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Fabrico Technology, Inc. SBIR Phase I Award, June 2018

A SBIR Phase I contract was awarded to Fabrico Technology, Inc. in June, 2018 for $149,999.0 USD from the U.S. Department of Defense and Defense Health Agency.

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Contents

sbir.gov/node/1589509
Is a
SBIR/STTR Awards
SBIR/STTR Awards

SBIR/STTR Award attributes

SBIR/STTR Award Recipient
Fabrico Technology, Inc.
Fabrico Technology, Inc.
0
Government Agency
U.S. Department of Defense
U.S. Department of Defense
0
Government Branch
Defense Health Agency
Defense Health Agency
0
Award Type
SBIR0
Contract Number (US Government)
W81XWH18C03440
Award Phase
Phase I0
Award Amount (USD)
149,9990
Date Awarded
June 11, 2018
0
End Date
January 10, 2019
0
Abstract

Stem cell apoptosis occurs when the interaction between the host brain extracellular matrix (ECM) and human induced pluripotent stem cell (iPSC) derived neural stem cell (NSCs) are directly injected into the injured brain regions. Establishment of a connection between ECM and iPSC/NSC could increase cell viability, differentiation, and promote functional recovery of the injured tissue. The critical gap technology is the development of a delivery system that would provide structural support for the endogenous tissue, prevent tissue collapse and finally provide an injected iPSC/NSC an ECM network with NO need for immunosuppressant therapies and introduction of neurotropic growth media or factors.Fabrico Technology, Dr. Charles Cox, MD, Director of Pediatric Trauma Programs, and Dr. Qi Lin Cao, Associate Professor of Department of Neurosurgery at the University of Texas Health Science Center at Houston (UTHealth) propose to develop a directly injectable gel composed of iPSC derived NSCs and porcine brain derived ECM. The injectable ECM-iPSC transplant provides for NSC survival and engraftment as neurons and would be directly injected into peri-contusional lesions, niche and voids resulting from TBI. The B-ECM provides a structural architecture to promote and stimulate iPSC to differentiate into neural cell lineage and reform into neural networks.

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