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Abstract CRISPR/Cas12a‐based detection of non‐nucleic acid targets faces two major challenges: 1) limited sensitivity due to the inherent inability to pre‐amplify non‐nucleic acid targets, and 2) suboptimal performance of traditional reporters caused by photobleaching of fluorescent dyes, rapid degradation, and slow reaction kinetics resulting from random molecular collisions. To overcome these limitations, we developed an innovative plasmonic CRISPR/Cas12a platform featuring positive‐feedback autocatalytic circular DNA (cir‐DNA) amplification. This system synergistically combines spatial confinement effects with plasmon‐enhanced fluorescence (PEF) to achieve ultrasensitive detection of non‐nucleic acid targets. The engineered cir‐DNA enables continuous Cas12a regeneration for autocatalytic signal amplification, while the designed plasmonic spherical nucleic acids significantly accelerate reaction kinetics while enhancing fluorescence signals. This integrated approach reduced the required reaction time to 15 min while improving the detection limit by approximately 52‐fold compared to conventional methods. Furthermore, by leveraging a convolutional neural network (CNN) machine learning model, not only the assessment of the risk level of perfluorooctanoic acid (PFOA) based on threshold‐positive and threshold‐negative serum concentrations but also highly accurate blind testing were both achieved, highlighting its potential for clinical applications such as pregnancy risk assessment.
Wang et al. (Mon,) studied this question.