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61,005 resultsShowing papers similar to AI-Enhanced Patient-Derived Cancer Organoids: Integrating Machine Learning for Precision Oncology
ClearThe Role of Artificial Intelligence in Microplastic Pollution Studies and Management
This review explores how artificial intelligence is transforming microplastic research, from automating detection in microscopy images and spectral analysis to predicting how plastics interact with pollutants and living organisms. AI-powered sensors and real-time monitoring systems are also being integrated into wastewater treatment to reduce microplastic release, making the technology a powerful tool for both understanding and managing plastic pollution.
Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology
Researchers reviewed how artificial intelligence combined with genomics (the study of genes) and multi-omics data is advancing personalized medicine and toxicology, enabling faster, more accurate predictions of how individuals will respond to drugs or toxic exposures. These tools could eventually help assess risks from environmental contaminants like microplastics based on a person's unique genetic makeup.
In-silico pharmacological insights into the therapeutic potential of microRNAs for microplastic-associated cancers
Researchers systematically screened published literature to identify cancer-related genes altered by microplastic exposure, then computationally evaluated microRNAs with anticancer activity that could target those genes, finding potential miRNA-based therapeutic candidates across breast, gastric, and other microplastic-associated tumor types.
Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review
This review covers how artificial intelligence and machine learning are being applied to nanomanufacturing for medicine, robotics, and electronics. While not about microplastics directly, the AI-powered nanoscale detection and characterization methods discussed could be applied to identifying and quantifying nanoplastics in the environment and human tissue. Advances in nano-scale imaging and analysis driven by AI may eventually help researchers better understand human exposure to nanoplastics.
An updated systematic review about various effects of microplastics on cancer: A pharmacological and in-silico based analysis
This systematic review with in-silico analysis found that microplastics have both tumor-promoting and tumor-suppressing effects on cancer cells, affecting viability, migration, metastasis, and apoptosis. The study identified key proteins (AP2M1, ASGR2, BI-1, Ferritin Heavy Chain) involved in microplastic-mediated cancer progression and used computational modeling to identify existing drugs that might counteract these pathways.
Integrating Artificial Intelligence with Quality by Design in the Formulation of Lecithin/Chitosan Nanoparticles of a Poorly Water-Soluble Drug
This study used artificial intelligence and quality-by-design methods to create optimized nanoparticles for delivering a cancer-fighting drug called silymarin. While not directly about microplastics, the research advances understanding of how nanoparticles interact with biological systems, which is relevant because nanoplastics behave similarly in the body. The techniques developed here could help researchers better predict how nanoscale plastic particles are absorbed and distributed in human tissues.
Exploring the prognostic implications of PET microplastic degradation products in colorectal cancer: insights from an integrated computational analysis on glucocorticoid pathway–mediated mechanisms
Researchers used network toxicology, machine learning, and molecular docking to investigate how PET degradation products—ethylene glycol and terephthalic acid—affect colorectal cancer prognosis through the glucocorticoid signaling pathway. The analysis identified 43 shared target genes, suggesting that PET breakdown products may worsen colorectal cancer outcomes by dysregulating glucocorticoid-mediated anti-inflammatory and cell survival signals.
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.
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.
Role of AI in Microplastic Pollution Detection and management studies
This review assessed how artificial intelligence approaches—including machine learning and deep learning—are being applied to detect, identify, and monitor microplastics in environmental and biological samples. The authors found AI substantially accelerates microplastic characterization workflows but that training data quality and standardization across studies remains a limiting factor.
Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors
This review summarizes how artificial intelligence and machine learning are being used to identify, track, and predict the environmental behavior of microplastics in soil and water. AI methods can analyze the chemical composition, shape, and distribution of microplastics faster and more accurately than traditional techniques. The technology could help scientists better understand where microplastics accumulate and what risks they pose to ecosystems and human health.
Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry
Researchers developed a label-free method using holographic flow cytometry and artificial intelligence to identify and classify neuroblastoma cancer cells without the need for traditional staining. The approach analyzes cell shape and structure to distinguish between different cancer cell subtypes. While not directly related to microplastics, the technique advances rapid screening capabilities for bioparticle analysis in fluid samples.
Exploring the prognostic implications of PET microplastic degradation products in colorectal cancer: insights from an integrated computational analysis on glucocorticoid pathway–mediated mechanisms
Combining network toxicology, machine learning, and molecular docking, this study found that PET plastic degradation products ethylene glycol and terephthalic acid may influence colorectal cancer prognosis through 43 shared genes linked to TNF/IL-17 signaling and glucocorticoid-mediated metabolic pathways.
Human organoids to assess environmental contaminants toxicity and mode of action: towards New Approach Methodologies
This review explores how human organoids, miniature lab-grown organ models, can be used to test the toxicity of environmental contaminants including microplastics. These 3D tissue models offer a more accurate picture of how pollutants affect human cells than traditional lab tests, though more work is needed to simulate the chronic, low-dose exposures people actually experience.
The supporting role of Artificial Intelligence and Machine/Deep Learning in monitoring the marine environment: a bibliometric analysis
This review examines the supporting role of artificial intelligence and machine learning in monitoring and managing plastic pollution, covering applications in remote sensing, image-based plastic detection, and predictive modeling of plastic fate. The authors identify deep learning for image classification and satellite-based detection as the most rapidly advancing AI applications in plastic pollution science.
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.
Artificial Intelligence and Machine Learning Approaches for Automatic Microplastics Identification and Characterization
This review examines how artificial intelligence and machine learning algorithms are being applied to identify, characterize, and model microplastic pollution in the environment. The authors found that these tools can analyze large sensor datasets to detect microplastics in water bodies, predict transport patterns, and model adsorption behavior under various environmental conditions. The study highlights the growing role of computational approaches in understanding and mitigating microplastic contamination.
Artificial intelligence for modeling and reducing microplastic in marine environments: A review of current evidence
This review examines how artificial intelligence is being applied to address marine microplastic pollution, including modeling accumulation zones, developing real-time detection systems using remote sensing and robotics, and creating AI-based filtration technologies. The study suggests that while AI holds significant promise for predicting microplastic flows and supporting targeted cleanup efforts, challenges remain around data availability, model refinement, and international collaboration.
A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges
Researchers reviewed the use of artificial intelligence and machine learning techniques for detecting and identifying microplastics in environmental samples. The study found that AI-based imaging tools can significantly speed up analysis and improve accuracy compared to traditional manual methods. However, challenges remain around standardizing datasets and making these tools accessible for routine environmental monitoring.
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.
Toward Nano- and Microplastic Sensors: Identification of Nano- and Microplastic Particles via Artificial Intelligence Combined with a Plasmonic Probe Functionalized with an Estrogen Receptor
Scientists created a sensor that combines artificial intelligence with a specialized light-based probe to detect and identify different types of nano- and microplastics in water. The AI-powered system could distinguish between various plastic types with high accuracy, offering a faster and more practical way to monitor plastic contamination in drinking water and environmental samples.
Advancing Microplastic and Nanoplastic Toxicity Assessment: Insights from Human Organoid Models
This review examines how human stem cell-derived organoids are being used to study the toxic effects of microplastics and nanoplastics on human tissues. Researchers found that organoid models of the gut, lung, brain, and other organs provide more human-relevant data than traditional animal testing for assessing plastic particle toxicity. The study suggests that organoid technology could significantly advance understanding of how microplastics affect human health at the tissue and organ level.
Microplastic Contamination: A Rising Environmental Crisis With Potential Oncogenic Implications
This review examines how microplastics detected in human tissues — blood, placenta, and organs — may act as vectors for carcinogens, including adsorbed heavy metals and persistent organic pollutants, and discusses emerging evidence linking MP accumulation to oncogenic processes.
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.