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Machine Learning-Aided 3D Dynamic SERS Strategy for Physiological Mapping: Biotoxicity of Environmentally Dimensional Aged Nanoplastics and Corresponding Protein Corona Complexes
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Researchers used a new combination of 3D surface-enhanced Raman spectroscopy and machine learning to study the toxicity of nanoplastics on cells. They found that aged nanoplastics and those coated with proteins from the environment caused different types of cell damage depending on the plastic type. This approach could help scientists more rapidly assess the biological hazards of nanoplastics found in the environment.
Nanoplastics (NPs) are emerging pollutants that undergo inevitable aging in the environment, raising concerns about human exposure and health hazards. Research on the cytotoxicity of various polymer types of NPs, aged nanoplastics (aNPs), and their interactions with proteins (aNPs-protein corona) is still nascent. Traditional cytotoxicity detection methods often rely on end point assays with restricted temporal resolution and analysis of single or multiple biomarkers. Here, we propose a novel approach integrating the 3D dynamic SERS strategy (DSS) with machine learning to rapidly analyze the cell fate and death modes induced by NPs, aNPs, and aNPs-protein corona complexes at the molecular level. PS, PVC, PMMA, and PC products from the water environment were used to prepare the corresponding NPs, and the impact of UV irradiation on their physicochemical properties was examined. DSS systematically maps the molecular changes in the cellular secretome caused by these NPs. Machine learning effectively extracts information from complex spectra, differentiating between biological samples. Results show prolonged UV exposure increases cell sensitivity to ferroptosis and cytotoxicity in various aNPs, while the protein corona on aNPs significantly mitigates toxicity associated with surface oxygen-containing functional groups, resulting in a reduced similarity to ferroptosis signatures. 3D DSS with machine learning technique analyzes the overall metabolite profile at the molecular level rather than individual biomarkers. This study is the first attempt to compare the biotoxicity of diverse polymer NPs, aNPs, and aNPs-protein coronas at cellular and molecular levels in human hepatocytes, enhancing our understanding of the complex biological impacts of NPs.
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Researchers developed a machine learning-assisted technique using surface-enhanced Raman spectroscopy to track how nanoplastics from environmental sources affect human cells. They found that environmentally derived nanoplastics were more toxic than pristine laboratory versions, largely because pollutants adsorbed onto their surfaces amplified the harmful effects. The study reveals that the real-world "carrier effect" of nanoplastics, where they transport other pollutants into cells, may pose a greater health risk than the plastic particles alone.
Integrating Metal Phenolic Networks-Mediated Separation and Machine Learning-Aided SERS for High-Precision Quantification and Classification of Nanoplastics
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Scientists combined metal-phenolic network chemistry — which rapidly concentrates and captures nanoplastics — with machine-learning-enhanced surface-enhanced Raman spectroscopy (SERS) to accurately identify and quantify nanoplastics at very low environmental concentrations. This integrated approach addresses one of the biggest technical obstacles in nanoplastic research: detecting particles that are too small and too sparse for conventional methods to reliably find.
Cell Response to Nanoplastics and Their Carrier Effects Tracked Real-Timely with Machine Learning-Driven Smart Surface-Enhanced Raman Spectroscopy Slides
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Researchers developed a novel smart sensor slide that can track in real time how living cells respond to nanoplastic exposure at the molecular level. Using specially designed core-shell plastic nanoparticles with embedded tracking signals, they could monitor each stage from initial cell contact through absorption and eventual cell damage. The technology offers a powerful new tool for studying how nanoplastics interact with human cells and carry other pollutants into the body.
Cell Responseto Nanoplastics and Their Carrier EffectsTracked Real-Timely with Machine Learning-Driven Smart Surface-EnhancedRaman Spectroscopy Slides
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Researchers developed smart SERS slides to monitor in real-time how cells respond to polystyrene nanoplastics and their ability to carry contaminants (the 'carrier effect'). The platform captured intracellular metabolic changes at the molecular level, showing nanoplastics extended cell cycle S and G2 phases in lung cells and that carried pollutants caused additional distinct cellular effects.
Integrating Metal–Phenolic Networks-Mediated Separation and Machine Learning-Aided Surface-Enhanced Raman Spectroscopy for Accurate Nanoplastics Quantification and Classification
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Researchers combined a metal-based separation technique with machine learning and surface-enhanced Raman spectroscopy to detect and classify nanoplastics in environmental samples. The method achieved high accuracy in identifying different types of nanoplastics at very low concentrations. This approach could make it significantly easier and more reliable to monitor nanoplastic contamination in real-world water and soil samples.
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