Lattuada Group has recently published an article in the journal Nanotoxicology entitled, "Chemometrical assessment of adverse effects in lung cells induced by vehicle engine emissions", in collaboration with Fink Research Group.

This study employs machine learning to identify key components in vehicle engine exhaust responsible for lung cell toxicity, using reanalyzed in vitro data from 3D airway models exposed to gasoline and diesel emissions.

For more information: https://www.tandfonline.com/doi/full/10.1080/17435390.2025.2489631#abstract

Abstract

Vehicle engine exhausts contain complex mixtures of gaseous and particulate pollutants, which are known to affect lung functions adversely. Many in vitro studies have shown that exposure to engine exhaust can induce oxidative stress in lung cells, leading to cellular inflammation and cytotoxicity. However, it remains challenging to identify key harmful components and their specific adverse effects via traditional toxicological assessments. Machine learning (ML) methods offer new ways of analyzing such complex datasets and have gained attention in predicting toxicity outcomes and identifying key pollutants in mixtures responsible for adverse effects in a non-biased way. This study aims to understand the contribution of exhaust components to lung cell toxicity using ML techniques. Data were reanalyzed from previous studies (2015–2018), where a 3D human epithelial airway tissue model was exposed to gasoline and diesel engine exhausts under air-liquid interface (ALI) conditions with different fuels and exhaust after-treatment systems. This dataset included exhaust characteristics (particle number (PN), carbon monoxide (CO), total gaseous hydrocarbons (THC), and nitrogen oxides (NOx) levels) and corresponding biological responses (cytotoxicity, oxidative stress, and inflammatory responses).