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61,005 resultsShowing papers similar to Prediction of the cytotoxicity of micro- and nanoplastics using machine learning combined with literature data mining
ClearCombining machine learning with meta-analysis for predicting cytotoxicity of micro- and nanoplastics
This meta-analysis used machine learning to predict how toxic different types of micro- and nanoplastics are to cells. By analyzing data from many studies, it identified that particle size, concentration, and exposure time are key factors determining toxicity — smaller particles and longer exposures tend to cause more cell damage.
Predicting the toxicity of microplastic particles through machine learning models
Researchers developed machine learning models to predict microplastic particle toxicity from physical and chemical descriptors, addressing the classification challenge posed by the enormous diversity of particle types that cannot be characterized using conventional chemical hazard methods. The models provided accurate toxicity predictions across diverse microplastic types, offering a practical screening tool for the field.
Predicting the toxicity of microplastic particles through machine learning models
Researchers applied machine learning models to predict the toxicity of microplastic particles from their physical and chemical properties, addressing the challenge that microplastics lack the standardized identifiers used for chemical hazard classification. The models successfully predicted toxicity outcomes from particle descriptors, offering a framework for hazard screening of the diverse and complex microplastic contaminant class.
Unraveling the ecotoxicity of micro(nano)plastics loaded with environmental pollutants using ensemble machine learning.
Researchers developed an ensemble machine learning algorithm to predict the ecotoxicity of micro(nano)plastics loaded with environmental pollutants, addressing a key knowledge gap where most studies examine plastic particles alone. The model revealed that co-pollutant loading substantially amplifies toxicity and that particle characteristics govern outcomes.
Potential threat of microplastics to humans: toxicity prediction modeling by small data analysis
Researchers developed a toxicity prediction model for microplastics using small data analysis techniques, enabling the anticipation of varying toxic effects depending on microplastic types and compositions found in nature.
Nanoplastics ToxicityIs a Subset of Nanotoxicology,Not a Separate Field
Using data mining and machine learning on the nanoplastics toxicity literature, researchers demonstrate that nanoplastics toxicological findings align closely with established nanotoxicology, arguing against treating nanoplastics as a separate research field and advocating for integrated approaches.
Quantifying the influence of micro and nanoplastics characteristics on cytotoxicity in caco-2 cells through machine learning modelling.
This systematic review uses machine learning to determine which properties of micro and nanoplastics drive toxicity in human intestinal cell models. The findings reveal that smaller particles and higher concentrations cause more cell damage, which is important for understanding how the microplastics we swallow in food and water might harm our gut lining.
Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective
This review evaluates how machine learning and artificial intelligence are being used to predict the toxic effects of nanomaterials, including nanoplastics, on human health and the environment. These computational tools can help screen thousands of materials for potential hazards much faster than traditional lab experiments, though the authors note that better data quality and standardized methods are still needed.
Transfer learning enables robust prediction of cellular toxicity from environmental micro- and nanoplastics
Researchers developed a transfer learning approach to predict cellular toxicity from micro- and nanoplastics, overcoming the challenge of limited experimental data. By pre-training a model on a large nanoparticle dataset and fine-tuning it on plastic-specific data, they achieved strong predictive accuracy. The tool allows researchers to estimate the toxicity of various plastic particles based on their physical and chemical properties without extensive new experiments.
Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics
Researchers used machine learning to predict the toxicity of five common microplastic types on human lung cells, finding that particle size, plastic type, and exposure concentration were the most important factors determining harm. This computational approach could help assess the health risks of different microplastics more efficiently than traditional lab testing alone.
Machine-Learning-Accelerated Prediction of Water Quality Criteria for Microplastics
Researchers developed a machine learning framework to predict microplastic toxicity in aquatic organisms and derive water quality criteria for five common polymer types. The random forest model outperformed other algorithms, with particle size, density, and aquatic species group accounting for 72% of prediction variability. The study found that polystyrene and PET exhibited the greatest toxicity, and that microplastics were generally more toxic in freshwater than saltwater environments.
Data-driven machine learning modeling reveals the impact of micro/nanoplastics on microalgae and their key underlying mechanisms
Researchers used machine learning to predict how micro- and nanoplastics affect freshwater algae, training models on a decade of published experimental data. The best-performing model identified plastic concentration, exposure time, and particle size as the most important factors determining toxicity. The study offers a data-driven framework that could reduce the need for time-consuming laboratory experiments when assessing microplastic risks to aquatic organisms.
Health impacts of micro- and nanoplastics: key influencing factors, limitations, and future perspectives
This review systematically analyzed how the physicochemical properties of micro- and nanoplastics — including size, shape, surface charge, and polymer type — determine their toxicological impacts across biological systems. The authors argue that property-based frameworks are essential for predicting MNP health risks and designing relevant research.
Quantifying the influence of micro and nanoplastics characteristics on cytotoxicity in caco-2 cells through machine learning modelling.
This systematic review uses machine learning to identify which characteristics of micro and nanoplastics are most toxic to intestinal cells. The researchers found that particle size, shape, and concentration all play important roles in how much damage these plastics cause to gut lining cells, helping us understand how ingested microplastics might affect digestive health.
Harmfulness Score: A Data‐Driven Framework for Ranking Environmental Risks of Microplastics
Researchers analyzed over 104,000 scientific abstracts on micro- and nanoplastics using bibliometric tools and machine learning to create a data-driven framework for ranking environmental risks. The resulting Harmfulness Score ranked polystyrene and polyethylene as the highest-risk polymers based on their association with oxidative stress, cytotoxicity, and genotoxicity in the scientific literature.
Data driven methods to increase the reliability of microplastics hazard assessment
Researchers applied statistical data-driven methods to improve the reliability of microplastic hazard assessments derived from a growing but inconsistent body of ecotoxicology literature. The analysis identified key study characteristics that explain variability in reported effect sizes.
Screening and prioritization of nano- and microplastic particle toxicity studies for evaluating human health risks – development and application of a toxicity study assessment tool
Researchers developed a standardized tool to screen and rank toxicity studies on nano- and microplastics by quality and relevance, addressing a critical gap in how scientists evaluate which studies should inform human health risk assessments for these widespread plastic pollutants.
Algorithm Comparison for Microplastic Classification: Evaluating Ensemble Models on Density and Measurement Features
Researchers compared machine learning algorithms for classifying microplastic types based on density and measurement features, evaluating ensemble models against standard classifiers. The ensemble approaches outperformed individual models, suggesting that combining multiple algorithms improves automated MP identification from physical measurement data.
Prediction of the joint toxicity of microplastics and organic pollutants on algae based on machine learning
Researchers used machine learning models to predict the combined toxicity of microplastics and organic pollutants on algae, achieving high accuracy with gradient-boosted decision tree models. They found that microplastic concentration, particle size, and the hydrophobicity of organic pollutants were the most important factors influencing toxic effects. The study provides a computational framework that could help assess environmental risks from microplastic-pollutant mixtures more efficiently than traditional laboratory testing.
Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles
Researchers used machine learning to predict how toxic different mixtures of metal nanoparticles are to bacteria. Their models outperformed traditional methods at predicting combined toxicity effects. While focused on engineered nanoparticles rather than microplastics, the computational approach could be adapted to predict health risks from the complex mixtures of nano-sized pollutants people encounter.
In vivo , in vitro , and in silico toxicology studies of nanoplastics and their modeling
This in vivo, in vitro, and in silico study assessed nanoplastic toxicity through multiple complementary methods, finding concentration-dependent toxic effects on cellular and organismal endpoints and using computational modeling to predict interaction mechanisms relevant to nanoplastic risk assessment.
Toxicity of nanoplastics: machine learning combined with meta-analysis
This meta-analysis combined data from multiple studies and used machine learning to assess nanoplastic toxicity in mice. The findings showed that nanoplastics cause a wide range of harmful effects across multiple body systems, with the severity depending on particle size, type, exposure route, and duration. These results suggest that the nanoplastics we encounter daily could have complex, varied effects on mammalian health.
Assessment of machine learning-based methods predictive suitability for migration pollutants from microplastics degradation
Researchers assessed the usefulness of machine learning methods for predicting the migration of chemical pollutants from microplastics. The study found that artificial neural networks and support vector methods showed strong potential for modeling and predicting the leaching of plasticizers and other contaminants, which could reduce the need for extensive laboratory analyses.
Nanoplastics Toxicity Is a Subset of Nanotoxicology, Not a Separate Field
Researchers used data mining and machine learning to analyze over 154,000 published articles and found that nanoplastics toxicity research closely mirrors the well-established field of engineered nanoparticle toxicology. The study argues that treating nanoplastics as a separate research area leads to inefficient use of resources and duplicated efforts. Evidence indicates that integrating nanoplastics research within the broader framework of nanotoxicology would accelerate progress and improve risk assessment.