A Rapid Blood Test to Differentiate Latent Tuberculosis from Active Disease

Abstract

The purpose of the study is to develop a blood-based TB test that meets or exceeds WHO Target Product Profiles for a rapid, biomarker-based, non-sputum triage test for detecting active TB disease. To accomplish this, activities in Year 1included improvements to the 3-gene mRNA signature and analysis of these improvements; development of a 9-genesignature; prototype cartridge development; recruitment and blood collection in Moldova; and development of a secure data transmission system. In Year 2, Aim 2 recruitment and blood collection/processing was completed in Moldova, Cepheid worked to develop two "open" prototype cartridges the Stanford 3-gene signature cartridge for non-stimulated blood, and a prototype antigen-stimulated cartridge -- and Stanford began discovery analysis toward a sub-9-gene signature using a machine learning framework. In Year 3, Cepheid will further develop the"open" prototype cartridges, complete biostatistics work necessary to lock the signatures, and assess and validate their performance; the result will be a final closed prototype cartridge which will be deployed in the field.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2020
Accession Number
AD1123382

Entities

People

  • Antonino Catanzaro
  • Laura Myhovich
  • Naomi Hillery
  • Timothy Rodwell

Organizations

  • University of California, San Diego

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Biological Markers
  • Biomedical Research
  • Biostatistics
  • California
  • Computational Biology
  • Covid-19
  • Data Analysis
  • Data Processing
  • Data Transmission Systems
  • Department Of Defense
  • Detection
  • Diseases And Disorders
  • Field Tests
  • Health
  • Hematologic Tests
  • Information Science
  • Machine Learning
  • Medical Personnel
  • Professional Development
  • Public Health
  • Students
  • Training
  • Tuberculosis
  • Validation

Readers

  • Munitions and Ordnance Engineering
  • Oncology and Biomarker-Based Cancer Detection.
  • Software Engineering

Technology Areas

  • AI & ML