Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Toxicity Testing in Animals

Toxicity Testing in Animals

Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Improved CenterNet-Based Multimodal Object Detection for Low-Light and Complex Environments.

Sensors (Basel, Switzerland)·2026

Same author

Minocycline as a therapeutic candidate in diabetic retinopathy: insights into pathophysiology and translational potential.

Experimental eye research·2026

Same author

Recent Progress in Mechanism-Based Therapies for <i>GJB2</i>-Related Hearing Loss.

International journal of molecular sciences·2026

Same author

Fibrovascular Retinal Membrane and Tractional Retinal Detachment in a Pediatric Patient With Neurofibromatosis Type 1: Case Report.

Journal of pediatric ophthalmology and strabismus·2026

Same author

Metabolomics reveals formonetin mitigates neurotoxicity of CL-20 on neuron cells.

Drug and chemical toxicology·2026

Same author

Molecular-level precursor engineering enables high utilization of closed nanopores in hard carbon for sodium-ion batteries.

Chemical science·2026

Same journal

Independent and interactive associations of green space and air pollution with blood lipid biomarkers: A longitudinal study.

Ecotoxicology and environmental safety·2026

Same journal

Degradation, adsorption, and migration characteristics of fluridone in three types of soils.

Ecotoxicology and environmental safety·2026

Same journal

Neurotoxicity mechanism of polychlorinated biphenyl mediated by dopaminergic system.

Ecotoxicology and environmental safety·2026

Same journal

Molecular transformation of synthetic cannabinoids in surface water.

Ecotoxicology and environmental safety·2026

Same journal

Associations between diet quality and multiple chemical exposures.

Ecotoxicology and environmental safety·2026

Same journal

Corrigendum to "Bone marrow mesenchymal stem cells senescence induced by LCCP through activation of cGAS-STING-mediated inflammation" [Ecotoxicol. Environ. Saf. 294 (2025) 118069].

Ecotoxicology and environmental safety·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 28, 2026

Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline

Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline

Published on: June 13, 2025

Metabolomics‑driven, data‑augmented machine learning for predicting toxicity of microplastic mixtures.

Beilei Yuan¹, Chengzhi Liu², Shuang Chen¹

¹College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu 210009, China.

Ecotoxicology and Environmental Safety

|February 25, 2026

Summary

This summary is machine-generated.

Predicting microplastic (MP) toxicity is challenging due to complex mixtures. A metabolomics-driven machine learning model effectively predicted MP cytotoxicity, offering insights into cellular energy metabolism reprogramming.

Keywords:

Cytotoxicity Data augmentation Machine learning Microplastics QSAR

More Related Videos

Extraction of Organochlorine Pesticides from Plastic Pellets and Plastic Type Analysis

Extraction of Organochlorine Pesticides from Plastic Pellets and Plastic Type Analysis

Published on: July 1, 2017

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Published on: July 28, 2018

Related Experiment Videos

Last Updated: Feb 28, 2026

Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline

Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline

Published on: June 13, 2025

Extraction of Organochlorine Pesticides from Plastic Pellets and Plastic Type Analysis

Extraction of Organochlorine Pesticides from Plastic Pellets and Plastic Type Analysis

Published on: July 1, 2017

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Published on: July 28, 2018

Area of Science:

Environmental Science
Toxicology
Computational Chemistry

Background:

Microplastics (MPs) exist as complex mixtures in the environment, hindering individual toxicity assessments.
Developing rapid and effective methods to evaluate the toxicity of MP mixtures is crucial for risk assessment.

Purpose of the Study:

To develop predictive models for evaluating the toxicity of microplastic mixtures.
To compare the performance of quantitative structure-activity relationship (QSAR), quantitative bioactivity relationship (QBAR), and quantitative structure-bioactivity relationship (QSBAR) models.

Main Methods:

Explored three model frameworks: QSAR, QBAR, and QSBAR.
Employed six machine learning algorithms with data augmentation strategies.
Utilized metabolomics data to screen biodescriptors for the QBAR model.

Main Results:

The QBAR-based eXtreme Gradient Boosting (XGB-qbar) model demonstrated superior performance (R²test = 0.8923).
Key biological descriptors influencing toxicity were identified.
Metabolomics analysis revealed MP mixture exposure may reprogram cellular energy metabolism.

Conclusions:

A metabolomics-driven, data-augmented machine learning approach efficiently predicts microplastic toxicity in complex mixtures.
This approach provides mechanistic insights and a feasible pathway for environmental risk assessment.