Nuguri Shreya Madhav, Hackshaw Kevin V, Castellvi Silvia de Lamo, Wu Yalan, Gonzalez Celeste Matos, Goetzman Chelsea M, Schultz Zachary D, Yu Lianbo, Aziz Rija, Osuna-Diaz Michelle M, Sebastian Katherine R, Brode W Michael, Giusti Monica M, Rodriguez-Saona Luis
Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA.
Department of Internal Medicine, Division of Rheumatology, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA.
Biomedicines. 2024 Jun 28;12(7):1447. doi: 10.3390/biomedicines12071447.
Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10-20% of individuals following COVID-19 infection. FM and LC share similarities with regard to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCAs) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, = 48 FM and = 46 LC) and volumetric absorptive micro-sampling tips (VAMS, = 39 FM and = 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra were acquired using SERS with gold nanoparticles (AuNPs). Soft independent modelling of class analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity, achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNP SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.
纤维肌痛（FM）是一种慢性中枢性敏感综合征，其特征是全身弥漫性部位的疼痛处理增强，并表现为一种多病症临床状况。长新冠（LC）是一种异质性临床综合征，在新冠病毒感染后的个体中，有10% - 20%会受到影响。FM和LC在疼痛及其他经历的临床症状方面有相似之处，因此对准确诊断构成挑战。本研究探讨了使用表面增强拉曼光谱（SERS）结合类类比软独立建模（SIMCAs）来开发区分LC和FM的分类模型的可行性。使用两种载体采集静脉血样本，即干血斑卡（DBS，FM组48例，LC组46例）和体积吸收微量采样吸头（VAMS，FM组39例，LC组39例）。使用半透膜（10 kDa）从血样中提取低分子部分（LMF），并使用金纳米颗粒（AuNPs）的SERS采集拉曼光谱。用VAMS吸头采集的血样光谱数据开发的类类比软独立建模（SIMCA）模型表现出卓越性能，验证性能的准确率、灵敏度和特异性均为100%，曲线下面积（AUC）达到了优异的0.86分类准确率。酰胺基团、芳香族和酸性氨基酸是FM和LC综合征鉴别模式的原因，这强调了我们之前研究的结果。总体而言，我们的结果证明了AuNP SERS能够识别独特的代谢物，这些代谢物有可能用作光谱生物标志物来区分FM和LC。
