在一个集成的微流体芯片中,用于口腔状细胞癌的蛋白质的自动电气检测,使用多频阻抗细胞计和机器学习
Muhammad Tayyab1, Zhongtian Lin1, Seyed Reza Mahmoodi2
1Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, USA.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
我们开发了一种使用机器学习和微流体进行蛋白质检测的自动化,无标签的方法. 这个系统准确地量化了像IL-6 (IL-6) 这样的生物标志物,帮助诊断疾病.
科学领域:
- 生物标志物发现发现
- 微流体学 微流体学
- 机器学习 机器学习
背景情况:
- 蛋白质作为疾病预后和诊断的关键生物标志物.
- 了解基本的生物过程依赖于蛋白质分析.
- 目前用于蛋白质检测的方法可能是复杂和耗时的.
研究的目的:
- 开发一种完全自动化的,无标签的电动蛋白质检测方法.
- 整合多频阻抗细胞计与微流体芯片进行增强分析.
- 使用现成组件创建可编程的流体控制系统.
主要方法:
- 使用了一种定制的聚甲基西洛 (PDMS) 微流体混合器,配有蛇形通道.
- 采用多频阻抗细胞计,用于无标签的电气检测.
- 使用光三明治免疫试验验验证了混合方法,并与商业混合器进行了比较.
- 开发了一种机器学习辅助的系统,用于自动化蛋白质量化.
主要成果:
- 在定制的微流体芯片中证明了强大的混合.
- 在溶液中成功检测和量化了INTERLEUQIN-6 (IL-6) 蛋白,准确率为96%.
- 证实了该系统对与口腔状细胞癌 (OSCC) 相关的生物标志物检测的能力.
结论:
- 开发的系统为蛋白质检测提供了准确和自动化的方法.
- 该平台可以适应检测各种蛋白质生物标志物,只需稍微修改.
- 这项技术有可能改善诊断工具和生物研究.
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