Publications by authors named "Fayeq Jeelani Syed"
Article Synopsis
- High-resolution typing of human leukocyte antigen (HLA) may enhance kidney transplantation by enabling selection of less immunogenic donors within a new 250-nautical mile allocation system.
- A study matched 501 transplant candidates from the University of Toledo Medical Center with 4812 donors, achieving a 99.8% success rate in finding optimal matches, which significantly improved HLA immunogenicity scores compared to a historical cohort.
- The results indicated that simulated transplants could lead to an increase in median graft survival from 13.4 years to 18.2 years, highlighting the benefits of the new donor matching approach for long-term patient outcomes.
HLA matching improves long-term outcomes of kidney transplantation, yet implementation challenges persist, particularly within the African American (Black) patient demographic due to donor scarcity. Consequently, kidney survival rates among Black patients significantly lag behind those of other racial groups. A refined matching scheme holds promise for improving kidney survival, with prioritized matching for Black patients potentially bolstering rates of HLA-matched transplants.
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- - Immunosuppressed kidney transplant recipients show a significantly weaker antibody response to COVID-19 vaccines compared to healthy individuals, with only 45.8% producing anti-SARS-CoV-2 IgG versus 74.1% in healthy volunteers.
- - At six months post-vaccination, a higher percentage of kidney transplant recipients were non-responders to the Pfizer-BioNTech vaccine (34.3%) compared to Moderna (10.8%), indicating varying efficacy between the two vaccines.
- - The study suggests that the stronger regulatory Tr1 cell response in kidney transplant recipients may explain the higher non-responder rate after Pfizer-BioNTech vaccination compared to Moderna, along with differences in T helper cell responses
Survival prediction aims to predict the time of occurrence of a particular event of interest, such as the time until a patient dies. The main challenge in survival prediction is the presence of incomplete observations due to censoring. The classical formulation for survival prediction treats the survival time as a continuous outcome, which leads to a problem.
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