通过人工智能诊断测量 CEACAM5 的多模微纤维斑点生物传感器

Biosensors Pub Date : 2024-01-22 DOI:10.3390/bios14010057
Yuhui Liu, Weihao Lin, Fang Zhao, Yibin Liu, Junhui Sun, Jie Hu, Jialong Li, Jinna Chen, Xuming Zhang, Mang I. Vai, Perry Ping Shum, Liyang Shao
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摘要

癌胚抗原(CEACAM5)作为一种广谱肿瘤生物标记物,在分析癌症疗效和进展方面发挥着至关重要的作用。在此,我们提出了一种基于锥形多模光纤(MMF)斑点图和二维卷积神经网络(2D-CNN)的新型生物传感器,用于检测 CEACAM5。微纤维经 CEA 抗体修饰,可特异性识别抗原。该生物传感器利用锥形 MMF 的干扰效应,针对不同浓度的 CEACAM5 生成高灵敏度的斑点图。利用零均值归一化交叉相关(ZNCC)函数计算斑点图的图像匹配度。利用斑点传感器极高的检测限,在实验中测量了抗体浓度从 1 到 1000 ng/mL 的斑点图变化。在 1 至 50 纳克/毫升的范围内,生物传感器的表面灵敏度为 0.0012 (纳克/毫升)-1。此外,还引入了 2D-CNN 来解决大动态范围内非线性检测表面灵敏度变化的问题,并在寻找图像特征时提高了评估精度,实现了更精确的 CEACAM5 监测,最大检测误差为 0.358%。所提出的纤维斑点图生物传感方案易于实现,在分析患者术后病情方面具有巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multimode Microfiber Specklegram Biosensor for Measurement of CEACAM5 through AI Diagnosis
Carcinoembryonic antigen (CEACAM5), as a broad-spectrum tumor biomarker, plays a crucial role in analyzing the therapeutic efficacy and progression of cancer. Herein, we propose a novel biosensor based on specklegrams of tapered multimode fiber (MMF) and two-dimensional convolutional neural networks (2D-CNNs) for the detection of CEACAM5. The microfiber is modified with CEA antibodies to specifically recognize antigens. The biosensor utilizes the interference effect of tapered MMF to generate highly sensitive specklegrams in response to different CEACAM5 concentrations. A zero mean normalized cross-correlation (ZNCC) function is explored to calculate the image matching degree of the specklegrams. Profiting from the extremely high detection limit of the speckle sensor, variations in the specklegrams of antibody concentrations from 1 to 1000 ng/mL are measured in the experiment. The surface sensitivity of the biosensor is 0.0012 (ng/mL)−1 within a range of 1 to 50 ng/mL. Moreover, a 2D-CNN was introduced to solve the problem of nonlinear detection surface sensitivity variation in a large dynamic range, and in the search for image features to improve evaluation accuracy, achieving more accurate CEACAM5 monitoring, with a maximum detection error of 0.358%. The proposed fiber specklegram biosensing scheme is easy to implement and has great potential in analyzing the postoperative condition of patients.
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