用光光谱学和机器学习算法对糖尿的非侵入性表征和糖尿病尿液中的生物标志物的鉴定
Imran Rehan1,2, Rahat Ullah3, Saranjam Khan2
1National Institute of Lasers and Optronics College, Pakistan Institute of Engineering and Applied Sciences, Islamabad, 45650, Pakistan.
Journal of fluorescence
|August 3, 2023
概括
这项研究引入了一种简单的光光谱法,用于在糖尿病中非侵入性测量葡萄糖尿. 尿液中的葡萄糖和托芬水平显示出明显的光信号,有助于糖尿病分析.
科学领域:
- 分析化学 分析化学
- 生物化学 生物化学
- 医学诊断 医学诊断 医学诊断
背景情况:
- 糖尿病的诊断往往依赖于侵入性方法.
- 对葡萄糖尿的非侵入性监测对于糖尿病管理至关重要.
- 光谱学提供了一种敏感的分析技术.
研究的目的:
- 开发一种简单的,非侵入性的方法,使用光光谱学来确定糖尿尿.
- 识别和量化尿液中的葡萄糖和二作为糖尿病的潜在生物标志物.
- 根据尿液光来区分糖尿病和非糖尿病患者.
主要方法:
- 用光光谱学分析的尿样用Xenon弧灯 (200-950 nm) 进行分析.
- 在370nm处的激发确定了在450nm处的葡萄糖排放峰值.
- 在357nm的同步光发射用于托芬 (Trp) 分析.
- 层次集群分析 (HCA) 用于数据差异化.
主要成果:
- 尿中的葡萄糖度与450nm的光强度正相关.
- 随着葡萄糖度的增加,尿液中的托芬水平下降.
- 根据光签名,HCA有效地分离了糖尿病和非糖尿病的尿样.
结论:
- 光谱学提供了一种可行的非侵入性方法来确定葡萄糖尿症.
- 尿液中的葡萄糖和托芬光排放作为糖尿病分析的潜在生物标志物.
- 开发的方法为糖尿病查提供了一种简单而有效的方法.
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