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.

Background Air pollution poses a significant threat to children's respiratory health. Baotou, a resource-dependent industrial city in northern China dominated by coal, power generation, and steel industries, has limited evidence on the associations of inhalable particulate matter (PM₁₀), fine particulate matter (PM_2.5), and ozone (O₃) with pediatric pneumonia hospitalizations, particularly with respect to season-specific lagged effects. Objective To evaluate the associations of PM₁₀, PM_2.5, and O₃ with pediactric pneumonia hospitalizations and to examine seasonal variations in these associations in the urban districts of Baotou. Methods A time-series study was conducted using daily data on air pollutant concentrations, meteorological factors, and pediatric pneumonia hospitalizations in Baotou from 2017 to 2022. Generalized additive models were applied to examine single-pollutant associations. Cumulative lag models were fitted to assess delayed and cumulative effects, and season-stratified models were constructed to identify season-specific risks. Two-pollutant and multi-pollutant models were further developed to evaluate the robustness of the observed associations. Results The annual mean concentrations of PM_2.5 and PM₁₀ were 38.62 μg·m⁻³ and 85.44 μg·m⁻³, respectively.PM₁₀ and O₃ were the most frequent primary pollutants, accounting for 32.6% and 30.7% of polluted days, respectively. Significant positive associations with lag effects were observed between air pollutants and pediatric pneumonia hospitalizations. In the full-year model, PM₁₀ showed the strongest association at lag 3 (ER=0.77%, 95%CI: 0.50%, 1.04%), whereas PM_2.5 showed the strongest association at lag 1 (ER=1.85%, 95%CI: 1.23%, 2.48%). Seasonal stratification analyses revealed significant associations for PM_2.5 in both the cold season (lag 1, ER=1.57%, 95%CI: 0.83%, 2.31%) and the warm season (lag 7, ER=1.84%, 95%CI: 0.35%, 3.35%). PM₁₀ showed a significant association at lag 7 in the warm season (ER=0.70%, 95%CI: 0.27%, 1.13%). O₃ exhibited significant acute effects only in the warm season, with same-day exposure (lag 0) associated with an ER of 3.79% (95%CI: 1.92%, 5.69%). The estimated risk contribution ranking was PM_2.5 > PM₁₀ for the full year and the cold season, and O₃ > PM_2.5 > PM₁₀ for the warm season. Cumulative lag analysis revealed that PM₁₀ and PM_2.5 exhibited the strongest cumulative associations at lag07 in the full-year and cold-season models, with ERs of 2.20% (95%CI: 1.68%, 2.72%) and 4.20% (95%CI: 3.01%, 5.40%), respectively, and the associations intensified as the lag period extended. In contrast, O₃ in the warm season showed the strongest cumulative association at lag02, with an ER of 5.37% (95%CI: 3.13%, 7.66%), followed by a rapid decline. O₃ in the cold season showed a significant cumulative association at lag07, with an ER of 5.12% (95%CI: 0.00%, 10.49%). In two-pollutant and multi-pollutant models, the associations of PM₁₀ and O₃ remained stable after adjustment for other pollutants. Conclusion From 2017 to 2022, ambient particulate matter and ozone are significantly associated with pediatric pneumonia hospitalizations in the urban districts of Baotou, with evident lagged effects and seasonal variations. Differentiated prevention strategies should be developed according to seasonal pollutant profiles, with priority given to PM_2.5 control in the cold season and O₃ control in the warm season.

Objective:To investigate the effect of ERMAP on imiquimod(IMQ)-induced psoriasis-like skin inflammation in mice and its related mechanism.Methods:The experimental mice were divided into 3 groups:Sham group,WT group and ERMAP-/-group,with 9 mice in each group.The Sham group was smeared with Vaseline,and the WT group and ERMAP-/-group were smeared with IMQ to induce psoriatic dermatitis.The severity of IMQ-induced psoriasis lesions in mice were evaluated according to the psoria-sis area and severity index(PASI)and the HE staining pathology score.The expressions of F4/80 and Ki67 in mouse skin lesions were observed by immunofluorescence staining.The relative expressions of IL-1β,IL-6,IFN-γ and iNOS in skin lesions were detected by qRT-PCR.Flow cytometry was used to detect the proliferation and activation of T cells and the proportion of macrophages in spleen.Results:In the IMQ-induced mouse model of psoriasis-like dermatitis,the skin lesions of ERMAP gene knock out mice showed more severe squamous accumulation and skin bulge,more inflammatory cells aggregation and cytokine production,and the proportion of immune cells in the spleen of mice increased compared with the WT group,and the proportion of M1 macrophages increased.Conclu-sion:ERMAP deficiency aggravates IMQ-induced psoriasis-like skin inflammation in mice by enhancing immune response.

Based on the national policy direction of improving the individual account crediting method for employees'basic medical insurance,it designed three individual account reform scenarios,considering the individual account reform and fund sustainability considerations.Based on the 2022 data of the basic medical insurance for employees in Shandong Province,simulations were conducted to predict the overall scale of individual account reform funds released and individual subtractions under each scenario after the full implementation of the individual account reform in 2024,as well as to conduct an early warning analysis of public opinion risks for key populations.The results indicated that Plan I had the largest total amount of released funds from individual accounts(16.55 billion yuan),but the highest per capita monthly reduction in credits(52.97 yuan),thereby increasing the risk of generating social public sentiment.Plan III minimized the resistance to the implementation of the individual account reform but has the smallest total amount of released funds from individual accounts(13.484 billion yuan).Plan II achieved areas such as the provincial government and oil fields have the highest public sentiment risk due to the highest reduction ratio for retirees(>90%);11 coordinated areas have a percentage of retiree subtraction of more than 80%.It provided empirical evidence for policymakers on different plans for the reform of individual accounts in the employee basic medical insurance and their impacts,and suggested that high-risk areas should be given special attention,adopting refined and differentiated policy measures to effectively alleviate social public sentiment pressure and ensure the smooth progress of the reform.

Objective:To investigate the predictive value of multimodal magnetic resonance imaging (MRI) techniques in assessing symptom remission of post-traumatic stress disorder (PTSD) of bereaved parents who lost their only child.Methods:In this prospective study, 34 parents with PTSD resulting from the loss of the only child were followed-up for 2 years. Based on the PTSD diagnostic status at the end of the follow-up, participants were divided into the remission group and the persistent group.R 3.6.1 and SPSS 20.0 software were used for statistical analysis.Baseline clinical data and neuroimaging findings were compared between the two groups. Logistic regression and LASSO regression analyses were used to identify independent predictors of PTSD symptom remission. The predictive performance of these factors was evaluated by receiver operating characteristic (ROC) curve analysis.Results:Initial screening with univariate Logistic regression and LASSO regression revealed that regional homogeneity (ReHo) in the left middle temporal gyrus, the combined predictive value based on ReHo, and the integrated predictive value combining gray matter volume (GMV) and ReHo (GMV-ReHo predictor) were significant factors influencing symptom remission (all P<0.05). Multivariate Logistic regression further demonstrated that the GMV-ReHo predictor retained independent predictive significance ( P<0.05), with ROC curve analysis showing an area under the curve (AUC) of 0.979 (95% CI=0.935-0.996, P<0.001) for its ability to predict PTSD remission. Notably, a combined model incorporating both the scores of the clinician administered PTSD scale (CAPS) and the GMV-ReHo predictor achieved an enhanced predictive performance, yielding an AUC of 0.984 (95% CI=0.952-0.998, P<0.001). Conclusion:The GMV-ReHo predictor effectively identifies symptom remission in PTSD resulting from the loss of the only child.

The paper reports a case of Fabry disease in a child with non-singular nocturnal enuresis as the first symptom. The boy developed unexplained nocturnal enuresis with frequent daytime urination since the age of 6. Fabry disease was detected and diagnosed by chance through high-risk screening. The activity of α-galactosidase A by dry blood spot was 1.97 μmol·L -1·h -1 , and there was c.640-801G>A mutation in GLA gene. Urine routine, urinary microprotein and renal function were normal. However, there were mulberry bodies found in urine deposition microscopy, suggesting the presence of kidney injury. This case suggests that enuresis can be the first symptom of Fabry disease, and mulberry bodies can be seen in the urine at the early stage of the disease.

Objective:To develop CNN-Dinov2, a deep learning-based automatic classification model for Gram-stained images, enabling rapid diagnosis of three prevalent Gram-negative bacilli in bloodstream infections: Escherichia coli ( E.coli), Klebsiella pneumoniae ( K.pneumoniae), and Pseudomonas aeruginosa ( P.aeruginosa). Methods:This evaluation study analyzed 1 425 Gram-stained microscopic images from patients with bloodstream infections at Houjie Hospital, in Dongguan City, collected between January 2023 and January 2024. The images, all positive for blood culture and identified as target strains, were categorized into Escherichia coli (419 images), Klebsiella pneumoniae (411 images), Pseudomonas aeruginosa (413 images), and other Gram-negative bacilli (182 images). They were randomly split into a training set (1 141 images), a validation set (141 images), and a test set (143 images) in an 8∶1∶1 ratio. A hybrid CNN-Dinov2 model was developed by integrating ResNet′s local feature extraction with Dinov2′s global pre-trained features, followed by a fully connected layer. The model was optimized by inputting the preprocessed images and adjusting parameters through loss calculation and backpropagation. AlexNet, Dinov2, and ResNet18 served as control models. The models′ classification performance was assessed using accuracy, precision, weighted F1 score, and recall rate, derived from the confusion matrix. The PR curve and AP value further evaluated each model′s classification capability across the four image categories. Results:The CNN-Dinov2 model achieved a training accuracy of 99.74%, a validation accuracy of 98.12%, and a validation loss of 0.070 6, demonstrating robust generalization without overfitting. Validation metrics revealed superior performance with an accuracy of 98.60%, precision of 98.65%, a weighted F1 score of 98.60%, and a recall rate of 98.60%, outperforming other models. The confusion matrix confirmed its strong classification capability, with the highest sum of diagonal values for identifying four types of bacteria. The macro average precision (AP) values under the precision-recall (PR) curves were all 1, indicating excellent discrimination across all categories. Overall, the CNN-Dinov2 model exhibited the best performance among the four models evaluated.Conclusion:This study successfully developed CNN-Dinov2, an automated classification model for Gram staining images. It offers valuable support for the rapid diagnosis of bloodstream infections caused by Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, demonstrating practical utility.

Objective:To investigate the effect of ERMAP on imiquimod(IMQ)-induced psoriasis-like skin inflammation in mice and its related mechanism.Methods:The experimental mice were divided into 3 groups:Sham group,WT group and ERMAP-/-group,with 9 mice in each group.The Sham group was smeared with Vaseline,and the WT group and ERMAP-/-group were smeared with IMQ to induce psoriatic dermatitis.The severity of IMQ-induced psoriasis lesions in mice were evaluated according to the psoria-sis area and severity index(PASI)and the HE staining pathology score.The expressions of F4/80 and Ki67 in mouse skin lesions were observed by immunofluorescence staining.The relative expressions of IL-1β,IL-6,IFN-γ and iNOS in skin lesions were detected by qRT-PCR.Flow cytometry was used to detect the proliferation and activation of T cells and the proportion of macrophages in spleen.Results:In the IMQ-induced mouse model of psoriasis-like dermatitis,the skin lesions of ERMAP gene knock out mice showed more severe squamous accumulation and skin bulge,more inflammatory cells aggregation and cytokine production,and the proportion of immune cells in the spleen of mice increased compared with the WT group,and the proportion of M1 macrophages increased.Conclu-sion:ERMAP deficiency aggravates IMQ-induced psoriasis-like skin inflammation in mice by enhancing immune response.

Objective:To investigate the predictive value of multimodal magnetic resonance imaging (MRI) techniques in assessing symptom remission of post-traumatic stress disorder (PTSD) of bereaved parents who lost their only child.Methods:In this prospective study, 34 parents with PTSD resulting from the loss of the only child were followed-up for 2 years. Based on the PTSD diagnostic status at the end of the follow-up, participants were divided into the remission group and the persistent group.R 3.6.1 and SPSS 20.0 software were used for statistical analysis.Baseline clinical data and neuroimaging findings were compared between the two groups. Logistic regression and LASSO regression analyses were used to identify independent predictors of PTSD symptom remission. The predictive performance of these factors was evaluated by receiver operating characteristic (ROC) curve analysis.Results:Initial screening with univariate Logistic regression and LASSO regression revealed that regional homogeneity (ReHo) in the left middle temporal gyrus, the combined predictive value based on ReHo, and the integrated predictive value combining gray matter volume (GMV) and ReHo (GMV-ReHo predictor) were significant factors influencing symptom remission (all P<0.05). Multivariate Logistic regression further demonstrated that the GMV-ReHo predictor retained independent predictive significance ( P<0.05), with ROC curve analysis showing an area under the curve (AUC) of 0.979 (95% CI=0.935-0.996, P<0.001) for its ability to predict PTSD remission. Notably, a combined model incorporating both the scores of the clinician administered PTSD scale (CAPS) and the GMV-ReHo predictor achieved an enhanced predictive performance, yielding an AUC of 0.984 (95% CI=0.952-0.998, P<0.001). Conclusion:The GMV-ReHo predictor effectively identifies symptom remission in PTSD resulting from the loss of the only child.