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Updated: May 22, 2025

Microglia as a Surrogate Biosensor to Determine Nanoparticle Neurotoxicity

Microglia as a Surrogate Biosensor to Determine Nanoparticle Neurotoxicity

Published on: October 25, 2016

Machine learning-driven nanoparticle toxicity.

Zied Hosni¹, Sofiene Achour², Fatma Saadi³

¹Institute for Materials Discovery, University College London, 40 Roberts Building, London WC1E 7 JE, United Kingdom.

Ecotoxicology and Environmental Safety

|May 20, 2025

Summary

This summary is machine-generated.

Machine learning models predict nanoparticle toxicity by analyzing key features. Smaller nanoparticles and higher concentrations increase toxicity, while surface charge significantly impacts cellular effects, guiding safer nanomaterial design.

Keywords:

Machine learning Nanoparticles Nanotoxicity QSPR Random forest Xgboost

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Last Updated: May 22, 2025

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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

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Published on: January 27, 2023

Area of Science:

Nanotechnology
Toxicology
Computational Biology

Background:

Nanoparticle (NP) applications in medicine and industry require understanding their toxicity.
Predicting NP toxicity is complex due to multifaceted interactions with biological systems.

Purpose of the Study:

To develop and interpret machine learning models for predicting nanoparticle toxicity.
To identify key NP features influencing cellular toxicity.

Main Methods:

Utilized Random Forest (RF) and Light Gradient Boosting Machine (LightGBM) algorithms.
Employed Shapley Additive exPlanations (SHAP) for model interpretability.
Analyzed a robust dataset of NP characteristics and toxicity outcomes.

Main Results:

Identified inverse relationship between NP concentration and cell viability.
Found smaller NPs exhibit higher toxicity due to increased surface-to-volume ratio.
LightGBM model highlighted zeta potential (surface charge) as a critical factor in cytotoxicity.

Conclusions:

Machine learning models, particularly LightGBM, effectively predict NP toxicity.
Key features like size, concentration, and zeta potential are crucial for mitigating NP toxicity.
Findings guide the design of safer nanoparticles for biomedical and industrial applications, promoting responsible nanotechnology.