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61,005 resultsShowing papers similar to What Drives Microplastic Exposure in Human Blood and Feces? Machine Learning Reveals Potential Key Influencing Factors
ClearAssessment of microplastics in human stool: A pilot study investigating the potential impact of diet-associated scenarios on oral microplastics exposure
In this pilot study, 15 volunteers followed different plastic-use and food consumption scenarios, and microplastics were detected in every stool sample collected, with polyethylene being the most common type. Using plastic packaging for food and eating highly processed foods were statistically linked to higher microplastic levels in stool, providing early evidence that dietary choices influence how many microplastics people ingest.
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.
Microplastics in human feces and their correlation with dietary behavior: A pilot study
This pilot study analyzed microplastics in human fecal samples and examined correlations with dietary habits, finding that seafood and packaged food consumption were associated with higher fecal microplastic counts. The results provide early evidence linking diet to human microplastic exposure levels.
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.
Appraisal of microplastic pollution and its related risks for urban indoor environment in Bangladesh using machine learning and diverse risk evolution indices
This first-of-its-kind study in Bangladesh found an average of about 26 microplastic particles per gram of household dust in urban homes, with polystyrene being the most common type. Children were estimated to ingest roughly twice as many microplastic particles per day as adults through household dust exposure. The pollution levels were rated moderate to high, suggesting that indoor environments are a significant source of microplastic exposure for people.
Application of machine learning in assessing spatial distribution patterns of soil microplastics: a case study of the Bang Pakong Watershed, Thailand
Machine learning models were applied to predict spatial distribution patterns of microplastics in soils across a Thai watershed, identifying land use types and proximity to water bodies as key factors driving contamination levels.
Key Physicochemical Properties Dictating Gastrointestinal Bioaccessibility of Microplastics-Associated Organic Xenobiotics: Insights from a Deep Learning Approach
A deep learning analysis of gastrointestinal bioaccessibility data for 18 microplastic types found that polymer structural rigidity and surface area were the key physicochemical properties controlling desorption of pyrene and 4-nonylphenol under digestive conditions, covering a bioaccessibility range of 16–83% across polymer types.
Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning
Researchers used machine learning to predict microplastic levels in ocean surface waters and identify the key factors driving contamination. Their models found that geographic location, ocean currents, and proximity to populated coastlines were major predictors of microplastic abundance. This approach could help scientists map pollution hotspots without costly and time-consuming physical sampling.
Improved multivariate quantification of plastic particles in human blood using non-targeted pyrolysis GC-MS
Scientists developed improved methods for measuring plastic particles in human blood, finding that standard techniques can produce significant errors, especially for PET plastic. The new multivariate approach reduced measurement errors by up to 38%, which is important because accurate blood measurements are essential for understanding how much microplastic exposure people actually face.
Machine Learning-Driven Prediction of Organic Compound Adsorption onto Microplastics in Freshwater
Seven machine learning algorithms were trained on 173 published measurements to predict how strongly organic contaminants adsorb onto different types of microplastics in freshwater. Accurate adsorption predictions are essential for assessing environmental risk, because microplastics that strongly bind pollutants become vectors that concentrate and transport toxic chemicals through aquatic food webs.
Machine Learning Prediction of Adsorption Behavior of Xenobiotics on Microplastics under Different Environmental Conditions
Researchers developed a machine learning model to predict how different xenobiotic chemicals adsorb onto microplastics under varying environmental conditions, providing a computational tool to assess microplastics as vectors for pollutant transport without requiring extensive laboratory experiments.
Machine learning-driven analysis of soil microplastic distribution in the Bang Pakong Watershed, Thailand
Researchers used machine learning techniques to analyze the distribution and influencing factors of soil microplastic contamination in the Bang Pakong Watershed in Thailand. The study identified key environmental and land-use variables that predict microplastic occurrence, providing a data-driven approach for understanding how microplastics distribute across agricultural and urban landscapes.
Current applications and future impact of machine learning in emerging contaminants: A review
This review examines how machine learning is being applied to emerging contaminant research including microplastics, covering identification, environmental behavior prediction, bioeffect assessment, and removal optimization of these pollutants.
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.
A central role for fecal matter in the transport of microplastics: An updated analysis of new findings and persisting questions
This review examines the central role of fecal matter in transporting microplastics through ecosystems, analyzing how organisms ingest and excrete microplastics and the implications for environmental fate and human exposure monitoring.
[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.
Predictive modeling of microplastic adsorption in aquatic environments using advanced machine learning models
Scientists used advanced machine learning models to predict how microplastics interact with and absorb organic pollutants in water. The results showed that microplastics with certain chemical properties attract more toxic compounds, which matters because contaminated microplastics in waterways can concentrate harmful chemicals that may eventually reach humans through drinking water and seafood.
Microplastics in human feces: a pilot study exploring links with dietary habits
Researchers analyzed fecal samples from 18 people in Norway and found microplastics in 17 of them, with polypropylene being the most common polymer, but found no significant link between seafood consumption and microplastic levels. The results suggest that dietary habits alone do not determine exposure, and that microplastics may enter the body through many everyday sources beyond food.
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.
An introduction to machine learning tools for the analysis of microplastics in complex matrices
This paper introduces machine learning tools that can speed up the identification and counting of microplastics in complex samples like soil and water. While focused on analytical methods rather than health effects, faster and more accurate detection of microplastics is essential for understanding how much exposure humans actually face through food, water, and the environment.
Microplastics’ journey into the gut : human exposure to microplastics and associated chemicals
This thesis investigates lifetime human exposure to microplastics worldwide and explores how plastic particles act as carriers that transport harmful chemicals into the body after ingestion, known as the vector effect. Using mechanistic models and experimental methods, the work quantifies how much microplastic people consume and how significantly this route contributes to chemical bioaccumulation.
Using machine learning to reveal drivers of soil microplastics and assess their stock: A national-scale study
Using machine learning on data from 621 sites across China, researchers identified nine key factors driving microplastic distribution in soil, including population density, plastic production, and agricultural practices. The study estimated that Chinese topsoil contains a substantial stock of microplastics, with concentrations varying widely by region. This large-scale analysis helps predict where microplastic contamination is worst, which is important for understanding human exposure through food grown in contaminated soil.
Assessment of environmental and socioeconomic drivers of urban stormwater microplastics using machine learning
Using machine learning on data from 107 urban areas worldwide, researchers identified the key factors driving microplastic levels in stormwater runoff, including weather patterns, land use, and waste management practices. The study found that inconsistent definitions of what counts as a "microplastic" across different studies is a major barrier to comparing contamination levels between cities.
Rank-In Integrated Machine Learning and Bioinformatic Analysis Identified the Key Genes in HFPO-DA (GenX) Exposure to Human, Mouse, and Rat Organisms
Researchers used integrated machine learning and bioinformatic analysis to identify key molecular markers and pathways associated with microplastic-induced biological effects, generating mechanistic hypotheses for further experimental validation.