
NSF Org: |
CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems |
Recipient: |
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Initial Amendment Date: | April 6, 2021 |
Latest Amendment Date: | October 11, 2022 |
Award Number: | 2129352 |
Award Instrument: | Standard Grant |
Program Manager: |
Steve Zehnder
szehnder@nsf.gov (703)292-7014 CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems ENG Directorate for Engineering |
Start Date: | April 1, 2021 |
End Date: | April 30, 2023 (Estimated) |
Total Intended Award Amount: | $300,000.00 |
Total Awarded Amount to Date: | $263,767.00 |
Funds Obligated to Date: |
FY 2022 = $59,996.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
3901 RAINBOW BLVD KANSAS CITY KS US 66160-7100 (913)588-6570 |
Sponsor Congressional District: |
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Primary Place of Performance: |
KS US 66160-8500 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | BioP-Biophotonics |
Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.041 |
ABSTRACT
Glioblastoma (GBM) is the most common and most aggressive adult primary brain tumor. GBM is known for the patient's poor survival of less than 16 months despite surgical resection, radiation, and/or chemotherapy. There is a growing awareness that the interaction of tumors with their microenvironment, together with metabolic factors, is responsible for altering gene expression patterns, which enable the tumor to adapt and escape tumor treatment. This collaborative research project aims to develop novel biophotonic methods to recognize genome-wide epigenetic mutations in GBM. This methodology will not only permit the early diagnosis of GBM, it will also lead to a combination of mechanic and metabolic stimuli, which will be able to restore apoptosis (programmed cell death) in GBM and other solid tumors.
This proposal has the following aims: 1: Development of Nanoscale Technologies for Visualization and Characterization of Chromatin Alteration Induced by Mechano-metabolic Cues; Aim 2: Chromatin Level Epigenetic Engineering; and Aim 3: A Deep Hybrid Learning Model to Recognize and Predict Mechano-metabolic Conditions for Introducing Programmed Cell Death in GBM. The development of a new toolbox for reprogramming of transformed cells will have implications that will reach far beyond glioblastoma. It will apply to virtually all diseases with epigenetic drivers, among them numerous cancers, neurodegenerative diseases, cardiovascular diseases, obesity and metabolic syndrome.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Glioblastoma (GBM) is the most common and aggressive adult primary brain tumor. The average age- adjusted incidence rate is 3.2 per 100,000 population. GBM is known for its poor survival rate. Surgical resection, radiation, and chemotherapy is only palliative. GBM cells are especially motile, compared to other cancers. This enables a novel strategy, to eradicate glioblastoma by introducing high motility conditions in the extracellular matrix, and directing glioblastoma cells by means of cytokine/chemokine gradients to a site from where they can be permanently removed from the brain. We have designed, tested, and validated a microfluidic device capable of quantitatively measuring the velocity of cell (cluster) migration following cytokine gradients. The principal outcome of this EAGER grant is that a novel glioblastoma treatment strategy based on mechanobiology is possible.
The following research & development has been successfully performed during this EAGER grant:
A 3D printing method was developed for molds to shape PDMS gradient devices. This method for fabricating PDMS devices will cost approximately 100 times less than traditional photolithography for mold production when considering the equipment and materials needed. 3D printing molds is also considerably easier and requires less time. A PDMS baking procedure was developed using the printed resin molds. Contact angle analysis was used to investigate if volatile species from the resin were transferring into the PDMS during baking. This work confirmed that the resin was not impacting the PDMS surface properties. In fact, after both single and replicate use of the resin molds, the mold surface became more like the native PDMS surface.
It was confirmed that the 3M? 9795R microfluidic tape could be used to seal PDMS devices. A method was developed to eliminate bubbles during device filling through periods of increasing and relatively constant applied pressure using the filling apparatus syringe and monitoring using the in-line pressure sensor.
A post-print treatment process was adopted to aid in removal of toxins from the cured resin material that could leach into PDMS during baking. Once the 3D-printed molds have been rinsed in isopropyl alcohol, dried, and exposed to the final post-print cure, the molds are sterilized with 70% ethanol and placed into a biosafety cabinet to dry under UV light. The prints are then soaked in sterile 1x phosphate buffered saline for 24 hours at 37 ?C. After soaking, the molds are rinsed in ultrapure water, dried, and stored in petri dishes before PDMS molding.
Last Modified: 08/29/2023
Modified by: Stefan H Bossmann
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