National Institute for Occupational Health and Poison Control

BackgroundThe extension of survival in patients with pneumoconiosis has led to a shifting mortality spectrum where non-pneumoconiosis causes increasingly act as competing risks. Traditional survival analyses frequently ignore these competing events, potentially biasing prognostic estimates.MethodsWe conducted a retrospective study of 18,064 patients with pneumoconiosis diagnosed between 1960 and 2024 in Jiangsu Province. The Fine–Gray model was used to identify independent predictors of pneumoconiosis-related death while accounting for competing mortality. We compared this evidence with the standard Cox proportional hazards model and established a prognostic nomogram.ResultsThe cumulative incidence of non-pneumoconiosis-related death progressively surpassed that of pneumoconiosis-related death during long-term follow-up. Older age at diagnosis, silicosis, an earlier era of diagnosis, and advanced baseline stage were identified as independent risk factors. The traditional Cox model overestimated risk effects for variables with differential impacts on competing outcomes. Subgroup analyses showed a significant interaction between disease type and stage regarding competing mortality risk. Specifically, patients with Stage II silicosis exhibited higher systemic vulnerability compared with those with coal workers’ pneumoconiosis. The constructed nomogram demonstrated high discrimination and calibration.ConclusionNon-pneumoconiosis-related death constitutes a critical competing risk that substantially affects the long-term survival outcomes of patients with pneumoconiosis. The Fine–Gray model provides accurate risk stratification by correcting for potential overestimation bias. Clinical management strategies must shift from singular pulmonary care to comprehensive health management that addresses comorbidities to improve overall survival outcomes.

Objective: To analyze the risk factors of occupational stress among female coal miners and construct a risk prediction model. Methods: In July 2023, a cluster sampling method was conducted to select all female workers on duty at a large coal mine in Gansu Province during a specific period (totaling 1437 individuals) as research subjects. Occupational stress levels were assessed through questionnaire surveys. A total of 1435 valid questionnaires were collected, with an effective recovery rate of 99.86%. Single-factor analysis was conducted using the rank sum test, while multiple linear regression models was used for multivariate analysis. Deep learning models, including XGBoost and LightGBM, were constructed to analyze factors influencing occupational stress among female coal miners. The performance of the optimal XGBoost model was further explained using the Shapley additive explanatory (SHAP) algorithm. Results: Among the 1435 female coal miners, 89 cases (6.2%) had no occupational stress (score ≤20 points); 930 cases (64.8%) had mild occupational stress (scores 21-30 points); 405 cases (28.2%) had moderate occupational stress (scores 31-40 points); and 11 cases (0.8%) had severe occupational stress (scores 41-50 points). Educational level, marital status, annual personal income, physical exercise, job type, average weekly working time, work form, work position, exposure to noise and noise intensity, heavy labor, coal dust exposure, exposure to occupational hazards and exposure time, injury or infringement during work, and whether to wear protective equipment were influencing factors affecting the occupational stress of female coal miners (P<0.05). For the XGBoost model on the test set, the coefficient of determination (R(2)), mean squared error (MSE), and root mean squared error (RMSE) were 0.994, 0.152, and 0.390, respectively, which were better than LightGBM's 0.933, 1.692, 1.301 and the multiple linear regression model's 0.268, 18.408, and 4.290. The SHAP results showed that work position and daily household chores were the main factors affecting the occupational stress of female coal miners. In addition, physical exercise, work-related injuries or violations, and coal dust exposure also had a certain impact on occupational stress. Conclusion: The occupational stress of coal miners is the result of the combined effects of occupational and lifestyle factors. The XGBoost model demonstrates superior predictive ability and proves the feasibility of the SHAP algorithm in analyzing the occupational stress of female coal miners using machine learning models.

Agricultural and urban wastewater acts as a primary vehicle for transporting human pathogens and antimicrobial resistance genes (ARGs) into aquatic ecosystems. This study presents an integrated framework combining metagenomic sequencing, quantitative PCR (qPCR), microbial source tracking (MST), meta-MLST genotyping, and quantitative microbial risk assessment (QMRA) to systematically assess the occurrence, sources, and public health risks of microbial contaminants in the Sanjiangyuan watershed from 2023 to 2024. Metagenomic profiling identified over 30 high-risk bacterial and viral pathogens, with Salmonella enterica, Campylobacter jejuni, Vibrio cholerae, and Adenovirus exhibiting clear seasonal peaks. Zoonotic pathogens, including Brucella and Leptospira, were exclusively detected in agricultural effluents. Meta-MLST analysis revealed epidemic sequence types of V. cholerae (e.g., ST69) and V. parahaemolyticus (e.g., ST925, ST2141) in agricultural effluents. MST markers (BacHum, BacCow, qC160F) revealed dual contributions of human and livestock fecal pollution, and were significantly correlated with nutrient indicators such as nitrate and COD. Meta-MLST confirmed epidemic sequence types of V. cholerae and V. parahaemolyticus, indicating environmental circulation of clinically relevant strains. Resistome analysis revealed 772 to 972 ARG subtypes across sites, dominated by multidrug, β-lactam, and tetracycline resistance genes. Notably, mobile ARGs were frequently co-localized with virulence factors (VFs) in opportunistic pathogens such as Pseudomonas aeruginosa and Acinetobacter baumannii, indicating elevated mobility and pathogenicity. Seasonal variation showed the highest resistome risk in October, with 55% of ARGs classified as high-risk and associated with mobile genetic elements. Our findings underscore the value of integrating resistome analysis into risk-based frameworks to enhance early warning of microbial threats and health-relevant ARGs. This approach supports targeted mitigation strategies in highland watersheds facing mixed-source pollution.

Objective:This study aims to evaluate the applicability and limitations of the equal energy hypothesis (EEH) in assessing noise-induced hearing loss (NIHL) and investigate the role of the kurtosis parameter (reflecting noise temporal structure) in assessing NIHL, providing a basis for refining EEH theory.Materials and Methods:A systematic review and meta-analysis were conducted to summarize studies related to EEH, systematically collecting its development, validation, and revision suggestions. The weighted mean difference (WMD) and odds ratio (OR) of hearing loss caused by different types of noise were used to evaluate the role of noise kurtosis in the evaluation of NIHL.Results:A total of 57 articles were included, containing experimental and cross-sectional studies. Evidence showed significant limitations in the EEH when assessing non-steady noise, leading to an underestimation of NIHL under equal energy conditions. Animal experiments verified a critical level for assessing NIHL when using the EEH, with ranges of 90–135 dB in chinchillas and 108–120 dB in guinea pigs. EEH could be revised in the frequency domain (spectrum) and time domain (e.g., kurtosis). Meta-analysis showed that in animal experiments, WMD for permanent threshold shifts (PTSs) at 2, 4, and 8 kHz (PTS at 2, 4, and 8 kHz) was 3.30 dB (95% confidence interval [95% CI]: 2.22–4.37); in population surveys, OR for high-frequency NIHL was 1.78 (95% CI: 1.36–2.32). Logistic regression analysis confirmed kurtosis as a risk factor for NIHL (P < 0.001). Kurtosis adjustment for cumulative noise exposure and equivalent sound level adequately improved the accuracy of hearing loss estimation.Conclusion:The EEH’s limitations in assessing NIHL caused by non-steady noise can be remedied by adjusting noise energy metrics using kurtosis. Our findings suggest that EEH could be revised as an “equal energy and equal kurtosis hypothesis.”

Exposure to microplastics (MPs) has emerged as a potential threat to chronic respiratory health. However, the association between MPs exposure and allergic rhinitis (AR) in children remains unclear. We evaluated the association between MP exposure and the prevalence of AR in children. We measured 11 types of MPs in bronchoalveolar lavage fluid (BALF) collected from 207 children aged 1–16 years using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) in 2023. Logistic regression models were employed to evaluate the association between MP concentration and prevalence of AR. Polyamide 66 (PA66), polyethylene (PE), polyvinyl chloride (PVC), and polystyrene (PS) were the predominant types detected, with median concentrations of 2.33, 0.45, 0.38, and 0.08 ​μg/mL in BALF, respectively. Higher concentrations of PA66 were associated with an increased prevalence of AR in all children, with odds ratios (ORs) of 3.00 (95% CI: 1.23, 7.34) after adjusting for potential confounders, indicating a statistically significant association (α ​< ​0.05). Higher concentrations of total MP exposure (Poverall ​= ​0.012, Pnonlinear ​= ​0.310) and PA66 exposure (Poverall ​= ​0.012, Pnonlinear ​= ​0.951) were significantly associated with the prevalence of AR in children aged ≤6 years but not in those aged >6 years. Our findings suggest that exposure to MPs, particularly PA66, may be associated with a higher risk of AR in younger children. Further large-scale, community-based pediatric cohort studies are needed to elucidate the underlying mechanisms.