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Mapping the hidden journey of microplastics: Multi-organ deposition patterns and organ-specific health risks revealed by AI-driven analysis
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Researchers analyzed microplastic distribution across seven human organs (lungs, heart, liver, spleen, brain, kidneys, small intestine) from eight donors using Raman imaging and AI, finding microplastics in all organs with a machine learning classifier achieving 91% accuracy in attributing particles to their organ of origin based on morphological features.
Microplastics (MPs), pervasive environmental pollutants, have infiltrated human tissues, raising global health concerns. This study investigated the distribution and characteristics of MPs across seven major human organs (lungs, heart, liver, spleen, brain, kidneys, and small intestine) using Raman imaging and machine learning. Tissue samples from eight donors were analyzed for MP presence and characteristics. A deep learning-enhanced U-Net model segmented MPs in Raman images, while a random forest classifier was employed to identify organ-specific MP attribution using 120 imaging features. Animal models supported the systemic distribution of MPs. MPs were ubiquitous across all organs examined. The highest MP abundance was observed in the liver (65.28 ± 23.94 particles/g), small intestine (61.06 ± 25.25 particles/g), and kidneys (58.63 ± 16.50 particles/g). Organ-specific variations in MP characteristics were identified: larger particles dominated the lungs (56.80 ± 57.70 μm), while smaller particles (<10 μm) prevailed in the liver and spleen. Distinct polymer compositions and shape profiles were observed for each organ. The random forest classifier achieved 72.73% accuracy in organ-specific MP attribution. MP abundance was linked to organ vascularity. The findings highlight organ-specific risks of MPs and provide a framework for assessing health impacts, thus guiding targeted interventions to mitigate exposure.
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