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

数字高分辨率熔解和机器学习用于新生儿败血症的快速和特异性诊断

基本信息

批准号：
9915874
负责人：
Stephanie Irene Fraley
金额：
$ 48.62万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-05-01 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9915874
关键词：
Address Adult Algorithms Antibiotic Resistance Antibiotics Bacteremia Bacteria Bacterial Antibiotic Resistance Bacterial Infections Biological Assay Birth Weight Blood Blood Volume Blood specimen Child Clinical DNA DNA Sequence Data Databases Detection Diagnosis Diagnostic Disasters Dyes Emerging Technologies Exposure to Fingerprint Fluorescence Funding Genes Genome Genotype Goals Gold Hour Immune response Individual Infection Machine Learning Measures Microbe Modernization Neonatal Nucleotides Optics Organism Patients Performance Predisposition Public Health RNA Reaction Reporting Research Resistance Resolution Sampling Sepsis Symptoms System Technology Testing Therapeutic Time Training Tube United States Validation Variant Very Low Birth Weight Infant Viral Whole Blood Woman antimicrobial base circulating DNA clinically actionable clinically relevant cost diagnosis standard digital early onset interdisciplinary approach intrapartum machine learning algorithm melting microbial neonatal sepsis neonate overtreatment pathogen pathogen genome pathogen genomics pathogenic fungus pathogenic virus point of care premature rapid diagnosis resistance gene sample collection septic therapy resistant viral detection

项目摘要

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.

项目概要新生儿的血培养敏感性较差，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、通过测量荧光来替代血培养检测，成本低、快速、现代。当 PCR 扩增的病原体 DNA 片段被加热并解离时插入染料，序列已确定我们拥有先进的 U- 闭管反应，以单核苷酸分辨率生成熔解曲线。 HRM 转换为 PCR 数字格式 (U-dHRM)，其中混合物中存在的 DNA 序列是单独的我们还根据多种微生物感染的需要进行了扩增和鉴定。 37 种细菌的熔解曲线，这些细菌通常感染年龄较大的儿童和成人，并自动识别他们使用机器学习技术，目标是及时创建准确有效的测试。新生儿败血症的诊断，我们将推进这项技术来识别独特的真菌、病毒和细菌 HRM 特征以及抗生素抗性基因，最小血容量的准确度为 99-100% (1mL)。我们的目标是：目标 1. 优化和评估用于新生儿菌血症诊断的 U-dHRM 平台。扩展我们的细菌数据库（另外 13 种细菌）以检测 > 99% 的新生儿细菌的原因感染，扩大我们的抗生素耐药性基因数据库，包括五个临床上可行的基因，以及评估模拟和临床全血样本中菌血症诊断系统的性能；目标 2. 通过升级推进 U-dHRM 平台，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、多重检测扩展到真菌和病毒检测该测定扩展到导致 > 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本对多重平台进行分析验证；目标 3. 推进通过开发和集成异常来检测新出现的病原体的机器学习算法检测算法，用于报告未包含在我们数据库中的新出现的病原体并验证算法使用目标 1 和 2 中生成的数据。因此，该提案直接解决了资金需求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。