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61,005 resultsShowing papers similar to Toxicity of nanoplastics: machine learning combined with meta-analysis
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.
Toxicological effects of micro/nano-plastics on mouse/rat models: a systematic review and meta-analysis
This meta-analysis pools data from mouse and rat studies to assess the toxic effects of micro and nanoplastics on mammalian health. The findings show that these particles can cause damage across multiple organ systems in lab animals, providing important evidence about the potential health risks that microplastic exposure may pose to humans.
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.
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.
Effects of Nanoplastics on Human Health: A Comprehensive Study
This comprehensive review examines the diverse health effects of nanoplastics, drawing on toxicology, environmental science, and epidemiology to document how these particles interact with human biological systems. The authors conclude that nanoplastics represent a growing public health concern requiring further investigation.
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 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.
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.
Toxicity of Microplastics and Nanoplastics in Mammalian Systems
This review summarizes recent findings on how micro- and nanoplastics affect mammalian health, drawing on mouse model experiments and human cell line studies. Researchers found evidence that these tiny plastic particles can disrupt gut microbiota, cause metabolic toxicity, and accumulate in tissues after ingestion or inhalation. The study suggests that long-term accumulation of micro- and nanoplastics in human tissues could have negative health consequences that are not yet fully understood.
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.
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.
Predicting Bioaccumulation of Nanomaterials: Modeling Approaches with Challenges
This review examines different computer modeling approaches for predicting how nanomaterials, including nanoplastics, accumulate in living organisms. Traditional models developed for dissolved chemicals often give inaccurate results for nanoparticles because they behave differently in biological systems. Newer machine learning approaches show promise for better predictions, which could help scientists estimate how much nanoplastic actually builds up in the body without needing extensive animal testing.
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.
A review of data for quantifying human exposures to micro and nanoplastics and potential health risks
This review synthesizes data on human exposure to micro- and nanoplastics through air, water, and food, and examines the potential health effects. Researchers found evidence of respiratory, liver, immune, and gastrointestinal impacts in humans and mammals exposed to elevated plastic particle levels, with toxicity varying by plastic type and size. The study highlights that while growing evidence links plastic particle exposure to health concerns, significant data gaps remain in quantifying actual human intake and long-term risks.
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.
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-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 adverse health effects of ingested micro- and nanoplastics on humans. Lessons learned from in vivo and in vitro mammalian models
This review compiles recent studies on the effects of ingested micro- and nanoplastics using mammalian in vivo and in vitro models to assess potential human health implications. The authors found that while substantial research effort has been made, significant gaps remain in understanding absorption, biodistribution, and toxicity of these particles in mammalian systems. The review provides recommendations for improved testing methods to generate more relevant and targeted data for human risk assessment.
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.
Adverse Effects of Nanoplastics Administration on the Metabolic Profile and Glucose Control in Mice
This systematic review examines how nanoplastic exposure in mice affects metabolism and blood sugar control. The findings suggest that ingesting nanoplastics may disrupt metabolic processes and glucose regulation in mammals, raising concerns about potential links between everyday plastic exposure and metabolic health conditions like diabetes in humans.
Micro- and nanoplastics (MNPs) and their potential toxicological outcomes: State of science, knowledge gaps and research needs
This review summarizes what is known about the toxicity of micro- and nanoplastics in mammals, drawing from both cell studies and animal experiments. Evidence suggests these particles can cause inflammation, oxidative stress, gut disruption, and reproductive harm, with effects depending on particle size, shape, and chemical composition. However, most studies use uniform lab-made particles rather than the irregular plastics humans actually encounter, making real-world risk assessment challenging.
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 computational framework for multi-scale data fusion in assessing the associations between micro- and nanoplastics and human hepatotoxicity
Researchers developed a computational toxicology framework integrating multi-source data and network analysis to map associations between micro- and nanoplastics and hepatotoxicity, identifying key molecular pathways through which MNPs may damage the liver, offering a scalable alternative to traditional in vivo testing.
Consequences of nano and microplastic exposure in rodent models: the known and unknown
This review summarizes what rodent studies have revealed about the health effects of micro and nanoplastic exposure, including inflammation, oxidative stress, metabolic disruption, and reproductive harm. Researchers found that toxic effects depend heavily on particle size, polymer type, shape, and exposure route, making it difficult to draw broad conclusions. The study highlights major gaps in current knowledge and calls for more standardized research to better assess human health risks.