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    Exploring new functions of nanomaterials can help facilitate the development of biosensors for the detection of antibiotics. Herein, a new detection modality based on monovalent antigen-induced aggregation (MAA) of immunomagnetic beads (IMBs) was proposed for rapid and label-free detection of enrofloxacin (ENR), which endowed IMBs with the abilities of both sample separation and signal generation. In the presence of ENR, the initially well-dispersed IMBs were aggregated and the degree of aggregation was in a concentration-dependent manner. After exploring the mechanism underlying IMB aggregation and investigating the key parameters affecting it, a label-free biosensing platform was developed for rapid and sensitive detection of ENR. Based on the significant differences in the magnetic separation speed and size between the aggregated and well-dispersed IMBs, two methods were proposed for quantitatively determining ENR, i.e., measuring the turbidity of the IMB supernatant after magnetic separation for a given time and visualizing and calculating the grayscale value of the aggregated IMBs trapped on the surface of a nitrocellulose membrane. A three-dimensional (3D)-printed syringe was designed and fabricated for automatic filtration of IMBs. This immunosensor allowed for sensitive detection of ENR in less than 15 min without any labels. It exhibited a satisfactory limit of detection of 0.79 ng mL-1 and showed the feasibility for ENR detection of spiked chicken meat with recovery rates ranging from 74.8 to 98.3%. The MAA immunosensor can act as a promising tool to detect trace levels of ENR and has the potential to be applied to complex food samples.

    Citation

    Yafang Shen, Fei Jia, Aoming Liang, Yawen He, Yaping Peng, Huang Dai, Yingchun Fu, Jianping Wang, Yanbin Li. Monovalent Antigen-Induced Aggregation (MAA) Biosensors Using Immunomagnetic Beads in Both Sample Separation and Signal Generation for Label-Free Detection of Enrofloxacin. ACS applied materials & interfaces. 2022 Feb 23;14(7):8816-8823

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    PMID: 35133806

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