Additive manufacturing (AM), or 3D printing, is a growing industry involving a wide range of different techniques and materials. The potential toxicological effects of emissions produced in the process, involving both ultrafine particles and volatile organic compounds (VOCs), are unclear, and there are concerns regarding possible health implications among AM operators. The objective of this study was to screen the presence of respiratory health effects among people working with liquid, powdered, or filament plastic materials in AM. Methods: In total, 18 subjects working with different additive manufacturing techniques and production of filament with polymer feedstock and 20 controls participated in the study. Study subjects filled out a questionnaire and underwent blood and urine sampling, spirometry, impulse oscillometry (IOS), exhaled NO test (FeNO), and collection of particles in exhaled air (PEx), and the exposure was assessed. Analysis of exhaled particles included lung surfactant components such as surfactant protein A (SP-A) and phosphatidylcholines. SP-A and albumin were determined using ELISA. Using reversed-phase liquid chromatography and targeted mass spectrometry, the relative abundance of 15 species of phosphatidylcholine (PC) was determined in exhaled particles. The results were evaluated by univariate and multivariate statistical analyses (principal component analysis). Results: Exposure and emission measurements in AM settings revealed a large variation in particle and VOC concentrations as well as the composition of VOCs, depending on the AM technique and feedstock. Levels of FeNO, IOS, and spirometry parameters were within clinical reference values for all AM operators. There was a difference in the relative abundance of saturated, notably dipalmitoylphosphatidylcholine (PC16:0_16:0), and unsaturated lung surfactant lipids in exhaled particles between controls and AM operators. Conclusion: There were no statistically significant differences between AM operators and controls for the different health examinations, which may be due to the low number of participants. However, the observed difference in the PC lipid profile in exhaled particles indicates a possible impact of the exposure and could be used as possible early biomarkers of adverse effects in the airways.
Annually, an estimated seven million deaths are linked to exposure to airborne pollutants. Despite extensive epidemiological evidence supporting clear associations between poor air quality and a range of short- and long-term health effects, there are considerable gaps in our understanding of the specific mechanisms by which pollutant exposure induces adverse biological responses at the cellular and tissue levels. The development of more complex, predictive, in vitro respiratory models, including two- and three-dimensional cell cultures, spheroids, organoids and tissue cultures, along with more realistic aerosol exposure systems, offers new opportunities to investigate the cytotoxic effects of airborne particulates under controlled laboratory conditions. Parallel advances in high-resolution microscopy have resulted in a range of in vitro imaging tools capable of visualizing and analysing biological systems across unprecedented scales of length, time and complexity. This article considers state-of-the-art in vitro respiratory models and aerosol exposure systems and how they can be interrogated using high-resolution microscopy techniques to investigate cell-pollutant interactions, from the uptake and trafficking of particles to structural and functional modification of subcellular organelles and cells. These data can provide a mechanistic basis from which to advance our understanding of the health effects of airborne particulate pollution and develop improved mitigation measures.
Background: GOLD 2023 defines an exacerbation of COPD (ECOPD) by a deterioration of breathlessness at rest (BaR), mucus and cough. The severity of an ECOPD is determined by the degree of BaR, ranging from 0 to 10. However, it is not known which symptom is the most important one to detect early of an ECOPD, and which symptom that predicts future ECOPDs best. Thus, the purpose of the present study was to find out which symptom is the most important one to monitor. Methods: We analysed data on COPD symptoms from the telehealth study The eHealth Diary. Frequent exacerbators (n = 27) were asked to daily monitor BaR and breathlessness at physical activity (BaPA), mucus and cough, employing a digital pen and symptom scales (0–10). Twenty-seven patients with 105 ECOPDs were analysed. The association between symptom development and the occurrence of exacerbations was evaluated using the Andersen–Gill formulation of the Cox proportional hazards model for the analysis of recurrent time-to-event data with time-varying predictors. Results: According to the criteria proposed by GOLD 2023, 42% ECOPDs were mild, 48% were moderate and 5% were severe, while 6% were undefinable. Mucus and cough improved over study time, while BaR and BaPA deteriorated. Mucus appeared earliest, which was the most prominent feature of the average exacerbation, and worsening of mucus increased the risk for a future ECOPD. There was a 58% increase in the risk of exacerbation per unit increase in mucus score. Conclusion: This study suggests that mucus worsening is the most important COPD symptom to monitor to detect ECOPDs early and to predict future risk för ECOPDs. In the present study, we also noticed a pronounced difference between GOLD 2022 and 2023. Hence, GOLD 2023 defined the ECOPD severity much lower than GOLD 2022 did. © 2023 Jacobson et al.
Introduction: In this article, we explore to what extent it is possible to leverage on very small data to build machine learning (ML) models that predict acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Methods: We build ML models using the small data collected during the eHealth Diary telemonitoring study between 2013 and 2017 in Sweden. This data refers to a group of multimorbid patients, namely 18 patients with chronic obstructive pulmonary disease (COPD) as the major reason behind previous hospitalisations. The telemonitoring was supervised by a specialised hospital-based home care (HBHC) unit, which also was responsible for the medical actions needed. Results: We implement two different ML approaches, one based on time-dependent covariates and the other one based on time-independent covariates. We compare the first approach with standard COX Proportional Hazards (CPH). For the second one, we use different proportions of synthetic data to build models and then evaluate the best model against authentic data. Discussion: To the best of our knowledge, the present ML study shows for the first time that the most important variable for an increased risk of future AECOPDs is “maintenance medication changes by HBHC”. This finding is clinically relevant since a sub-optimal maintenance treatment, requiring medication changes, puts the patient in risk for future AECOPDs. Conclusion: The experiments return useful insights about the use of small data for ML. © 2023 Jacobson et al.
Background: Welders are exposed to gas and particle emissions that can cause severe lung disease, such as chronic obstructive pulmonary disease (COPD), a leading cause of mortality and morbidity worldwide. It is difficult to detect COPD early and therefore mitigating measures may be delayed. The aim of this study was to investigate lung health in welders and evaluate new sensitive methods with potential to assess early onset pulmonary changes in occupational settings. Methods: This study assessed the lung health and symptoms in active welders (n = 28) and controls (n = 17). Lung measurements were performed with standard spirometry and new methods: airspace dimension assessment (AiDA), oscillometry, blood serum biomarkers (club cell secretory protein 16, surfactant protein D, matrix metalloproteinases, fibroblast, hepatocyte growth factor, interleukins), and one urine biomarker (desmosine). Results: According to spirometry measurements, all participants had normal lung function. However, prevalence of cough was significantly higher among welders compared with controls and lung changes were found in welders with the novel methods. Welders had significantly higher respiratory system resistance assessed with oscillometry, serum levels of metalloproteinases 9 and hepatocyte growth factor, compared with controls. Airspace dimensions were on average higher among welders compared with controls, but the difference was not significant. The number of welding years correlated with decreased respiratory system reactance and increased serum levels of matrix metalloproteinases 9, interleukin 6, and hepatocyte growth factor. Airspace dimension assessment indices were correlated with increasing levels of inflammatory markers and matrix metalloproteinases. Conclusions: This study indicated the potential to use new and more sensitive methods for identification of changes in lungs when standard spirometry failed to do so. © 2023 The Authors