Digital High Resolution Melt and Machine Learning for Rapid and Specific Diagnosis in Neonatal Sepsis

项目摘要 新生儿血培养敏感性较差，但却是诊断败血症的“金标准”。 使用高分辨率熔解（U-HRM）对病原体基因组序列进行通用基因分型提供了一种简单， 低成本、快速和现代化的血液培养检测替代品。通过测量一种 插入染料作为PCR扩增的病原体DNA片段被加热并解离，序列确定 在闭管反应中用单核苷酸分辨率产生解链曲线。我们有先进的U- HRM转化为数字PCR格式（U-dHRM），其中存在于混合物中的DNA序列分别 扩增并鉴定为多微生物感染所需。我们还建立了独特的签名 37种细菌的熔解曲线，这些细菌通常感染较大的儿童和成人， 使用机器学习技术。目的是为及时提供准确有效的测试， 诊断新生儿败血症，我们将推进这项技术，以确定独特的真菌，病毒和细菌 HRM签名沿着抗生素耐药基因，在最小血容量上的准确率为99-100% （1 mL）。我们的目标是：目标1。优化和评估新生儿菌血症诊断的U-dHRM平台， 扩大我们的细菌数据库（13种额外的细菌），以检测>99%的新生儿细菌感染的原因。 感染，扩大我们的抗生素耐药基因数据库，包括五个临床可操作的基因， 在模拟和临床全血样本中评估系统用于菌血症诊断的性能; 目标二。升级U-dHRM平台，用于同时检测真菌和病毒病原体 我们的光学系统能够以高通量格式扩展到真菌和病毒检测， 该测定扩大到引起>99%非细菌感染的病毒和真菌病原体，并进行 使用模拟全血样品的多重平台的分析验证;和目的3.推进 用于通过开发和整合异常来检测新出现的病原体的机器学习算法 用于报告未包含在我们数据库中的新出现病原体的检测算法， 算法使用目标1和2中生成的数据。因此，本提案通过以下方式直接解决供资问题： 应用多学科方法克服快速诊断脓毒症的生物医学挑战， 隐藏的公共卫生灾难。

Project Summary Blood culture sensitivity in neonates is poor but is the “Gold Standard” for the diagnosis of sepsis. Universal genotyping of pathogen genomic sequences using High Resolution Melt (U-HRM) provides a simple, low cost, rapid, and modern alternative to blood culture testing. By measuring the fluorescence of an intercalating dye as PCR-amplified pathogen DNA fragments are heated and disassociate, sequence defined melt curves are generated with single-nucleotide resolution in a closed-tube reaction. We have advanced U- HRM into a digital PCR format (U-dHRM), where DNA sequences that are present in mixtures are individually amplified and identified as is needed for polymicrobial infections. We have also established unique signature melt curves for 37 bacterial species that commonly infect older children and adults and automatically identify them using machine learning technology. With the goal of creating an accurate and valid test for the timely diagnosis of neonatal sepsis, we will advance this technology to identify unique fungal, viral, and bacterial HRM signatures along with antibiotic resistance genes with an accuracy of 99-100% on minimal blood volume (1mL). Our aims are: Aim 1. Optimize and assess the U-dHRM platform for neonatal bacteremia diagnosis by expand our bacterial database (13 additional bacteria) to detect causes of >99% of neonatal bacterial infections, expand our antibiotic resistance gene database to include five clinically actionable genes, and assessing the performance of the system for bacteremia diagnosis in mock and clinical whole blood samples; Aim 2. Advance the U-dHRM platform for simultaneous detection of fungal and viral pathogens by upgrading our optical system to enable expansion to fungal and viral detection in a high-throughput format, multiplexing the assay to expand to viral and fungal pathogens causing >99% non-bacterial infections, and conducting analytical validation of the multiplexed platform using mock whole blood samples; and Aim 3. Advance the machine learning algorithm for detection of emerging pathogens by developing and integrating an anomaly detection algorithm for reporting emerging pathogens that are not included in our database and validating the algorithm using data generated in Aims 1 and 2. Thus, this proposal directly addresses the funding call by applying a multidisciplinary approach to overcome the biomedical challenge of rapidly diagnosis sepsis, a hidden public health disaster.