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Chronic Inflammation Prediction for Inhaled Particles, the Impact of Material Cycling and Quarantining in the Lung Epithelium

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Mus musculus

PROVIDER: GSE157856 | GEO | 2020/09/12

REPOSITORIES: GEO

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Chronic Inflammation Prediction for Inhaled Particles, the Impact of Material Cycling and Quarantining in the Lung Epithelium

Project description:We are daily exposed to a multitude of health hazardous airborne particulate matter with notable deposition in the fragile alveolar region of our lungs. Hence, there is a great need for identification and prediction of material-associated diseases, currently hindered due to the lack of in-depth understanding of causal relationships, in particular between acute exposures and chronic symptoms. By applying advanced microscopies and omics to in vitro and in vivo systems, together with in silico molecular modelling, we have here determined that the long-lasting response to a single exposure can originate from the interplay between the newly discovered nanomaterial quarantining and nanomaterial cycling between different lung cell types. This new insight finally allows us to predict the spectrum of lung inflammation associated with materials of interest using only in vitro measurements and in silico modelling potentially relating outcomes to material properties for large number of materials thus boosting safe-by-design-based material development. Because of its profound implications for animal-free predictive toxicology, our work paves the way to a more efficient and hazard-free introduction of numerous new advanced materials into our lives.

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Chronic Inflammation Prediction for Inhaled Particles, the Impact of Material Cycling and Quarantining in the Lung Epithelium [Tube_TiO2_NP]

Project description:In this study, using advanced microscopy, in vivo/in vitro omics experiments and in silico molecular modelling, the long-lasting response to a single exposure of different lung cell types to nanomaterial was determined. The information was further used to build a predictive algorithm that would allow identification of nanomaterials that have the potential to induce inflammation.

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Chronic Inflammation Prediction for Inhaled Particles, the Impact of Material Cycling and Quarantining in the Lung Epithelium [Cube_TiO2_NP]

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Chronic Inflammation Prediction for Inhaled Particles, the Impact of Material Cycling and Quarantining in the Lung Epithelium

Project description:Chronic Inflammation Prediction for Inhaled Particles, the Impact of Material Cycling and Quarantining in the Lung Epithelium

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Chronic Inflammation Prediction for Inhaled Particles, the Impact of Material Cycling and Quarantining in the Lung Epithelium

Project description:Chronic Inflammation Prediction for Inhaled Particles, the Impact of Material Cycling and Quarantining in the Lung Epithelium

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Chronic Inflammation Prediction for Inhaled Particles, the Impact of Material Cycling and Quarantining in the Lung Epithelium [Tube_TiO2_NP]

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