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双模式SERS侧流吸管测定与机器学习驱动的高度敏感的干扰素-γ检测

Jiali Jin1, Jiaying Hu1, Jiliang Yan2

  • 1School of Public Health, Zhejiang Key Laboratory of Pathophysiology, Health Science Center, Ningbo University, 818 Fenghua Road, Ningbo 315211, Zhejiang Province, China.

ACS synthetic biology
|July 7, 2025
PubMed

Insights

这项研究提出了一个新的生物传感平台,用于在非常低的度下检测干扰素-γ (IFN-γ). 该测试将视觉和定量检测与机器学习相结合,用于准确诊断与免疫相关的疾病.

科学领域:

  • 生物医学工程 生物医学工程
  • 分析化学 分析化学
  • 免疫学 免疫学 免疫学

背景情况:

  • 干扰素-γ (IFN-γ) 是一种重要的促炎性细胞因子和免疫状况的生物标志物.
  • 检测IFN-γ的低pg/mL度需要超敏感的方法进行早期诊断.

研究的目的:

  • 开发一种双模式的阿巴美尔试剂,用于超敏感检测IFN-γ.
  • 整合机器学习以提高诊断准确性和结果的解释.

主要方法:

  • 一个具有竞争力的绑定侧流阿普坦体试验,采用表面增强拉曼散射 (SERS).
  • 定量检测,检测极限为2.23 pg/mL,动态范围为5-2000 pg/mL.
  • 用人类血清和机器学习算法 (MLR,MLP,随机森林) 进行临床验证以进行分类.

主要成果:

  • 该试验达到2.23 pg/mL的检测极限,从而能够超敏感地测量IFN-γ.
  • 临床验证表明,在区分IFN-γ度级别方面具有很高的准确性.
  • 该MLR模型实现了94.12%的整体准确性和优异的特定组敏感性和特异性.

结论:

  • 双模式的SERS胺测定为超敏感细胞因子检测提供了强大而实用的解决方案.
  • 机器学习集成显著提高了免疫相关疾病的诊断性能.
  • 该平台显示了在精确诊断领域的临床应用的潜力.
抽象的

No abstract available in PubMed .

关键词:
这就是 SERS SERS.一个aptamer的应用程序.干扰素 - 玛干扰素侧向流量检测试验机器学习是机器学习.

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