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20 resultsShowing papers similar to Quantifying the influence of micro and nanoplastics characteristics on cytotoxicity in caco-2 cells through machine learning modelling.
ClearQuantifying 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.
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.
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.
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.
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 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.
Effect of microplastics and nanoplastics in gastrointestinal tract on gut health: A systematic review.
This systematic review of 30 in vitro studies found that microplastics and nanoplastics cause size- and concentration-dependent damage to human gastrointestinal cells, including increased oxidative stress, mitochondrial dysfunction, inflammation, and apoptosis. Smaller particles consistently showed greater cellular uptake and biological effects, though chronic low-dose exposure generally produced minimal impacts.
Quantitative evaluation of microplastic interference with gut microbiota: Identifying sensitive indicators and key factors
This meta-analysis combined with machine learning found that the Firmicutes-to-Bacteroidetes ratio is the most sensitive biomarker of microplastic-induced gut microbiome disruption, with exposure concentration, particle size, and duration as the key drivers. The resulting predictive model (R=0.91) offers a quantitative tool for assessing gastrointestinal harm from microplastic exposure.
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.
Effect of microplastics and nanoplastics in gastrointestinal tract on gut health: A systematic review.
This systematic review provides the first comprehensive look at how microplastics and nanoplastics affect the human gut using laboratory models. The findings help explain how these tiny particles may damage the digestive tract lining and trigger inflammation, which is important for understanding the health risks of swallowing microplastics in food and water.
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.
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.
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.
Assessment of micro and nanoplastic toxicity and their protein corona using in vitro and in silico new approach methodologies
This research improved laboratory and computational methods for assessing microplastic and nanoplastic toxicity in the human intestine. Researchers found that realistic, irregularly shaped secondary nanoplastics were more toxic to intestinal cells than the pristine spherical particles typically used in lab studies. The study also showed that proteins from the body rapidly coat nanoplastic surfaces, forming a corona that influences how particles interact with and affect gut cells.
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.
Microplastics in our diet: complementary in vitro gut and epithelium models to understand their fate in the human digestive tract.
Researchers used complementary in vitro gut models to study how microplastics behave during human digestion, finding that digestive conditions alter microplastic surface properties and their interactions with gut cells. The work advances understanding of how ingested microplastics may affect the human digestive system.
In vitro toxicity study of micro- and nanoplastics, with co-contamination of metals, on human intestinal models
This French-language study used human intestinal cell models to evaluate the in vitro toxicity of micro- and nanoplastics co-contaminated with heavy metals, finding that combined exposure was more toxic than plastic particles alone and that nanoplastics were more harmful than microplastics.
Impact of Environmental Microplastic Exposure on Caco-2 Cells: Unraveling Proliferation, Apoptosis, and Autophagy Activation
Researchers exposed human intestinal cells to polyethylene and PET microplastics of different sizes and observed significant decreases in cell survival along with increased oxidative stress. The microplastics triggered both programmed cell death (apoptosis) and the cell's self-recycling process (autophagy), with effects varying by particle size. The study suggests that microplastic exposure may compromise the intestinal barrier through multiple pathways of cellular damage.
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.
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.