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Abstract Purpose The purpose of this study is to propose an efficient coal workers' pneumoconiosis (CWP) clinical prediction system and put it into clinical use for clinical diagnosis of pneumoconiosis. Methods Patients with CWP and dust‐exposed workers who were enrolled from August 2021 to December 2021 were included in this study. Firstly, we chose the embedded method through using three feature selection approaches to perform the prediction analysis. Then, we performed the machine learning algorithms as the model backbone and combined them with three feature selection methods, respectively, to determine the optimal predictive model for CWP. Results Through applying three feature selection approaches based on machine learning algorithms, it was found that AaDO2 and some pulmonary function indicators played an important role in prediction for identifying CWP of early stage. The support vector machine (SVM) algorithm was proved as the optimal machine learning model for predicting CWP, with the ROC curves obtained from three feature selection methods using SVM algorithm whose AUC values of 97.78%, 93.7%, and 95.56%, respectively. Conclusion We developed the optimal model (SVM algorithm) through comparisons and analyses among the performances of different models for the prediction of CWP as a clinical application.

Abstract The human microbiome plays a crucial role in maintaining health, with advances in high‐throughput sequencing technology and reduced sequencing costs triggering a surge in microbiome research. Microbiome studies generally incorporate five key phases: design, sampling, sequencing, analysis, and reporting, with sequencing strategy being a crucial step offering numerous options. Present mainstream sequencing strategies include Amplicon sequencing, Metagenomic Next‐Generation Sequencing (mNGS), and Targeted Next‐Generation Sequencing (tNGS). Two innovative technologies recently emerged, namely MobiMicrobe high‐throughput microbial single‐cell genome sequencing technology and 2bRAD‐M simplified metagenomic sequencing technology, compensate for the limitations of mainstream technologies, each boasting unique core strengths. This paper reviews the basic principles and processes of these three mainstream and two novel microbiological technologies, aiding readers in understanding the benefits and drawbacks of different technologies, thereby guiding the selection of the most suitable method for their research endeavours.

Abstract Purpose This paper aims to develop a successful deep learning model with data augmentation technique to discover the clinical uniqueness of chest X-ray imaging features of coal workers' pneumoconiosis (CWP). Patients and methods We enrolled 149 CWP patients and 68 dust-exposure workers for a prospective cohort observational study between August 2021 and December 2021 at First Hospital of Shanxi Medical University. Two hundred seventeen chest X-ray images were collected for this study, obtaining reliable diagnostic results through the radiologists' team, and confirming clinical imaging features. We segmented regions of interest with diagnosis reports, then classified them into three categories. To identify these clinical features, we developed a deep learning model (ShuffleNet V2-ECA Net) with data augmentation through performances of different deep learning models by assessment with Receiver Operation Characteristics (ROC) curve and area under the curve (AUC), accuracy (ACC), and Loss curves. Results We selected the ShuffleNet V2-ECA Net as the optimal model. The average AUC of this model was 0.98, and all classifications of clinical imaging features had an AUC above 0.95. Conclusion We performed a study on a small dataset to classify the chest X-ray clinical imaging features of pneumoconiosis using a deep learning technique. A deep learning model of ShuffleNet V2 and ECA-Net was successfully constructed using data augmentation, which achieved an average accuracy of 98%. This method uncovered the uniqueness of the chest X-ray imaging features of CWP, thus supplying additional reference material for clinical application.

Objectives To determine the incidence of pneumoconiosis worldwide and its influencing factors.Design Systematic review and meta-analysis.Setting Cohort studies on occupational pneumoconiosis.Participants PubMed, Embase, the Cochrane Library and Web of Science were searched until November 2021. Studies were selected for meta-analysis if they involved at least one variable investigated as an influencing factor for the incidence of pneumoconiosis and reported either the parameters and 95% CIs of the risk fit to the data, or sufficient information to allow for the calculation of those values.Primary outcome measures The pooled incidence of pneumoconiosis and risk ratio (RR) and 95% CIs of influencing factors.Results Our meta-analysis included 19 studies with a total of 335 424 participants, of whom 29 972 developed pneumoconiosis. The pooled incidence of pneumoconiosis was 0.093 (95% CI 0.085 to 0.135). We identified the following influencing factors: (1) male (RR 3.74; 95% CI 1.31 to 10.64; p=0.01), (2) smoking (RR 1.80; 95% CI 1.34 to 2.43; p=0.0001), (3) tunnelling category (RR 4.75; 95% CI 1.96 to 11.53; p

Silicosis is a leading cause of occupational disease-related morbidity and mortality worldwide, but the molecular basis underlying its development remains unclear. An accumulating body of evidence supports gasdermin D (GSDMD)-mediated pyroptosis as a key component in the development of various pulmonary diseases. However, there is little experimental evidence connecting silicosis and GSDMD-driven pyroptosis. In this work, we investigated the role of GSDMD-mediated pyroptosis in silicosis. Single-cell RNA sequencing of healthy and silicosis human and murine lung tissues indicated that GSDMD-induced pyroptosis in macrophages was relevant to silicosis progression. Through microscopy we then observed morphological alterations of pyroptosis in macrophages treated with silica. Measurement of interleukin-1β release, lactic dehydrogenase activity, and real-time propidium iodide staining further revealed that silica induced pyroptosis of macrophages. Additionally, we verified that both canonical (caspase-1-mediated) and non-canonical (caspase-4/5/11-mediated) signaling pathways mediated silica-induced pyroptosis activation, in vivo and in vitro. Notably, Gsdmd knockout mice exhibited dramatically alleviated silicosis phenotypes, which highlighted the pivotal role of pyroptosis in this disease. Taken together, our results demonstrated that macrophages underwent GSDMD-dependent pyroptosis in silicosis and inhibition of this process could serve as a viable clinical strategy for mitigating silicosis.

Coal workers’ pneumoconiosis (CWP) is a fatal occupational disease caused by inhalation of coal dust particles, which leads to progressive pulmonary fibrosis. Recently, as new signal carriers for intercellular communication, exosomal miRNAs have been validated in the pathogenesis of multiple diseases. However, the research on exosomal miRNAs in CWP is still in the preliminary stage. Here, using miRNA sequencing, exosomal miRNA profiles in bronchoalveolar lavage fluid (BALF) from rats with pulmonary fibrosis induced by coal dust particles were analyzed, and the underlying biological function of putative target genes was explored by GO term analysis and KEGG pathway enrichment analysis. According to the results, intratracheal instillation of coal dust particles can alter the exosomal miRNAs expression in the BALF of rats. Further bioinformatics analysis provided some clues to reveal their function in pathological process of pneumoconiosis. More importantly, we identified 4 differentially expressed exosomal miRNAs (miRNA-21–5p, miRNA-29a-3p, miRNA-26a-5p, and miRNA-34a-5p) by qRT‑PCR and further verified the temporal changes in the expression of these exosomal miRNAs in animal models from 2 weeks to 16 weeks postexposure. In addition, we conducted a preliminary study on Smad7 as a potential target of miRNA-21–5p and found that exosomal miRNA 21–5p/Smad7 may contribute to the pulmonary fibrosis induced by coal dust particles. Our study confirmed the contribution of exosomal miRNAs to coal dust particle-induced pulmonary fibrosis and provided new insights into the pathogenesis of CWP.

Abstract Glucose-regulating protein 78 (GRP78) is a molecular chaperone in the endoplasmic reticulum (ER) that promotes folding and assembly of proteins, controls the quality of proteins, and regulates ER stress signaling through Ca2+ binding to the ER. In tumors, GRP78 is often upregulated, acting as a central stress sensor that senses and adapts to changes in the tumor microenvironment, mediating ER stress of cancer cells under various stimulations of the microenvironment to trigger the folding protein response. Increasing evidence has shown that GRP78 is closely associated with the progression and poor prognosis of lung cancer, and plays an important role in the treatment of lung cancer. Herein, we reviewed for the first time the functions and mechanisms of GRP78 in the pathological processes of lung cancer, including tumorigenesis, apoptosis, autophagy, progression, and drug resistance, giving a comprehensive understanding of the function of GRP78 in lung cancer. In addition, we also discussed the potential role of GRP78 as a prognostic biomarker and therapeutic target for lung cancer, which is conducive to improving the assessment of lung cancer and the development of new therapeutic interventions.

Background: Ambient sulfur dioxide (SO2) has been associated with morbidity and mortality of respiratory diseases, however, its effect on length of hospital stays (LOS) and cost for these diagnoses remain unclear. Methods: We collected hospital admission information for respiratory diseases from all 11 cities in the Shanxi Province of China during 2017–2019. We assessed individual-level exposure by using an inverse distance weighting approach based on geocoded residential addresses. A generalized additive model was built to delineate city-specific effects of SO2 on hospitalization, hospital expenditure, and length of hospital stay for respiratory diseases. The overall effects were obtained by random-effects meta-analysis. We further estimated the respiratory burden attributable to SO2 by comparing different reference concentrations. Results: We observed significant effects of SO2 exposure on respiratory diseases. At the provincial level, each 10 μg/m3 increase in SO2 on lag03 was associated with a 0.63% (95% CI: 0.14–0.11) increase in hospital admission, an increase of 4.56 days (95% CI: 1.16–7.95) of hospital stay, and 3647.97 renminbi (RMB, Chinese money) (95% CI: 1091.05–6204.90) in hospital cost. We estimated about 6.13 (95% CI: 1.33–11.10) thousand hospital admissions, 65.77 million RMB (95% CI: 19.67–111.87) in hospital expenditure, and 82.13 (95% CI: 20.87–143.40) thousand days of hospital stay could have potentially been avoided had the daily SO2 concentrations been reduced to WHO’s reference concentration (40 µg/m3). Variable values in correspondence with this reference concentration could reduce the hospital cost and LOS of each case by 52.67 RMB (95% CI: 15.75–89.59) and 0.07 days (95% CI: 0.02–0.117). Conclusion: This study provides evidence that short-term ambient SO2 exposure is an important risk factor of respiratory diseases, indicating that continually tightening policies to reduce SO2 levels could effectively reduce respiratory disease burden in Shanxi Province.

Objective: It aimed to analyze the epidemic situation of new coronary pneumonia (COVID-19) based on the epidemiological Markov model, and to study the clinical risk factors of the patients based on the patient’s cardinal data and clinical symptoms. Methods: A total of 500 patients with COVID-19 diagnosed by nucleic acid testing in the X hospital from January 2020 to May 2020 were collected. According to the severity of the disease, they were classified into general group (200 cases) and acute critical group (300 cases). Markov model to predict the number of COVID-19 infections was constructed. Patient’s general information, clinical characteristics, and prevention methods were analyzed. Results: According to Markov model statistics, the developmental expected stay time of patients infected with COVID-19 was 14 days. 2. The two groups of patients had statistically considerable differences in complications such as gender, age, hypertension, coronary heart disease, shortness of breath, myocardial damage, and thrombocytopenia (P

Ivosidenib is an isocitrate dehydrogenase mutant inhibitor that the US Food and Drug Administration recently approved for the treatment of leukemia. Studies suggested that ivosidenib may inhibit the progression of non-small cell lung cancer (NSCLC). In the present study, we explored RNAs and their potential regulatory mechanisms by which ivosidenib treats NSCLC cells. We used MTT assays, Transwell assays, and flow cytometry to measure the anti-tumor effects of ivosidenib in NSCLC cells. We performed whole transcriptome sequencing to determine differentially expressed mRNAs (DE-mRNAs) and non-coding RNAs (ncRNA). We used GO and KEGG pathway enrichment analyses to identify the functions and potential mechanisms. According to miRNA target interactions, we constructed a competing endogenous network. Ivosidenib inhibited the proliferation, invasion, and migration of NSCLC cells and inhibited tumor growth in vivo. We identified 212 DE-mRNAs, four DE-miRNAs, and 206 DE-lncRNAs in ivosidenib-treated NSCLC cells compared to untreated NSCLC cells. DE-mRNAs were significantly enriched in the cancer-associated pathways, including the TGF-β signaling pathway, the PI3K-Akt signaling pathway, the Jak-STAT signaling pathway, the MAPK signaling pathway, the Rap1 signaling pathway, and cell adhesion molecules. Based on the competing endogenous RNA hypothesis, we constructed lncRNA-miRNA-mRNA networks to elucidate the regulatory relationships between mRNA and ncRNA. We found that qRT-PCR results showed corresponding expression trends of differential genes with sequencing data. Our results provide insights into the molecular basis of ivosidenib suppression of NSCLC.

Cinnamaldehyde (CA) is the main component extracted from the traditional Chinese medicine cinnamon. Recent studies revealed that CA has antiviral and anti-tumor effects. However, the effect and mechanism of CA on non-small cell lung cancer (NSCLC) through whole transcriptome sequencing integrated analysis have not been systematically investigated. In this study, whole transcriptome sequencing was used to identify differentially expressed messenger RNAs (mRNAs), micro RNAs (miRNAs), and long non-coding RNAs (lncRNAs) that were influenced by CA and screen regulatory pathways. The results showed that CA significantly inhibited proliferation, invasion, and migration, whereas it induced the apoptosis of NSCLC cells. CA inhibited tumor growth in vivo. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis revealed that these differentially expressed mRNAs were potentially implicated in the CA-suppressing malignant phenotypes of NSCLC. According to the competing endogenous RNA (ceRNA) hypothesis, a ceRNA network was constructed, including 13 mRNAs, 6 miRNAs, and 11 lncRNAs. Kyoto Encyclopedia of Genes and Genomes analysis of the 13 mRNAs in the ceRNA network showed that suppressors of cytokine signaling 1 (SOCS1), BTG anti-proliferation factor 2 (BTG2), and Bruton tyrosine kinase (BTK) were significantly enriched in the JAK/STAT signaling pathway, RNA degradation, and nuclear factor-κB (NF-κB) signaling pathway related to cancer. These findings indicated that SOCS1, BTG2, and BTK play an essential role in CA against NSCLC. Meanwhile, based on the ceRNA network, three lncRNAs (long intergenic non-protein coding RNA 1504 [LINC01504], LINC01783, and THUMPD3 antisense RNA 1 [THUMPD3-AS1]) and three miRNAs (has-miR-155-5p, has-miR-7-5p, and has-miR-425-5p) associated with SOCS1, BTG2, and BTK may be important in CA against NSCLC. Taken together, the present study demonstrated the activity of CA against lung cancer and its potential use as a therapeutic agent.

Circulating tumor cells (CTCs) have been considered as an alternative to tissue biopsy for providing both germline-specific and tumor-derived genetic variations. Single-cell analysis of CTCs enables in-depth investigation of tumor heterogeneity and individualized clinical assessment. However, common CTC enrichment techniques generally have limitations of low throughput and cell damage. Herein, based on micropore-arrayed filtration membrane and microfluidic chip, we established an integrated CTC isolation platform with high-throughput, high-efficiency, and less cell damage. We observed a capture rate of around 85% and a purity of 60.4% by spiking tumor cells (PC-9) into healthy blood samples. Detection of CTCs from lung cancer patients demonstrated a positive detectable rate of 87.5%. Additionally, single CTCs, ctDNA and liver biopsy tissue of a representative advanced lung cancer patient were collected and sequenced, which revealed comprehensive genetic information of CTCs while reflected the differences in genetic profiles between different biological samples. This work provides a promising tool for CTCs isolation and further analysis at single-cell resolution with potential clinical value.

Progressive fibrosing interstitial lung diseases (PF-ILDs) result in high mortality and lack effective therapies. The pathogenesis of PF-ILDs involves macrophages driving inflammation and irreversible fibrosis. Fc-g receptors (FcγRs) regulate macrophages and inflammation, but their roles in PF-ILDs remain unclear. We characterized the expression of FcγRs and found upregulated FcγRIIB in human and mouse lungs after exposure to silica. FcγRIIB deficiency aggravated lung dysfunction, inflammation, and fibrosis in silica-exposed mice. Using single-cell transcriptomics and in vitro experiments, FcγRIIB was found in alveolar macrophages, where it regulated the expression of fibrosis-related genes Spp1 and Ctss. In mice with macrophagespecific overexpression of FcγRIIB and in mice treated with adenovirus by intratracheal instillation to upregulate FcγRIIB, silica-induced functional and histological changes were ameliorated. Our data from three genetic models and a therapeutic model suggest that FcγRIIB plays a protective role that can be enhanced by adenoviral overexpression, representing a potential therapeutic strategy for PF-ILDs. [ABSTRACT FROM AUTHOR]

Background: Few studies have examined the effects of ambient particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) on hospital cost and length of hospital stay for respiratory diseases in China.Methods: We estimated ambient air pollution exposure for respiratory cases through inverse distance-weighted averages of air monitoring stations based on their residential address and averaged at the city level. We used generalised additive models to quantify city-specific associations in 11 cities in Shanxi and a meta-analysis to estimate the overall effects. We further estimated respiratory burden attributable to PM2.5 using the standards of WHO (25 µg/m3) and China (75 µg/m3) as reference.Results: Each 10 µg/m3 increase in lag03 PM2.5 corresponded to 0.53% (95% CI: 0.33% to 0.73%) increase in respiratory hospitalisation, an increment of 3.75 thousand RMB (95% CI: 1.84 to 5.670) in hospital cost and 4.13 days (95% CI: 2.51 to 5.75) in length of hospital stay. About 9.7 thousand respiratory hospitalisations, 132 million RMB in hospital cost and 145 thousand days of hospital stay could be attributable to PM2.5 exposures using WHO's guideline as reference. We estimated that 193 RMB (95% CI: 95 to 292) in hospital cost and 0.21 days (95% CI: 0.13 to 0.30) in hospital stay could be potentially avoidable for an average respiratory case.Conclusion: Significant respiratory burden could be attributable to PM2.5 exposures in Shanxi Province, China. The results need to be factored into impact assessment of air pollution policies to provide a more complete indication of the burden addressed by the policies. [ABSTRACT FROM AUTHOR]