通过FT-拉曼光谱检测多重物质使用患者的血清变化
Başak Ünübol1, Devrim Sarıbal2, Zeynep Ceylan3
1Department of Psychiatry, University of Health Sciences, Erenköy Mental Health and Neurological Diseases Training and Research Hospital, Istanbul, Türkiye.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|October 10, 2024
概括
多种物质的使用会改变体液化学,影响蛋白质和脂质水平. FT-拉曼光谱和机器学习准确地识别了这些化学变化,有助于对物质使用障碍的诊断.
科学领域:
- 生物化学 生物化学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 药物使用障碍 (SUDs) 由于多种物质之间的相互作用而带来复杂的健康挑战.
- 多重物质使用对体液的化学影响仍然不完全理解.
- 研究这些化学变化对于推进SUD诊断和治疗至关重要.
研究的目的:
- 研究由阿片类药物,酒精和大麻的联合使用引起的体液中的化学变化.
- 使用先进的光谱技术,将这些化学变化与对照组进行比较.
- 评估机器学习算法在基于光谱数据的多种物质使用分类中的有效性.
主要方法:
- 福里埃转换-拉曼 (FT-Raman) 光谱法用于分析血清样本.
- 两轨二维相关谱 (2T2D-COS) 用于分析光谱数据.
- 主要组件分析 (PCA) 和部分最小平方区分分析 (PLS-DA) 用于数据分析和分类.
- 进行了接收器操作特征 (ROC) 分析,以验证分类模型.
主要成果:
- FT-拉曼光谱揭示了多重物质组中胺和脂质功能组的结构变化.
- 观察到胺-脂质比率的不平衡,表明血清蛋白质和脂质水平发生了变化.
- 与对照组不同的是,2T2D-COS在多物质组中显示了转向脂质主导的转变.
- PLS-DA实现了高分类准确度 (100%的训练,94.74%的测试),并从ROC分析中获得了完美的AUC值.
结论:
- 多种物质的使用会导致血清蛋白质和脂质概况的大量和质量变化.
- FT-拉曼光谱能有效地检测到体液中的这些分子变化.
- 机器学习模型显示了在药物滥用监测和管理中的临床应用的巨大潜力.
- 了解多种物质使用的化学影响对于改善SUD诊断和治疗策略至关重要.
相关概念视频
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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
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