Using advanced modeling to understand blood clotting related to inflammation in COVID-19
Official title: A deep learning-enhanced multiphysics and multiscale framework for predictive modeling of inflammation-induced thrombosis
Funded at University of Georgia · NIH-11015825 · Data updated 2026-11-12
This study is looking at how inflammation can cause blood clots in people with COVID-19 by examining blood samples, hoping to find clues that could help doctors treat patients more effectively.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Georgia NIH-funded |
| Lab location | 1 site (Athens, United States) |
| Project start | February 1, 2024 |
| Project end | January 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $171,215 |
| NIH project number | 5R21HL168507-02 |
| Project ID | NIH-11015825 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include patients diagnosed with COVID-19 who are experiencing severe symptoms or complications related to blood clotting.
✕Less likely to help
Patients with mild COVID-19 symptoms or those not diagnosed with COVID-19 may not benefit from this research.
What this research studies
This research aims to develop a sophisticated modeling framework that utilizes deep learning to predict how inflammation can lead to blood clotting, particularly in patients with COVID-19. By analyzing blood samples, the study will focus on identifying circulating cell clusters as potential markers for disease severity and outcomes. The research will explore the mechanisms behind the formation of microthrombi, which are small blood clots that can cause serious complications in vital organs. This approach seeks to provide insights that could lead to timely therapeutic interventions for affected patients.
Why it matters
Potential benefit: If successful, this research could improve the understanding and management of blood clotting complications in patients with COVID-19 and similar inflammatory conditions.
How similar studies have gone: While the approach of using deep learning for predictive modeling in this context is innovative, similar studies have shown promise in understanding thrombosis in other inflammatory diseases.
Where this research is happening
-
University of Georgia
Athens, United States Active
Researchers
- Principal investigator: Li, He — University of Georgia
- Study coordinator: Li, He
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.