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About Collaborative Institutional Resources

Meharry Medical College has several multi-institutional grants and partnerships that expand our research capabilities through collaborations with universities, non-profits, government agencies, and community organizations.

About Collaborative Institutional Resources Publications

Genomics-informed drug-repurposing strategy identifies two therapeutic targets for preventing liver disease associated with metabolic dysfunction.
Hannah M Seagle, Alexis T Akerele, Joseph A DeCorte, Jacklyn N Hellwege, Joseph H Breeyear, Jeewoo Kim, Michael G Levin, Samuel Khodursky, Adam Bress, Kyung Min Lee, Jens Meiler, Dipender Gill, Jennifer S Lee, Kent Heberer, Donald R Miller, Peter D Reaven, Kyong-Mi Chang, Julie A Lynch, Nikhil K Khankari, Megan M Shuey, Todd L Edwards, Marijana Vujkovic

Hydroxypropyl-Beta-Cyclodextrin (HP-BCD) inhibits SARS-CoV-2 replication by modulating intracellular lipid dynamics and preventing viral replication complex formation.
Bruno Braz Bezerra, Keylla Vitória Gomes Macedo, Isadora Alonso Correa, Sharton Vinicius Antunes Coelho, Marcos Romario Matos de Souza, Barbara Martins Cordeiro, Carlos Frederico Leite Fontes, Fabiana Avila Carneiro, Flavio Matassoli, Luciana Jesus Costa, James E K Hildreth, Luciana Barros Arruda

Priapism Before and After Hematopoietic Stem Cell Therapy in Individuals with Sickle Cell Disease.
Jose A Mejias, Santosh L Saraf, Clifford M Takemoto, Mark Rodeghier, Michael R DeBaun

Perlecan is a novel target of autoantibodies in anti-glomerular basement membrane disease.
Huang Kuang, Bei-Ning Wang, Xiao- Yu Jia, Zhao Cui, Xiao-Juan Yu, Nan Jiang, Dorin-Bogdan Borza, Ming-Hui Zhao

Haplo-stem cell transplant post liver transplantation to cure sickle cell disease with related liver dysfunction: a case series.
Ali D Alahmari, Saad Alghamdi, Reem Alasbali, Sara Hisham Samarkandi, Saleh Algahtani, Hadeel Samarkandi, Syed Osman Ahmed, Dieter Broering, Hazzaa Alzahrani, Adetola Kassim, Mahmoud Aljurf, Fahad Almohareb, Waleed Al-Hamoudi

Improving automated deep phenotyping through large language models using retrieval-augmented generation.
Brandon T Garcia, Lauren Westerfield, Priya Yelemali, Nikhita Gogate, E Andres Rivera-Munoz, Haowei Du, Moez Dawood, Angad Jolly, James R Lupski, Jennifer E Posey