Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach.

BACKGROUND: Pneumoconiosis has a significant impact on the quality of patient survival due to its difficult staging diagnosis and poor prognosis. This study aimed to develop a computer-aided diagnostic system for the screening and staging of pneumoconiosis based on a multi-stage joint deep learning approach using X-ray chest radiographs of pneumoconiosis patients. METHODS: In this study, a total of 498 medical chest radiographs were obtained from the Department of Radiology of West China Fourth Hospital. The dataset was randomly divided into a training set and a test set at a ratio of 4:1. Following histogram equalization for image enhancement, the images were segmented using the U-Net model, and staging was predicted using a convolutional neural network classification model. We first used Efficient-Net for multi-classification staging diagnosis, but the results showed that stage I/II of pneumoconiosis was difficult to diagnose. Therefore, based on clinical practice we continued to improve the model by using the Res-Net 34 Multi-stage joint method. RESULTS: Of the 498 cases collected, the classification model using the Efficient-Net achieved an accuracy of 83% with a Quadratic Weighted Kappa (QWK) score of 0.889. The classification model using the multi-stage joint approach of Res-Net 34 achieved an accuracy of 89% with an area under the curve (AUC) of 0.98 and a high QWK score of 0.94. CONCLUSIONS: In this study, the diagnostic accuracy of pneumoconiosis staging was significantly improved by an innovative combined multi-stage approach, which provided a reference for clinical application and pneumoconiosis screening.

[EQ-5D-3L based quality of life for patients of pneumoconiosis combined with tuberculosis and its influencing factors].

Objective: To understand the health-related quality of life for patients of pneumoconiosis combined with tuberculosis and its main influencing factors. Methods: This was a cross-sectional study, and 951 patients of pneumoconiosis combined with tuberculosis from the pneumoconiosis survey in 27 provinces and autonomous regions in China from December 2017 to December 2021 were selected for the study. The nonparametric Mann-Whitney test and the Kruskal-Wallis H test were used to compare the health utility values, and multiple linear regression was used for multifactor analysis. AMOS 24.0 was used to establish a structural equation modeling. Results: The mean age of 951 patients of pneumoconiosis combined with tuberculosis was (59.3±12.4) years. The main types were silicosis combined with tuberculosis (62.2%, 591/951) and coal-worker's pneumoconiosis combined with tuberculosis (34.9%, 332/951), and other type pneumoconiosis-combined tuberculosis was 2.9% (28/951). The proportion of patients with stage Ⅰ, Ⅱ, Ⅲ, and unstaged clinical diagnosis was 27.4% (261/951), 26.6% (253/951), 32.5% (309/951) and 13.5% (128/951), respectively. 63.3% (602/951) of study participants suffered from other chronic diseases, and the percentage of patients combined the number of chronic diseases with 1, 2, and more than 3 respectively were 24.1% (229/951), 16.3% (155/951) and 22.9% (218/951). The median and quartiles of health utility values and the mean±standard deviation of self-rating scores of patients of pneumoconiosis combined with tuberculosis were 0.562 (0.482, 0.766) and (53.7±18.4), respectively, which were lower than patients of pneumoconiosis without tuberculosis (Z=-11.29, P<0.001; t=8.97, P<0.01). The health utility values and self-rating scores for patients of pneumoconiosis combined with tuberculosis were significantly different between urban and rural areas (Z= -2.22, P=0.027; t=4.85, P<0.01). Pain/discomfort was the most frequently reported problem in the five-dimensional distribution of problems, followed by daily activities and anxiety/depression, and the difference in the percentage reported by anxiety/depression between urban and rural areas was significant (χ(2)=30.28, P<0.01). The results of multiple linear regression showed that the survey area, body mass index, education level, age, employment status, annual personal income, stage of pneumoconiosis, number of multi-morbidities, hemoptysis, acute exacerbation of symptoms in two-week, social support and minimum living standard were the main influences on the health utility values of the patients of pneumoconiosis combined with tuberculosis (P<0.05). The results of structural equation model showed that economic security and health status directly affected the health-related quality of life among patients of pneumoconiosis combined with tuberculosis and played a chain-mediating effect in the influence of socioeconomic status on the health-related quality of life among patients of pneumoconiosis combined with tuberculosis. Conclusion: Health-related quality of life was poorer in patients of pneumoconiosis with tuberculosis, with pain and discomfort and anxiety/depression problems being more pronounced, and economic status and health status played multiple mediating roles in the influence of general socio-demographic characteristics on quality of life in pneumoconiosis.

[Utilization of outpatient service of pneumoconiosis patients in Chongqing and its influencing factors].

Objective: To understand the utilization and characteristics of outpatient services for pneumoconiosis patients within two weeks in Chongqing, and analyze the influencing factors, so as to provide reference for relevant policy making. Methods: From October 2020 to October 2022, 1771 pneumoconiosis patients who met the inclusion criteria were selected by multi-stage stratified random cluster sampling. A questionnaire survey was conducted on their basic situation, utilization of outpatient services within two weeks, treatment for pneumoconiosis-related symptoms, and selection of medical service institutions using χ(2)-test and logistic regression analysis. Results: All the 1771 pneumoconiosis patients were male, with the average age of (56.1±10.19) years old. In the pneumoconiosis patients were treated in outpatient department within 2 weeks.40.0% (204/510) of aged 41~50 years Rural patients accounted for 87.8% (448/510) ; 65.1% (332/510) of silicosis patients, 37.5% (191/510) of stage II patients, 75.1% (383/510) of patients did not continue to engage in dust work after diagnosis of pneumoconiosis, and 57.1% (291/510) of patients never had work-related injury insurance at work. The outpatient rate within two weeks of pneumoconiosis related assistance and subsistence allowance was 17.6% (90/510) and 12.5% (64/510), respectively. The average self-health score of the patients was (52.9±16.2). 28.2% of the patients had purchased work-related injury insurance; Among the 1204 patients who received the treatment within two weeks, 42.2% were in the outpatient department, 20.7% were in the inpatient department, and 36.9% were self-buyers. There was a significant difference between the different treatment methods of the patients (χ(2)=27.53, P<0.05). There was a significant difference in patients from different residence choosing to visit different medical institutions (χ(2)=13.97, P<0.05). The stage of pneumoconiosis, presence of complications, presence of work injury insurance, self-health score, and whether he/she has been hospitalized in the past year are the important factors affecting the outpatient treatment of pneumoconiosis patients. Conclusion: The utilization of outpatient service of pneumoconiosis patients is influenced by demographic sociology, social support and disease characteristics. The quality of occupational disease medical service in primary health institutions should be strengthened so that pneumoconiosis patients can get convenient and effective treatment. Establish a more perfect social security support system to reduce the disease burden of pneumoconiosis patients.

[Analysis of the epidemiological traits of occupational pneumoconiosis in Qinhuangdao between 1961 and 2020].

Objective: To study the prevalence of occupational pneumoconiosis in Qinhuangdao from 1961 to 2020 and offer a foundation for developing occupational pneumoconiosis prevention and control methods. Methods: In December 2020, the data of occupational pneumoconiosis cases diagnosed by medical institutions with occupational disease diagnosis qualifications in Qinhuangdao City from 1961 to 2020 were collected Anova or kruskal-Walls tests and chi-square tests were used for inter-group comparisons of continuous and categorical variables, and LSD tests or Tamhane T2 tests were used for multiple comparisons. Results: Between 1961 and 2020, 384 cases of pneumoconiosis were documented in Qinhuangdao, of which 382 (99.5%) patients were men and 2 (0.5%) were women. The average dust service duration is 15 (9, 25) years, with a minimum duration of 0.5 years and a maximum duration of 49 years; Cases were primarily distributed in Qinglong Manchu Autonomous County (187 cases, 48.7%) and the Haigang district (160 cases, 41.7%) ; Type of pneumoconiosis was silicosis (340 cases, 88.5%), mainly 273 cases (71.1%) of stage I, 88 cases (22.9%) of stage II, and 23 cases (6.0% of stage III) ; Cases of Phase II and III and with short lengths of service are mainly concentrated in medium-sized, small, private limited liability companies and collective enterprises. Rrock work (166 cases, 43.2%), and loading kiln workers (42 cases, 10.9%) were the main types. Conclusion: Because the distribution of pneumoconiosis cases in Qinhuangdao city is concentrated and the length of service is decreasing, it is important to enhance the oversight of important area, businesses, industries, and job categories in line with the growth of the region's mineral resources.

Pneumoconiosis among artisanal miners in Chunya district, Mbeya, Tanzania.

This case series sheds light on the pulmonary diseases afflicting artisanal gold miners in Chunya district, Mbeya, Tanzania. We present 3 cases from a group of 21 miners. The patients, ranging in age and mining exposure, exhibited symptoms of severe pulmonary conditions, including pneumoconiosis, pulmonary hypertension and Cor pulmonale, attributed to prolonged exposure to dust and inadequate protective measures in mining environments. These cases underscore the urgent need for enhanced occupational health standards and preventive strategies in artisanal mining communities.

Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study.

The diagnosis of pneumoconiosis is complex and subjective, leading to inevitable variability in readings. This is especially true for inexperienced doctors. To improve accuracy, a computer-assisted diagnosis system is used for more effective pneumoconiosis diagnoses. Three models (Resnet50, Resnet101, and DenseNet) were used for pneumoconiosis classification based on 1250 chest X-ray images. Three experienced and highly qualified physicians read the collected digital radiography images and classified them from category 0 to category III in a double-blinded manner. The results of the 3 physicians in agreement were considered the relative gold standards. Subsequently, 3 models were used to train and test these images and their performance was evaluated using multi-class classification metrics. We used kappa values and accuracy to evaluate the consistency and reliability of the optimal model with clinical typing. The results showed that ResNet101 was the optimal model among the 3 convolutional neural networks. The AUC of ResNet101 was 1.0, 0.9, 0.89, and 0.94 for detecting pneumoconiosis categories 0, I, II, and III, respectively. The micro-average and macro-average mean AUC values were 0.93 and 0.94, respectively. The accuracy and Kappa values of ResNet101 were 0.72 and 0.7111 for quadruple classification and 0.98 and 0.955 for dichotomous classification, respectively, compared with the relative standard classification of the clinic. This study develops a deep learning based model for screening and staging of pneumoconiosis is using chest radiographs. The ResNet101 model performed relatively better in classifying pneumoconiosis than radiologists. The dichotomous classification displayed outstanding performance, thereby indicating the feasibility of deep learning techniques in pneumoconiosis screening.

[Analysis of disease burden of welder's pneumoconiosis in Jiangsu Province].

Objective: To investigate the current status of disease burden and its influencing factors among welder's pneumoconiosis patients, and provide scientific basis for taking targeted intervention measures. Methods: From June 2022 to June 2023, the patients with welder's pneumoconiosis in Jiangsu Province were selected from 1956 to 2020 as the research objects, and disability adjusted life years (DALY) were used as the comprehensive index to study the disease burden. The direct and indirect economic losses caused by the diseases were calculated, and the factors affecting the disease burden were discussed by multiple linear regression method. Results: A total of 974 cases of welder's pneumoconiosis were reported in Jiangsu Province, the cumulative loss of DALY was 6300.73 person-years, and the per capita loss was 6.47 person-years. Among them, the healthy life years lost due to disability (YLD) was 6156.50 person-years (97.71%) , and the healthy life years lost due to premature death (YLL) was 144.23 person-years (2.29%) . Multiple linear regression analysis showed that the main factors affecting DALY were disability grade, diagnostic age, pneumoconiosis grade and length of dust exposure (P<0.05) . The total economic loss caused by 974 welder's pneumoconiosis patients was 1831838160.18 yuan, and the per capita loss was 1880737.33 yuan. Among them, the direct economic loss was 970917563.75 yuan (53.00%) , and the indirect economic loss was 860920596.43 yuan (47.00%) . Conclusion: Welder's pneumoconiosis causes serious disease burden to patients, and at the same time causes huge economic losses to individuals and society, which seriously hinders the development of society. Taking effective control measures to prevent the incidence of welder's pneumoconiosis is the key to reduce the disease burden.

[The relationship between the comprehensive blood inflammation indexes and stage I pneumoconiosis and its combined lung infections].

Objective: To analyze the comprehensive blood inflammation index of the patients with stage I pneumoconiosis complicated with pulmonary infection, and to explore its value in predicting the patients' disease. Methods: In September 2023, 83 patients with stage I pneumoconiosis who were treated in Tianjin Occupational Diseases Precaution and Therapeutic Hospital from November 2021 to August 2023 were selected and divided into non-infected group (56 cases) and infected group (27 cases) according to whether they were combined with lung infection. Workers with a history of dust exposure but diagnosed without pneumoconiosis during the same period were selected as the control group (65 cases) . By referring to medical records and collecting clinical data such as gender, age, occupational history, past medical history, hematology testing, the differences in the comprehensive blood inflammation indexes among the three groups were compared, ROC curve was drawn, and the relationship between comprehensive blood inflammation indexes and stage I pneumoconiosis and its combined lung infection was analyzed. Results: There were significtant differences in the number of neutrophils (N) , the number of lymphocytes (L) , the number of monocytes (M) , C-reactive protein (CRP) , the neutrophil to lymphocyte ratio (NLR) , the monocyte to lymphocyte ratio (MLR) , the platelet to lymphocyte ratio (PLR) , the systemic immune-inflammatory index (SII) , the systemic inflammation response index (SIRI) , the aggregate index of systemic inflammation (AISI) , the derived neutrophil to lymphocyte ratio (dNLR) , the neutrophil to lymphocyte and platelet ratio (NLPR) , and the C-reactive protein to lymphocyte ratio (CLR) (P<0.05) . Compared with the control group, MLR, SIRI and AISI in the non-infected group were significantly increased (P<0.05) . NLR, MLR, PLR, SII, SIRI, AISI, dNLR, NLPR, CLR were significantly increased (P<0.05) . Compared with the non-infected group, NLR, PLR, SII, SIRI, AISI, dNLR, NLPR and CLR were significantly increased in the infected group (P<0.05) . ROC analysis showed that NLR, MLR, PLR, SII, SIRI and AISI had a certain predictive capability for stage I pneumoconiosis (P<0.05) , among which MLR had the highest efficacy, with an AUC of 0.791 (95% CI: 0.710-0.873) , the cut-off value was 0.18, the sensitivity was 71.4%, and the specificity was 78.5%. NLR, MLR, PLR, SII, SIRI, AISI, dNLR, NLPR and CLR all had a certain predictive capability forstage I pneumoconiosis combined lung infection (P<0.05) , among which CLR had the highest efficacy, with an AUC of 0.904 (95%CI: 0.824~0.985) , the cut-off value was 5.33, sensitivity was 77.8%, specificity was 98.2%. Conclusion: The comprehensive blood inflammation index may be an auxiliary predictor of stage I pneumoconiosis and its combined lung infections.

[Research progress on pneumoconiosis markers based on multi-omics analysis].

The etiology of pneumoconiosis is relatively clear, but the pathogenic mechanism is not fully understood, and there is no effective cure for pneumoconiosis. Clarifying the pathogenesis of pneumoconiosis and exploring relevant markers can help screen high-risk groups of dust exposure, and relevant markers can also be used as targets to intervene in the process of pulmonary fibrosis. The in-depth development of genomics, transcriptomics and proteomics has provided a new way to discover more potential markers of pneumoconiosis. In the future, the combination of multi-omics and multi-stage interactive analysis can systematically and comprehensively identify key genes (proteins) , metabolites and metabolic pathways in the occurrence and development of pneumoconiosis, build a core regulatory network, and then screen out sensitive markers related to early diagnosis and treatment of pneumoconiosis. This article summarizes the research progress of pneumoconiosis markers from the perspective of multi-omics, hoping to provide more basic data for the early prevention and diagnosis of pneumoconiosis, pathogenesis research, and therapeutic intervention.

[Study on evaluation method of circular small shadow profusion in chest CT reconstruction images of pneumoconiosis].

Objective: To select chest CT image patterns for the diagnosis of pneumoconiosis and establish a method for determining the profusion of circular small shadows in chest CT. Methods: In April 2021, 66 cases of occupational pneumoconiosis patients with digital radiography (DR) chest radiographs and chest CT imaging data with circular small shadow as the main manifestations were selected as the study objects. 1.5 mm and 5 mm chest CT axial images, 1 mm and 5 mm chest CT coronal multi-plane recombination (MPR) images, and 5 mm chest CT coronal maximum intensity projection (MIP) images were used to observe the different characteristics of pneumoconiosis patients, and were compared and analyzed with DR chest radiographs to establish the experimental chest CT standards. The consistency of the profusion results between the experimental chest CT standards and GBZ 70-2015 Diagnosis of Occupational Pneumoconiosis was verified. Results: All the 66 objects were male, including 33 cases of stage Ⅰ pneumoconiosis, 17 cases of stage Ⅱ pneumoconiosis and 16 cases of stage Ⅲ pneumoconiosis. By observing five chest CT images of 66 objects, we found that chest CT images of different modes could clearly display and identify abnormal images such as small circular shadow, large shadow, small shadow aggregation, honeycomb glass shadow, flake glass shadow, uniform low-profusion glass shadow, mesh glass shadow, cable shadow, linear shadow, subpleural spinous shadow, subpleural nodules, various kinds of emphysema and lung texture distortion and fracture. Small shadow aggregation was usually accompanied by the appearance of large shadow. The vascular shadows in 5 mm CT images had good ductility, and small nodules were easy to distinguish. The coronal MIP image of 5 mm chest CT used edge enhancement technology, which was prone to small shadow fusion and fibrotic shadow fusion. The coronal MPR image of 5 mm chest CT was highly consistent with the DR chest radiographs in terms of the integrity of film reading. GBZ 70-2015 standard was used to compare the profusion of DR chest radiographs and 5 mm chest CT coronal MPR images of 66 objects, and the consistency test Kappa=0.64. GBZ 70-2015 standard and experimental chest CT standard were used to compare the profusion results of DR chest radiographs and 5 mm chest CT coronal MPR images of 66 objects, respectively, and the consistency test Kappa=0.80, with high consistency. Conclusion: 5 mm coronal MPR image is suitable for chest CT imaging in the diagnosis of pneumoconiosis. Following the selection path and method of GBZ 70-2015 profusion criterion, the established experimental chest CT standard in determining the profusion of small circular shadows in 5 mm coronal MPR images of chest CT with pneumoconiosis has a high consistency with GBZ 70-2015 standard.

Survival and disease burden analyses of occupational pneumoconiosis during 1958-2021 in Huangshi city, China: a retrospective cohort study.

BACKGROUND: Pneumoconiosis, a chronic disease stemming from prolonged inhalation of dust particles, stands as a significant global burden of occupational diseases. This study aims to investigate the survival outcomes of pneumoconiosis patients in Huangshi city, China, while also evaluating the disease burden on afflicted patients. METHODS: Data for this study were sourced from the Huangshi Center for Disease Control and Prevention. Survival analyses of pneumoconiosis patients were conducted employing life tables and the Kaplan-Meier method. The Cox proportional hazards models were deployed to identify factors influencing pneumoconiosis patients' survival duration. Competing risks models were employed to confirm the validity of the model outcomes. Additionally, in the disease burden assessment, disability-adjusted life years (DALYs) were computed for various demographic groups and time frames. RESULTS: A total of 5,641 pneumoconiosis cases, diagnosed in Huangshi City, Hubei Province between 1958 and 2021, were incorporated into the cohort analysis. The probability of mortality and the risk ratio increased with advancing age. Notably, the median survival time of stage III pneumoconiosis patients was significantly shorter compared with those in stages I and II. The Cox proportional hazards model and competing risks analyses underscored several significant factors influencing survival time, including dust exposure duration (HR = 1.197, 95% CI: 1.104-1.298), age at first diagnosis (HR = 3.149, 95% CI: 2.961-3.349), presence of silicosis (HR = 1.378, 95% CI: 1.254-1.515), and stage II-III pneumoconiosis (HR = 1.456, 95% CI: 1.148-1.848). Cumulatively, DALYs amounted to 7,974.35 person-years, with an average of 1.41 person-years. The period between 2000 and 2019 witnessed the highest disease burden. CONCLUSION: Our findings highlight the urgent need for improved prevention, earlier detection, and more effective management strategies for the occupational pneumoconiosis population. This study not only underscores the persistent issue of pneumoconiosis in industrial environments but also serves as a crucial call to action for policymakers and healthcare providers.

DLA-Net: dual lesion attention network for classification of pneumoconiosis using chest X-ray images.

Accurate and early detection of pneumoconiosis using chest X-rays (CXR) is important for preventing the progression of this incurable disease. It is also a challenging task due to large variations in appearance, size and location of lesions in the lung regions as well as inter-class similarity and intra-class variance. Compared to traditional methods, Convolutional Neural Networks-based methods have shown improved results; however, these methods are still not applicable in clinical practice due to limited performance. In some cases, limited computing resources make it impractical to develop a model using whole CXR images. To address this problem, the lung fields are divided into six zones, each zone is classified separately and the zone classification results are then aggregated into an image classification score, based on state-of-the-art. In this study, we propose a dual lesion attention network (DLA-Net) for the classification of pneumoconiosis that can extract features from affected regions in a lung. This network consists of two main components: feature extraction and feature refinement. Feature extraction uses the pre-trained Xception model as the backbone to extract semantic information. To emphasise the lesion regions and improve the feature representation capability, the feature refinement component uses a DLA module that consists of two sub modules: channel attention (CA) and spatial attention (SA). The CA module focuses on the most important channels in the feature maps extracted by the backbone model, and the SA module highlights the spatial details of the affected regions. Thus, both attention modules combine to extract discriminative and rich contextual features to improve classification performance on pneumoconiosis. Experimental results show that the proposed DLA-Net outperforms state-of-the-art methods for pneumoconiosis classification.

[A survey on the application of convolutional neural networks in the diagnosis of occupational pneumoconiosis].

Pneumoconiosis ranks first among the newly-emerged occupational diseases reported annually in China, and imaging diagnosis is still one of the main clinical diagnostic methods. However, manual reading of films requires high level of doctors, and it is difficult to discriminate the staged diagnosis of pneumoconiosis imaging, and due to the influence of uneven distribution of medical resources and other factors, it is easy to lead to misdiagnosis and omission of diagnosis in primary healthcare institutions. Computer-aided diagnosis system can realize rapid screening of pneumoconiosis in order to assist clinicians in identification and diagnosis, and improve diagnostic efficacy. As an important branch of deep learning, convolutional neural network (CNN) is good at dealing with various visual tasks such as image segmentation, image classification, target detection and so on because of its characteristics of local association and weight sharing, and has been widely used in the field of computer-aided diagnosis of pneumoconiosis in recent years. This paper was categorized into three parts according to the main applications of CNNs (VGG, U-Net, ResNet, DenseNet, CheXNet, Inception-V3, and ShuffleNet) in the imaging diagnosis of pneumoconiosis, including CNNs in pneumoconiosis screening diagnosis, CNNs in staging diagnosis of pneumoconiosis, and CNNs in segmentation of pneumoconiosis foci to conduct a literature review. It aims to summarize the methods, advantages and disadvantages, and optimization ideas of CNN applied to the images of pneumoconiosis, and to provide a reference for the research direction of further development of computer-aided diagnosis of pneumoconiosis.

Trends in work-related respiratory diseases attributed to nickel, chromium and cobalt in the UK: descriptive findings from The Health and Occupation Research (THOR) network 1996-2019.

BACKGROUND: Occupational exposure to metals can be associated with respiratory diseases which can adversely affect the individual's health, finances and employment. Despite this, little is known about the incidence of these respiratory conditions over prolonged periods of time. AIMS: This study aimed to investigate the trends in the incidence of work-related respiratory diseases attributed to nickel, chromium and cobalt in the UK. METHODS: Cases of occupational respiratory diseases caused by nickel, chromium or cobalt reported to Surveillance of Work-related and Occupational Respiratory Disease (SWORD), the UK-based surveillance scheme between 1996 and 2019 (inclusive), were extracted and grouped into six 4-year time periods. Cases were characterised by causative metal exposure, occupational and industrial sector. Incidence rates diseases (adjusted for physician participation and response rate) were calculated using ONS employment data. RESULTS: Of cases reported to SWORD during the study period, 1% (173 actual cases) of respiratory problems were attributed to nickel, chromium or cobalt. Diagnoses of asthma compromised the largest proportion of diagnoses (74.4%), followed by lung cancer (8.9%) and pneumoconiosis (6.7%). Cases had a mean age of 47 years (SD 13); 93% were men. The annual incidence fell from 1.6 per million employed in the first 4-year period, to 0.2 in the most recent period. CONCLUSIONS: Over 24 years, a decline in the incidence of metal-related occupational respiratory diseases was observed in the UK. This could be attributed to improvements in working conditions which resulted in reduced metal exposure but could also be due to closure of industries that might have generated case returns.

No causal association between pneumoconiosis and three inflammatory immune diseases: a Mendelian randomization study.

Objectives: To investigate the causal relationships between pneumoconiosis and rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and gout. Methods: The random-effects inverse variance weighted (IVW) approach was utilized to explore the causal effects of the instrumental variables (IVs). Sensitivity analyses using the MR-Egger and weighted median (WM) methods were did to investigate horizontal pleiotropy. A leave-one-out analysis was used to avoid the bias resulting from single-nucleotide polymorphisms (SNPs). Results: There was no causal association between pneumoconiosis and SLE, RA or gout in the European population [OR = 1.01, 95% CI: 0.94-1.10, p = 0.74; OR = 1.00, 95% CI: 0.999-1.000, p = 0.50; OR = 1.00, 95% CI: 1.000-1.001, p = 0.55]. Causal relationships were also not found in pneumoconiosis due to asbestos and other mineral fibers and SLE, RA and gout [OR = 1.01, 95% CI: 0.96-1.07, p = 0.66; OR = 1.00, 95% CI: 1.00-1.00, p = 0.68; OR = 1.00, 95% CI: 1.00-1.00, p = 0.20]. Conclusion: Our study suggests that pneumoconiosis may have no causal relationship with the three inflammatory immune diseases.

Time-/dose- series transcriptome data analysis and traditional Chinese medicine treatment of pneumoconiosis.

Pneumoconiosis' pathogenesis is still unclear and specific drugs for its treatment are lacking. Analysis of series transcriptome data often uses a single comparison method, and there are few reports on using such data to predict the treatment of pneumoconiosis with traditional Chinese medicine (TCM). Here, we proposed a new method for analyzing series transcriptomic data, series difference analysis (SDA), and applied it to pneumoconiosis. By comparison with 5 gene sets including existing pneumoconiosis-related genes and gene set functional enrichment analysis, we demonstrated that the new method was not inferior to two existing traditional analysis methods. Furthermore, based on the TCM-drug target interaction network, we predicted the TCM corresponding to the common pneumoconiosis-related genes obtained by multiple methods, and combined them with the high-frequency TCM for its treatment obtained through literature mining to form a new TCM formula for it. After feeding it to pneumoconiosis modeling mice for two months, compared with the untreated group, the coat color, mental state and tissue sections of the mice in the treated group were markedly improved, indicating that the new TCM formula has a certain efficacy. Our study provides new insights into method development for series transcriptomic data analysis and treatment of pneumoconiosis.

[Global trends in the incidence and prevalence of pneumoconiosis in 204 countries/territories from 1990 to 2019].

Objective: To analyze the changing trend of incidence and prevalence of pneumoconiosis globally, and provide scientific basis for the formulation of health policy. Methods: In June 2022, through the Global Health Data exchange (GHDx) query tool (http: //ghdx.healthdata.org/gbd-results-tool) , the pneumoconiosis incidence and prevalence data was downloaded and organized. Estimated annual percentage change (EAPC) and age-standardized rate (ASR) were used to estimate the trends of pneumoconiosis from 1990 to 2019. EAPC was estimated by linear regression model based on ASR. Results: The overall ASR of the incidence and prevalence of pneumoconiosis decreased from 1990 to 2019, and their EAPCs were-0.85% (95%CI: -1.11%--0.60%) and -0.78% (95%CI: -1.08%--0.49%) . Over the past 30 years, the incidence and prevalence of pneumoconiosis in all SDI areas showed decreasing trends, especially in high SDI areas, their EAPCs were -1.46% (95%CI: -1.76%--1.15%) and -1.99% (95%CI: -2.44%--1.53%) . 110 countries/areas showed increasing trends in age standardized incidence rate (ASIR) , with Iran and Georgia showing the most pronounced upward trend, their EAPCs were 5.32% (95%CI: 4.43%-6.22%) and 4.39% (95%CI: 3.81%-4.97%) . 125 countries/areas showed anincreasing trends in prevalence ASR, with Iran had the fastest rise in prevalence (EAPC=6.40%, 95%CI: 5.33%-7.49%) . Conclusion: Although decreasing trends in the burden of pneumoconiosis are observed globally from 1990 to 2019, but the burden of pneumoconiosis in low-and middle-income countries or regions are still heavy. We need more effective strategies to prevent and reduce the burden of pneumoconiosis.

[Bibliometric and visual analysis of pneumoconiosis based on Cite Space].

Objective: Through the bibliometrics analysis and visual analysis of Chinese and English literature related to pneumoconiosis through CiteSpace, to understand the research situation, research trend and hotspots of pneumoconiosis, so as to provide reference for further research. Methods: In August 2022, CNKI (China National Knowledge Infrastructure) data baseand Web of Science core collection database were used as data sources for literature retrieval. Cite Space.5.8.R3c software was used to analyze the cooperation between authors and institutions, keyword co-occurrence analysis, keyword clustering analysis and keyword emergence analysis. Results: A total of 4726 Chinese literature and 2490 English literature related to pneumoconiosis were included; The annual publication volume of Chinese literature shows a fluctuating downward trend, while the annual publication volume of English literature shows a fluctuating upward trend. The Institute of Labor Health and Occupational Disease of the Chinese Academy of Preventive Medical Sciences and the Institute of Occupational Health and Poisoning Control of the Chinese Center for Disease Control and Prevention have the highest publication volume (55 articles) in the institutional cooperation network; The National Institute for Occupational Safety and Health (NIOSH) in the United States has the highest publication volume (153 articles) in the institutional collaboration network. The results of keyword co-occurrence, clustering, and prominence analysis show that Chinese literature focuses more on clinical research on pneumoconiosis, while English literature focuses more on experimental research related to the pathogenesis of pneumoconiosis. Conclusion: In the related field of pneumoconiosis research, the experimental research and clinical research on the pathogenesis are the main research hotspots.

Research on factors affecting the spread of dust pollution in conveyor belt workshop and research on wet dust reduction technology.

At this stage, there are many dust-hazardous industries, and occupational pneumoconiosis has a high incidence for a long time. To solve the dust pollution problem in coal processing plant workshops, the dust particle field and liquid droplet particle field were numerically simulated using computational fluid dynamics (CFD), and the influences of the induced airflow and corridor wind speed on the internal airflow field of the workshop were investigated to derive the dust pollution mechanism in the coal plant workshop under the change in the wind flow field. In this study, it was shown that the wind flow rate in the coal processing plant workshop is mainly affected by the corridor wind speed, and the higher the corridor wind speed is, the higher the wind flow rate. The induced airflow mainly affected the direction of the wind flow field in the workshop. According to the conclusions obtained from the simulations, a spray dust reduction system was designed for the coal processing plant workshop and applied in the Huangyuchuan coal processing plant. On-site measurement revealed that the dust reduction effect inside the coal processing plant workshop is obvious, and the overall dust reduction efficiency in the workshop reaches more than 94%, which meets the requirements of environmentally sustainable development and clean production.

Predicting coal workers' pneumoconiosis trends: Leveraging historical data with the GARCH model in a Chinese Miner Cohort.

Coal workers' pneumoconiosis (CWP) is one of the most common and severe occupational diseases worldwide. The main risk factor of CWP is exposure to respirable mine dust. Prediction theory was widely applied in the prediction of the epidemic. Here, it was used to identify the characteristics of CWP today and the incidence trends of CWP in the future. Eight thousand nine hundred twenty-eight coal workers from a state-owned coal mine were included during the observation period from 1963 to 2014. In observations, the dust concentration gradually decreased over time, and the incidence of tunnels and mine, transportation, and assistance workers showed an overall downward trend. We choose a better prediction model by comparing the prediction effect of the Auto Regression Integrate Moving Average model and Generalized Autoregressive Conditional Heteroskedasticity model. Compared with the Auto Regression Integrate Moving Average model, the Generalized Autoregressive Conditional Heteroskedasticity model has a better prediction effect. Furthermore, the status quo and future trend of coal miners' CWP are still at a high level.