PhD I. (Iacopo) Cristoferi

Below is a brief summary of the dissertation:

Kidney transplantation is often the best treatment for people with end-stage kidney disease, but there’s a big challenge: not enough kidneys are available. This thesis looks at how new technologies, like artificial intelligence (AI) and molecular tools, can help solve this problem by improving how we check the quality of kidneys before a transplant and spot problems after surgery.

One key tool is the NanoString® B-HOT gene panel, which helps identify genes that affect transplant outcomes. This tool can predict how well a kidney will work after a transplant and can also help detect early signs of issues like organ rejection. By checking the kidney’s health before the transplant, doctors can better match kidneys to patients and spot potential problems early.

The first part of the thesis looks at how AI can help analyze complex data, like DNA changes (specifically DNA methylation), to predict how well a kidney transplant might work. It also tests how well the NanoString® B-HOT panel can identify different types of rejection using both public and new data.

The second part of the thesis focuses on using these technologies to predict transplant outcomes and detect problems early. By combining gene data with advanced imaging and AI tools, doctors can better understand how a transplant is doing and catch issues like rejection sooner.

In summary, this thesis shows how AI and molecular tools can improve kidney transplant outcomes and patient care.