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Papers
61,005 resultsShowing papers similar to A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations
ClearMetabolomics‑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.
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.
Microplastics and PFAS as ubiquitous pollutants affect potencies of highly toxic chemicals in mixtures
Researchers investigated how ubiquitous pollutants like PFAS and microplastics affect the toxicity of other highly toxic chemicals when present together in mixtures. They found that even at non-toxic concentrations, PFAS and microplastics could alter the potency of co-occurring toxic compounds. The study highlights the importance of considering pollutant interactions in complex environmental mixtures rather than assessing chemicals in isolation.
The emerging threat of hybrid microplastics: Impacts on per(poly)fluoroalkyl substance bioaccumulation and phytotoxicity in floating macrophytes
This study examined how mixtures of different microplastic types interact with PFAS (forever chemicals) and found that more diverse microplastic mixtures increased the absorption and toxicity of PFAS in aquatic plants. The complexity of real-world microplastic pollution, where multiple plastic types coexist, appears to make forever chemical contamination worse. This finding is important because most lab studies test single plastic types, potentially underestimating the actual environmental risk.
Unraveling the complexities of microplastics and PFAS synergy to foster sustainable environmental remediation and ecosystem protection: A critical review with novel insights
This review examines how microplastics and PFAS (sometimes called 'forever chemicals') interact in the environment, since both often come from the same everyday products. The authors found that microplastics can carry PFAS on their surface, and when organisms are exposed to both together, the combined toxic effects including oxidative stress and reproductive harm can be worse than either pollutant alone.
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.
Combining 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.
Toxicity of microplastics and per- and polyfluoroalkyl substances in sentinel freshwater models, Daphnia, Zebrafish and unicellular green algae: A systemic review
Researchers reviewed 68 studies on how microplastics and PFAS ("forever chemicals") affect freshwater organisms like Daphnia, zebrafish, and algae, finding that both contaminants are more toxic at chronic low doses than in short-term exposures, and that combining them tends to amplify harm — while noting almost no research has studied them together.
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.
The toxicological impact of PET-MPs exposure on atherosclerosis: insights from network toxicology, molecular docking, and machine learning
Researchers used network toxicology, molecular docking, and machine learning to identify how PET microplastics may promote atherosclerosis, narrowing 28 candidate targets to seven key genes and predicting interactions with atherosclerosis-relevant pathways including inflammation and lipid metabolism.
Development of AOP relevant to microplastics based on toxicity mechanisms of chemical additives using ToxCast™ and deep learning models combined approach
Researchers used ToxCast high-throughput screening data and deep learning models to build adverse outcome pathway (AOP) models for common plastic additive chemicals, identifying molecular initiating events and potential health endpoints relevant to human and environmental microplastic toxicity.
The Microplastic–PFAS Nexus: From Co-Occurrence to Combined Toxicity in Aquatic Environments
This review examines the interconnected environmental behavior of microplastics and PFAS ("forever chemicals"), showing that microplastics can act as carriers, concentrators, and secondary sources of PFAS contamination. Researchers found that co-exposure to both pollutants often produces synergistic toxic effects in aquatic organisms, disrupting processes from photosynthesis to neurological development. The study argues that current regulations assessing these pollutants individually are inadequate and must evolve to address their combined effects.
Microplastics as carriers of per- and polyfluoroalkyl substances (PFAS) in aquatic environment: interactions and ecotoxicological effects
Researchers reviewed how microplastics serve as carriers for per- and polyfluoroalkyl substances (PFAS), sometimes called forever chemicals, in aquatic environments. The study found that PFAS can attach to microplastic surfaces and accumulate in organisms through the food chain, potentially amplifying the toxic effects of both pollutants. The findings suggest that the combined presence of microplastics and PFAS poses a greater environmental and health risk than either pollutant alone.
Predicting aqueous sorption of organic pollutants on microplastics with machine learning
Researchers developed machine learning models to predict how organic pollutants bind to microplastics in water, using data from 475 published experiments. The models outperformed traditional approaches by accounting for properties of both the microplastics and the pollutants simultaneously. The study provides a more universal tool for understanding how microplastics can transport and concentrate harmful chemicals in freshwater systems.
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.
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.
Interaction of microplastics with perfluoroalkyl and polyfluoroalkyl substances in water: A review of the fate, mechanisms and toxicity
This review examines how microplastics act as carriers for PFAS ("forever chemicals") in water, with the two pollutants interacting through various chemical mechanisms that affect their movement through the environment. The combined presence of microplastics and PFAS raises concerns about increased toxicity, since microplastics can transport these persistent chemicals into organisms and potentially concentrate their harmful effects.
Molecular-Scale Insights into the Interactions between Perfluoroalkyl Substances and Polyethylene
Scientists found that tiny plastic particles called microplastics can strongly attract and hold onto toxic "forever chemicals" called PFAS, which are already found in drinking water and food. This means microplastics in our environment could act like sponges that collect these harmful chemicals and potentially transport them to new places, including into our bodies. The research helps explain why these two types of pollution might work together to create bigger health risks than either one alone.
Synergistic toxicity of PFAS and microplastic mixtures across five human cell lines
Researchers tested the combined toxicity of PFAS chemicals and microplastics on five types of human cells representing the kidney, liver, prostate, skin, and lung. They found that mixtures of these common environmental contaminants produced synergistic harmful effects, particularly in kidney and liver cells, including increased oxidative stress and DNA damage. The study suggests that the combined exposure to PFAS and microplastics, which frequently co-occur in the environment, may pose greater health risks than either pollutant alone.
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.
In Silico Analysis of Contaminant Persistence: From QSARs to Machine Learning Models
Researchers reviewed six decades of computational approaches for predicting environmental persistence of contaminants, tracing the evolution from classical QSAR models to modern machine learning methods, and proposed a practical roadmap for applying these tools to small molecules, polymers, and microplastics in decision-ready regulatory contexts.
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.