机器学习增强的药物测试用于在尿液生物流体中同时检测吗啡和甲.
Mohammad Mehdi Habibi1, Mitra Mousavi1, Maryam Shekofteh-Gohari1
1School of Chemistry, University College of Science, University of Tehran, P.O. Box 14155-6455, Tehran, Iran.
Scientific reports
|April 6, 2024
概括
这项研究介绍了一种新的电化学传感器,用于同时检测尿液中的药物. 该g-C3N4-CNT传感器使用机器学习准确测量吗啡,甲和尿酸,为临床药物分析提供了可靠的工具.
科学领域:
- 电化学 电化学 电化学
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
背景情况:
- 由于复杂的生物矩阵和分析物相互作用,同时识别药物具有挑战性.
- 现有的方法往往在真实世界样本的精度和灵敏度上扎.
研究的目的:
- 开发一种创新的电化学传感器,用于精确同时测定尿液中的吗啡 (MOR),甲 (MET) 和尿酸 (UA).
- 克服当前复杂生物样本分析技术的局限性.
主要方法:
- 使用碳纳米管 (CNT) 与石墨碳化物 (g-C3N4) 纳米板进行修改的新型电化学传感器的制造.
- 使用快速里埃转换 (FFT) 电压计量用于定量测量.
- 采用部分最小平方 (PLS) 机器学习用于预测建模和验证.
主要成果:
- 传感器实现了高灵敏度和精度,同时检测MOR,MET和UA.
- 较低的RMSECV和RMSEP值证明了模型的准确性 (例如,MOR RMSECV:0.1827微米,RMSEP:0.1925微米).
- 在真实尿样中表现出色,RSD低 (3.71-5.26%) 和恢复高 (96-106%).
结论:
- 开发的g-C3N4-CNT电化学传感器为复杂的生物矩阵中同时进行药物分析提供了强大而可靠的平台.
- 先进材料和机器学习的整合意味着电化学传感在临床和实际应用中的重大进步.
- 这项技术有望改变药物分析,使得诊断更准确,更有效.
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