Silicosis not a disease of the past. It is an irreversible, fibrotic lung disease specifically caused by exposure to respirable crystalline silica (RCS) dust. Over 20,000 incident cases of silicosis were identified in 2017 and millions of workers continue to be exposed to RCS. Identified case numbers are however a substantial underestimation due to deficiencies in reporting systems and occupational respiratory health surveillance programmes in many countries. Insecure workers, immigrants and workers in small businesses are at particular risk of more intense RCS exposure. Much of the focus of research and prevention activities has been on the mining sector. Hazardous RCS exposure however occurs in a wide range of occupational setting which receive less attention, in particular the construction industry. Recent outbreaks of silicosis associated with the fabrication of domestic kitchen benchtops from high‐silica content artificial stone have been particularly notable because of the young age of affected workers, short duration of RCS exposure and often rapid disease progression. Developments in nanotechnology and hydraulic fracking provide further examples of how rapid changes in technology and industrial processes require governments to maintain constant vigilance to identify and control potential sources of RCS exposure. Despite countries around the world dealing with similar issues related to RCS exposure, there is an absence of sustained global public health response including lack of consensus of an occupational exposure limit that would provide protection to workers. Although there are complex challenges, global elimination of silicosis must remain the goal.

Chronic obstructive pulmonary disease (COPD) is one of the commonest causes of death in the world and poses a substantial burden on healthcare systems and patients’ quality of life. The largest component of the related healthcare costs is attributable to admissions due to acute exacerbation (AECOPD). The evidence that might support the effectiveness of the telemonitoring interventions in COPD is limited partially due to the lack of useful predictors for the early detection of AECOPD. Electronic stethoscopes and computerised analyses of respiratory sounds (CARS) techniques provide an opportunity for substantial improvement in the management of respiratory diseases. This exploratory study aimed to evaluate the feasibility of using: (a) a respiratory sensor embedded in a self-tailored housing for ageing users; (b) a telehealth framework; (c) CARS and (d) machine learning techniques for the remote early detection of the AECOPD. In a 6-month pilot study, 16 patients with COPD were equipped with a home base-station and a sensor to daily record their respiratory sounds. Principal component analysis (PCA) and a support vector machine (SVM) classifier was designed to predict AECOPD. 75.8% exacerbations were early detected with an average of 5 ± 1.9 days in advance at medical attention. The proposed method could provide support to patients, physicians and healthcare systems.

Background: Engineered stone silicosis is an emerging disease in many countries worldwide produced by the inhalation of respirable dust of engineered stone. This silicosis has a high incidence among young workers, with a short latency period and greater aggressiveness than silicosis caused by natural materials. Although the silica content is very high and this is the key factor, it has been postulated that other constituents in engineered stones can influence the aggressiveness of the disease. Different samples of engineered stone countertops (fabricated by workers during the years prior to their diagnoses), as well as seven lung samples from exposed patients, were analyzed by multiple techniques. Results: The different countertops were composed of SiO₂ in percentages between 87.9 and 99.6%, with variable relationships of quartz and cristobalite depending on the sample. The most abundant metals were Al, Na, Fe, Ca and Ti. The most frequent volatile organic compounds were styrene, toluene and m-xylene, and among the polycyclic aromatic hydrocarbons, phenanthrene and naphthalene were detected in all samples. Patients were all males, between 26 and 46 years-old (average age: 36) at the moment of the diagnosis. They were exposed to the engineered stone an average time of 14 years. At diagnosis, only one patient had progressive massive fibrosis. After a follow-up period of 8 ± 3 years, four patients presented progressive massive fibrosis. Samples obtained from lung biopsies most frequently showed well or ill-defined nodules, composed of histiocytic cells and fibroblasts without central hyalinization. All tissue samples showed high proportion of Si and Al at the center of the nodules, becoming sparser at the periphery. Al to Si content ratios turned out to be higher than 1 in two of the studied cases. Correlation between Si and Al was very high (r = 0.93). Conclusion: Some of the volatile organic compounds, polycyclic aromatic hydrocarbons and metals detected in the studied countertop samples have been described as causative of lung inflammation and respiratory disease. Among inorganic constituents, aluminum has been a relevant component within the silicotic nodule, reaching atomic concentrations even higher than silicon in some cases. Such concentrations, both for silicon and aluminum showed a decreasing tendency from the center of the nodule towards its frontier.

Despite the impressive performance of current deep learning models in the field of medical imaging, transferring the lung segmentation task in X-ray images to clinical practice is still a pending task. In this study, the performance of a fully automatic framework for lung field segmentation in chest X-ray images was evaluated. The framework is rooted in the combination of the Segment Anything Model (SAM) with prompt capabilities, and the You Only Look Once (YOLO) model to provide effective prompts. Transfer learning, loss functions, and several validation strategies were thoroughly assessed. This provided a complete benchmark that enabled future research studies to fairly compare new segmentation strategies. The results achieved demonstrated significant robustness and generalization capability against the variability in sensors, populations, disease manifestations, device processing, and imaging conditions. The proposed framework was computationally efficient, could address bias in training over multiple datasets, and had the potential to be applied across other domains and modalities.

Long-term oxygen therapy (LTOT) has become standard care for the treatment of patients with chronic obstructive pulmonary disease (COPD) and other severe hypoxemic lung diseases. The use of new portable O₂ concentrators (POC) in LTOT is being expanded. However, the issue of oxygen titration is not always properly addressed, since POCs rely on proper use by patients. The robustness of algorithms and the limited reliability of current oximetry sensors are hindering the effectiveness of new approaches to closed-loop POCs based on the feedback of blood oxygen saturation. In this study, a novel intelligent portable oxygen concentrator (iPOC) is described. The presented iPOC is capable of adjusting the O₂ flow automatically by real-time classifying the intensity of a patient’s physical activity (PA). It was designed with a group of patients with COPD and stable chronic respiratory failure. The technical pilot test showed a weighted accuracy of 91.1% in updating the O₂ flow automatically according to medical prescriptions, and a general improvement in oxygenation compared to conventional POCs. In addition, the usability achieved was high, which indicated a significant degree of user satisfaction. This iPOC may have important benefits, including improved oxygenation, increased compliance with therapy recommendations, and the promotion of PA.