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The aim of this study was to synthesize and analyze the burden of disease of polycystic ovary syndrome (PCOS) in women of reproductive age globally and in China from 1990 to 2021. The study utilized data from the Global Burden of Disease 2021 database, which contains detailed epidemiologic information from 204 countries and territories. Incidence, prevalence and disability-adjusted life years (DALYs) of polycystic ovary syndrome (PCOS) were assessed. Bayesian age-period cohort modeling was applied to project trends in the burden of disease up to 2035. Global trend: the incidence of polycystic ovary syndrome (PCOS) increased slightly from 58.84/100,000 in 1990 to 60.30/100,000 in 2021. there was a significant increase in prevalence of 29.66% and an increase in disability-adjusted life-years (DALYs) of 28.37%. Trends in China: Despite a slight decrease in prevalence (-1.96%), the prevalence of polycystic ovary syndrome (PCOS) and the number of DALYs in China increased substantially by 86.95% and 86.56%, respectively. Sociodemographic impact: Countries with higher sociodemographic indices tend to face a higher burden of PCOS. Future projections: BAPC model projections suggest that the prevalence of PCOS will continue to increase globally and in China through 2035. These findings highlight the growing public health challenge posed by polycystic ovary syndrome and emphasize the need to strengthen early identification, health management, and lifestyle interventions, especially in areas with high SDI.

Liver fibrosis is a common pathologic stage of the development of liver failure. It has showed that exosomes loaded with therapeutic circRNAs can be manufactured in bulk by exosome secreted cells in vitro, thus enabling personalized treatment. This study aimed to investigate the role of exosome-based delivery of circDIDO1 in liver fibrosis. Levels of genes and proteins were examined by qRT-PCR and Western blot. Cell proliferation, apoptosis, and cell cycle were analyzed by using cell counting kit-8 (CCK-8) assay, EdU assay, and flow cytometry, respectively. The binding between circDIDO1 and miR-141-3p was confirmed by dual-luciferase reporter, RNA pull-down and RIP assays. Exosomes were isolated by ultracentrifugation, and qualified by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA) and Western blot. CircDIDO1 overexpression or miR-141-3p inhibition suppressed the proliferation, reduced pro-fibrotic markers, and induced apoptosis as well as cell cycle arrest in hepatic stellate cells (HSCs) by blocking PTEN/AKT pathway. Mechanistically, circDIDO1 acted as an endogenous sponge for miR-141-3p, further rescue experiments showed that circDIDO1 suppressed HSC activation by targeting miR-141-3p. Extracellular circDIDO1 could be incorporated into exosomes isolated from mesenchymal stem cells (MSCs), and transmitted to HSCs to restrain HSC activation. Clinically, low levels of serum circDIDO1 in exosome were correlated with liver failure, and serum exosomal circDIDO1 had a well diagnostic value for liver fibrosis in liver failure patients. Transfer of circDIDO1 mediated by MSC-isolated exosomes suppressed HSC activation through the miR-141-3p/PTEN/AKT pathway, gaining a new insight into the prevention of liver fibrosis in liver failure patients.

Diabetes is a chronic metabolic disorder that has become a growing global health concern. Waist circumference-glucose index (WyG) is an effective predictor of diabetes; however, its predictive performance in the Japanese population and potential sex-specific differences remain unexplored. This study evaluated the dynamic prediction capability of WyG for diabetes in Japanese individuals, focusing on the differences between sexes. We analyzed data from 15,464 Japanese adults initially without diabetes (7,034 women and 8,430 men). Multivariate Cox regression analysis was used to evaluate the association between WyG and diabetes. Sex-based subgroup analyses were conducted to assess the impact of potential confounders. Sensitivity analysis was conducted to exclude specific populations to test for robustness, and E-values were calculated to evaluate the impact of unmeasured cofounders. Predictive performance was assessed using time-dependent receiver operating characteristic curve analysis. WyG was significantly associated with diabetes in both sexes. This association remained consistent across female subgroups, whereas in males, it was influenced by age and the presence of fatty liver. The area under the curve values ranged from 0.73 to 0.78 in women and 0.73 to 0.77 in men. Sex-specific thresholds (WyG > 8.19 for women, > 8.32 for men) were identified, with greater relative risk elevation for individuals above the threshold. WyG is a reliable predictor of diabetes, with its predictive performance varying across sexes.

Currently, fabric defect detection methods predominantly rely on CNN models. However, due to the inherent limitations of CNNs, such models struggle to capture long-distance dependencies in images and fail to accurately detect complex defect features. While Transformers excel at modeling long-range dependencies, their quadratic computational complexity poses significant challenges. To address these issues, we propose combining CNNs with Transformers and introduce Kolmogorov–Arnold Networks (KANs) to enhance feature extraction capabilities. Specifically, we designed a novel network for fabric defect segmentation, named HKAN, consisting of three components: encoder, bottleneck, and decoder. First, we developed a simple yet effective KANConv Block using KAN convolutions. Next, we replaced the MLP in PoolFormer with KAN, creating a lightweight KANTransformer Block. Finally, we unified the KANConv Block and the KANTransformer Block into a Hybrid KAN Block, which serves as both the encoder and bottleneck of HKAN. Extensive experiments on three fabric datasets demonstrate that HKAN outperforms mainstream semantic segmentation models, achieving superior segmentation performance and delivering prominent results across diverse fabric images.

The inspection of power transmission lines using unmanned aerial vehicles primarily relies on object detection. However, the continuous emergence of new obstacle types necessitates frequent updates to detection models, leading to substantial retraining costs. To address this challenge, we propose a novel framework named IViT, which integrates incremental learning with a hybrid CNN-Transformer architecture for improved identification. We combined knowledge distillation with the elastic response selection distillation strategy to enhance detection performance for old classes and strengthen knowledge retention through star convolutional residual blocks constructed via element-wise multiplication. We designed a separable convolution aggregation block that integrates PConv with an attention mechanism, effectively merging global and local information to improve detection accuracy. Finally, we unified the two modules into a hybrid block. In the static detection task, IViT achieves a mAP of 55.3%, a mAP50 of 83.6%, and a mAP75 of 61.0%. For the incremental detection task, it attains a mAP of 57.8%, a mAP50 of 79.7%, and a mAP75 of 62.3%. Extensive experiments on the transmission line corridor external damage dataset and the INSPLAD dataset demonstrate that IViT exhibits outstanding detection performance compared to mainstream static object detection models and incremental object detection models.

Depression, a leading contributor to the global disease burden, exhibits alarming increases in incidence and prevalence, with pronounced disparities across regions and genders. This study provides the first comprehensive analysis of depression burden from 1990 to 2021, integrating the latest Global Burden of Disease (GBD) 2021 data to identify critical hotspots and policy-relevant trends. Estimated global, regional, and national burden of disease for depression from 1990-2021 by extracting incidence, prevalence, and DALYS from the Global Burden of Disease(GBD) database 2021. From 1990 to 2021, the global incidence of depression surged by 15.6% (3,749-4,334 per 100,000), with Sub-Saharan Africa emerging as an unexpected epicenter. Uganda and The Gambia recorded the highest incidence rates globally (9,644 and 7,624 per 100,000, respectively), likely linked to civil instability and healthcare deficits. Women bore a disproportionate burden, with adolescent females (15-19 years) showing 64% higher incidence than males (5,584 vs. 3,401 per 100,000). High-income regions paradoxically exhibited steeper annual percentage increases (EAPC: 1.0 in North America), suggesting improved detection or escalating stressors. This study highlights urgent priorities: (1) integrating mental health services into primary care in conflict-affected African nations. (2) gender-sensitive interventions targeting adolescent females. (3) global equity in mental health resource allocation.

Introduction: Obesity and constipation are both global problems, but the factors associated with constipation in individuals with obesity are currently understudied. The aim of our study was to explore the factors associated with constipation in people with obesity. Methods: From three cycles of the National Health and Nutrition Examination Survey (NHANES) 2005–2010, data from 14,048 persons aged ≥20 years were collected. Variables included demographics, lifestyle, comorbidities, and dietary data. Multiple logistic regression analysis was used to calculate adjusted prevalence odds ratio (OR) and assess the relationship between different variables and constipation in population with obesity. Results: Using stool consistency definition, multivariate analysis revealed that education ≥12th grade (OR: 0.456; 95% CI: 0.300, 0.694; p = 0.00024), hypertension (OR: 0.505; 95% CI: 0.334, 0.763; p = 0.00119), polypharmacy (OR: 1.669; 95% CI: 1.104, 2.521; p = 0.01507), high cholesterol (OR: 0.400; 95% CI: 0.213, 0.750; p = 0.00430), and high dietary fiber (OR: 0.454; 95% CI: 0.245, 0.841; p = 0.01206) were substantially linked with constipation in the population with obesity. For constipation defined using stool frequency, multivariate regression analysis show constipation in people with obesity had a significant association with the female sex (OR: 2.684; 95% CI: 1.379, 5.223; p = 0.00366 multivariate), Mexican American (OR: 0.142; 95% CI, 0.033, 0.616; p = 0.00914 multivariate), hypertension (OR: 0.569; 95% CI: 0.324, 0.998; p = 0.04916), depression (OR: 2.280; 95% CI: 1.240, 4.195; p = 0.00803), occasional/often milk consumption (OR: 0.473; 95% CI: 0.286, 0.782; p = 0.00356), medium energy (OR: 0.318; 95% CI: 0.118, 0.856; p = 0.02338), polypharmacy (OR: 1.939; 95% CI: 1.115, 3.373; p = 0.01907), and medium moisture (OR: 0.534; 95% CI: 0.285, 0.999; p = 0.04959). In nonobese people, constipation was significantly associated with the female sex and high moisture but not with hypertension and polypharmacy. Conclusion: This study suggests that the population with obesity has many factors that affect constipation such as hypertension, polypharmacy, cholesterol, dietary fiber, depression, and so on, of which hypertension and polypharmacy were significant associated with constipation, regardless of definitions of constipation. Notably, hypertension might be associated with a reduced risk of constipation in people with obesity.

The soft-sensor method of carbon content in fly ash is to predict and calculate the carbon content of boiler fly ash by modeling the distributed control system (DCS) data of thermal power stations. A novel data-driven soft-sensor model that combines data pre-processing, feature engineering and hyperparameter optimization for application in the carbon content of fly ash is presented. First, extract steady-state data by data mining technology. Second, twenty characteristics that may affect the carbon content in fly ash are identified as variables by feature engineering. Third, a LightGBM prediction model that captures the relation between the carbon content in fly ash and various DCS parameters is established and improves the prediction accuracy by the Bayesian optimization (BO) algorithm. Finally, to verify the prediction accuracy of the proposed model, a case study is carried out using the data of a coal-fired boiler in China. Results show that the proposed method yielded the best prediction accuracy and closely approximates the non-linear relationships between variables.

Background Preventive treatment of tuberculosis infection (TBI) is considered a crucial strategy to prevent and control tuberculosis (TB). However, the acceptance and completion rates of preventive therapy for TBI are still far from optimistic. Evidence is mounting that TB knowledge and stigma may have a substantial effect on acceptance of TBI treatment. This study aimed to explore the effect of stigma on the relationship between the level of TB knowledge and acceptance of TBI treatment. Methods 7017 general population were included in the study. We adjusted for the covariates at the individual. Stepwise logistic regression was used to examine the moderating role of TB stigma and also explore the association between TB knowledge and acceptance of TBI treatment. Results The acceptance rate of TBI treatment among the respondents was 84.38% ( n  = 5921). Among respondents, a significant positive correlation between acceptance of TBI treatment and TB knowledge (OR = 1.096,95%CI = 1.073,1.118). Additionally, the association between TB knowledge and acceptance of TBI treatment was found to be moderated by TB stigma. In other words, TB stigma was found to weaken the impact of TB knowledge on acceptance of TBI treatment (OR = 0.994,95%CI = 0.991,0.996). Conclusion The findings of the study indicated that having a high level of awareness about TB can enhance the general population’s acceptability of TBI treatment. TB stigma moderated this association; it weakened the relationship between TB knowledge and individuals’ willingness to accept TBI treatment. To mitigate TB stigma and enhance the intention of individuals with TBI to accept preventive therapy, it is imperative to enhance TB-related health education.

Background Environmental exposure to toxic brominated flame retardants (BFRs) has been confirmed to have detrimental effects on human health. The impact of serum BFRs on hyperlipidemia risk has not been sufficiently examined. Our objective is to identify both the individual and combined effects of serum BFRs on hyperlipidemia and to further investigate the most influential chemicals. Methods We included 7,009 individuals with complete details on 9 types of serum BFRs, hyperlipidemia, and other covariates from the NHANES in 2007–2016. Multivariate logistic regression was conducted to evaluate the individual impact of BFRs exposure on hyperlipidemia risk. We assessed the cumulative effect of BFRs on hyperlipidemia risk through weighted quantile sum (WQS) regression, quantile g-computation (QGC), and Bayesian kernel machine regression models. Results PBDE 28, PBDE 47, PBDE 85, PBDE 99, PBDE 100, PBDE 154, PBDE 209, and PBB153 were found to be positively associated with hyperlipidemia risk. The results of WQS and QGC revealed consistent positive correlation. PBDE209 emerged as the most significant chemicals exerting influence. The restricted cubic splines regression further identified significant dose-response relationship. Conclusion Exposure to individual and combined serum BFRs has been associated with an increased risk of hyperlipidemia. The causal relationship still requires confirmation through large-scale cohort studies.