| NSF Org: |
ECCS Div Of Electrical, Commun & Cyber Sys |
| Recipient: |
|
| Initial Amendment Date: | July 22, 2023 |
| Latest Amendment Date: | July 22, 2023 |
| Award Number: | 2315376 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Svetlana Tatic-Lucic
staticlu@nsf.gov (703)292-0000 ECCS Div Of Electrical, Commun & Cyber Sys ENG Directorate For Engineering |
| Start Date: | August 1, 2023 |
| End Date: | July 31, 2026 (Estimated) |
| Total Intended Award Amount: | $420,000.00 |
| Total Awarded Amount to Date: | $420,000.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
3 RUTGERS PLZ NEW BRUNSWICK NJ US 08901-8559 (848)932-0150 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
3 RUTGERS PLZA NEW BRUNSWICK NJ US 08901-8559 |
| Primary Place of Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): |
BioP-Biophotonics, CCSS-Comms Circuits & Sens Sys |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Monitoring patients? individual response to infections is vital not only for early diagnosis but also to determine effective treatment for the patients. In United States, around 14 million operative procedures are performed in inpatient hospital settings with more than hundred thousand people subsequently developing surgical site infections (SSI). Its management and treatment cost another US $10 billion yearly in excess costs. Further, 77% of the deaths of surgical patients are attributed to SSI. This award which is being made to Rutgers University New Brunswick will develop a biomedical platform to perform prescribed diagnostics tests rapidly and accurately. The award will enable a significant advance compared to currently employed instruments used for these tests which are bulky, costly, and requires manual sample processing by technically trained staff to perform these tests. In future, proposed platform can translated in multiple healthcare settings including primary care offices, emergency care settings, intensive care units and testing laboratories. Biomedical platform resulting from this award will have a great potential to benefit human health and welfare. This project will train undergraduate and graduate students in the fields of biosensors, microfluidics, microfabrication, optics, and machine learning. The project will also enable the integration of proposed research into investigators educational efforts. Further, the award will facilitate the investigators to implement various outreach activities including engaging K-12 students in research, and the public through educational lectures and making them available online for broad dissemination of knowledge.
Personalized immune response monitoring of patients to infections is critical not only for early diagnosis but also for determining effective therapy, thereby having a significant impact in patients? outcome. SSI is a common condition faced by patients? post-surgical procedures. Early visual indications of SSI include inflammation and puss at the site of the wound, pain, fever, and discomfort. This is followed by a microbial culture which takes multiple days to get the results, leaving huge diagnostic gaps in the treatment pathway. Post-operative frequent quantification of clinically approved biomarkers (e.g., C-reactive protein (CRP), procalcitonin (PCT)) on high-risk patients could provide an early indication of an SSI, however, their testing requires centralized lab facility, trained professionals and longer wait times to get the results. In this work, investigators envision a biomedical platform for inflammatory proteins quantification using only a drop of whole blood. The proposed innovation is based on a cross-cut integration of photonics, microfluidics, smartphone enabled optical sensing, and 3-D multi-layer microfluidic architectures on a single biochip with automated whole blood processing to provide clinical test results from patient samples. Sensors will be equipped with real-time measurement capability and machine learning models to train the sensors data and provide test results. Sensors will be benchmarked with patient samples collected from Robert Wood Johnson Medical Hospital. Investigators envisions proposed biosensor platform to be generic and the pre-functionalized cartridges can be swapped out for different infectious diseases biomarkers testing. This transformative research will also open new educational initiatives to train the next generation of engineers and scientists.
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.
Please report errors in award information by writing to: awardsearch@nsf.gov.
