Isaac D Juárez, Aidan P Holman, Elizabeth J Horn, Artem S Rogovskyy, Dmitry Kurouski
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摘要

由勃氏杆菌(Borreliella burgdorferi)引起的莱姆病(LD)是美国最常见的蜱媒疾病,但由于目前血清学诊断的局限性,早期诊断仍具有挑战性。拉曼光谱(RS)与偏最小二乘判别分析(PLS-DA)相配合,有望成为一种替代诊断工具。利用 RS,我们分析了从莱姆病生物库中获得的 107 份编码人体血液样本(42 份 LD 阳性样本和 65 份 LD 阴性样本)。PLS-DA 模型显示出近乎完美的内部验证性能,灵敏度和特异性分别为 97.1% 和 100.0%,显示出强大的预测能力。通过对所有光谱进行 80/20 的训练/验证分配,对所开发的化学计量学模型进行了外部验证,结果显示血清学阳性和阴性光谱的真阳性率分别为 92.7% 和 87.3%。这些发现凸显了 RS 作为一种快速、无创的 LD 诊断平台的潜力,尤其是在与机器学习相结合时。
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External Validation of Raman Spectroscopy for Lyme Disease Diagnostics.

Lyme disease (LD), caused by Borreliella burgdorferi, is the most common tick-borne illness in the United States, yet early-stage diagnosis remains challenging due to the limitations of current serological diagnostics. Raman spectroscopy (RS), paired with partial least squares discriminant analysis (PLS-DA), showed promise as an alternative diagnostic tool. Using RS, we analyzed 107 coded human blood samples (42 LD-positive and 65 LD-negative) obtained from the Lyme Disease Biobank. PLS-DA models showed nearly perfect internal validation performance with a sensitivity and specificity of 97.1% and 100.0%, respectively, indicating robust predictive capabilities. External validation of the developed chemometrics model with 80/20 training/validation split of all spectra gave true positive rates of 92.7% and 87.3% for serological positive and negative spectra, respectively. These findings highlight the potential of RS as a rapid and noninvasive diagnostic platform for LD, particularly when integrated with machine learning.

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