通过自组织地图解释SPR衍生反应动力学,用于诊断应用
Jaqueline Volpe1, Floriatan S Costa1, Beatriz Sachuk1
1Laboratório de Espectrometria, Sensores e Biossensores─Department of Chemistry, Federal University of Paraná (UFPR), Curitiba, Paraná 81530-900, Brazil.
ACS sensors
|September 25, 2025
概括
这项研究引入了与人工智能 (AI) 结合的表面等离子体共振 (SPR) 生物传感器,用于快速诊断犬内脏莱什曼病 (CVL). 由人工智能驱动的方法提高了在资源有限的环境中对传染病查的准确性和速度.
科学领域:
- 生物医学工程 生物医学工程
- 传染病诊断 传染病诊断 传染病诊断
- 医疗保健中的人工智能
背景情况:
- 生物传感器为传染病提供具有成本效益的,可在现场部署的诊断,对于资源有限的环境至关重要.
- 表面等离子共振 (SPR) 生物传感器擅长无标签,实时分析生物分子相互作用.
- 狗内脏雷什曼病 (CVL) 的诊断往往会延迟,阻碍了人类和狗群体的有效疾病控制.
研究的目的:
- 开发和评估一个与自我组织地图 (SOMs) 集成的SPR生物传感器,以加强CVL的血清诊断.
- 评估用于CVL检测的多皮托普化学蛋白 (PQ20) 的诊断性能和动力参数.
- 展示人工智能驱动的数据分析对感染者与健康个体的快速准确分类的实用性.
主要方法:
- 使用SPR生物传感器分析PQ20蛋白与抗PQ20抗体的结合动力学.
- 雇员自组织地图 (SOM) 用于高维数据投影和样本的自动分类.
- 使用原始血清样本评估诊断性能,包括检测极限,灵敏度和特异性.
主要成果:
- 在PQ20中确定了两个免疫主导表位,表现出高的关联率 (k_a1 = 2.4 × 10^5 L mol^-1 s^-1) 和解离率 (k_d1 = 5.5 × 10^-4 L mol^-1 s^-1).
- 达到SPR生物传感器的5.1nmol L^-1的检测极限.
- 与单变量分析相比,SOM分析显示出更好的诊断准确性 (灵敏度和特异性),使分类能够在15分钟内进行,反应时间缩短 (100秒).
结论:
- 将SPR生物传感与人工智能驱动的SOM分析集成,为CVL血清诊断提供了快速,无标签和准确的方法.
- 这种方法增强了诊断能力,特别是在资源有限的环境中被忽视的热带疾病.
- 开发的生物传感器系统显示了改善传染病监测和管理的巨大潜力.
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