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纸喷雾质谱与机器学习相结合,用于慢性病的快速诊断
Igor Pereira1, Jindar N S Sboto1, Jason L Robinson2
1Applied Environmental Research Laboratories (AERL), Chemistry Department, Vancouver Island University, 900 Fifth Street, Nanaimo, BC, V9R 5S5, Canada. chris.gill@viu.ca.
The Analyst
|March 26, 2024
概括
一种新的快速方法使用纸喷雾质谱和机器学习来早期检测慢性病 (CKD). 这种方法分析尿液代谢物,在诊断CKD及其阶段时达到91%以上的准确性.
科学领域:
- 分析化学 分析化学
- 生物化学 生物化学
- 计算生物学 计算生物学
背景情况:
- 慢性病 (CKD) 是一个重要的全球健康问题.
- 早期发现CKD对于有效管理和改善患者的治疗结果至关重要.
- 目前的诊断方法可能是侵入性的或耗时的.
研究的目的:
- 开发和验证用于CKD检测的快速,简单的分析方法.
- 使用纸喷雾质谱法 (PS-MS) 结合机器学习进行尿液代谢分析.
- 评估CKD和白尿分类的拟议方法的诊断性能.
主要方法:
- 用高分辨率质谱仪使用纸喷雾质谱法 (PS-MS) 分析尿液样本.
- 采用快速分析方案 (每个样本15秒),分析代谢概况的变化.
- 随机森林机器学习算法用于数据分类和代谢物识别.
主要成果:
- 通过PS-MS和机器学习方法,训练数据的诊断准确率达到96%以上,验证/测试数据的诊断准确率达到91%以上.
- 识别的代谢物概况与关于CKD的现有文献一致.
- 该方法证明了在区分常态白血病,小白血病和大白血病之间有潜力.
结论:
- 与机器学习相结合的PS-MS为CKD检测提供了快速简单的方法.
- 这种方法有可能成为CKD的有价值的非侵入性诊断工具.
- 进一步用于区分白蛋白尿症阶段的进一步应用显示了个性化患者管理的前景.
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