Background: Lung fibroblasts are implicated in abnormal tissue repair in chronic obstructive pulmonary disease (COPD). The exact mechanisms are unknown and comprehensive analysis comparing COPD- and control fibroblasts is lacking. Aim: To gain insight in the role of lung fibroblasts in COPD pathology using unbiased proteomic and transcriptomic analysis. Methods: Protein and RNA was isolated from cultured parenchymal lung fibroblasts of 17 stage IV COPD patients and 16 non-COPD controls. Proteins were analyzed using LC-MS/MS and RNA through RNA sequencing. Differential protein and gene expression in COPD was assessed via linear regression, followed by pathway enrichment, correlation analysis and immunohistological staining in lung tissue. Proteomic and transcriptomic data was compared to investigate the overlap and correlation between both levels of data. Results: We identified 40 differentially expressed (DE) proteins and zero DE genes between COPD and control fibroblasts. The most significant DE proteins were HNRNPA2B1 and FHL1. Thirteen of the 40 proteins were previously associated with COPD, including FHL1 and GSTP1. Six of the 40 proteins were related to telomere maintenance pathways, and were positively correlated with the senescence marker LMNB1. No significant correlation between gene and protein expression was observed for the 40 proteins. Conclusions: The 40 DE proteins in COPD fibroblasts include previously described COPD proteins (FHL1, GSTP1) and new COPD research targets like HNRNPA2B1. Lack of overlap and correlation between gene and protein data supports the use of unbiased proteomics analysis and indicates that different types of information are generated with both methods.
Epidemiological miner cohort data used to estimate lung cancer risks related to occupational radon exposure often lack cohort-wide information on exposure to tobacco smoke, a potential confounder and important effect modifier. We have developed a method to project data on smoking habits from a case-control study onto an entire cohort by means of a Monte Carlo resampling technique. As a proof of principle, this method is tested on a subcohort of 35,084 former uranium miners employed at the WISMUT company (Germany), with 461 lung cancer deaths in the follow-up period 1955–1998. After applying the proposed imputation technique, a biologically-based carcinogenesis model is employed to analyze the cohort's lung cancer mortality data. A sensitivity analysis based on a set of 200 independent projections with subsequent model analyses yields narrow distributions of the free model parameters, indicating that parameter values are relatively stable and independent of individual projections. This technique thus offers a possibility to account for unknown smoking habits, enabling us to unravel risks related to radon, to smoking, and to the combination of both.
This research examines the impact of transitioning to an autonomous operation on the airside of Schiphol airport, with a specific focus on emissions that affect both the environment and the staff working within airport premises. This study will explore current emissions from vehicles on Schiphol's airside, assessing their environmental impact and identifying harmful emissions. It will evaluate potential solutions, notably the role of electric vehicles, comparing this to the status quo before mapping the transition to an autonomous airside and its environmental consequences. A significant focus will be on the implications for staff working in these conditions. Additionally, it will review relevant laws and regulations to propose improvements, aiming to enhance Schiphol's environmental footprint. Conducted by Bright Sky for Schiphol Airport, this research aims to address overlooked harmful substances at the airport, seeking prompt solutions. Utilized by Schiphol, the findings will shed light on the necessity for innovation towards electric and autonomous vehicles, underlining the urgency for environmental improvements and technological advancements to tackle pollution issues effectively.
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