Impact of reactive iron in coal mine dust on oxidant generation and epithelial lung cell viability

Fine particulate matter potentiates Th17-cell pathogenicity in experimental autoimmune uveitis via ferroptosis

The effect of fine particulate matter (PM2.5) on the development of uveitis remains unclear. Therefore, this study was designed to investigate the role of PM2.5 in experimental autoimmune uveitis (EAU) and its potential mechanism. Our results showed that PM2.5 could exacerbate the activity of EAU, as evidenced by severer clinical and pathological changes, correlated with elevated Th17 cells frequency and IL-17A expression. Proteomic analysis revealed ferroptosis was the most significant pathway. In vivo, the levels of Fe2+, ROS, lipid ROS, and malondialdehyde, as well as the expression of TFRC, HMOX1, FTH1, and FTL1 in CD4+ T cells were increased, while GSH/GSSG ratio and the expression of ACSL1 and GPX4 were decreased after PM2.5 exposure. In vitro, the expression of TFRC and HMOX1 were increased, while the expression FTH1, FTL1, ACSL1, and GPX4 were decreased after PM2.5 exposure. Ferrostatin-1 effectively alleviated PM2.5-induced intraocular inflammation and suppressed the frequency of Th17 cells. These results suggest that PM2.5 could aggravate intraocular inflammation and immune response in EAU mice through ferroptosis. Ferroptosis could be a potential marker for the prevention and treatment of uveitis.

Research progress on the pathogenesis and prediction of pneumoconiosis among coal miners

Pneumoconiosis is the most common occupational disease among coal miners, which is a lung disease caused by long-term inhalation of coal dust and retention in the lungs. The early stage of this disease is highly insidious, and pulmonary fibrosis may occur in the middle and late stages, leading to an increase in patient pain index and mortality rate. Currently, there is a lack of effective treatment methods. The pathogenesis of pneumoconiosis is complex and has many influencing factors. Although the characteristics of coal dust have been considered the main cause of different mechanisms of pneumoconiosis, the effects of coal dust composition, particle size and shape, and coal dust concentration on the pathogenesis of pneumoconiosis have not been systematically elucidated. Meanwhile, considering the irreversibility of pneumoconiosis progression, early prediction for pneumoconiosis patients is particularly important. However, there is no early prediction standard for pneumoconiosis among coal miners. This review summarizes the relevant research on the pathogenesis and prediction of pneumoconiosis in coal miners in recent years. Firstly, the pathogenesis of coal worker pneumoconiosis and silicosis was discussed, and the impact of coal dust characteristics on pneumoconiosis was analyzed. Then, the early diagnostic methods for pneumoconiosis have been systematically introduced, with a focus on image collaborative computer-aided diagnosis analysis and biomarker detection. Finally, the challenge of early screening technology for miners with pneumoconiosis was proposed.

Prevalence of nano-sized coal mine dust in North and Central Appalachian coal mines - Insights from SEM-EDS imaging

The increasing prevalence of coal mine dust-related lung diseases in coal miners calls for urgent and meticulous scrutiny of airborne respirable coal mine dust (RCMD), specifically focusing on particles at the nano-level. This necessity is driven by expanding research, including the insights revealed in this paper, that establish the presence and significantly increased toxicity of nano-sized coal dust particles in contrast to their larger counterparts. This study presents an incontrovertible visual proof of these tiny particulates in samples collected from underground mines, utilizing advanced techniques such as scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). The intricate elemental composition of nano-sized coal dust identified through EDS analysis reveals the presence of elements such as silica and iron, which are known to contribute to lung pathologies when inhaled over prolonged periods. The outcomes of the statistical analyses reveal significant relationships between particle size and elemental composition, highlighting that smaller particles tend to have higher carbon content, while larger particles exhibit increased concentrations of elements like silica and aluminum. These analyses underscore the complex interactions within nano-sized coal dust, providing critical insights into their behavior, transport, and health impacts. The nano-sized coal dust could invade the alveoli, carrying these toxic elements from where they are impossible to exhale. The revelation of nano-sized coal dust's existence and the associated health hazards necessitate their incorporation into the regulatory framework governing the coal mining industry. This study lays the groundwork for heightened protective measures for miners, urging the invention of state-of-the-art sampling instruments, comprehensive physicochemical profiling of RCMD nanoparticles, and the pursuit of groundbreaking remedies to neutralize their toxic impact. These findings advocate for a paradigm shift in how the coal mining industry views and handles particulate matter, proposing a re-evaluation of occupational health standards and a call to action for protecting coal miners worldwide.

Associations of coal mine dust exposure with arterial stiffness and atherosclerotic cardiovascular disease risk in chinese coal miners

OBJECTIVE

Whether coal mine dust exposure increases cardiovascular diseases (CVDs) risk was rarely explored. Our objective was to examine the association between coal mine dust exposure and cardiovascular risk.

METHODS

We estimated cumulative coal mine dust exposure (CDE) for 1327 coal miners by combining data on workplace dust concentrations and work history. We used brachial-ankle pulse wave velocity (baPWV, a representative indicator of arterial stiffness) and ten-year atherosclerotic cardiovascular disease (ASCVD) risk to assess potential CVD risk, exploring their associations with CDE.

RESULTS

Positive dose-response relationships of CDE with baPWV and ten-year ASCVD risk were observed after adjusting for covariates. Specifically, each 1 standard deviation (SD) increase in CDE was related to a 0.27 m/s (95% CI: 0.21, 0.34) increase in baPWV and a 1.29 (95% CI: 1.14, 1.46) elevation in OR (odds ratio) of risk of abnormal baPWV. Moreover, each 1 SD increase in CDE was associated with a 0.74% (95% CI: 0.63%, 0.85%) increase in scores of ten-year ASCVD and a 1.91 (95% CI: 1.62, 2.26) increase in OR of risk of ten-year ASCVD. When compared with groups unexposed to coal mine dust, significant increase in the risk of arterial stiffness and ten-year ASCVD in the highest CDE groups were detected.

CONCLUSION

The study suggested that cumulative exposure to coal mine dust was associated with elevated arterial stiffness and ten-year ASCVD risk in a dose-response manner. These findings contribute valuable insights for cardiovascular risk associated with coal mine dust.

Machine Learning Demonstrates Dominance of Physical Characteristics over Particle Composition in Coal Dust Toxicity

Mine dust has been linked to the development of pneumoconiotic diseases such as silicosis and coal workers' pneumoconiosis. Currently, it is understood that the physicochemical and mineralogical characteristics drive the toxic nature of dust particles; however, it remains unclear which parameter(s) account for the differential toxicity of coal dust. This study aims to address this issue by demonstrating the use of the partial least squares regression (PLSR) machine learning approach to compare the influence of D50 sub 10 μm coal particle characteristics against markers of cellular damage. The resulting analysis of 72 particle characteristics against cytotoxicity and lipid peroxidation reflects the power of PLSR as a tool to elucidate complex particle-cell relationships. By comparing the relative influence of each characteristic within the model, the results reflect that physical characteristics such as shape and particle roughness may have a greater impact on cytotoxicity and lipid peroxidation than composition-based parameters. These results present the first multivariate assessment of a broad-spectrum data set of coal dust characteristics using latent structures to assess the relative influence of particle characteristics on cellular damage.

Effect of Sophorolipid Adsorption on the Coal Microstructure: Experimental and Wettability Mathematical Model Discussion

Green biosurfactants are emerging as a promising area of research. However, there is a limited focus on the adsorption and wetting characteristics of biosurfactants on coal dust. This study explores the effects of sophorolipid (SL) biosurfactants on the microstructure and wettability of different coalification degree coal. The microstructure parameters of SL adsorbed on coal dust were measured using a surface tensiometer, contact angle analyzer, and particle size analyzer. The results indicate that SL has the lowest critical surface tension, leading to a 9.25° decrease in the contact angle for low-rank bituminous coal (YZ-LRBC). Furthermore, SL significantly altered the particle size distribution of lignite (NM-LC) and YZ-LRBC. The pore size structure of SL-infiltrated coal dust was quantified using a specific surface area analyzer, revealing a decrease in the specific surface area and an increase in the average pore size. The infrared analysis demonstrated that SL permeation significantly increased the percentage of hydrophilic functional groups (hydroxyl structures) while reducing the hydrophobic functional groups (aliphatic hydrocarbon and aromatic structure). Based on the measured microstructure parameters, a regression equation for contact angle was established: [contact angle (°)] = 73.800 - 0.860 × [D10 (nm)] + 4.280 × [specific surface area (m2/g)]. Notably, the characteristic particle size D10 had a significant negative effect on the contact angle, while the specific surface area had a significant positive effect. These findings provide a theoretical foundation for the application of biosurfactants in water injection to reduce dust and improve the wetting efficiency.

The impact of coal mine dust characteristics on pathways to respiratory harm: investigating the pneumoconiotic potency of coals

Exposure to dust from the mining environment has historically resulted in epidemic levels of mortality and morbidity from pneumoconiotic diseases such as silicosis, coal workers' pneumoconiosis (CWP), and asbestosis. Studies have shown that CWP remains a critical issue at collieries across the globe, with some countries facing resurgent patterns of the disease and additional pathologies from long-term exposure. Compliance measures to reduce dust exposure rely primarily on the assumption that all "fine" particles are equally toxic irrespective of source or chemical composition. For several ore types, but more specifically coal, such an assumption is not practical due to the complex and highly variable nature of the material. Additionally, several studies have identified possible mechanisms of pathogenesis from the minerals and deleterious metals in coal. The purpose of this review was to provide a reassessment of the perspectives and strategies used to evaluate the pneumoconiotic potency of coal mine dust. Emphasis is on the physicochemical characteristics of coal mine dust such as mineralogy/mineral chemistry, particle shape, size, specific surface area, and free surface area-all of which have been highlighted as contributing factors to the expression of pro-inflammatory responses in the lung. The review also highlights the potential opportunity for more holistic risk characterisation strategies for coal mine dust, which consider the mineralogical and physicochemical aspects of the dust as variables relevant to the current proposed mechanisms for CWP pathogenesis.

The dependence of particle size on cell toxicity for modern mining dust

Progressive massive pulmonary fibrosis among coal miners has unexpectedly increased. It would likely due to the greater generation of smaller rock and coal particles produced by powerful equipment used in modern mines. There is limited understanding of the relationship between micro- or nanoparticles with pulmonary toxicity. This study aims to determine whether the size and chemical characteristics of typical coal-mining dust contribute to cellular toxicity. Size range, surface features, morphology, and elemental composition of coal and rock dust from modern mines were characterized. Human macrophages and bronchial tracheal epithelial cells were exposed to mining dust of three sub- micrometer and micrometer size ranges at varying concentrations, then assessed for cell viability and inflammatory cytokine expression. Coal had smaller hydrodynamic size (180–3000 nm) compared to rock (495–2160 nm) in their separated size fractions, more hydrophobicity, less surface charge, and consisted of more known toxic trace elements (Si, Pt, Fe, Al, Co). Larger particle size had a negative association with in-vitro toxicity in macrophages (p < 0.05). Fine particle fraction, approximately 200 nm for coal and 500 nm for rock particles, explicitly induced stronger inflammatory reactions than their coarser counterparts. Future work will study additional toxicity endpoints to further elucidate the molecular mechanism causing pulmonary toxicity and determine a dose–response curve.

Predicting the risk of nodular thyroid disease in coal miners based on different machine learning models

Background

Nodular thyroid disease is by far the most common thyroid disease and is closely associated with the development of thyroid cancer. Coal miners with chronic coal dust exposure are at higher risk of developing nodular thyroid disease. There are few studies that use machine learning models to predict the occurrence of nodular thyroid disease in coal miners. The aim of this study was to predict the high risk of nodular thyroid disease in coal miners based on five different Machine learning (ML) models.

Methods

This is a retrospective clinical study in which 1,708 coal miners who were examined at the Huaihe Energy Occupational Disease Control Hospital in Anhui Province in April 2021 were selected and their clinical physical examination data, including general information, laboratory tests and imaging findings, were collected. A synthetic minority oversampling technique (SMOTE) was used for sample balancing, and the data set was randomly split into a training and Test dataset in a ratio of 8:2. Lasso regression and correlation heat map were used to screen the predictors of the models, and five ML models, including Extreme Gradient Augmentation (XGBoost), Logistic Classification (LR), Gaussian Parsimonious Bayesian Classification (GNB), Neural Network Classification (MLP), and Complementary Parsimonious Bayesian Classification (CNB) for their predictive efficacy, and the model with the highest AUC was selected as the optimal model for predicting the occurrence of nodular thyroid disease in coal miners.

Result

Lasso regression analysis showed Age, H-DLC, HCT, MCH, PLT, and GGT as predictor variables for the ML models; in addition, heat maps showed no significant correlation between the six variables. In the prediction of nodular thyroid disease, the AUC results of the five ML models, XGBoost (0.892), LR (0.577), GNB (0.603), MLP (0.601), and CNB (0.543), with the XGBoost model having the largest AUC, the model can be applied in clinical practice.

Conclusion

In this research, all five ML models were found to predict the risk of nodular thyroid disease in coal miners, with the XGBoost model having the best overall predictive performance. The model can assist clinicians in quickly and accurately predicting the occurrence of nodular thyroid disease in coal miners, and in adopting individualized clinical prevention and treatment strategies.

Association of coal mine dust lung disease with Nodular thyroid disease in coal miners: A retrospective observational study in China

Background

Coal dust is a major risk factor for the occupational health of coal miners, and underground workers with coal mine dust lung disease (Coal miners with coal mine dust lung disease (CMDLD) may have a higher risk of developing Nodular thyroid disease (NTD). The aim of this study was to investigate the relationship between coal mine dust lung disease and the development of Nodular thyroid disease in coal miners.

Methods

This was a clinical retrospective observational study that included 955 male coal miners from 31 different coal mining companies in Huainan, Anhui Province, China, who were examined in April 2021 at the Huainan Occupational Disease Prevention and Control Hospital to collect all their clinical physical examination data, including general conditions, laboratory test indices and imaging indices. Based on the presence or absence of Nodular thyroid disease, 429 cases with Nodular thyroid disease were classified as the diseased group and 526 cases without Nodular thyroid disease were classified as the control group. Logistic regression was used to analyse the correlation between the occurrence of Nodular thyroid disease in coal miners, and further single- and multi-factor logistic regression was used to screen the risk exposure factors for Nodular thyroid disease in coal miners.

Results

Age, coal mine dust lung disease (CMDLD), red blood cells (RBC), mean red blood cell volume (MCV), albumin (ALB), albumin/globulin (A/G), indirect bilirubin (IBIL), globulin (GLOB), total bilirubin (TBil) and myeloperoxidase (MPO) were associated with the development of Nodular thyroid disease in coal miners (p < 0.05) The results of univariate and multifactorial logistic regression analysis showed that CMDLD (OR:4.5,95%CI:2.79-7.51) had the highest OR and CMDLD was the strongest independent risk exposure factor for the development of Nodular thyroid disease in coal miners.

Conclusions

There is a strong correlation between coal mine dust lung disease and Nodular thyroid disease in underground coal miners, and clinicians need to be highly aware of the high risk of NTD in coal miners with CMDLD and adopt individualized clinical prevention strategies.

Assessment of Genetic Damage in Coal Miners of Punjab, Pakistan

Coal miners are continuously exposed to coal mine dust and airborne particulate that act as a potential risk to their health. The present study evaluates the DNA damage in coal miners using the Buccal Micronucleus Cytome (BMCyt) assay. The samples of the blood and buccal epithelial cells of 40 coal miners and 20 control subjects were taken from coal mines of Pail and Padhrar, Pakistan, to establish buccal anomaly frequencies of metal levels in the blood. Besides this, work history and duration hours were also analyzed. Results revealed that micronucleus frequencies positively correlated with the metal concentrations in the miner's blood. The change in the extent of nuclear damage per unit change in the year was 0.170 for micronuclei; however, with addition in each year of working experience, nuclear buds and broken egged nuclei increased by 0.316 and 0.194 units, where each year increases karyolysis by 0.349 units and karyorrhexis by 0.308 units, respectively. An increase in work hours and working years was positively correlated with cytogenetic damage. Nuclear damage in coal miners due to occupational exposure is obvious and increases with increasing work experience. Hence, the Buccal Micronucleus Cytome assay has proved to be an effective cytogenetic biomonitoring tool for assessing genetic and nuclear damage in coal miners.

Laboratory investigation of pollutant emissions and PM2.5 toxicity of underground coal fires

Widespread underground coal fires (UCFs) release large amounts of pollutants, thus leading to air pollution and health impacts. However, this topic has not been widely investigated, especially regarding the potential health hazards. We quantified the pollutant emissions and analyzed the physicochemical properties of UCF PM_2.5 in a laboratory study of coal smoldering under a simulated UCF background. The emission factors of CO₂, CO, and PM_2.5 were 2489 ± 35, 122 ± 9, 12.90 ± 1.79 g/kg, respectively. UCF PM_2.5 are carbonaceous particles with varied morphology and complex composition, including heavy metals, silica and polycyclic aromatic hydrocarbons (PAHs). The main PAHs components were those with 2-4 rings. Benzoapyrene (BaP) and indeno[1,2, 3-cd]pyrene (IcdP) were important contributors to the carcinogenesis of these PAHs. We quantitatively evaluate the toxicity of inhaled UCF PM_2.5 using a nasal inhalation exposure system. The target organs of UCF PM_2.5 are lungs, liver, and kidneys. UCF PM_2.5 presented an enriched chemical composition and induced inflammation and oxidative stress, which together mediated multiple organ injury. Long-term PM_2.5 metabolism is the main cause of persistent toxicity, which might lead to long-term chronic diseases. Therefore, local authorities should recognize the importance and effects of UCF emissions, especially PM_2.5, to establish control and mitigation measures.

Review of Lung Particle Overload, Rat Lung Cancer, and the Conclusions of the Edinburgh Expert Panel—It's Time to Revisit Cancer Hazard Classifications for Titanium Dioxide and Carbon Black

Chronic inhalation of titanium dioxide or carbon black by rats at concentrations which overload lung particle clearance can result in lung cancer. Based on this rat lung response, IARC, NIOSH, and ECHA classified titanium dioxide, and IARC classified carbon black, as potential human carcinogens. These classifications have been questioned based on an extensive data base demonstrating: the rat lung cancer occurred only under conditions of extreme lung particle overload; the lung cancer response in rats has not been seen in other animal species; and studies in titanium dioxide and carbon black exposed human populations have not shown an increased incidence of cancer. In 2019 an international panel of science and regulatory experts was convened to document the state of the science on lung particle overload and rat lung cancer after exposure to poorly soluble low toxicity particles. Regarding hazard identification, the expert panel concluded, in the absence of supporting data from other species, lung particle overload-associated rat lung cancer does not imply a cancer hazard for humans. Regarding high to low dose extrapolation, the expert panel concluded rat lung tumors occurring only under conditions of lung particle overload are not relevant to humans exposed under non-overloading conditions. The conclusions of the Edinburgh Expert Panel directly conflict with IARC, ECHA and NIOSH's extrapolation of lung particle overload associated rat lung cancer to hazard for humans. The hazard classifications for titanium dioxide and carbon black inhalation should be assessed considering the state-of-the-science on lung particle overload and rat lung cancer.