Latent tuberculosis infection (LTBI) is estimated in nearly one quarter of the world's population, and of those immunocompetent and infected ~10% will proceed to active tuberculosis (TB). Current diagnostics cannot definitively identify LTBI and provide no insight into reactivation risk, thereby defining an unmet diagnostic challenge of incredible global significance. We introduce a new machine-learning-driven approach to LTBI diagnostics that leverages a high throughput, multiplexed cytokine detection technology and powerful bioinformatics to reveal multi-marker signatures for LTBI diagnosis and risk stratification. This approach is enabled through an individualized normalization procedure that allows disease-relevant biomarker signatures to be revealed despite heterogeneity in basal immune response. Specifically, cytokines secreted from antigen-challenged peripheral blood mononuclear cells were detected using silicon photonic sensor arrays and multidimensional data correlation of individually-normalized immune responses revealed signatures important for LTBI status. These results demonstrate a powerful combination of multiplexed biomarker detection technologies, precision immune normalization, and feature selection algorithms that revealed positively correlated multi-biomarker signatures for LTBI status and reactivation risk stratification from a relatively simple blood-based assay.

中文翻译：

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精确的免疫分析可揭示潜伏性结核感染和再激活风险分层的诊断特征。

据估计，潜伏性结核感染（LTBI）占世界人口的近四分之一，在那些具有免疫能力和感染能力的人群中，约有10％会发展为活动性结核（TB）。当前的诊断无法确定LTBI，也无法提供重新激活的风险，因此定义了具有令人难以置信的全球意义的未满足的诊断挑战。我们为LTBI诊断引入了一种新的由机器学习驱动的方法，该方法利用了高通量，多重细胞因子检测技术和强大的生物信息学来揭示LTBI诊断和风险分层的多标记签名。这种方法可通过个性化的标准化程序启用，该程序尽管基础免疫反应存在异质性，但仍可显示与疾病相关的生物标记物特征。特别，使用硅光子传感器阵列检测了抗原激发的外周血单核细胞分泌的细胞因子，单个归一化免疫反应的多维数据相关性揭示了对LTBI状态重要的特征。这些结果证明了多种生物标志物检测技术，精确的免疫归一化和特征选择算法的强大组合，这些特征通过相对简单的基于血液的测定揭示了LTBI状态和再激活风险分层的正相关多生物标志物签名。