Investigation of complex dynamics of donor specific antibodies in high risk kidney transplantation. Category Control engineering and Healthcare tech

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Renal failure is one of the fastest-rising death triggers worldwide. Due to the lack of affordable treatment, only 10% of patients receive treatment, and a million die yearly. The optimal treatment for renal failure is a kidney transplant, ideally performed when a compatible donor is available. Providing a compatible kidney transplant is challenging, resulting in more than half of patients waiting an average of 2.5 years to get treatment. Luckily, incompatible kidney transplant has evolved significantly in the last few decades. Advancement in diagnoses and assay techniques allows for transplantation in the presence of HLA antibodies. However, the prediction of clinical outcomes is still challenging. A lot of research has been carried out focusing on the characteristics of donor-specific antibodies (DSA). Yet, determining the impact of specific DSA antibodies is only just undertaken. In particular, in recent studies, the dynamic of total DSA during the early post-transplant period was investigated for the first time. Unsupervised machine learning was applied to cluster the DSA responses into five groups (no response, fast modulation, slow modulation, rise to sustained and sustained). The research findings suggested that fast modulation dynamics are highly related to the increasing rate of early acute rejection (80%), opposite to the sustained group that showed an association with the lowest kidney rejection rates (19%). In contrast, modulated response in the early post-transplant period was protective for a long-term graft outcome. This project aims to investigate the DSA dynamic patterns further, emphasising DSA-specific responses and linking them to the transplantation outcome. The success of this project will contribute to the area of HLA incompatible transplants, which provides hope for millions of patients waiting for a compatible donor. Control