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 construct a predictive model for high-grade pathological components of early invasive lung adenocarcinoma(ILAC)based on radiomics.Methods Collecting information on total 495 patients who underwent radical operation and were pathologically diagnosed as stage Ⅰ in the cardiothoracic surgery of Zhoushan Hospital from January 2015 to December 2019,including gender,age,pathological findings,tumor markers and preoperative chest CT images.The micropapillary and solid components in postoperative pathology were defined as"high-grade pathological components",while those without high-grade pathological components were classified into the low-grade group and those with high-grade pathological components were classified into the high-grade group.And patients were randomly divided into the training set(343 cases)and the validation set(152 cases)with a ratio of 7∶3 using the simple randomization grouping method.The region of interest of nodules on CT images were delineated layer by layer by scientific research platform and 1950 radiomics features were extracted.And then those features were filtrated by F-test,Pearson correlation coefficient,and L1 based feature selection.A model was built by using Logistic regression machine learning classifier,named mod 2,and radscore was also obtained.Differences between general information and CT features were analyzed.Binary Logistic regression analysis was used to construct a model for statistically significant variables,named mod 1.At the same time,Radscore was added to build the mod and named comb mod.The area under the curve(AUC),sensitivity and specificity of the three models were calculated.A nomogram was also drawn.Results A total of 495 patients were divided into the training set(n=343)and the validation set(n=152).Gender,carcinoma embryonic antigen(CEA),nodule,and maximum diameter were screened out in clinical features and involved in constructing the mod 1.Twelve features were selected from the radiomics features to build mod 2.Comb mod performed best,training set AUC:0.887,validation set AUC:0.875,and had good clinical practicability.Conclusion The model composed of general feature,CT feature and radiomics features could accurately predict high-grade pathological components in early ILAC,and provide references for clinicians to choose surgical method for patients.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To study the role of cadherin 1 (CDH1) gene DNA methylation in autoimmune thyroiditis (AIT) in population from different water-iodine regions.Methods:From May to June 2019, the information of AIT cases and healthy individuals in Shandong Province were collected in three types of water-iodine regions: iodine-fortification (IF) region, iodine-adequate (IA) region and iodine-excess (IE) region. A case-control study design was applied to match 176 AIT cases (case group) with age, gender, body mass index, and place of residence in a 1 ∶ 1 ratio to 176 healthy individuals (control group). Fasting urine and whole blood samples were collected to test the contents of urinary iodine, thyroid function indicators [serum free triiodothyronine (FT 3), free thyroxine (FT 4), thyroid stimulating hormone (TSH)], and serum iodine. The DNA methylation levels of the target region of the CDH1 gene and its four CpG sites in whole blood were determined using methylation sequencing technology for target regions (MethylTarget TM). Results:The DNA methylation level of the target region of CDH1 gene in the case group was 0.832 ± 0.044, and that in the control group was 0.828 ± 0.049, there was no statistically significant difference between the two groups ( t = 0.76, P = 0.448). There was no statistically significant difference in DNA methylation levels of the four CpG sites in the target region of CDH1 gene between the case group and the control group ( P > 0.05). There was no statistically significant difference in the DNA methylation level of the CDH1 gene target region between the case group and the control group in IF, IA and IE regions ( P > 0.05). The detection results of DNA methylation levels at CpG sites in the target region of CDH1 gene in different water iodine regions showed that the DNA methylation level at site 83 in case group in IF region was higher than that in the control group ( t = 2.30, P = 0.023). However, there was no statistically significant difference in the DNA methylation levels of the four CpG sites between the case group and the control group in IA and IE regions ( P > 0.05). The DNA methylation level of CDH1 gene target region in AIT patients was not significantly correlated with urinary iodine, serum iodine, and serum FT 3, FT 4, and TSH contents ( P > 0.05), but was significantly negatively correlated with age ( r =-0.19, P = 0.014). Conclusions:The DNA methylation level at CpG site 83 of CDH1 gene in AIT patients in IF region is significantly higher than that in control population, indicating that DNA methylation at this locus may be involved in the occurrence and development of AIT after iodine fortification. The DNA methylation level of CDH1 gene is negatively correlated with age.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Objective:To study the role of cadherin 1 (CDH1) gene DNA methylation in autoimmune thyroiditis (AIT) in population from different water-iodine regions.Methods:From May to June 2019, the information of AIT cases and healthy individuals in Shandong Province were collected in three types of water-iodine regions: iodine-fortification (IF) region, iodine-adequate (IA) region and iodine-excess (IE) region. A case-control study design was applied to match 176 AIT cases (case group) with age, gender, body mass index, and place of residence in a 1 ∶ 1 ratio to 176 healthy individuals (control group). Fasting urine and whole blood samples were collected to test the contents of urinary iodine, thyroid function indicators [serum free triiodothyronine (FT 3), free thyroxine (FT 4), thyroid stimulating hormone (TSH)], and serum iodine. The DNA methylation levels of the target region of the CDH1 gene and its four CpG sites in whole blood were determined using methylation sequencing technology for target regions (MethylTarget TM). Results:The DNA methylation level of the target region of CDH1 gene in the case group was 0.832 ± 0.044, and that in the control group was 0.828 ± 0.049, there was no statistically significant difference between the two groups ( t = 0.76, P = 0.448). There was no statistically significant difference in DNA methylation levels of the four CpG sites in the target region of CDH1 gene between the case group and the control group ( P > 0.05). There was no statistically significant difference in the DNA methylation level of the CDH1 gene target region between the case group and the control group in IF, IA and IE regions ( P > 0.05). The detection results of DNA methylation levels at CpG sites in the target region of CDH1 gene in different water iodine regions showed that the DNA methylation level at site 83 in case group in IF region was higher than that in the control group ( t = 2.30, P = 0.023). However, there was no statistically significant difference in the DNA methylation levels of the four CpG sites between the case group and the control group in IA and IE regions ( P > 0.05). The DNA methylation level of CDH1 gene target region in AIT patients was not significantly correlated with urinary iodine, serum iodine, and serum FT 3, FT 4, and TSH contents ( P > 0.05), but was significantly negatively correlated with age ( r =-0.19, P = 0.014). Conclusions:The DNA methylation level at CpG site 83 of CDH1 gene in AIT patients in IF region is significantly higher than that in control population, indicating that DNA methylation at this locus may be involved in the occurrence and development of AIT after iodine fortification. The DNA methylation level of CDH1 gene is negatively correlated with age.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective To study the bioequivalence and safety of two olmesartan medoxomil and hydrochlorothiazide tablets in Chinese healthy subjects.Methods A total of 24 healthy subjects underwent fasting and postprandial tests in a single-center,randomized,open-label,single-dose,two-formulation,two-sequence,two-period,self-cross-over controlled design.The subjects were administered a single oral dose of the test formulation and reference formulation(each containingolmesartan medoxomil 20 mg and hydrochlorothiazide 12.5 mg)in a random cross-over fashion.The plasma concentrations of olmesartan and hydrochlorothiazide were determined by LC-MS/MS.The non-compartmental model analysis of olmesartan and hydrochlorothiazide was conducted using WinNonlin 7.0 software to calculate pharmacokinetic parameters and assess bioequivalence.Results In the fasting test,the pharmacokinetic parameters of olmesartan of test and reference were as follows:Cmax were(798.35±206.78)and(664.52±168.25)ng·mL-1,AUC0-t were(4 430.71±1 294.87)and(3 976.67±1 083.54)h·ng·mL-1,AUC0-∞ were(4 551.67±1 303.06)and(4 090.37±1 103.97)h·ng·mL-1.The pharmacokinetic parameters of hydrochlorothiazide of test and reference were as follows:Cmax were(92.39±35.96)and(96.15±38.76)ng·mL-1,AUC0_t were(548.69±217.11)and(564.41±208.68)h·ng·mL-1,AUC0-∞ were(603.04±228.59)and(619.26±223.27)h·ng·mL-1.In the fed test,the pharmacokinetic parameters of olmesartan of T and R were as follows:Cmax were(583.15±149.48)and(550.57±104.76)ng·mL-1,AUC0-t were(3 585.18±952.72)and(3 292.19±904.58)h·ng·mL-1,AUC0-∞ were(3 696.05±996.55)and(3 396.30±923.41)h·ng·mL-1.The pharmacokinetic parameters of hydrochlorothiazide of test and reference were as follows:Cmax were(70.30±17.88)and(74.70±21.65)ng·mL-1,AUC0-t were(476.60±119.39)and(492.91±144.81)h·ng·mL-1,AUC0-∞ were(523.37±132.67)and(535.81±151.92)h·ng·mL-1.In fasting and fed condition,the 90％confidence interval(90％CI)of Cmax,AUC0-t and AUC0-∞ of olmesartan and hydrochlorothiazide were in 80.00％-125.00％.Conclusion The two olmesartan medoxomil and hydrochlorothiazide tablets were bioequivalent under fasting and fed conditions,and good security.

Objective:To assess the accuracy of two-dimensional (2D) photographs in measuring esthetic parameters of the maxillary anterior teeth by comparing them with measurements obtained from three-dimensional (3D) dental models.Methods:A total of one hundred volunteers (49 males, 51 females, aged 18-23 years) were recruited from School and Hospital of Stomatology, Wuhan University from January to February 2024. 3D digital models of their dentitions were obtained using an intraoral scanner, and standardized frontal 2D intraoral photographs were captured with a digital camera. The lengths, widths and width/length ratio of the bilateral incisors, lateral incisors and canines were measured on both the 3D digital models and the 2D intraoral photographs. The width ratios of adjacent maxillary anterior were also calculated on the 2D intraoral photographs and the frontal view of 3D digital models.Results:The widths of lateral incisors [(5.85±0.60) mm] and canines [(4.73±0.71) mm] and the lengths of canines [(8.72±0.96) mm] in the 2D intraoral photographs were significantly lower than those in 3D digital models [(6.65±0.59), (7.76±0.60), (8.90±0.86) mm] ( t=-18.24, P<0.001; t=-54.43, P<0.001; t=-4.40, P<0.001), while there were no significant differences in the lengths and widths of the other teeth ( P>0.05). The width/length ratios measured from the 2D intraoral photographs for the lateral incisors and canines (0.74±0.08, 0.55±0.08) were significantly lower than those measured in the 3D digital models (0.84±0.09, 0.88±0.09) ( t=-19.68, P<0.001; t=-50.21, P<0.001), and the width/length ratio of the central incisors showed no significant difference between the two groups ( P>0.05). The width ratios of canines/lateral incisors and lateral incisors/central incisors measured on the 2D intraoral photographs (0.72±0.06, 0.85±0.11) were significantly smaller than those measured in the frontal view of 3D digital models (0.75±0.06, 0.89±0.11) ( t=-9.31, P<0.001; t=-6.58, P<0.001). Conclusions:There is a difference between 2D and 3D measurement results of teeth in the esthetic area and the magnitude of the difference varies with their position in the dental arch. When analyzing the measurement of the anterior teeth, it is necessary to choose the appropriate method according to the target tooth position.