Machine Learning Demonstrates Dominance of Physical Characteristics over Particle Composition in Coal Dust Toxicity
Conchita Kamanzi, Megan Becker, Johanna von Holdt, Nai‐Jen Hsu, Petr Konečný, Jennifer L. Broadhurst, Muazzam Jacobs
Abstract
High Resolution Image Download MS PowerPoint Slide Mine dust has been linked to the development of pneumoconiotic diseases such as silicosis and coal workers’ pneumoconiosis. Currently, it is understood that the physicochemical and mineralogical characteristics drive the toxic nature of dust particles; however, it remains unclear which parameter(s) account for the differential toxicity of coal dust. This study aims to address this issue by demonstrating the use of the partial least squares regression (PLSR) machine learning approach to compare the influence of D 50 sub 10 μm coal particle characteristics against markers of cellular damage. The resulting analysis of 72 particle characteristics against cytotoxicity and lipid peroxidation reflects the power of PLSR as a tool to elucidate complex particle-cell relationships. By comparing the relative influence of each characteristic within the model, the results reflect that physical characteristics such as shape and particle roughness may have a greater impact on cytotoxicity and lipid peroxidation than composition-based parameters. These results present the first multivariate assessment of a broad-spectrum data set of coal dust characteristics using latent structures to assess the relative influence of particle characteristics on cellular damage.