Implantable Graphene Fiber Sensor Functionalized with Enzyme-Mimicking Fe-Porphyrin for In Vivo Simultaneous Monitoring of NO and H2O2 in Cancer

石墨烯 体内 材料科学 生物传感器 纳米技术 过氧化氢 生物标志物 微电极 生物医学工程 电化学气体传感器 癌症 电化学 多电极阵列 信号(编程语言) 癌细胞 电极 癌症生物标志物 癌症检测 检出限 持续监测 化学 选择性
作者
Shanting Li,Kaiyuan Yao,Min Hu,Wei Huang,Dan Wang,Hengrui Zhang,Tengyun Chang,Fei Xiao,Anshun Zhao
出处
期刊:ACS Sensors [American Chemical Society]
标识
DOI:10.1021/acssensors.6c02929
摘要

Nitric oxide (NO) and hydrogen peroxide (H2O2) are critical in redox homeostasis, cell signaling, and tumor progression, yet the simultaneous detection of both remains challenging due to their low concentrations and high reactivity. Herein, we developed an implantable, fully flexible electrochemical biosensor based on a Fe-porphyrin metal-organic framework (i.e., PCN-224(Fe)) for real-time monitoring of NO and H2O2 in complex biological environments. This platform employs PCN-224(Fe) with enzyme-mimicking activity as the catalytic material, combined with nitrogen and boron codoped graphene fiber as the freestanding and flexible microelectrode substrate. The resultant electrochemical sensor demonstrated outstanding performance, with detection limits of 3.0 nM for NO and 0.5 μM for H2O2, and sensitivities of 2.99 mA cm-2 mM-1 and 1.04 mA cm-2 mM-1, respectively. This performance surpasses most previously reported electrochemical sensors. The sensor also shows excellent selectivity and reproducibility, facilitating continuous monitoring of NO and H2O2 signals in the tumor microenvironment. The proposed fiber-based sensor enables in situ, real-time, continuous monitoring of NO and H2O2 in cancer cells, tissues, and living organisms, thereby effectively discriminating between cancerous and normal samples based on significantly elevated biomarker levels in malignant tissues. When implanted directly into living tissues, the sensing device captures dynamic biomarker fluctuations in vivo with high fidelity, offering a distinct advantage over traditional in vitro assays, which often introduce signal loss and concentration artifacts during sample processing. As a result, this approach provides a more accurate reflection of actual in vivo conditions and holds great promise for assessing tumor progression as well as monitoring therapeutic responses.
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