Deborah H. Yates

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, computed tomography 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 analyze 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 classifier demonstrated the highest 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.

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.

Landscape fires are increasing in frequency and severity globally. In Australia, extreme bushfires cause a large and increasing health and socioeconomic burden for communities and governments. People with asthma are particularly vulnerable to the effects of landscape fire smoke (LFS) exposure. Here, we present a position statement from the Thoracic Society of Australia and New Zealand. Within this statement we provide a review of the impact of LFS on adults and children with asthma, highlighting the greater impact of LFS on vulnerable groups, particularly older people, pregnant women and Aboriginal and Torres Strait Islander peoples. We also highlight the development of asthma on the background of risk factors (smoking, occupation and atopy). Within this document we present advice for asthma management, smoke mitigation strategies and access to air quality information, that should be implemented during periods of LFS. We promote clinician awareness, and the implementation of public health messaging and preparation, especially for people with asthma.

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Dermatological manifestations of connective tissue diseases (CTDs) are common and frequently precede other symptoms. Thus, dermatologists may be the first clinicians to diagnose these disorders. Silica exposure is an acknowledged cause of several CTDs, but this is under-appreciated by clinicians, who may also be unaware of the wide range of jobs in which silica exposure can occur. The CTDs associated with silica exposure include systemic sclerosis (SSc), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), anti-neutrophil cytoplasmic antibody (ANCA) positive vasculitis and overlap syndromes. Silica-related systemic sclerosis (Si-SSc) is associated with a specific antibody profile and more severe disease. Silicosis has re-emerged worldwide recently due to several new workplace exposures, including a new type of silicosis (artificial stone (AS) silicosis), which is associated with a particularly high rate of auto-antibody formation. Dangerous work practices are still occurring. This article summarises recent literature on the topic of the resurgence of silicosis and silica-induced CTDs and reminds dermatologists of the importance of taking a thorough occupational history in all patients. Early intervention in CTDs and reduction in dust exposure can reduce risk and improve prognosis. Treatment options are rapidly improving.

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Non-invasive medical diagnosis by analysing volatile organic compounds (VOCs) at the point-of-care is becoming feasible owing to recent advances in portable instrumentation. A number of studies have assessed the performance of a state-of-the-art VOC analyser (micro-chip high-field asymmetric waveform ion mobility spectrometry, FAIMS) for medical diagnosis. However, a comprehensive meta-analysis is needed to investigate the overall diagnostic performance of these novel methods across different medical conditions. An electronic search was performed using the CAplus and MEDLINE database through the SciFinder platform. The review identified a total of 23 studies and 2312 individuals. Eighteen studies were used for meta-analysis. A pooled analysis found an overall sensitivity of 80% (95% CI, 74%–85%, I ² = 62%), and specificity of 78% (95% CI, 70%–84%, I ² = 80%), which corresponds to the overall diagnostic performance of micro-chip FAIMS across many different medical conditions. The diagnostic accuracy was particularly high for coeliac and inflammatory bowel disease (sensitivity and specificity from 74% to 97%). The overall diagnostic performance was similar across breath, urine, and faecal matrices with sparse logistic regression and random forests algorithms resulting in higher diagnostic accuracy. Sources of variability likely arise from differences in sample storage, sampling protocol, method of data analysis, type of disease, sample matrix, and potentially to clinical and disease factors. The results of this meta-analysis indicate that micro-chip FAIMS is a promising candidate for disease screening at the point-of-care, particularly for gastroenterology diseases. This review provides recommendations that should improve the techniques relevant to diagnostic accuracy of future VOC and point-of-care studies.

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