The role of inflammation in silicosis

Liu, Tong-Tong; Sun, Hai-Fei; Han, Yan-Xing; Zhan, Yun; Jiang, Jian-Dong

doi:10.3389/fphar.2024.1362509

Cited by 4 publications

(1 citation statement)

References 157 publications

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“…It is thought that the pathophysiology of ES-silicosis starts with the deposition of CS particles into alveoli, where they are ingested by alveolar macrophages and, due to their inability to eliminate them, an inflammatory reaction occurs in which the lung epithelial cells and the immune system are involved [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Immune Cell Subsets in Peripheral Blood from Patients with Engineered Stone Silica-Induced Lung Inflammation

Jiménez-Gómez,

Campos-Caro,

García-Núñez

et al. 2024

IJMS

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Silicosis caused by engineered stone (ES-silicosis) is an emerging worldwide issue characterized by inflammation and fibrosis in the lungs. To our knowledge, only a few reports have investigated leukocyte/lymphocyte subsets in ES-silicosis patients. The present study was designed to explore the proportions of the main lymphocyte subsets in ES-silicosis patients stratified into two groups, one with simple silicosis (SS) and the other with a more advanced state of the disease, defined as progressive massive fibrosis (PMF). The proportions of B (memory and plasmablasts) cells, T (helper, cytotoxic, regulatory) cells, and natural killer (NK) (regulatory and cytotoxic) cells were investigated by multiparameter flow cytometry in 91 ES-silicosis patients (53 SS patients and 38 PMF patients) and 22 healthy controls (HC). Although the total number of leukocytes did not differ between the groups studied, lymphopenia was observed in patients compared to healthy controls. Compared with those in healthy controls, the proportions of memory B cells, naïve helper T cells, and the CD4+/CD8+ T cells’ ratio in the peripheral blood of patients with silicosis were significantly decreased, while the percentages of plasma cells, memory helper T cells, and regulatory T cells were significantly increased. For the NK cell subsets, no significant differences were found between the groups studied. These results revealed altered cellular immune processes in the peripheral blood of patients with ES-silicosis and provided further insight into silicosis pathogenesis.

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Section: Introductionmentioning

confidence: 99%

Analysis of Immune Cell Subsets in Peripheral Blood from Patients with Engineered Stone Silica-Induced Lung Inflammation

Jiménez-Gómez,

Campos-Caro,

García-Núñez

et al. 2024

IJMS

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Inhibition of IRF3-STING axis interaction in silicosis using natural compounds: an in-silico study using molecular docking, ADMET, molecular dynamics and MMPBSA approach

Sood,

Kulharia

2024

In Silico Pharmacol.

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Rapid, Non-Invasive Breath Analysis for Enhancing Detection of Silicosis Using Mass Spectrometry and Interpretable Machine Learning

Baker,

Gordon,

Thiruvarudchelvan

et al. 2024

Preprint

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Occupational lung diseases, such as silicosis, are a significant global health concern, especially with increasing exposure to engineered stone dust. Early detection of silicosis is helpful for preventing disease progression, but existing diagnostic methods, including X-rays, CT scans, and spirometry, often detect the disease only at late stages. This study investigates a rapid, non-invasive diagnostic approach using atmospheric pressure chemical ionization-mass spectrometry (APCI-MS) to analyse volatile organic compounds (VOCs) in exhaled breath from 31 silicosis patients and 60 healthy controls. Six different interpretable machine learning (ML) models with Shapley additive explanations (SHAP) were applied to classify these samples and determine VOC features that contribute the most significantly to model accuracy. The extreme gradient boosting (XGB) classifier demonstrated the best performance, achieving an area under the receiver-operator characteristic curve of 0.933 with the top ten SHAP features. The m/z 442 feature, potentially corresponding to leukotriene-E3, emerged as a significant predictor for silicosis. The VOC sampling and measurement process takes less than five minutes per sample, highlighting its potential suitability for large-scale population screening. Moreover, the ML models are interpretable through SHAP, providing insights into the features contributing to the model's predictions. This study suggests that APCI-MS breath analysis could enable early and non-invasive diagnosis of silicosis, helping to improve disease outcomes.

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The role of inflammation in silicosis

Cited by 4 publications

References 157 publications

Analysis of Immune Cell Subsets in Peripheral Blood from Patients with Engineered Stone Silica-Induced Lung Inflammation

Analysis of Immune Cell Subsets in Peripheral Blood from Patients with Engineered Stone Silica-Induced Lung Inflammation

Inhibition of IRF3-STING axis interaction in silicosis using natural compounds: an in-silico study using molecular docking, ADMET, molecular dynamics and MMPBSA approach

Rapid, Non-Invasive Breath Analysis for Enhancing Detection of Silicosis Using Mass Spectrometry and Interpretable Machine Learning

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