纤维肌痛的早期诊断使用表面增强的拉曼光谱与化学测量结合使用
Haona Bao1, Kevin V Hackshaw2, Silvia de Lamo Castellvi1,3
1Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA.
Biomedicines
|January 23, 2024
概括
使用金纳米粒子 (AuNPs) 的表面增强拉曼光谱 (SERS) 显示出用于诊断纤维肌痛 (FM) 和其他类风湿性疾病的前景. 这种无标签的方法使用血液样本分析准确地区分了FM和非FM疾病.
科学领域:
- 生物医学光谱学 生物医学光谱学
- 诊断技术 诊断技术 诊断技术
- 类风湿病学 类风湿病学
背景情况:
- 纤维肌痛 (Fibromyalgia,简称FM) 是一种慢性疼痛疾病,其症状与其他类风湿病症重叠.
- 精确诊断FM和相关疾病,如类风湿性关节炎 (RA),全身性红斑狼 (SLE),骨关节炎 (OA) 和慢性腰部疼痛 (CLBP) 仍然是一个临床挑战.
- 需要新的诊断工具来精确区分这些复杂的疾病.
研究的目的:
- 调查使用金纳米粒子增强拉曼光谱 (AuNP SERS) 作为FM诊断指纹采集方法的可行性.
- 评估AuNP SERS在将FM与其他类风湿病区分开来方面的潜力.
- 确定潜在的光谱生物标志物,以免标签诊断FM.
主要方法:
- 血液样本从 FM 患者,非 FM 患者和健康对照组中采集.
- 采用半透膜过方法来分离血清中低分子量分数 (LMF).
- 在LMF上进行了标准化的AuNP SERS测量,随后对光谱数据进行了OPLS-DA分析 (750-1720 cm-1).
主要成果:
- OPLS-DA模型实现了频谱的优异分类,分为FM和非FM组,Rcv > 0.99.
- 该方法在区分组中显示出100%的准确性,灵敏性和特异性.
- 与氨基酸相关的光谱区域被确定为关键的歧视因素,表明它们作为生物标志物的潜力.
结论:
- 结合OPLS-DA分析,AuNP SERS方法是一种高度准确和可行的方法,用于无标签诊断FM.
- 这种技术显示出区分FM与其他类风湿病的巨大潜力.
- 对与氨基酸相关的光谱生物标志物的进一步研究可能会增强类风湿性疾病的诊断能力.
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