Background: Globally, silicosis accounts for 90% of all pneumoconiosis cases and is a serious public health issue. It is characterized by progressive inflammation and irreversible pulmonary fibrosis. A comprehensive analysis at temporal, spatial and population levels with the most updated data from GBD 2019 is provided in this study to estimate the disease burden of silicosis from 1990 to 2019 and make predictions to 2029. Methods: We delineated silicosis data on incidence, prevalence, and disability-adjusted life years (DALYs) as well as age-standardized rates (ASRs) across 30 years from GBD 2019. Joinpoint regression analysis was employed to detect temporal changes and estimate annual percentage change (APC) of each trend segment. Measures were stratified by time, location, age, and sociodemographic index (SDI). Back propagation artificial neural network (BP-ANN) model was applied to elaborate ASR trends from 1990 to 2019 and projections to the next 10 years. Results: Globally, silicosis incident, prevalent cases, and DALYs increased by 64.6%, 91.4%, and 20.8%, respectively. However, all the corresponding ASRs showed overall downward trends with an estimated average APC (AAPC) of -0.5(-0.7 to -0.3), -0.2(-0.5 to 0.0), and − 2.0(-2.2 to -1.8), respectively. Middle and high-middle SDI regions carried the heaviest disease burden. The highest disease burden of silicosis was mainly transferred to the older from 1990 to 2019. The trend of ASRs demonstrated a rapid decline between 2005 and 2019, followed by a continuous decline until 2029. Conclusion: Though disease burden of silicosis has been on a decline in general from 1990 to 2019, which shows a promising prospect but cannot be ignored. We should pay more attention to implementing preventive tactics and improving the life quality of present sufferers.

Background: Silicosis is one of the most common occupational pulmonary fibrosis caused by respirable silica-based particle exposure, with no ideal drugs at present. Metformin, a commonly used biguanide antidiabetic agent, could activate AMP-activated protein kinase (AMPK) to exert its pharmacological action. Therefore, we sought to investigate the role of metformin in silica-induced lung fibrosis. Methods: The anti-fibrotic role of metformin was assessed in 50 mg/kg silica-induced lung fibrosis model. Silicon dioxide (SiO₂)-stimulated lung epithelial cells/macrophages and transforming growth factor-beta 1 (TGF-β1)-induced differentiated lung fibroblasts were used for in vitro models. Results: At the concentration of 300 mg/kg in the mouse model, metformin significantly reduced lung inflammation and fibrosis in SiO₂-instilled mice at the early and late fibrotic stages. Besides, metformin (range 2–10 mM) reversed SiO₂-induced cell toxicity, oxidative stress, and epithelial-mesenchymal transition process in epithelial cells (A549 and HBE), inhibited inflammation response in macrophages (THP-1), and alleviated TGF-β1-stimulated fibroblast activation in lung fibroblasts (MRC-5) via an AMPK-dependent pathway. Conclusions: In this study, we identified that metformin might be a potential drug for silicosis treatment.

Background: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. Objective: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. Methods: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. Results: Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. Conclusions: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.

Background: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. Objective: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. Methods: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants’ clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. Results: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). Conclusions: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study’s hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.

Coal Workers’ Pneumoconiosis (CWP) is the primary occupational disease in China. However, information about the definite prevalence of CWP is only partially available. The aims of our study were to assess the prevalence characteristics of CWP in a state-owned coal mine, evaluate the effects of control measures and develop further preventive strategies for CWP. The total study population included 495 cases who were diagnosed with CWP from the construction of this coal mine to the end of October 2014. Individuals’ information, including duration of dust exposure, job titles, age as first diagnosis, stages of CWP, CWP progress, complications with pulmonary tuberculosis, death and others were collected and analyzed. The results showed that 71.11% of 495 CWP cases were stage I and 90.71% were involved in tunneling or coal mining. The mean dust exposure period in CWP patients was 26.7 years, the mean latent period was 29.3 years and the mean diagnosed age was 50.3 years old. The proportion of CWP diagnosed after ending dust exposure were remarkably increased with the time passing. Among the CWP cases, 36 (7.27%) were complicated with pulmonary tuberculosis. The mortality of patients with stage III was the highest (60.71%) (p < 0.0001). Our data obviously show that more strict policies to protect coal miners are needed to be implemented in China, especially for tunneling and mining workers.

To investigate the mortality probability, life expectancy of coal workers’ pneumoconiosis (CWP), and related factors of life expectancy, a total of 495 patients with CWP were diagnosed and reported from 1963 to 2014 in a state-owned mine in the east of China. The life table method, log rank method, and Cox regression model were used for survival analysis. 95 out of 495 CWP died during this period. The mortality rate was 19.19%. The average life span was 12.1 (0.0–33.2) years and average death age was 57.4 (33.0–83.0) years. The life table indicated that overall mortality probability increased with the age of CWP patients. Life expectancy of CWP patients was prolonged to 4.3, 1.4, 1.2, and 1.4 years without death caused by pneumoconiosis, tuberculosis, lung cancer, and pulmonary heart disease respectively. The survival curve of CWP patients without pulmonary tuberculosis was higher (average 37.9 years) than patients with pulmonary tuberculosis (average 34.1 years). There was significant difference observed (χ² = 6.196, p < 0.05). Three risk factors that include initial dust exposure year, age of onset, and first diagnostic stage were put into the Cox regression model for evaluation. The data indicated that prevention and treatment of CWP complication is important to improve patients’ survival rates.