Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.

Objective:To address the critical issue of missing dynamic border molding information in edentulous direct digital impression technology, this study explores innovative digital solutions and conducts preliminary application validation.Methods:Based on the myostatic line theory, a methodology was established: intraoral scanner (IOS) high-frequency video was utilized to dynamically capture functional molding data of soft tissues, integrated with a self-developed mobility gradient recognition algorithm to achieve dynamic threshold segmentation between the muscle dynamic zone and myostatic zone, termed "optical digital molding technology". Ten edentulous patients with well-fitting complete dentures, treated at the Department of Prosthodontics, Peking University School and Hospital of Stomatology from January 2024 to December 2024, were enrolled. The standard deviation between the muscle static line (generated by mobility gradient algorithm with thresholds of 0.3-0.7 mm) and the denture border curve was analyzed to optimize the dynamic threshold, followed by single-case clinical validation.Results:Among the mobility thresholds of 0.3-0.7 mm, the 0.5 mm threshold yielded the smallest standard deviation between the myostatic line and denture border. Clinical validation demonstrated that dentures designed with this threshold exhibited no displacement during dynamic functional tests, with marginal sealing meeting clinical standards.Conclusions:The optical digital border molding technique for edentulous soft tissue boundaries translates the myostatic line theory into quantifiable parameters for the first time. Based on data from 10 cases, a mobility threshold of 0.5 mm is recommended for clinical application.

Objective:To analyze the influencing factors of low fall alertness in elderly inpatients,construct a risk prediction model and validate it,providing a reference for clinical medical staff to identify elderly inpatients with low fall alertness in the early stage.Methods:A total of 605 elderly inpatients treated in Yijishan Hospital affiliated to Wannan Medical College from Oct 2023 to Mar 2024 were enrolled and randomly divided into the training group(n=423)and validation group(n=182)at a ratio of 7∶3.The patients were evaluated using a general information questionnaire,the Social Frailty Screening Tool(HALFT),the Tilburg Frailty Indicator(TFI),and the Self-Awareness of Falls in Elderly scale(SAFE).Multivariate logistic analysis was used to determine the influencing factors of low fall alertness in elderly inpatients.RStudio was used to construct a risk prediction model of low fall alertness.The discrimination,calibration,and clinical net benefit of the model were verified using the receiver operating characteristic(ROC)curves,calibration plots,and decision curve analysis(DCA).Results:Multivariate logistic analysis showed that the history of falls,monthly income,previous physical activity time,social frailty score and TFI score were independent risk factors for low fall alertness in elderly inpatients.The Hosmer-Lemeshow χ2 test showed that χ2=8.863,P=0.354,indicating good calibration of the prediction model.The area under the ROC curve of the training group and the validation group were 0.860(95%CI:0.815-0.904)and 0.937(95%CI:0.888-0.986),respectively,and the maximum Youden indices of the model was 0.576 and 0.788,respectively,indicating good discrimination of the model.The DCA decision curve showed that the model had good clinical effectiveness.Conclusion:The constructed model has a good prediction effect and can help clinical medical staff quickly and effectively screen out elderly inpatients at risk of low fall alertness.

Objective:To address the critical issue of missing dynamic border molding information in edentulous direct digital impression technology, this study explores innovative digital solutions and conducts preliminary application validation.Methods:Based on the myostatic line theory, a methodology was established: intraoral scanner (IOS) high-frequency video was utilized to dynamically capture functional molding data of soft tissues, integrated with a self-developed mobility gradient recognition algorithm to achieve dynamic threshold segmentation between the muscle dynamic zone and myostatic zone, termed "optical digital molding technology". Ten edentulous patients with well-fitting complete dentures, treated at the Department of Prosthodontics, Peking University School and Hospital of Stomatology from January 2024 to December 2024, were enrolled. The standard deviation between the muscle static line (generated by mobility gradient algorithm with thresholds of 0.3-0.7 mm) and the denture border curve was analyzed to optimize the dynamic threshold, followed by single-case clinical validation.Results:Among the mobility thresholds of 0.3-0.7 mm, the 0.5 mm threshold yielded the smallest standard deviation between the myostatic line and denture border. Clinical validation demonstrated that dentures designed with this threshold exhibited no displacement during dynamic functional tests, with marginal sealing meeting clinical standards.Conclusions:The optical digital border molding technique for edentulous soft tissue boundaries translates the myostatic line theory into quantifiable parameters for the first time. Based on data from 10 cases, a mobility threshold of 0.5 mm is recommended for clinical application.

Objective:To analyze the influencing factors of low fall alertness in elderly inpatients,construct a risk prediction model and validate it,providing a reference for clinical medical staff to identify elderly inpatients with low fall alertness in the early stage.Methods:A total of 605 elderly inpatients treated in Yijishan Hospital affiliated to Wannan Medical College from Oct 2023 to Mar 2024 were enrolled and randomly divided into the training group(n=423)and validation group(n=182)at a ratio of 7∶3.The patients were evaluated using a general information questionnaire,the Social Frailty Screening Tool(HALFT),the Tilburg Frailty Indicator(TFI),and the Self-Awareness of Falls in Elderly scale(SAFE).Multivariate logistic analysis was used to determine the influencing factors of low fall alertness in elderly inpatients.RStudio was used to construct a risk prediction model of low fall alertness.The discrimination,calibration,and clinical net benefit of the model were verified using the receiver operating characteristic(ROC)curves,calibration plots,and decision curve analysis(DCA).Results:Multivariate logistic analysis showed that the history of falls,monthly income,previous physical activity time,social frailty score and TFI score were independent risk factors for low fall alertness in elderly inpatients.The Hosmer-Lemeshow χ2 test showed that χ2=8.863,P=0.354,indicating good calibration of the prediction model.The area under the ROC curve of the training group and the validation group were 0.860(95%CI:0.815-0.904)and 0.937(95%CI:0.888-0.986),respectively,and the maximum Youden indices of the model was 0.576 and 0.788,respectively,indicating good discrimination of the model.The DCA decision curve showed that the model had good clinical effectiveness.Conclusion:The constructed model has a good prediction effect and can help clinical medical staff quickly and effectively screen out elderly inpatients at risk of low fall alertness.

In response to the new requirements for course instruction outlined in the revised training program for medical imaging program, this study integrated medical imaging technology courses based on the principle of outcome-oriented education and by leveraging self-developed digital resources, with imaging methods as the entry point. The core elements of the course teaching were re-optimized and reorganized to transcend the temporal and spatial limitations of course delivery, enabling the rational application of diverse teaching methods. This approach facilitated the integration of knowledge across three specialized courses, namely medical imaging physics, medical imaging equipment, and medical imaging examination techniques, and achieved full-dimensional and whole-process teaching evaluation. While reducing the number of hours allocated to theoretical instruction, the teaching objectives were achieved with high quality, providing a reference for the integration of digital technologies into the teaching of medical imaging and related disciplines.

Objective To establish a rat model of venous thrombosis in a plateau hypobaric hypoxic environment and to investigate the effect of this environment on venous thrombosis.Methods A total of 144 healthy male SD rats were assigned randomly to four groups(n=36 rats per group):a plains sham operation(A)group,plains operation(B)group,plateau altitude 6000 m+sham operation(C)group,and plateau altitude 6000 m+surgery(D)group.Rats in A and B groups were maintained in a plains normoxic environment,while rats in C and D groups C and D were subjected to a plateau environment.Rats in the surgical groups underwent quantitative constriction to incompletely obstruct the inferior vena cava blood flow.Each group was further divided into subgroups based on time:1,3,5,7,14,and 21 d(n=6 rats per group).Regular vascular ultrasound monitoring was conducted,and blood samples were taken for whole blood viscosity testing and the assessment of inflammatory indicators,including endothelin-1(ET-1),interleukin-6(IL-6)and tissue factor(TF).Coagulation function was evaluated through the activated partial thromboplastin time(APTT),prothrombin time(PT),thrombin time(TT),fibrinogen(FIB)and D-dimer.After the observation period,the experimental animals were sacrificed and the limbs were removed.Thrombus samples were stained with hematoxylin/eosin(HE),and the thrombus wet mass was measured.Results The thrombosis incidence was significantly higher in the plateau D group than in B group,accompanied by a marked increase in blood viscosity and hematocrit(P＜0.01).Additionally,levels of ET-1,IL-6,and TF were significantly elevated(P＜0.05),indicating a coagulation disorder.Conclusions A plateau hypoxic environment model can be successfully simulated by quantitative coarctation of the inferior vena cava,combined with a specialized environmental chamber.The findings of this study suggest that a plateau hypoxic environment promotes venous thrombosis.

In response to the new requirements for course instruction outlined in the revised training program for medical imaging program, this study integrated medical imaging technology courses based on the principle of outcome-oriented education and by leveraging self-developed digital resources, with imaging methods as the entry point. The core elements of the course teaching were re-optimized and reorganized to transcend the temporal and spatial limitations of course delivery, enabling the rational application of diverse teaching methods. This approach facilitated the integration of knowledge across three specialized courses, namely medical imaging physics, medical imaging equipment, and medical imaging examination techniques, and achieved full-dimensional and whole-process teaching evaluation. While reducing the number of hours allocated to theoretical instruction, the teaching objectives were achieved with high quality, providing a reference for the integration of digital technologies into the teaching of medical imaging and related disciplines.

Objective To establish a rat model of venous thrombosis in a plateau hypobaric hypoxic environment and to investigate the effect of this environment on venous thrombosis.Methods A total of 144 healthy male SD rats were assigned randomly to four groups(n=36 rats per group):a plains sham operation(A)group,plains operation(B)group,plateau altitude 6000 m+sham operation(C)group,and plateau altitude 6000 m+surgery(D)group.Rats in A and B groups were maintained in a plains normoxic environment,while rats in C and D groups C and D were subjected to a plateau environment.Rats in the surgical groups underwent quantitative constriction to incompletely obstruct the inferior vena cava blood flow.Each group was further divided into subgroups based on time:1,3,5,7,14,and 21 d(n=6 rats per group).Regular vascular ultrasound monitoring was conducted,and blood samples were taken for whole blood viscosity testing and the assessment of inflammatory indicators,including endothelin-1(ET-1),interleukin-6(IL-6)and tissue factor(TF).Coagulation function was evaluated through the activated partial thromboplastin time(APTT),prothrombin time(PT),thrombin time(TT),fibrinogen(FIB)and D-dimer.After the observation period,the experimental animals were sacrificed and the limbs were removed.Thrombus samples were stained with hematoxylin/eosin(HE),and the thrombus wet mass was measured.Results The thrombosis incidence was significantly higher in the plateau D group than in B group,accompanied by a marked increase in blood viscosity and hematocrit(P＜0.01).Additionally,levels of ET-1,IL-6,and TF were significantly elevated(P＜0.05),indicating a coagulation disorder.Conclusions A plateau hypoxic environment model can be successfully simulated by quantitative coarctation of the inferior vena cava,combined with a specialized environmental chamber.The findings of this study suggest that a plateau hypoxic environment promotes venous thrombosis.

Objective:To investigate the factors contributing to frailty in very elderly patients with multimorbidity.Methods:This cross-sectional study enrolled 119 very elderly patients with multimorbidity who were hospitalized in the Department of Geriatrics of Huadong Hospital Affiliated to Fudan University from August 2022 to March 2023.The study aimed to understand the basic status of multimorbidity by collecting general information, the number and types of diseases, and frailty status.The subjects were divided into frail and non-frail groups through comprehensive geriatric assessment.Various factors including gender, age, Tinetti balance gait score, risk of sarcopenia, dementia, depression, risk of deep vein thrombosis, dysphagia, comorbidity index, medication count, Basic Activities of Daily Living(BADL)score, Instrumental Activities of Daily Living(IADL)score, Nutritional Risk Screening 2002(NRS-2002)score, Norton pressure injury risk assessment score, and Social Support Rating Scale(SSRS)score were compared.The correlation between each factor and the occurrence of frailty was analyzed using univariate analysis and multivariate Logistic regression analysis.Results:A total of 119 elderly inpatients with multimorbidity, with an average age of 90.8±5.9 years old, were included in the study.The incidence of frailty was 68.9%(82 cases).Univariate analysis revealed significant statistical differences between the frail group and the non-frail group in various factors including age( t=-3.131, P=0.002), Tinetti score( Z=-5.544, P<0.001), risk of sarcopenia( χ2=39.205, P<0.001), dysphagia( χ2=5.937, P=0.015), Charlson comorbidity index( Z=-2.565, P=0.010), medication count( Z=-3.325, P<0.001), BADL( Z=-5.871, P<0.001), IADL( Z=-5.062, P<0.001), Norton score( Z=-5.922, P<0.001), and SSRS social support( Z=-2.637, P=0.008).Multivariate logistic regression analysis showed that the Tinetti score( OR=0.843, 95% CI: 0.737-0.966, P=0.014), decreased muscle strength( OR=11.226, 95% CI: 2.157-58.432, P=0.004), sarcopenia( OR=18.084, 95% CI: 2.041-106.211, P=0.009), Norton score( OR=0.462, 95% CI: 0.254-0.838, P=0.011), and medication count( OR=1.153, 95% CI: 1.000-1.329, P=0.049)were independently associated with frailty. Conclusions:In very elderly patients with multimorbidities, the occurrence of frailty is notably increased.Frailty is linked to multiple risks including falls, muscle weakness/sarcopenia, pressure ulcer risk, and polypharmacy, and these risks are independent of other factors.