免疫染色学SERS传感器深度学习结合策略,用于对慢性病的智能诊断
Zengshan Yu1,2, Zhibin Zhang3, Shan Guo1,2
1Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, 067000, Hebei, China.
Mikrochimica acta
|February 3, 2026
概括
一种新型的免疫染色学表面增强拉曼散射 (SERS) 传感器与深度学习相结合,可快速,灵敏地检测中性粒细胞凝酶相关性脂卡林 (NGAL). 这项技术在早期诊断慢性病方面表现有前途.
科学领域:
- 生物医学工程 生物医学工程
- 分析化学 分析化学
- 人工智能的人工智能
背景情况:
- 中性粒细胞凝酶相关的脂卡林 (NGAL) 是慢性病 (CKD) 的潜在生物标志物.
- 准确而敏感地检测NGAL对于早期CKD诊断和管理至关重要.
- 现有的检测方法可能缺乏广泛临床应用所需的灵敏度,速度或特异性.
研究的目的:
- 为NGAL.开发一种新,高度敏感和特定的检测策略.
- 将表面增强的拉曼散射 (SERS) 传感与深度学习相结合,以进行增强的光谱分析.
- 为了验证这种结合方法对CKD歧视的诊断潜力.
主要方法:
- 制造一种免疫染色体SERS传感器,用于在血清中特定的NGAL识别.
- 深度学习算法的应用 (包括剩余网络 - ResNet) 用于SERS光谱数据预处理,特征提取和模式识别.
- 使用来自志愿者和患者的血清样本进行临床验证,评估分类准确性,损失值和AUC.
主要成果:
- 对于NGAL,SERS传感器实现了0.1059 ng/mL的低检测极限.
- 集成的深度学习模型在光谱分析中表现出高性能.
- 在临床样本分析中,ResNet模型实现了98.81%的分类准确率和99.97%的AUC.
- 在患者和正常血清样本中成功分类和预测NGAL水平.
结论:
- 结合免疫染色学SERS传感器和深度学习策略,为快速,灵敏和特定的NGAL检测提供了一个强大的平台.
- 这种方法克服了分析复杂光谱数据的传统算法的局限性.
- 该技术具有显著的潜力,可以对慢性病进行智能歧视,为临床诊断提供关键的技术支持.
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