Research Areas
- Traumatic Brin Injury
- MRI on Glucose metabolism
- Computational Pathology using Deep Learning
- Radiological-Pathological Correlation Analysis
Scientific Achievements
- Develop an integrated CEST-DCE imaging paradigm for preclinical MRI.
- Pathological Correlation and Validation: Generate glucose utilization and blood flow maps in TBI brains and correlate with immunohistochemistry to enhance interpretation.
- Collaboration with MPI at Howard University and
- Children’s National Research Institute.
- Communication Biology, 2025; doi:10.1038/s42003- 025-07926-y; PMID: 40114030; PMCID: PMC11926354.
Funding
RCMI Funding: U54MD007597, NIH/NIMHD: Pilot Project “Imaging Neurovascular Uncoupling in Traumatic Brain Injury using CEST CEST-DCE MRI”
Other funding obtained with RCMI support:
Other funding obtained with RCMI support:
- NIH/NINDS Award: R01NS123442.
- NSF Awards: 2200489, 2200585.
Scientific Advance
StainAI: quantitative mapping of stained microglia and insights into brain wide neuroinflammation and therapeutic effects in cardiac arrest.
Published in Communication Biology, Volume 8, 2025, PMCID: PMC11926354.
Published in Communication Biology, Volume 8, 2025, PMCID: PMC11926354.
We developed StainAI, a deep learning tool for high throughput analysis of microglial morphology from 20x immunohistochemistry images. It maps microglia to a brain atlas, classifies morphology, quantifies features, and computes activation scores across the entire brain. In a rat model of pediatric cardiac arrest, StainAI classified millions of microglia, surpassing existing methods and revealing novel activation patterns. Application to a primate model of simian immunodeficiency virus confirmed its cross species utility. StainAI provides a scalable platform for advancing research in microglial biology and neuroinflammation.
NIH/NIMHD #U54MD007597, NIH/NINDS #R01NS123442, NSF 2200489, 2200585
