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DOI 10.1016/j.bios.2026.119225

Медицинская библиотека · Biosensors & bioelectronics · 2026

A molecularly imprinted sensor based on NiO/MnO-loaded carbon nanoframework for monitoring glucose metabolism of cells in real-time.

Zhang Hao, Long Yunwei, You Zhanghao, Zhang Zhuang, Wang Hui, Yang Guocheng, Li Siying, Shan Yuping

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Аннотация

Cellular uptake of glucose is the first step for glucose metabolism that is closely related to the physiological activities of cells, it is effective to monitor the glucose consumption of cell culture medium in real time. Herein, a sensitive and selective electrochemical glucose sensor was developed by coating a molecularly imprinted polymer (MIP) layer on a carbon nanoframework co-loaded with NiO and MnO (Ni&Mn/CNFs). The Ni&Mn/CNFs were prepared via electrospinning and subsequent carbonization, and the MIP layer was electropolymerized using glucose as the template, creating specific recognition cavities. The bimetallic oxide synergistically enhances electrocatalytic activity, while the MIP layer endows excellent anti-interference capability. The sensor shows a linear response from 10 μM to 300 μM, with a sensitivity of 4.28 μA·loge(μM-1)·cm-2 and a detection limit of 0.87 μM. Real-time monitoring of glucose consumption in cell culture media revealed that the highly malignant cancer cells (e.g., U87 and H1975) exhibit higher glucose metabolism than less aggressive cells (e.g., MCF-7 and HEK-293T), correlating metabolic levels with invasive/proliferative potential. Moreover, it is found that glucose consumption is higher during the S phase of the cell cycle, reflecting increased energy demand for DNA replication. This work provides a simple and reliable strategy for monitoring glucose metabolism in real-time and screening drugs targeting glucose metabolism.

Для цитирования: Zhang Hao, Long Yunwei, You Zhanghao, Zhang Zhuang, Wang Hui, Yang Guocheng, Li Siying, Shan Yuping. A molecularly imprinted sensor based on NiO/MnO-loaded carbon nanoframework for monitoring glucose metabolism of cells in real-time.. Biosensors & bioelectronics. 2026. DOI: 10.1016/j.bios.2026.119225.
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