癌胚抗原（CEA）是一种具有人类胚胎抗原特性的酸性糖蛋白，存在于从内胚层细胞分化而来的癌细胞表面。本文提出了一种无标记电化学免疫分析方法，使用载有聚吡咯聚多巴胺（Au/PPy-PDA）和开环聚合（ROP）制备的聚合聚己内酯（Ng-PCL）的金纳米粒子来双扩增检测CEA。首先，将复合材料 Au/PPy-PDA 粘附到电极表面。然后，金纳米颗粒与 Apt1 中的巯基形成 Au-S 键，将其固定在电极表面。随后，电极表面的非特异性结合位点被牛血清白蛋白（BSA）封闭。接下来，将CEA滴到电极表面，通过抗原-抗体特异性识别将其固定化，羧基功能化的Apt2通过特异性识别形成抗体-抗原-抗体的“三明治结构”。聚合物 Ng-PCL 粘附在电极表面，导致电化学阻抗信号增加，从而形成完整的信号分析链。最后，通过电化学阻抗谱（EIS）检测响应信号。在最佳实验条件下，该方法具有灵敏度高、线性范围宽（1 pg mL-1∼100 ng mL-1）等优点，检测下限（LOD）为0.234 pg mL-1。并且具有与真实样品检测同样的高灵敏度、选择性和抗干扰性。因此，它提供了一种关于生物医学和临床诊断的新思维方式。版权所有 © 2024 Elsevier B.V. 保留所有权利。

Carcinoembryonic Antigen (CEA), an acidic glycoprotein with human embryonic antigen properties, is found on the surface of cancer cells that have differentiated from endodermal cells. This paper presents a label-free electrochemical immunoassay for the dual amplification detection of CEA using gold nanoparticles loaded with polypyrrole polydopamine (Au/PPy-PDA) and polymerized polycaprolactone (Ng-PCL) prepared by ring-opening polymerization (ROP). First, the composite Au/PPy-PDA was adhered to the electrode surface. Then, gold nanoparticles form a Au-S bond with the sulfhydryl group in Apt1 to secure it on the electrode surface. Subsequently, the non-specific binding sites on the electrodes surface are closed by bovine serum albumin (BSA). Next, CEA is dropped onto the electrode surface, which is immobilized by antigen-antibody specific recognition, and the carboxyl-functionalized Apt2 forms a "sandwich structure" of antibody-antigen-antibody by specific recognition. Polymeric Ng-PCL is adhered to the electrode surface, leading to an increase in the electrochemical impedance signal, resulting in a complete chain of signal analysis. Finally, the response signal is detected by electrochemical impedance spectroscopy (EIS). Under optimal experimental conditions, the method has the advantages of high sensitivity and wide linear range (1 pg mL-1∼100 ng mL-1), and the lower limit of detection (LOD) is 0.234 pg mL-1. And it has the same high sensitivity, selectivity and interference resistance for the real samples detection. Thus, it provides a new way of thinking about biomedical and clinical diagnosis.Copyright © 2024 Elsevier B.V. All rights reserved.