ArticleFood chemistry: X2026
Machine learning prioritization of antibiotic residues in aquatic foods reveals exposure-driven genotoxic risk mediated by BCL2.
Article in Food chemistry: X, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
Antibiotic residues in aquatic foods pose genotoxic risks. Traditional monitoring focuses on individual Maximum Residue Limit (MRL) compliance, often overlooking cumulative multi-residue risks. We developed an interpretable machine learning (ML) framework integrating surveillance data (3719 samples, 17 antibiotics) with mechanistic validation to prioritize risks in Guangzhou (2021-2023). Exposure metrics and in silico hazard predictions were analyzed via clustering and ranking. Enrofloxacin, sulfamethoxazole, and 3-amino-2-oxazolidinone were prioritized, with risk drivers deconstructed via Explainable AI (SHAP). In L-02 hepatocytes, prioritized mixtures reduced viability (70.21 ± 7.49%), increased apoptosis (7.12 ± 2.75%), and induced DNA damage (tail DNA% 5.25 ± 1.03%) (all
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