed to examine the enamel cross-sections of Wwp1 KO and control mice. Energy dispersive spectroscopy (EDS) was used to analyze the calcium and phosphorus content of the enamel rod of incisors. Immunofluorescence was performed to detect the expression of amelogenin (AMELX) in the ameloblasts of Wwp1 KO and control mice. Additionally, LS-8 ameloblast-like epithelial cells were cultured, and Wwp1 siRNA or overexpression plasmids were transfected to knock down or overexpress WWP1. The protein levels of AMELX were then assessed by Western blotting.Results:Single-cell sequencing result showed a high Wwp1 mRNA expression level in the epithelial cells of mouse incisors and mandibular molar tooth germs. Immunohistochemistry revealed the expression of WWP1 in presecretory, secretory, transitional, and mature ameloblasts. Wwp1 KO mice exhibited enamel developmental defects. The enamel volumes of molars and incisors in Wwp1 KO mice [(0.155±0.016), (0.300±0.017) μm 3] were reduced by 23.95% ( P<0.001) and 28.31% ( P<0.001) compared with the control group [(0.203±0.062), (0.418±0.023) μm 3] respectively. Scanning electron microscopy showed disorganized enamel structures in Wwp1 KO incisors and molars. EDS results showed the weight percent of calcium in the enamel rod of incisors decreased in Wwp1 KO mice [(20.74±0.91)%] compared with the control group [(30.30±3.83)%] ( P<0.001), and the calcium-to-phosphorus ratio decreased in Wwp1 KO mice (1.93±0.01) compared with the control group (2.02±0.01) ( P<0.001). Immunofluorescence showed weaker AMELX expression in ameloblasts of mandibular first molar tooth germs from P1 and P7 Wwp1 KO mice compared with the control group ( P<0.001, P<0.001). In LS-8 cells, Wwp1 knocked-down led to a decrease of AMELX protein expression, while WWP1 overexpression resulted in an increased AMELX protein level. Conclusions:WWP1 promotes ameloblast differentiation and enamel matrix mineralization, playing a critical role in enamel formation.

Objective:To investigate the role of WW domain containing E3 ubiquitin protein ligase 1 (WWP1) in enamel development of mice.Methods:Single-cell RNA sequencing data of incisor tissues of postnatal day 7 (P7) mice and mandibular first molar tooth germs of P3.5 mice were used to analyze the expression of Wwp1 in dental epithelial cells. Immunohistochemistry was performed to observe the distribution and expression levels of WWP1 in the epithelium of mouse incisors and mandibular first molar tooth germs. Wwp1 knockout (Wwp1 KO) mice were generated and collected with their control littermates at P1, P7, three mice per group, as well as at P14, P28, 2 months (2M), and 3M, six mice per group. The enamel volumes of molars and incisors were analyzed using micro-CT. Scanning electron microscopy was employed to examine the enamel cross-sections of Wwp1 KO and control mice. Energy dispersive spectroscopy (EDS) was used to analyze the calcium and phosphorus content of the enamel rod of incisors. Immunofluorescence was performed to detect the expression of amelogenin (AMELX) in the ameloblasts of Wwp1 KO and control mice. Additionally, LS-8 ameloblast-like epithelial cells were cultured, and Wwp1 siRNA or overexpression plasmids were transfected to knock down or overexpress WWP1. The protein levels of AMELX were then assessed by Western blotting.Results:Single-cell sequencing result showed a high Wwp1 mRNA expression level in the epithelial cells of mouse incisors and mandibular molar tooth germs. Immunohistochemistry revealed the expression of WWP1 in presecretory, secretory, transitional, and mature ameloblasts. Wwp1 KO mice exhibited enamel developmental defects. The enamel volumes of molars and incisors in Wwp1 KO mice [(0.155±0.016), (0.300±0.017) μm 3] were reduced by 23.95% ( P<0.001) and 28.31% ( P<0.001) compared with the control group [(0.203±0.062), (0.418±0.023) μm 3] respectively. Scanning electron microscopy showed disorganized enamel structures in Wwp1 KO incisors and molars. EDS results showed the weight percent of calcium in the enamel rod of incisors decreased in Wwp1 KO mice [(20.74±0.91)%] compared with the control group [(30.30±3.83)%] ( P<0.001), and the calcium-to-phosphorus ratio decreased in Wwp1 KO mice (1.93±0.01) compared with the control group (2.02±0.01) ( P<0.001). Immunofluorescence showed weaker AMELX expression in ameloblasts of mandibular first molar tooth germs from P1 and P7 Wwp1 KO mice compared with the control group ( P<0.001, P<0.001). In LS-8 cells, Wwp1 knocked-down led to a decrease of AMELX protein expression, while WWP1 overexpression resulted in an increased AMELX protein level. Conclusions:WWP1 promotes ameloblast differentiation and enamel matrix mineralization, playing a critical role in enamel formation.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

Objective:To explore the value of 18F-fluorodeoxyglucose ( 18F-FDG) PET-CT dual-modality habitat imaging technology in predicting the epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma. Methods:This study was designed as a cross-sectional study. Clinical and imaging data of 403 patients with lung adenocarcinoma who underwent 18F-FDG PET-CT imaging with definitive EGFR results from January 2018 to April 2022 at the Third Affiliated Hospital of Soochow University were retrospectively analyzed.The patients were divided into a development set (282 cases) and a validation set (121 cases) using a stratified random sampling method at a 7∶3 ratio. An adaptive clustering algorithm was used to segment the regions of interest, forming different habitats and obtaining derived parameters. Independent samples t-test or Mann-Whitney U test were used to compare clinical, imaging indicators, and habitat-derived parameters between EGFR mutant and wild-type patient. The clinical, imaging indicators, and habitat-derived parameters that showed statistically significant differences in univariate analysis were included in multivariate logistic regression to construct clinical and clinical-habitat combined models, respectively. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s ability to predict EGFR mutations in lung adenocarcinoma. Additionally, the net reclassification index (NRI) was employed to assess the model′s classification improvement capability. Results:There were 249 cases of EGFR mutation and 154 cases of wild type. The optimal number of habitats was two, namely Habitat 1 and Habitat 2. The parameters included in the clinical model were smoking history, bronchial sign, pleural indentation sign, and tumor diameter. The parameters incorporated into the clinical-habitat combined model were smoking history, bronchial sign, pleural indentation sign, Habitat 2, and Habitat 1 voxel count. In the development set, the AUCs for predicting EGFR mutations in lung adenocarcinoma using the clinical model and the clinical-habitat combined model were 0.723 and 0.733, respectively, with no statistically significant difference ( Z=0.60, P=0.549); In the validation set, the AUCs were 0.684 and 0.715, respectively, with no statistically significant difference ( Z=1.32, P=0.186). The accuracy (0.694) and specificity (0.609) of the clinical-habitat combined model in the validation set were slightly higher than those of the clinical model (0.686 and 0.565, respectively). NRI analysis confirmed that the clinical-habitat combined model improved the correct classification of EGFR wild-type lung adenocarcinoma by 10.9% compared to the clinical model ( P=0.018). Conclusion:18F-FDG PET-CT dual-modality habitat imaging technology can be used to analyze the microenvironment of lung adenocarcinoma and has the potential in non-invasively predicting EGFR mutation status, providing an important basis for personalized and accurate treatment of patients with lung adenocarcinoma.