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From metabolic reprogramming to diagnostic tool: A machine learning framework identifies biomarkers for soil microplastic stress in rice
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Microplastics in soil don't just pollute farmland—they actually change the internal chemistry of rice plants, disrupting how they process energy and nutrients. Scientists used AI to spot just seven chemical "warning signs" in rice that can reveal how contaminated the soil is with 90% accuracy, potentially offering a fast way to screen crops for microplastic pollution before they reach your plate. This matters because it could help catch contaminated food supplies early, though more research is needed to know exactly how these changes affect the safety or nutrition of the rice we eat.
As emerging environmental contaminants, microplastics (MPs) increasingly infiltrate agricultural ecosystems, threatening crop safety and productivity. While the phytotoxic effects of MPs on plants like rice are becoming apparent, a significant gap exists in developing high-throughput tools for diagnosing MP contamination levels in soil. This study integrates untargeted metabolomics with machine learning to comprehensively elucidate the metabolic responses of rice to MP exposure and to establish a diagnostic model. Our results demonstrate that MPs significantly disrupt key metabolic pathways, including ABC transporters, the tricarboxylic acid (TCA) cycle, and alanine, aspartate, and glutamate metabolism. A machine learning-based feature selection pipeline identified a minimal panel of seven metabolic biomarkers. A predictive model built on this panel accurately distinguished rice samples under different soil pollution levels (Low, Medium, High) and demonstrated robust diagnostic capability in an independent external validation set (ROC-AUC = 0.90). This integrative analytical framework provides a scalable strategy for monitoring and early warning of microplastic pollution in agricultural systems, offering significant implications for ensuring food security and technical support for the future promotion of in-situ soil microplastic risk assessment methods.
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Tiny plastic particles from everyday products are contaminating farm soil and making rice plants more vulnerable to fungal diseases by interfering with the natural chemical signals plants use to fight off infections. This matters because it could reduce crop yields and potentially allow more pesticides to be used, while these same plastic particles can eventually end up in our food and water. This review suggests that plastic pollution in soil is a bigger problem for farming than scientists previously realized.
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Researchers developed a non-destructive, interpretable deep learning framework using excitation-emission matrix fluorescence spectra of root exudates to detect microplastic stress in rice seedlings at early stages. The approach enables early-warning detection of plastic contamination effects on crop health without damaging the plants.
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