{"id":7403,"date":"2020-10-13T12:14:45","date_gmt":"2020-10-13T12:14:45","guid":{"rendered":"https:\/\/cdtrp.ca\/?p=7403"},"modified":"2021-02-11T13:19:59","modified_gmt":"2021-02-11T13:19:59","slug":"congratulations-to-dr-mamatha-bhat-ast-research-network","status":"publish","type":"post","link":"https:\/\/cdtrp.ca\/en\/congratulations-to-dr-mamatha-bhat-ast-research-network\/","title":{"rendered":"Congratulations to Dr. Mamatha Bhat: AST Research Network – CareDx Award"},"content":{"rendered":"

Congratulations to Dr. Mamatha Bhat: AST Research Network – CareDx Award<\/h4>\n

Warm congratulations to Dr. Mamatha Bhat<\/strong> for being awarded a $100,000 AST Research Network\/CareDx Directed Research Grant on \u201cOptimizing the Long-Term Health of Liver Transplant Recipients using Machine Learning Algorithms\u201d.<\/p>\n

Lay abstract<\/h5>\n

Liver Transplantation has been lifesaving for patients with end-stage liver disease. While one-year survival of liver transplant recipients has significantly improved over the last few decades, long-term survival beyond a year has remained stagnant. The lifespan of liver transplant recipients has been compromised by a heightened incidence of cancer, cardiometabolic disease, and infection. A key risk factor is the lifelong immunosuppression required to mitigate against graft rejection. It is currently not feasible to balance risks versus benefits of immunosuppression in the individual liver transplant recipient.<\/p>\n

We anticipate that each individual liver transplant recipient has a specific immunosuppression regimen and level of immunosuppression that would best balance long-term risks (cancer, cardiometabolic, fatty liver in the graft, infection) versus benefits (free of graft rejection) of immunosuppression. We will develop and validate an MLA-based risk calculator that predicts the major post-transplant complications and associated mortality, identify specific etiologies of ongoing graft damage by examining methylation patterns on dd-cfDNA (cfMeDIP), and finally delineate data-driven patient subtypes based on the risk: benefit ratio of immunosuppression. The ranked features for these patient subtypes would generate personalized preventive and therapeutic recommendations. We anticipate that our study findings will allow us to generate personalized immunosuppressive and preventive strategies for enhanced well-being after transplant.<\/p>\n

CONGRATULATIONS!<\/strong><\/h5>\n<\/div>
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