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61,005 resultsShowing papers similar to Combining machine learning with meta-analysis for predicting cytotoxicity of micro- and nanoplastics
ClearMachine 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.
Deciphering the cytotoxicity of micro- and nanoplastics in Caco-2 cells through meta-analysis and machine learning
This meta-analysis uses data from multiple studies and machine learning to determine which properties of micro- and nanoplastics make them most toxic to human intestinal cells. The findings show that smaller particles and certain plastic types cause more cell damage, which is important for understanding how ingested microplastics may affect gut health.
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.
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.
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.
Prediction of the cytotoxicity of micro- and nanoplastics using machine learning combined with literature data mining
Researchers developed a machine learning framework using decision tree ensemble classifiers trained on 1,824 literature-derived data points to predict the cytotoxicity of micro- and nanoplastics based on nine physicochemical and experimental features. The full-feature model achieved 95% accuracy with 86% recall and precision, and feature selection identified six key predictors, providing a tool to guide experimental design and harmonize MNP toxicity research.
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.
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.
Metabolomics‑driven, data‑augmented machine learning for predicting toxicity of microplastic mixtures
Scientists developed a computer model that can predict how harmful mixtures of microplastics (tiny plastic particles) might be to our cells without testing each combination individually. The model works by analyzing how these plastic particles change the way cells produce energy, which helps explain why microplastics can be toxic. This breakthrough could help researchers quickly assess health risks from the complex mix of microplastics we're exposed to in real life through food, water, and air.
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 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.
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.
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.
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.
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.
Effects of the co-exposure of microplastic/nanoplastic and heavy metal on plants: Using CiteSpace, meta-analysis, and machine learning
This meta-analysis found that co-exposure to micro/nanoplastics and heavy metals produces stronger toxic effects on plants than heavy metal exposure alone, with toxicity increasing at higher concentrations, longer durations, and with nanoparticles. Notably, polyolefin plastics partially reduced plant toxicity from heavy metals, while modified polystyrene and biodegradable polymers worsened it.
Machine Learning Advancements and Strategies in Microplastic and Nanoplastic Detection
This systematic review looks at how machine learning is improving our ability to detect tiny microplastics and nanoplastics in the environment. Better detection methods matter because accurately measuring plastic contamination is the first step toward understanding — and reducing — human exposure.
A rapid review and meta-regression analyses of the toxicological impacts of microplastic exposure in human cells
Researchers conducted a systematic review and statistical analysis of studies examining the effects of microplastic exposure on human cells in the laboratory. They found evidence that microplastics can cause cell damage, inflammation, and oxidative stress, with smaller particles and higher doses generally producing stronger effects. The study provides the first pooled estimate of dose-response thresholds for microplastic toxicity in human cells, helping to frame the potential health risks of daily exposure.
Machine LearningAdvancements and Strategies in Microplasticand Nanoplastic Detection
This systematic review summarizes how machine learning technology is being used to detect microplastics and nanoplastics in the environment. Better detection methods are important because understanding where these particles are and how much is present is the first step toward assessing risks to human health.
Machine learning-enhanced meta-analysis unravels the global patterns of microplastic-heavy metal co-toxicity in terrestrial ecosystems
This meta-analysis of 1,820 datasets found that combined microplastic and heavy metal exposure significantly inhibits plant growth (22% height decrease), reduces microbial diversity, and increases animal intestinal damage and mortality. Nanoscale microplastics amplified heavy metal toxicity the most, suggesting that smaller plastic particles in soil pose the greatest combined pollution risk to ecosystems and food safety.
Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics
Researchers used machine learning models to predict how microplastics affect algal growth and found that exposure time, microplastic concentration, and particle size are the most important factors. Smaller microplastics and longer exposure periods had the greatest negative effects on algae, particularly when particles were smaller than the algal cells. The study provides a data-driven approach for assessing the ecological risks of microplastic pollution in aquatic environments.
[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].
This review examines the application of machine learning (ML) methods for identifying microplastics and assessing their environmental risks, covering techniques for improving the accuracy and reliability of microplastic detection across different environmental media. Researchers highlight how ML can systematically analyse pollution characteristics and support ecological risk evaluation of microplastic contamination.