Community Engagement Core

The Meharry Community Engagement Core connects community members and organizations with Meharry researchers to conduct research and share results that will improve health for communities that experience the poorest health outcomes.

Vision

To eradicate health and healthcare disparities through long-term, collaborative, mutually beneficial community/academic research partnership.

Mission

The mission of the Meharry Community Engagement Core is to “combine the passion, knowledge, and skills of community partners and academic researchers to carry out research, share research results, and put into practice research findings that advance health equity and improve health for communities that experience the poorest health outcomes.”

Apart from efforts to increase community capacity to engage in research and partner with them in advancing their own health-related priorities, the Community Engagement Core provides direct support to Meharry researchers, including:

  1. providing community-engaged research training;
  2. identifying and facilitating collaborators with various community members, such as community-based organizations;
  3. identifying potential research study participants; and;
  4. providing assistance for sharing research study results beyond traditional academic venues.

A Community Advisory Board, including representatives form health advocacy, health equity, and faith-based organizations, provides guidance to the Community Engagement Core and serves as one avenue for researchers to get community feedback about their research and explore potential community-academic research partnerships.

Resources

  • Consultations for researcher and community members/organizations
  • Partnerships with more than 100 community members and organizations
  • Community Advisory Board
  • Training modules for researchers and community member
  • Research Volunteer Database
  • Community Engagement Studios
  • Community/Academic partnership development support
  • Lay Research Dissemination Procedures and Methods

For any inquires contact, Community Engagement Core Director, Stephania Miller-Hughes PhD MS, smiller@mmc.edu or Co-Director, Leah Alexander PhD MPH, lalexander@mmc.edu or Program Manager, Mariah Chambers MHA, mchambers@mmc.edu

Partnership Opportunities

To learn more about the Community Engagement Core, visit their website below.

Community Engagement Core 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