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Development of Autonomous Glycemic Control Mechanism for Patients Suffering Glycemic Abnormalities as a Result of Critical Illnesses

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W911NF19C0026
Agency Tracking Number: D18C-004-0043
Amount: $224,864.20
Phase: Phase I
Program: STTR
Solicitation Topic Code: ST18C-004
Solicitation Number: 18.C
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-03-18
Award End Date (Contract End Date): 2020-01-13
Small Business Information
345 Allerton Ave.
San Francisco, CA 94080
United States
DUNS: 830219338
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Natalie Wisniewski
 Chief Technology Officer
 (415) 655-9861
Business Contact
 Sean Givens
Phone: (415) 655-9861
Research Institution
 The Ohio State University
 Sunny Zong Sunny Zong
1960 Kenny Road
Columbus, OH 43210
United States

 (614) 292-4342
 Nonprofit College or University

The use of continuous glucose monitors can be an invaluable management tool for patients afflicted by glycemic variability due to critical illness or trauma. Maintaining stable glucose levels enhances health and lowers care costs, and individuals equipped with continuous glucose data have significantly improved outcomes. Profusa has developed highly miniaturized, injectable, tissue-like, glucose sensors that minimize the foreign body response, enabling long-term monitoring. Sensors are wirelessly, fluorescently read through the skin (continuously or on-demand) and data is transmitted to a smart phone for viewing and analysis. In this STTR, we aim to transition our successfully demonstrated glucose sensors into a biodegradable format and to apply artificial intelligence to optimize the signal accuracy. We will utilize degradable hydrogels already used in other FDA-approved degradable devices to adapt our sensors to dissolve harmlessly, leaving no lasting implant in the body after their useful life for trauma care. Furthermore, our system collects multi-parameter data streams (oxygen, temperature, etc.), and we will apply artificial intelligence approaches to optimize the measurement accuracy. By integrating a machine learning approach into the analysis, different characteristic signatures will be inherently trained into the signal interpretation to ultimately lead to better closed loop glycemic control.

* Information listed above is at the time of submission. *

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