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result(s) for
"Kadushkin, Aliaksei"
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Coronas of micro/nano plastics: a key determinant in their risk assessments
by
Kadushkin, Aliaksei
,
Wang, Fangjun
,
Cao, Jiayu
in
Biomedical and Life Sciences
,
Biomedicine
,
Biotransformation
2022
As an emerging pollutant in the life cycle of plastic products, micro/nanoplastics (M/NPs) are increasingly being released into the natural environment. Substantial concerns have been raised regarding the environmental and health impacts of M/NPs. Although diverse M/NPs have been detected in natural environment, most of them display two similar features, i.e.,high surface area and strong binding affinity, which enable extensive interactions between M/NPs and surrounding substances. This results in the formation of coronas, including eco-coronas and bio-coronas, on the plastic surface in different media. In real exposure scenarios, corona formation on M/NPs is inevitable and often displays variable and complex structures. The surface coronas have been found to impact the transportation, uptake, distribution, biotransformation and toxicity of particulates. Different from conventional toxins, packages on M/NPs rather than bare particles are more dangerous. We, therefore, recommend seriously consideration of the role of surface coronas in safety assessments. This review summarizes recent progress on the eco–coronas and bio-coronas of M/NPs, and further discusses the analytical methods to interpret corona structures, highlights the impacts of the corona on toxicity and provides future perspectives.
Journal Article
CD206 and dust particles are prognostic biomarkers of progressive fibrosing interstitial lung disease associated with air pollutant exposure
2025
Current management strategies for progressive fibrosing interstitial lung disease (PF-ILD) and non-PF-ILD differ significantly, underscoring the need for early identification of PF-ILD patients. We analyzed the expression of macrophage markers and the number of dust particles (DP) in lung tissue, as well as complete blood count and blood chemistry tests to identify biomarkers of PF-ILD, and examined the effect of certain pollutants on these biomarkers. Lung biopsies were collected from 73 non-PF-ILD patients and 36 PF-ILD patients. DP were quantified in alveolar wall cells (DP-aw) and desquamated epithelial cells (DP-desq) using polarizing light microscopy. Expression of CD206, transforming growth factor β1 (TGF-β1), connective tissue growth factor (CTGF), C-X-C motif ligand 13 (CXCL13), fibroblast growth factor 2 (FGF-2), tumor necrosis factor α (TNFα), and interleukin 1β (IL-1β) was assessed in lung tissue by immunohistochemistry. The numbers of DP-desq, pulmonary expression of CXCL13, IL-1β and CD206 were higher in ILD patients resided for ≥ 15 days per year in places with 24-hour ambient PM
10
level of ≥ 50 µg/m
3
compared with ILD patients exposed for < 15 days per year to the similar PM
10
concentration. Additionally, CXCL13 expression in lung tissue was higher in smoking ILD patients than in non-smoking ILD patients. Compared with non-PF-ILD patients, PF-ILD patients exhibited higher numbers of DP-aw and DP-desq, as well as increased expression of CD206, CXCL13, IL-1β, TGF-β1, and CTGF in lung tissue. Elevated blood neutrophil-to-lymphocyte (NLR) and platelet-to-lymphocyte ratios were also observed in PF-ILD patients. These biomarkers were found to be independent predictors of PF-ILD. A regression logistic model incorporating NLR, CD206, and DP-desq predicted PF-ILD with an AUC of 0.847, sensitivity of 84.6%, and specificity of 83.3%. Our findings may be useful in predicting PF-ILD and highlight the need for reducing pollutant emission.
Journal Article
Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosis
2025
BackgroundThe advancement of nanotechnology underscores the imperative need for establishing in silico predictive models to assess safety, particularly in the context of chronic respiratory afflictions such as lung fibrosis, a pathogenic transformation that is irreversible. While the compilation of predictive descriptors is pivotal for in silico model development, key features specifically tailored for predicting lung fibrosis remain elusive. This study aimed to uncover the essential predictive descriptors governing nanoparticle-induced pulmonary fibrosis.MethodsWe conducted a comprehensive analysis of the trajectory of metal oxide nanoparticles (MeONPs) within pulmonary systems. Two biological media (simulated lung fluid and phagolysosomal simulated fluid) and two cell lines (macrophages and epithelial cells) were meticulously chosen to scrutinize MeONP behaviors. Their interactions with MeONPs, also referred to as nano-bio interactions, can lead to alterations in the properties of the MeONPs as well as specific cellular responses. Physicochemical properties of MeONPs were assessed in biological media. The impact of MeONPs on cell membranes, lysosomes, mitochondria, and cytoplasmic components was evaluated using fluorescent probes, colorimetric enzyme substrates, and ELISA. The fibrogenic potential of MeONPs in mouse lungs was assessed by examining collagen deposition and growth factor release. Random forest classification was employed for analyzing in chemico, in vitro and in vivo data to identify predictive descriptors.ResultsThe nano-bio interactions induced diverse changes in the 4 characteristics of MeONPs and had variable effects on the 14 cellular functions, which were quantitatively evaluated in chemico and in vitro. Among these 18 quantitative features, seven features were found to play key roles in predicting the pro-fibrogenic potential of MeONPs. Notably, IL-1β was identified as the most important feature, contributing 27.8% to the model’s prediction. Mitochondrial activity (specifically NADH levels) in macrophages followed closely with a contribution of 17.6%. The remaining five key features include TGF-β1 release and NADH levels in epithelial cells, dissolution in lysosomal simulated fluids, zeta potential, and the hydrodynamic size of MeONPs.ConclusionsThe pro-fibrogenic potential of MeONPs can be predicted by combination of key features at nano-bio interfaces, simulating their behavior and interactions within the lung environment. Among the 18 quantitative features, a combination of seven in chemico and in vitro descriptors could be leveraged to predict lung fibrosis in animals. Our findings offer crucial insights for developing in silico predictive models for nano-induced pulmonary fibrosis.
Journal Article