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Demonstrates improved identification of aminoglycosides, suggesting a new strategy for complex environments.
Developed sensor arrays commonly rely on directly sensing the effects of antibiotics on oxidoreductase-like activity, which has faced significant challenges in discriminating between structurally similar antibiotics. In this study, we constructed a hydrolysis-cascade sensor array in which hydrolase-like activity decoding was employed to amplify the chemical features input into the sensor array, thereby enabling more accurate identification of aminoglycosides. Specifically, two Ce-based nanozymes with glycoside hydrolase-like activity were synthesized to perform the pH-regulated hydrolytic decoding of aminoglycosides. Hydrolysis products subsequently modulated the laccase-like activity of an Arac-Cu nanozyme, producing distinct responses across the sensor array. The hydrolysis-cascade sensor array with machine learning assistance achieved the accurate identification of concentration-independent aminoglycosides, highlighting the great potential of the hydrolysis-cascade sensor array for analytical applications in complex environments. This work provides a new strategy for improving the accurate identification of aminoglycosides under complex environmental conditions.
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J et al. (2026) studied this question.
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