Background With China's socio-economic development, the dietary structure of Chinese residents has gradually shifted from a traditional Eastern pattern characterized by high carbohydrate intake to a relatively high-fat Western dietary model, alongside a growing burden of chronic diseases. However, dietary changes may vary across different occupational groups. Objective To analyze the long-term trends in dietary energy and three major macronutrient intake among various occupational groups aged 18-59 years in nine provinces of China from 1989 to 2018, providing a scientific basis for developing occupation-specific dietary intervention strategies. Methods Based on 11 waves of data (1989–2018) from the China Health and Nutrition Survey (CHNS), 57458 employed individuals aged 18-59 years were selected as observation subjects after applying inclusion and exclusion criteria. Joinpoint regression was used to analyze temporal trends by calculating the annual percentage change (APC) and average annual percentage change (AAPC) with 95% confidence intervals. The analysis covered the energy supply ratio from the three major macronutrients and the proportion of the participants within the acceptable macronutrient distribution ranges (AMDR) for the total occupational population and specific occupational group. Results From 1989 to 2018, the dietary structure of the occupational population in the nine provinces gradually Westernized. The carbohydrate energy supply ratio showed a decreasing trend (AAPC=−0.77%, 95%CI: −1.00%, −0.55%, P<0.05), while the fat energy supply ratio increased (AAPC=1.42%, 95%CI: 1.07%, 1.77%, P<0.05). The protein energy supply ratio remained largely unchanged (AAPC=−0.19%, 95%CI: −0.08%, 0.46%, P>0.05), and the total energy intake declined (AAPC=−0.62%, 95%CI: −0.94%, −0.30%, P<0.05). Among the occupational groups studied, white-collar workers were the first to show characteristics of dietary Westernization. Using the AMDR as the criterion, their average fat energy supply ratio first exceeded the AMDR upper limit (30%) in 1993, and their average carbohydrate energy supply ratio first fell below the AMDR lower limit (50%) in 2006, both time points being earlier than other occupational groups. Agricultural workers lagged in this transition (their average fat energy supply ratio first reached ≥30% in 2011), but their rate of increase in the fat energy supply ratio was the fastest among all occupational groups (AAPC=1.59%, 95%CI: 0.90%, 2.30%, P<0.05; P for trend comparison between groups<0.05). The other three occupational groups (blue-collar workers, service workers, and others) generally fell between these two extremes. Taking the fat energy supply ratio as an example, their AAPCs of increase were 1.02% (95%CI: 0.73%, 1.32%, P<0.05) for blue-collar workers, 0.85% (95%CI: 0.61%, 1.09%, P<0.05) for service workers, and 0.88% (95%CI: 0.58%, 1.17%, P<0.05) for others. Conclusion From 1989 to 2018, the macronutrient structure of the occupational population aged 18-59 years in nine China provinces is gradually Westernized. The energy supply ratios of carbohydrates and proteins primarily face issues of potential inadequacy, while the fat energy supply ratio is excessively high. Furthermore, the patterns of change vary among different occupational groups. It is recommended to propose personalized dietary advice tailored to specific occupations.

Background With China's socioeconomic development, significant lifestyle changes have occurred among occupational groups, leading to alterations in cardiovascular metabolic risk factors. However, few studies have examined the secular trends of these risk factors in China's working population. Objective To analyze the trends in cardiovascular metabolic risk factors among the occupational population in nine provinces of China from 2009 to 2018, and to explore the associations between different occupational types and these risk factors, along with their clustering patterns, thereby providing evidence for targeted interventions. Methods This study utilized data from the China Health and Nutrition Survey (CHNS) in 2009, 2015, and 2018. The dataset covered 13274 employed individuals aged 18-59 years. Only participants with complete blood test results and physical measurement data were included in the final analysis. The average annual percentage change (AAPC) in the positive rates of risk factors was calculated using Joinpoint regression to analyze temporal trends. Multinomial logistic regression and binary logistic regression models were employed to examine the cross-sectional associations between occupational types (categorized as white-collar workers, agricultural workers, blue-collar workers, service workers, and others) and nine cardiovascular metabolic risk factors and their clustering (defined as having ≥2 risk factors) in 2018. Results A total of 8451 participants were included. From 2009 to 2018, significant increasing trends (AAPC>0, P<0.05) were observed for overweight, obesity, central obesity, borderline elevated triglycerides (TG), borderline elevated to elevated total cholesterol (TC), borderline elevated low-density lipoprotein cholesterol (LDL-C), elevated blood pressure and hypertension, diabetes. The most rapid increases were found for obesity (AAPC=6.07%, 95%CI: 3.42%, 8.88%), hypertension (AAPC=4.10%, 95%CI: 0.62%, 7.77%), and central obesity (AAPC=3.27%, 95%CI: 1.24%, 5.36%). The clustering rate of risk factors increased from 62.3% in 2009 to 73.2% in 2018 (AAPC=1.87%, 95%CI: 1.02%, 2.73%). The cross-sectional analysis in 2018, after adjusting for confounders, showed that compared with white-collar workers, blue-collar workers had a significantly lower risk of overweight (OR=0.67, 95%CI: 0.48, 0.95), central obesity (OR=0.65, 95%CI: 0.45, 0.92), and borderline high TG (OR=0.54, 95%CI: 0.35, 0.82). In contrast, agricultural workers had a higher risk of hypertension (OR=1.76, 95%CI: 1.14, 2.72). Conclusion Between 2009 and 2015, the burden of most cardiometabolic risk factors increases significantly among the Chinese occupational population, with the rate of increase slowing by 2018. Risks vary across occupational types, among which blue- collar workers report lower target outcomes. Targeted interventions focusing on the rapidly increasing risk factors and high-risk occupational groups are recommended.

Background The global prevalence of obesity is on the rise and is closely associated with various chronic non-communicable diseases such as cardiovascular diseases and diabetes. There is a relative lack of long-term dynamic studies on compound obesity among occupational populations. Objective To explore the changing trends of compound obesity among different occupational groups aged 18–59 years in nine provinces (autonomous regions, municipalities) of China from 1993 to 2018, and to provide a scientific basis for formulating targeted weight management strategies for occupational populations. Methods A total of 34519 employed adults aged 18–59 years from nine waves of the China Health and Nutrition Survey in1993, 1997, 2000, 2004, 2006, 2009, 2011, 2015, and 2018 were included. Obesity types were determined according to the Chinese Adult Weight Management Guidelines. The Joinpoint regression model was used to calculate the average annual percentage change (AAPC) and annual percentage change (APC) of compound obesity rates. Results From 1993 to 2018, the age-standardized compound obesity rate among the occupational population aged 18–59 years showed an upward trend (AAPC=6.24%, 95%CI: 5.09%, 7.41%, P<0.05). The AAPCs (95%CI) for age-standardized compound obesity rates among agricultural workers, blue-collar workers, other occupational groups, service workers, and white-collar workers were 9.55% (7.51%, 11.64%), 6.18% (4.70%, 7.69%), 5.53% (2.27%, 8.90%), 4.07% (2.40%, 5.77%), and 3.89% (2.16%, 5.65%), respectively, with statistically significant differences (P<0.05). Among males, the AAPCs (95%CI) for age-standardized compound obesity rates among agricultural workers, blue-collar workers, other occupational groups, service workers, and white-collar workers were 11.52% (7.71%, 15.47%), 7.28% (6.18%, 8.40%), 5.92% (2.28%, 9.69%), 5.56% (2.11%, 9.12%), and 4.22% (2.33%, 6.15%), respectively, with statistically significant differences (P<0.05). Among females, the AAPCs (95%CI) for age-standardized compound obesity rates among agricultural workers, blue-collar workers, and white-collar workers were 7.45% (6.22%, 8.69%), 3.72% (0.84%, 6.67%), and 2.41% (0.17%, 4.70%), respectively, with statistically significant differences (P<0.05). A turning point was observed in the compound obesity rate among female white-collar workers in 2006, with an APC (95%CI) of 9.69% (5.24%, 14.33%) from 2006 to 2008, showing a statistically significant difference (P<0.05), while no significant trend was found from 1993 to 2006 (P>0.05). No significant trends were observed in the age-standardized compound obesity rates among females in other occupational groups and service workers (P>0.05). Conclusion The compound obesity rate among China's occupational population is increasing rapidly, with the growth rate among agricultural workers being particularly prominent. In 2018, male white-collar workers and female agricultural workers exhibit relatively high compound obesity rates, warranting focused attention. Although the overall growth rate among service workers is slower, the expanding workforce in this sector indicates that obesity remains a serious issue. Targeted occupational health and weight management interventions are needed in the future.

Background With rising obesity rates and earlier hypertension onset among occupational populations, there is an urgent need to elucidate the long-term cardiovascular impacts of dynamic body weight patterns. Current evidence lacks trajectory modeling studies examining occupation-specific prevention strategies. Objective To investigate the association between long-term body mass index (BMI) trajectories and incident hypertension risk in Chinese working adults, and to examine occupation-specific heterogeneity in this relationship. Methods A dynamic sub-cohort of 4 413 occupational participants was constructed from ten survey waves (1991–2018) of the China Health and Nutrition Survey (CHNS). Eligible individuals had valid key BMI records at three or more independent follow-ups before the outcome event; the individual baseline was set as the year of their first participation in the survey. Group-based trajectory modeling (GBTM) was used to identify BMI change patterns. Cox proportional hazards regression was used to calculate hazard ratios (HRs) and 95% confidence interval (CI) for hypertension incidence across trajectory groups, with stratified analysis by occupational categories. Results Among 4413 occupational participants, 1441 incident hypertension cases were reported during the follow-up window. Three long-term BMI trajectories were identified: low-stable, moderate-slowly rising, and high-rising. In Model 3 with all selected confounders adjusted, compared with the low-stable group, the moderate-slowly rising group showed a significantly higher risk of incident hypertension (HR=1.28, 95%CI: 1.12, 1.45; P < 0.001), as did the high-increasing group (HR=1.80, 95%CI: 1.45, 2.23; P < 0.001). The stratified analyses revealed occupational differences in the association. Among agricultural workers, the moderate-slowly rising group (HR=1.35, 95%CI: 1.15, 1.57) and the high-rising group (HR=2.00, 95%CI: 1.50, 2.66) both exhibited significantly increased risks of hypertension (P<0.001). Conversely, among white-collar workers, an increased risk of hypertension was observed only in the high-rising group (HR=1.97, 95%CI: 1.23, 3.17; P<0.05). Conclusion Rapid BMI escalation constitutes a significant risk factor for incident hypertension in occupational populations. Targeted long-term weight monitoring and tailored interventions are recommended for high-risk occupations.

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

ObjectiveTo establish a geographical origin identification model for Foeniculi Fructus from Haiyuan， providing a new technical reference for the protection of Haiyuan's geo-authentic medicinal materials and its designation as a national geographical indication agricultural product. MethodsSamples of Foeniculi Fructus were collected from eight producing areas， including Minqin （Gansu）， Bozhou （Anhui）， Qingdao （Shandong）， Dezhou （Shandong）， Urumqi （Xinjiang）， Nujiang （Yunnan）， Gutuo （Inner Mongolia）， and Haiyuan （Ningxia）. Gas chromatography-ion mobility spectrometry （GC-IMS） was used to detect the volatile organic compounds （VOCs） in samples from these geographic origins. VOCs were qualitatively analyzed through dual matching with the National Institute of Standards and Technology （NIST） mass spectral database and the IMS drift time database. Using the Reporter module and Gallery Plot visualization tools within the LAV analytical platform， VOC fingerprint profiles characterizing geographic origins were constructed. A non-targeted analytical strategy was adopted， and 97 VOCs detected via GC-IMS were subjected to principal component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） based on their differential distribution patterns to construct an origin identification model for Foeniculi Fructus from Haiyuan region. Key discriminative markers were screened using variable importance in projection （VIP） values greater than 1. ResultsA total of 97 VOCs were identified， including alcohols， aldehydes， ketones， esters， organic acids， terpenoids， ethers， alkenes， and benzenes. The PLS-DA model， based on VOCs data obtained by GC-IMS， effectively distinguished Foeniculi Fructus in Haiyuan region from those of other origins. During cross-validation， the model achieved a prediction parameter （Q²） of 0.976 and a goodness-of-fit parameter （R²） of 0.936， with no overfitting observed in permutation testing. Twelve key flavor markers with VIP > 1 were identified as characteristic indicators of Haiyuan origin. ConclusionA stable and highly predictive origin identification model for Foeniculi Fructus from Haiyuan was successfully established using GC-IMS technology， PLS-DA， and VIP-based marker screening. This model provides a novel technical strategy for accurately distinguishing Foeniculi Fructus in Haiyuan region from other regional varieties and offers new technical support for its protection as a geo-authentic medicinal material and a nationally designated geographical indication agricultural product in China.

ObjectiveTo establish a geographical origin identification model for Foeniculi Fructus from Haiyuan， providing a new technical reference for the protection of Haiyuan's geo-authentic medicinal materials and its designation as a national geographical indication agricultural product. MethodsSamples of Foeniculi Fructus were collected from eight producing areas， including Minqin （Gansu）， Bozhou （Anhui）， Qingdao （Shandong）， Dezhou （Shandong）， Urumqi （Xinjiang）， Nujiang （Yunnan）， Gutuo （Inner Mongolia）， and Haiyuan （Ningxia）. Gas chromatography-ion mobility spectrometry （GC-IMS） was used to detect the volatile organic compounds （VOCs） in samples from these geographic origins. VOCs were qualitatively analyzed through dual matching with the National Institute of Standards and Technology （NIST） mass spectral database and the IMS drift time database. Using the Reporter module and Gallery Plot visualization tools within the LAV analytical platform， VOC fingerprint profiles characterizing geographic origins were constructed. A non-targeted analytical strategy was adopted， and 97 VOCs detected via GC-IMS were subjected to principal component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） based on their differential distribution patterns to construct an origin identification model for Foeniculi Fructus from Haiyuan region. Key discriminative markers were screened using variable importance in projection （VIP） values greater than 1. ResultsA total of 97 VOCs were identified， including alcohols， aldehydes， ketones， esters， organic acids， terpenoids， ethers， alkenes， and benzenes. The PLS-DA model， based on VOCs data obtained by GC-IMS， effectively distinguished Foeniculi Fructus in Haiyuan region from those of other origins. During cross-validation， the model achieved a prediction parameter （Q²） of 0.976 and a goodness-of-fit parameter （R²） of 0.936， with no overfitting observed in permutation testing. Twelve key flavor markers with VIP > 1 were identified as characteristic indicators of Haiyuan origin. ConclusionA stable and highly predictive origin identification model for Foeniculi Fructus from Haiyuan was successfully established using GC-IMS technology， PLS-DA， and VIP-based marker screening. This model provides a novel technical strategy for accurately distinguishing Foeniculi Fructus in Haiyuan region from other regional varieties and offers new technical support for its protection as a geo-authentic medicinal material and a nationally designated geographical indication agricultural product in China.

ObjectiveTo explore the association between short-term exposure to atmospheric fine particulate matter （PM_2.5） and ozone （O₃） and systemic inflammatory indicators in patients with pneumonia， and to identify the susceptible populations. MethodsFrom September 2018 to April 2020， data of 1 480 patients admitted for pneumonia was collected from a tertiary hospital in Taiyuan City. Generalized additive models （GAMs） were used to explore the associations between PM_2.5 and O₃ exposure and inflammatory indicators of patients with pneumonia； and to explore the susceptibility factors and susceptible populations to PM_2.5 and O₃ exposures through stratified analyses. ResultsThe short-term exposure to PM_2.5 was associated with changes in peripheral blood C-reation protein （CRP）， erythrocyte sedimentation （ESR）， easinophil （EOS）， neutrophil （NEU） and neutrophil-lymphocyte ratio （NLR） in patients with pneumonia， and there were different degrees of hysteresis effects， with the effect values reaching a maximum at lag03， lag03， lag0， lag03， lag03， respectively， which were 4.13% （95%CI： 0.43%‒7.84%）， 3.10% （95%CI： 0.24%‒5.97%）， 5.27% （95%CI： 3.12%‒7.42%）， 1.85% （95%CI： 0.36%‒3.34%）， and 2.53% （95%CI： 0.53%‒4.74%） for every 10 μg·m^-3 of PM_2.5. The changes in O₃ concentration were associated with the elevation of peripheral blood PCT and ESR in patients with pneumonia， and their effect values all reached the maximum at lag01 d， every 1 μg·m^-3 of O₃ elevation increased by 0.38% （95%CI： 0.04%‒0.73%） and 0.47% （95%CI： 0.19%‒0.76%）， respectively. Stratified analyses showed that the associations of PM_2.5 with peripheral blood CRP， ESR， NEU， and NLR in pneumonia patients were more significant in males， the elderly， and those with onset in the cold season； the associations of O₃ with peripheral blood PCT and ESR in pneumonia patients were more significant in the elderly and those with onset in the warm season， and the peripheral blood CRP and PCT in female patients with pneumonia were more susceptible to the changes of O₃. ConclusionShort-term exposure to atmospheric PM_2.5 and O₃ are positively associated with changes in inflammatory indicators in patients with pneumonia， and the effects of PM_2.5 on patients with pneumonia are more extensive than those of O₃， with a longer lag effect. In addition， elderly patients with pneumonia are more sensitive to air pollution， male patients with pneumonia are more sensitive to PM_2.5， and female patients with pneumonia are more sensitive to O₃. Cold and warm seasons can exacerbate the effects of PM_2.5 and O₃ on inflammatory indicators in patients with pneumonia， respectively， and the patients must be protected well.

Background: Twenty-four-hour urinary free cortisol (UFC) measurement is the initial diagnostic test for Cushing’s syndrome (CS). We compared UFC determination by both direct and extraction immunoassays using Abbott Architect, Siemens Atellica Solution, and Beckman DxI800 with liquid chromatography-tandem mass spectrometry (LC-MS/MS). In addition, we evaluated the value of 24-hr UFC measured by six methods for diagnosing CS. Methods: Residual 24-hr urine samples of 94 CS and 246 non-CS patients were collected.A laboratory-developed LC-MS/MS method was used as reference. UFC was measured by direct assays (D) using Abbott, Siemens, and Beckman platforms and by extraction assays (E) using Siemens and Beckman platforms. Method was compared using Passing–Bablok regression and Bland–Altman plot analyses. Cut-off values for the six assays and corresponding sensitivities and specificities were calculated by ROC analysis. Results: Abbott-D, Beckman-E, Siemens-E, and Siemens-D showed strong correlations with LC-MS/MS (Spearman coefficient r = 0.965, 0.922, 0.922, and 0.897, respectively), while Beckman-D showed weaker correlation (r = 0.755). All immunoassays showed proportionally positive bias. The areas under the curve were 0.975 for Abbott-D, 0.972 for LCMS/MS, 0.966 for Siemens-E, 0.948 for Siemens-D, 0.955 for Beckman-E, and 0.877 for Beckman-D. The cut-off values varied significantly (154.8–1,321.5 nmol/24 hrs). Assay sensitivity and specificity ranged from 76.1% to 93.2% and from 93.0% to 97.1%, respectively. Conclusions Commercially available immunoassays for measuring UFC show different levels of analytical consistency compared to LC-MS/MS. Abbott-D, Siemens-E, and Beckman-E have high diagnostic accuracy for CS.