嗜铬粒蛋白A
石墨烯
免疫分析
氧化物
信号(编程语言)
电化学
材料科学
纳米技术
化学
计算机科学
医学
电极
内科学
物理化学
抗体
冶金
程序设计语言
免疫学
免疫组织化学
作者
Supakeit Chanarsa,Patrawadee Yaiwong,Siriporn Anuthum,Kullapa Chanawanno,Jaroon Jakmunee,Kontad Ounnunkad
出处
期刊:ACS Measurement Au
[American Chemical Society]
日期:2025-05-15
卷期号:5 (3): 388-396
被引量:1
标识
DOI:10.1021/acsmeasuresciau.5c00048
摘要
Neuroendocrine tumors (NETs), often misdiagnosed and mistreated, require early detection for precise therapeutic interventions. This study presents a newly developed competitive electrochemical immunosensor for sensitive and selective detection of chromogranin A (CgA), a key biomarker for diagnosing and monitoring NETs. The sensor, featuring a sandwiched structure with versatile and multifunctional graphene oxide (GO), utilizes polyethylenimine-capped gold nanoparticles (PEI-AuNPs) to enhance the electroreactivity and biocompatibility of a screen-printed electrode (SPE). The immunosensor operates by immobilizing standard CgA antigens on the PEI-AuNPs/GO-modified SPE surface and employing GO nanotags loaded with anti-CgA antibodies (Ab) and ferrocene monocarboxylic acid (Fc) redox probes to capture target CgA. As the CgA concentration increases, the current response of the immunosensor decreases due to a reduction in the amount of Fc/Ab/GO tags on the electrode surface. This reduction occurs because the nanotags bind to the free CgA in the sample rather than the CgA immobilized on the electrode. The immunosensor demonstrates a good linearity (0.10-50 ng mL-1), a low detection limit of 90 pg mL-1, and high accuracy in detecting CgA levels in human serum samples. With its high specificity, long-term stability, and excellent reproducibility, our cost-effective and user-friendly platform holds promise for clinical screening and point-of-care diagnosis of NETs. Further optimization of the immunosensor's design and exploration of its use for additional biomarkers could enhance NETs' diagnosis and provide advancements in managing other related health conditions.
科研通智能强力驱动
Strongly Powered by AbleSci AI