Autonomous Diagnostics to Enable Prevention and Therapeutics (ADEPT)*
Abstract
*Previously funded in Synthetic Biology in PE 0601101E, Project TRS-01 The overarching goal of the Autonomous Diagnostics to Enable Prevention and Therapeutics (ADEPT) program is to create an ability to rapidly respond to a disease or threat and improve individual readiness and total force health protection. Service members in deployed settings have limited access to health care. The ability to perform continuous monitoring of physiological status to automatically and autonomously report a warning of a detrimental change and enable immediate diagnostic or therapeutic action would expand healthcare capabilities to these service members. Additionally, in vivo production of a vaccine would potentially eliminate the time to manufacture a vaccine ex vivo. This basic research effort will develop in vivo nucleic-acid circuits to control cellular machinery for diagnostic or vaccine applications and include research to: optimize orthogonality and modularity of genetic control elements; identify methods to increase sensitivity and specificity; and demonstrate methods to control cellular machinery in response to changes in physiological status. An additional strategic thrust is to develop methodologies for measuring health-specific biomarkers from a collected biospecimen to enable diagnostics at the point-of-need, in-garrison or deployed. This basic research effort will: develop new molecular methods for isolating and detecting health-associated biomarkers for application at the point-of-need or resource limited clinical facilities (point-of -care); develop new chemical and material methods for optimizing the analytical utility of minimal sample volumes; and, develop capabilities to archive and distribute biospecimens in a stable dried format without tubes, collection vials, or additional reagents. This program also has applied research efforts budgeted in PE 0602115E, Project BT-01.
Document Details
- Document Type
- Accomplishment
- Publication Date
- Oct 01, 2012
- Source ID
- eef35967121aaec25b0007ae666e5e11