Search Results (32)

In recent years, the Mineral Liberation Analyser (MLA) has played a pivotal role in analysing respirable and inhalable ambient air samples collected on filters from both underground coal and metalliferous mines. Leveraging backscattered electron (BSE) image analysis and X-ray mineral identification, the MLA offers automated quantitative mineral characterization. The escalating prevalence and severity of mine dust lung diseases, particularly among young miners, have reignited interest in comprehensively understanding the dust’s characterization, encompassing mineralogy, particle size, and shape. Merely measuring total respirable dust exposure and its duration based on gravimetrically determined weight is no longer deemed sufficient in addressing the evolving landscape of occupational health challenges in mining environments. Since the publication of previous studies, efforts have been dedicated to refining the Mineral Liberation Analyser (MLA) methodology for respirable dust sampling. This refinement, discussed in detail in this paper, encompasses various enhancements, such as the implementation of data checks to identify carbon contamination, backscattered electron (BSE) drift, and the misclassification of X-ray spectra. Additionally, an examination of sampling efficiency led to the exploration of using smaller samples as an alternative to the time-intensive analysis of entire filters. Furthermore, this paper presents a reanalysis of paired filter sample sets previously reported using the Sarver Group Methodology. These samples are subjected to analysis using the Mineral Liberation Analyser, providing a more detailed illustration of the outputs derived from the updated methodology and compared to previously published MLA data. Full article

The United States has witnessed a concerning surge in the incidence of diseases like Coal Workers’ Pneumoconiosis (CWP), despite numerous efforts aimed at prevention. This study delves into the realm of respiratory health by investigating the deposition of dust particles within the respiratory tract and lungs. By analyzing particles of varying sizes, shapes, velocities, and aerodynamic diameters, we aim to gain a comprehensive understanding of their impact on deposition patterns. This insight could potentially drive changes in dust exposure protocols within mining environments and improve monitoring practices. The interplay of several critical factors, including particle characteristics and an individual’s breathing patterns, plays a pivotal role in determining whether particles settle in the lungs or are exhaled. This paper provides a comprehensive literature review on Respirable Coal Mine Dust (RCMD), with a specific focus on examining particle deposition across different regions of the airway system and lungs. Additionally, we explore the utility of Computational Fluid Dynamics (CFD) in simulating particle behavior within the respiratory system. Predicting the precise behavior of dust particles within the respiratory airway poses a significant challenge. However, through numerical simulations, we aspire to enhance our understanding of strategies to mitigate total lung deposition by comprehensively modeling particle interactions within the respiratory system. Full article

Respirable coal mine dust (RCMD) exposure is one of the utmost health hazards to the mining community causing various health issues, including coal worker pneumoconiosis (CWP). Considering multiple potential sources of RCMD having different physicochemical properties within the same mine suggests a wide range of health impacts that have not yet been studied extensively. In this work, we investigate the toxicity of lab-created RCMD based on different sources: coal seam, rock dust, host floor, and host roof collected from the same mine. Comparative samples obtained from several mines situated in various geographic locations were also assessed. This work quantifies metal leaching in simulated lung fluids and correlates dissolution with in vitro immune responses. Here, dissolution experiments were conducted using two simulated lung fluids; Gamble solution (GS) and artificial lysosomal fluid (ALF). In vitro studies were performed using a lung epithelial cell line (A549) to investigate their immune responses and cell viability. Si and Al are the most dissolved metals, among several other trace metals, such as Fe, Sr, Ba, Pb, etc. RCMD from the coal seam and the rock dust showed the least metal leaching, while the floor and roof samples dissolved the most. Results from in vitro studies showed a prominent effect on cell viability for floor and roof dust samples suggesting high toxicity. Full article

Objective: This study aims to summarize the characteristics of diagnosed pneumoconiosis and pneumoconiosis death in the Hubei Province of China, between the years 1949 and 2019, and provide clues for the scientific prevention of pneumoconiosis. Methods: We recruited 23,069 pneumoconiosis cases in Hubei Province, China, from 1949 to 2019. Basic information and occupational surveillance information were obtained from the Hubei Occupational Diseases and Health Risk Factors Information Surveillance System. Results: The annually diagnosed pneumoconiosis cases showed an overall increasing trend from 1949 to 2019 in Hubei Province. The major types of pneumoconiosis were coal workers’ pneumoconiosis (CWP, 49.91%) and silicosis (43.39%). Pneumoconiosis cases were mainly engaged in mining (75.32%) and manufacturing (12.72%), and were distributed in Huangshi (35.48%), Yichang (16.16%), and Jingzhou (7.97%). CWP (47.50%) and silicosis (44.65%) accounted for most of the deaths. Conclusions: The number of pneumoconiosis cases and deaths in Hubei increased in the period of 1949 to 2019. Silicosis and CWP contributed to the predominant types of pneumoconiosis. Prevention and control measures should continue to be taken to reduce the morbidity and mortality of pneumoconiosis. Full article

Background: The purpose of this study is to see if exfoliative pulmonary airway pathology in cancer-free coal workers’ pneumoconiosis (CWP) can be used as a biomarker for predicting pulmonary morbidity. Methods: We investigated persistent metaplastic changes in bronchoscopic washing cytology and differential cell counts in bronchoalveolar lavages (BAL) in 97 miners with CWP and 80 miners without CWP as the control. Clinicopathological parameters were examined including pulmonary function tests and the presence of progressive massive fibrosis. Results: When compared to the control group, severe alveolitis, severe goblet cell hyperplasia (GCH), severe hyperplastic epithelial change, and severe squamous metaplasia were the distinguishing biomarkers in CWP. Multivariate analysis revealed that severe alveolitis and severe GCH, along with miner duration and current smoker, were independent predictors of pulmonary mortality. The survival analysis revealed a significantly different survival rate between the three groups: no evidence of severe alveolitis and severe GCH, presence of severe alveolitis or severe GCH but not both, and both severe alveolitis and severe GCH. Conclusions: The severities of alveolitis and goblet cell hyperplasia in the bronchoscopic study are independent prognostic factors for CWP. A pathologic grading system based on these two parameters could be used in the stratification and clinical management of CWP patients. Full article

Globally, coal remains one of the natural resources that provide power to the world. Thousands of people are involved in coal collection, processing, and transportation. Particulate coal dust is produced during these processes, which can crush the lung structure of workers and cause pneumoconiosis. There is no automated system for detecting and monitoring diseases in coal miners, except for specialist radiologists. This paper proposes ensemble learning techniques for detecting pneumoconiosis disease in chest X-ray radiographs (CXRs) using multiple deep learning models. Three ensemble learning techniques (simple averaging, multi-weighted averaging, and majority voting (MVOT)) were proposed to investigate performances using randomised cross-folds and leave-one-out cross-validations datasets. Five statistical measurements were used to compare the outcomes of the three investigations on the proposed integrated approach with state-of-the-art approaches from the literature for the same dataset. In the second investigation, the statistical combination was marginally enhanced in the ensemble of multi-weighted averaging on a robust model, CheXNet. However, in the third investigation, the same model elevated accuracies from 87.80 to 90.2%. The investigated results helped us identify a robust deep learning model and ensemble framework that outperformed others, achieving an accuracy of 91.50% in the automated detection of pneumoconiosis. Full article

Pneumoconiosis is a group of occupational lung diseases induced by mineral dust inhalation and subsequent lung tissue reactions. It can eventually cause irreparable lung damage, as well as gradual and permanent physical impairments. It has affected millions of workers in hazardous industries throughout the world, and it is a leading cause of occupational death. It is difficult to diagnose early pneumoconiosis because of the low sensitivity of chest radiographs, the wide variation in interpretation between and among readers, and the scarcity of B-readers, which all add to the difficulty in diagnosing these occupational illnesses. In recent years, deep machine learning algorithms have been extremely successful at classifying and localising abnormality of medical images. In this study, we proposed an ensemble learning approach to improve pneumoconiosis detection in chest X-rays (CXRs) using nine machine learning classifiers and multi-dimensional deep features extracted using CheXNet-121 architecture. There were eight evaluation metrics utilised for each high-level feature set of the associated cross-validation datasets in order to compare the ensemble performance and state-of-the-art techniques from the literature that used the same cross-validation datasets. It is observed that integrated ensemble learning exhibits promising results (92.68% accuracy, 85.66% Matthews correlation coefficient (MCC), and 0.9302 area under the precision–recall (PR) curve), compared to individual CheXNet-121 and other state-of-the-art techniques. Finally, Grad-CAM was used to visualise the learned behaviour of individual dense blocks within CheXNet-121 and their ensembles into three-color channels of CXRs. We compared the Grad-CAM-indicated ROI to the ground-truth ROI using the intersection of the union (IOU) and average-precision (AP) values for each classifier and their ensemble. Through the visualisation of the Grad-CAM within the blue channel, the average IOU passed more than 90% of the pneumoconiosis detection in chest radiographs. Full article

As a serious occupational pulmonary fibrosis disease, pneumoconiosis still lacks effective biomarkers. Previous studies suggest that pneumoconiosis may affect the body’s lipid metabolism. The purpose of this study was to explore lipidomics profiles and lipid metabolite biomarkers in the serum of coal workers’ pneumoconiosis (CWP) by a population case-control study. A total of 150 CWP cases and 120 healthy controls from Beijing, China were included. Blood lipids were detected in serum biochemistry. Lipidomics was performed in serum samples for high-throughput detection of lipophilic metabolites. Serum high density lipoprotein cholesterol (HDL-C) decreased significantly in CWP cases. Lipidomics data found 131 differential lipid metabolites between the CWP case and control groups. Further, the top eight most important differential lipid metabolites were screened. They all belonged to differential metabolites of CWP at different stages. However, adjusting for potential confounding factors, only three of them were significantly related to CWP, including acylhexosylceramide (AHEXCER 43:5), diacylglycerol (DG 34:8) and dimethyl-phosphatidylethanolamine (DMPE 36:0|DMPE 18:0_18:0), of which good sensitivity and specificity were proven. The present study demonstrated that lipidomics profiles could change significantly in the serum of CWP patients and that the lipid metabolites represented by AHEXCER, DG and DMPE may be good biomarkers of CWP. Full article

Coal mine workers are continuously exposed to respirable coal mine dust (RCMD) in workplaces, causing severe lung diseases. RCMD characteristics and their relations with dust toxicity need further research to understand the adverse exposure effects to RCMD. The geographic clustering of coal workers’ pneumoconiosis (CWP) suggests that RCMD in the Appalachian region may exhibit more toxicity than other geographic regions such as the Rocky Mountains. This study investigates the RCMD characteristics and toxicity based on geographic location. Dissolution experiments in simulated lung fluids (SLFs) and in vitro responses were conducted to determine the toxicity level of samples collected from five mines in the Rocky Mountains and Appalachian regions. Dust characteristics were investigated using Fourier-transform infrared spectroscopy, scanning electron microscopy, the BET method, total microwave digestion, X-ray diffraction, and X-ray photoelectron spectroscopy. Inductively coupled plasma mass spectrometry was conducted to determine the concentration of metals dissolved in the SLFs. Finer particle sizes and higher mineral and elemental contents were found in samples from the Appalachian regions. Si, Al, Fe, Cu, Sr, and Pb were found in dissolution experiments, but no trends were found indicating higher dissolutions in the Appalachian region. In vitro studies indicated a proinflammatory response in epithelial and macrophage cells, suggesting their possible participation in pneumoconiosis and lung diseases development. Full article

Exposure monitoring and health surveillance of coal mine workers has been improved in Australia since coal workers’ pneumoconiosis was reidentified in 2015 in Queensland. Regional variations in the prevalence of mine dust lung disease have been observed, prompting a more detailed look into the size, shape, and mineralogical classes of the dust that workers are being exposed to. This study collected respirable samples of ambient air from three operating coal mines in Queensland and New South Wales for characterization analysis using the Mineral Liberation Analyser (MLA), a type of scanning electron microscope (SEM) that uses a combination of the backscattered electron (BSE) image and characteristic X-rays for mineral identification. This research identified 25 different minerals present in the coal samples with varying particle size distributions for the overall samples and the individual mineralogies. While Mine 8 was very consistent in mineralogy with a high carbon content, Mine 6 and 7 were found to differ more significantly by location within the mine. Full article

Pneumoconiosis remains one of the most serious global occupational diseases. However, effective treatments are lacking, and early detection is crucial for disease prevention. This study aimed to explore serum biomarkers of occupational coal workers’ pneumoconiosis (CWP) by high-throughput metabolomics, combining with machine learning strategy for precision screening. A case–control study was conducted in Beijing, China, involving 150 pneumoconiosis patients with different stages and 120 healthy controls. Metabolomics found a total of 68 differential metabolites between the CWP group and the control group. Then, potential biomarkers of CWP were screened from these differential metabolites by three machine learning methods. The four most important differential metabolites were identified as benzamide, terazosin, propylparaben and N-methyl-2-pyrrolidone. However, after adjusting for the influence of confounding factors, including age, smoking, drinking and chronic diseases, only one metabolite, propylparaben, was significantly correlated with CWP. The more severe CWP was, the higher the content of propylparaben in serum. Moreover, the receiver operating characteristic curve (ROC) of propylparaben showed good sensitivity and specificity as a biomarker of CWP. Therefore, it was demonstrated that the serum metabolite profiles in CWP patients changed significantly and that the serum metabolites represented by propylparaben were good biomarkers of CWP. Full article

Coal worker’s pneumoconiosis (CWP) is an occupationally induced progressive fibrotic lung disease. This irreversible but preventable disease currently affects millions across the world, mainly in countries with developed coal mining industries. Here, we report a pilot study that explores the sputum microbiome as a potential non-invasive bacterial biomarker of CWP status. Sputum samples were collected from 35 former and active coal miners diagnosed with CWP and 35 healthy controls. Sequencing of bacterial 16S rRNA genes was used to study the taxonomic composition of the respiratory microbiome. There was no difference in alpha diversity between CWP and controls. The structure of bacterial communities in sputum samples (β diversity) differed significantly between cases and controls (pseudo-F = 3.61; p = 0.004). A significant increase in the abundance of Streptococcus (25.12 ± 11.37 vs. 16.85 ± 11.35%; p = 0.0003) was detected in samples from CWP subjects as compared to controls. The increased representation of Streptococcus in sputum from CWP patients was associated only with the presence of occupational pulmonary fibrosis, but did not depend on age, and did not differ between former and current miners. The study shows, for the first time, that the sputum microbiota of CWP subjects differs from that of controls. The results of our present exploratory study warrant further investigations on a larger cohort. Full article

Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers’ pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers’ survival rate. The development of the CAD systems will reduce risk in the workplace and improve the quality of chest screening for CWP diseases. This systematic literature review (SLR) amis to categorise and summarise the feature extraction and detection approaches of computer-based analysis in CWP using chest X-ray radiographs (CXR). We conducted the SLR method through 11 databases that focus on science, engineering, medicine, health, and clinical studies. The proposed SLR identified and compared 40 articles from the last 5 decades, covering three main categories of computer-based CWP detection: classical handcrafted features-based image analysis, traditional machine learning, and deep learning-based methods. Limitations of this review and future improvement of the review are also discussed. Full article

Coal miners with coal workers’ pneumoconiosis (CWP, J60 according to ICD-10) were previously found to have a significantly higher risk of lung carcinoma compared to the general male population. The presented study aimed to analyze the (i) incidence of lung carcinoma in miners, (ii) histopathological findings in cohorts with and without CWP, and (iii) effect of smoking cessation on the histopathological profile. Analyzed cohorts consisted of miners with (n = 3476) and without (n = 6687) CWP. Data on personal and working history obtained from the medical records were combined with information on lung cancer from the Czech Oncological Register and histopathological findings. Statistical analysis was performed using non-parametric tests and the incidence risk ratio at the significance level of 5%. In 1992–2015, 180 miners (2.7%) without CWP and 169 (4.9%) with CWP, respectively, were diagnosed with lung carcinoma. The risk of lung cancer in miners with CWP was 1.82 (95% CI: 1.48–2.25) times higher than in those without CWP. Squamous cell carcinoma (37%) was the most common histopathological type, followed by adenocarcinoma (22%) and small cell carcinoma (21%). A statistically significant difference between the cohorts (p = 0.003) was found in the histopathological subtypes, with the incidence of small cell carcinoma being 2 times higher in miners without CWP than in those with CWP. Only a few individuals with lung carcinoma were non-smokers. The incidence of small cell carcinoma, which is strongly associated with smoking, is significantly higher in miners without CWP. Smoking constitutes the most important risk factor for developing lung carcinoma even in that cohort. However, CWP remains a very important risk factor. Full article

Pneumoconiosis has become one of the biggest threats to the occupational health and life safety of mining workers in China. The number of pneumoconiosis cases has continued to rise in recent years. The main task of occupational health development is to study the pathogenesis of pneumoconiosis and to develop mine dust prevention and control technology. Therefore, this paper summarizes the research progress of coal worker pneumoconiosis and dust prevention and control in mines. Firstly, the research progress of coal worker pneumoconiosis is analyzed from the aspects of pathogenesis, animal model and pathological changes of coal worker pneumoconiosis. Then, the existing basic theory and technology of dust prevention are described, including ventilation and dust removal, spray and dust suppression, and chemical dust suppression methods. Finally, based on the dust removal theory of wet shotcrete, the progress of shotcrete dust control technology and equipment used for shotcrete is summarized from the aspects of shotcrete technology process and shotcrete materials. At the same time, in view of the shortcomings of the existing research, the next research prospect is given in the pathogenesis of pneumoconiosis, intelligent dust prevention, jet spraying dust removal and so on. This paper provides theoretical support for realizing the separate source and efficient treatment of mine dust control and helps to improve the clean production level of mine, control and prevent pneumoconiosis. Full article