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Updated: May 2, 2026

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机器学习辅助的蛋白质生物标记物的高通量识别和定量

Xiangyu Pan^1,2, Zeying Zhang^1,2, Yang Yun^1,2

¹Key Laboratory of Green Printing, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, PR China.

Journal of the American Chemical Society

|July 1, 2024

概括

我们开发了一种新的多重蛋白质生物传感器, 这种先进的诊断工具为早期发现和监测疾病提供了更高的灵敏度和准确性.

科学领域:

生物医学工程
纳米技术
诊断方法

背景情况:

早期发现疾病,预后和监测需要先进的体外诊断技术.
目前的方法如ELISA套件在灵敏度和吞吐量方面存在局限性.

研究的目的:

设计一种多重蛋白质生物传感策略,
开发一个纳米光子生物传感器,用于快速和高通量蛋白质检测.

主要方法:

使用可调节的多材料异质链的液体封闭自组装.
具有受控联体纳米粒子功能化的工程聚合物半导体异质链.
开发了一种可见纳米光子生物传感器,具有抗体功能化和神经网络分析.

主要成果:

在异链中实现了强大的近场增强和强大的电磁共振.
证明了对半导体纳米粒子协调号的线性光学信号响应.
能够对多种蛋白质生物标志物进行特定的一步检测,检测极限超低 (1 pg/mL).
在多种临床样本中达到97.3%的平均精度.

结论:

基于异质链的生物传感器代表了下一代诊断工具的重大进步.
这项技术适用于各种临床环境,提供了更好的疾病检测和监测能力.

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