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
概括
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.
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