Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
Electrical & Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Nat Commun. 2024 Aug 20;15(1):7124. doi: 10.1038/s41467-024-51067-5.
Point-of-care serological and direct antigen testing offers actionable insights for diagnosing challenging illnesses, empowering distributed health systems. Here, we report a POC-compatible serologic test for Lyme disease (LD), leveraging synthetic peptides specific to LD antibodies and a paper-based platform for rapid, and cost-effective diagnosis. Antigenic epitopes conserved across Borrelia burgdorferi genospecies, targeted by IgG and IgM antibodies, are selected to develop a multiplexed panel for detection of LD antibodies from patient sera. Multiple peptide epitopes, when combined synergistically with a machine learning-based diagnostic model achieve high sensitivity without sacrificing specificity. Blinded validation with 15 LD-positive and 15 negative samples shows 95.5% sensitivity and 100% specificity. Blind testing with the CDC's LD repository samples confirms the test accuracy, matching lab-based two-tier results, correctly differentiating between LD and look-alike diseases. This LD diagnostic test could potentially replace the cumbersome two-tier testing, improving diagnosis and enabling earlier treatment while facilitating immune monitoring and surveillance.
即时血清学和直接抗原检测为诊断疑难疾病提供了切实可行的见解,为分布式医疗系统赋能。在这里,我们报告了一种即时检测(POC)兼容的莱姆病(LD)血清学检测方法,该方法利用针对 LD 抗体的合成肽和基于纸张的平台,实现快速、经济高效的诊断。选择针对 IgG 和 IgM 抗体的 Borrelia burgdorferi 种系保守抗原表位,开发一个用于检测患者血清中 LD 抗体的多重检测panel。当与基于机器学习的诊断模型协同使用时,多种肽表位可实现高灵敏度,而不会牺牲特异性。对 15 份 LD 阳性和 15 份阴性样本的盲法验证显示,敏感性为 95.5%,特异性为 100%。对 CDC 的 LD 存储库样本进行的盲法测试证实了该检测方法的准确性,与实验室的两阶段检测结果相匹配,正确地区分了 LD 和类似疾病。这种 LD 诊断检测方法有可能取代繁琐的两阶段检测,改善诊断,更早开始治疗,并有助于免疫监测和监测。