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.

AimPneumoconiosis is a group of chronic occupational lung diseases caused by inhalation of mineral dust particles leading to inflammation and fibrosis. This study is to characterize the disease burden through disability-adjusted life years (DALYs), incidence, prevalence and mortality rates from year 1991 to 2023 across the nation.MethodsGlobal Burden of Disease database was utilized to obtain incidence, prevalence, DALYs and death rates of pneumoconiosis for all sex and age from all U.S states from 1991 to 2023. The change is calculated in percent.ResultsNationally, the total pneumoconiosis mortality fell by 51.08%. DALYs to pneumoconiosis likewise dropped by 47.12%. Annual incidence of pneumoconiosis decreased more modestly, by 23.62%. In contrast, the prevalence of pneumoconiosis nearly doubled over this period, rising 69.34%. This substantial increase in living cases aligns with global trends (∼66% increase in pneumoconiosis cases from 1990-2017). There were large geographic differences in pneumoconiosis burden. In 1991, the state with the highest pneumoconiosis mortality was Pennsylvania (n = 394), which also led in total DALYs (n = 6812). The most populous states dominated non-fatal metrics - California had the highest incidence (n = 1,663) and prevalence (n = 1,493) in 1991. Despite overall decline, Pennsylvania still incurred the most pneumoconiosis deaths (n = 65) and DALYs (n = 1,160), while California remained highest in incidence (n = 1,479) and prevalence (n = 3,039). Broad declines in pneumoconiosis burden were observed across most of the United States from 1991 to 2023. Historically high-burden mining regions in Appalachia (e.g. Pennsylvania, West Virginia, Kentucky, Virginia) showed substantial absolute declines in mortality and incidence, yet many of these states remain among the highest in pneumoconiosis burden in 2023.ConclusionOverall, the geographic pattern of pneumoconiosis burden in 2023 still highlights legacy industrial exposures - with Appalachian coal mining states and several large-population states (California, Ohio, Florida, New York) accounting for the greatest burdens - even though the incidence, DALYs and death rates have improved over the years. Increased prevalence likely reflects improved survival hence accumulation of chronic cases. These findings emphasize that regulatory actions and workplace safety improvements have been effective but not uniformly sufficient. Continued surveillance is necessary to identify and address emerging occupational risks. Sustained prevention efforts, targeted interventions in high-burden regions, and enhanced long-term management strategies are essential to achieving further reductions and advancing worker respiratory health nationwide.This abstract is funded by: None

RATIONALE:To date, there has been no research on the co-occurrence of pneumoconiosis with varying degrees of resting hypoxemia at home and abroad. An increasing number of studies show that many patients with chronic respiratory diseases do not meet the criteria for hypoxemia at rest; they may only have exercise-induced hypoxemia (EID). However, there have been no reports on the co-occurrence of pneumoconiosis with EID either domestically or internationally. Therefore, this study aims to understand the prevalence of different types of hypoxemia in patients with pneumoconiosis and to identify the associated risk factors through a cross-sectional survey.METHODS:A survey of hospitalized pneumoconiotics in two comprehensive Grade IIIA hospitals in Kunming, was conducted from October 2022 to December 2023,. The pneumoconiosis patients were divided into non-resting hypoxemia group and resting hypoxemia group.The resting hypoxemia group was divided into mild resting hypoxemia group and moderate and severe resting hypoxemia group.Patients with pneumoconiosis who had PaO2 ≥60mmHg/SpO2≥90% and no contraindication were selected for 6-minute walk test(6MWT), They were divided into no exercise hypoxemia group and exercise hypoxemia group, according to the lowest PaO2 or SpO2 during 6 MWT after correction.Multivariate Logistic regression analysis was used to screen the risk factors of resting hypoxemia and exercise-induced hypoxemia.RESULTS:(1)After adjusting for age and altitude, there were 108 patients with pneumoconiosis complicated with resting hypoxemia, the prevalence rate was 43.03%,37 patients with mild resting hypoxemia, the prevalence rate was 14.74%, 71 patients with moderate-to-severe resting hypoxemia, the prevalence rate was 28.29%. 6MWT was performed in 180 patients, and 63 patients with pneumoconiosis complicated by exercise hypoxemia had a prevalence of 35.00%.(2)Multifactorial logistic regression analysis showed that mMRC classification(OR=7.540,95%CI 1.955−14.376, P<0.001)and pneumoconiosis stage(OR=4.537,95%CI 2.089−9.851, P<0.001)were independent risk factors for the complication of resting hypoxemia in patients with pneumoconiosis;mMRC classification(OR=3.708,95%CI 2.044−6.725, P<0.001), pneumoconiosis stage(OR=2.893,95%CI 1.417−5.906, P=0.004), and FEV1/FVC(OR=0.984,95%CI 0.974−0.994, P=0.002)were independent risk factors for the complication of exercise hypoxemia in patients with pneumoconiosis.CONCLUSIONS:Pneumoconiosis has a high prevalence of complicated hypoxemia. The mMRC grade and pneumoconiosis stage are the independent risk factors of resting hypoxemia in pneumoconiosis patients, while mMRC grade, pneumoconiosis stage and FEV1/FVC are the independent risk factors of exercise-induced hypoxia in pneumoconiosis patients.

Accurate prediction of pneumoconiosis is essential for individualized early prevention and treatment. However, the different manifestations and high heterogeneity among radiologists make it difficult to diagnose and stage pneumoconiosis accurately. Here, based on DR images collected from two centers, a novel deep learning model, namely Multi-scale Lesion-aware Attention Networks (MLANet), is proposed for diagnosis of pneumoconiosis, staging of pneumoconiosis, and screening of stage I pneumoconiosis. A series of indicators including area under the receiver operating characteristic curve, accuracy, recall, precision, and F1 score were used to comprehensively evaluate the performance of the model. The results show that the MLANet model can effectively improve the consistency and efficiency of pneumoconiosis diagnosis. The accuracy of the MLANet model for pneumoconiosis diagnosis on the internal test set, external validation set, and prospective test set reached 97.87%, 98.03%, and 95.40%, respectively, which was close to the level of qualified radiologists. Moreover, the model can effectively screen stage I pneumoconiosis with an accuracy of 97.16%, a recall of 98.25, a precision of 93.42%, and an F1 score of 95.59%, respectively. The built model performs better than the other four classification models. It is expected to be applied in clinical work to realize the automated diagnosis of pneumoconiosis digital chest radiographs, which is of great significance for individualized early prevention and treatment.

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.

<p>Pneumoconiosis is characterized by pulmonary fibrosis. The activation of fibroblasts play an important role in the pathological development of pulmonary fibrosis. Chest CT, as a conventional examination to diagnose pulmonary fibrosis of pneumoconiosis, cannot evaluate the fibrosis activity. The application value of FAPI in pneumoconiosis is unclear. This study aimed to clarify the feasibility of FAPI PETCT in noninvasively monitoring the activity evolution of pulmonary fibrosis in pneumoconiosis and the anti-fibrotic treatment. A preliminary clinical study was conducted on 6 pneumoconiosis patients and 4 healthy control individuals, correlation analysis was performed between the FAPI uptake in pulmonary fibrosis areas and the pulmonary diffusing function. Sprague Dawley rat experiments were performed on three groups concluding pneumoconiosis model, pirfenidone treated, and normal control groups. FAPI and FDG PETCT, histopathologic, and hematological analysis were assessed monthly from modeling until 6 months. FAPI uptake in fibrotic areas was found in the pneumoconiosis.FAPI PET/CT imaging holds promise for the early identification of pulmonary fibrosis activity and monitoring its evolution in pneumoconiosis, offering a precise clinical opportunity for targeted anti-fibrotic treatment.</p>