完全自动化的深度学习启用微型质谱系统用于精神活性治疗药物监测
Yuanhao Zhou1,2, Jiawen Ai3,4, Zi Ye1,2
1School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China.
概括
这项研究引入了一种自动化微型质谱系统,用于在生物流体中快速检测生物标志物. 这种创新方法通过对精神活性药物的高通量,准确的分析来增强精确医学.
科学领域:
- 分析化学 分析化学
- 生物技术是生物技术.
- 计算生物学 计算生物学
背景情况:
- 精准医学需要在生物流体中有效地检测小分子生物标记物.
- 目前的方法面临复杂性,可移植性和吞吐量方面的局限性.
- 临床诊断需要先进的分析系统.
研究的目的:
- 开发一个集成的微型系统,用于自动化血液处理和质谱分析.
- 提高临床环境中生物标志物检测的吞吐量和准确性.
- 为了使血清中精神活性药物的快速定量分析.
主要方法:
- 自动磁性固体相提取用于样品制备.
- 一个自吸取样本的微型质谱仪用于快速分析.
- 深度学习算法 (U-net) 用于自动定量分析和峰值识别.
- 双目标离子并行联质谱技术.
主要成果:
- 从样本准备到检测实现了完全的自动化.
- 分析了血清精神活性药物与15秒MS收购和8样本并行处理在30分钟内.
- 证明识别准确度>98%,区域预测偏差<0.2%.
- 在定量分析中获得了高相关系数 (>0.99) 和准确性 (<3.5%).
- 临床验证显示与液体染色学-并联质谱学 (LC-MS/MS) 有很强的一致性.
结论:
- 综合系统为高通量临床检测提供了一个范式.
- 自动化处理,微型MS和人工智能驱动分析的结合提高了准确性和效率.
- 这项技术在临床检测和个性化医疗方面具有重大潜力.
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