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Machine Learning Combined with Spectral Imaging for Inferring the Toxicity of Micro- and Nanoplastics

Project description

Advancing risk assessment of micro- and nanoplastics

Micro- and nanoplastics (MNPs) pose significant risks to human health, potentially affecting the gastrointestinal system,, yet their small size and widespread presence complicate toxicity assessments. Traditional risk evaluation methods are costly and time-consuming, highlighting the need for innovative approaches. In this context, the ERC-funded PlasTox project seeks to advance our understanding of MNP risks by combining experimental, computational, and machine learning techniques. It will develop a framework to characterise MNPs using spectral imaging and bioassays, integrating this data with machine learning models to predict toxicity. By leveraging deep learning, PlasTox aims to uncover toxicological pathways and create predictive models to assess human health impacts, revolutionising risk assessment and toxicology research.

Objective

The project aims to advance our understanding of potential risks posed by micro- and nanoplastics (MNPs) to human gastrointestinal health through a combination of quantitative, experimental, and computational approaches, leveraging powerful machine learning (ML) algorithms and versatile spectral imaging techniques. Towards this goal, the project will first deliver a framework to extensively characterise MNPs using multiple spectral imaging techniques covering from micro- to nanoscale coupled with complementary instruments. The fused characterisation data will be combined with experimental in vitro bioassays to develop ML models, enabling the prediction of toxicity patterns and unveiling key drivers of MNP toxicity. Harnessing the broad literature data, a knowledge-based deep learning approach will be employed to unlock mechanistic insights into toxicological pathways. The most ambitious part of the proposal is to integrate previously acquired knowledge to develop innovative predictive models for predicting human health impacts of MNPs based on their physicochemical properties. This will be achieved through two independent pathways: one built on insights from in vitro experiments and another rooted in extensive literature data. The ground-breaking approaches hold the potential to revolutionise the characterisation and risk assessment of MNPs, significantly reducing reliance on expensive in vitro and in vivo experiments.

This project offers a unique integration of approaches, competencies and resources in environmental science, life science, analytical chemistry, machine learning, and computer vision and technological developments of spectral imaging instruments. The outcomes could yield potential breakthroughs in numerous key applications of tremendous human, technological, and environmental importance, such as toxicological screening of drugs, safety assurance and environmental hazard monitoring and open a whole new field of research in toxicology.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2024-STG

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Host institution

UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 499 949,00
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 499 949,00

Beneficiaries (1)

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