Emmanouil Mavrogeorgis, Tianlin He, Harald Mischak, Agnieszka Latosinska, Antonia Vlahou, Joost P Schanstra, Lorenzo Catanese, Kerstin Amann, Tobias B Huber, Joachim Beige, Harald Rupprecht, Justyna Siwy
BACKGROUND AND HYPOTHESIS: Specific urinary peptides hold information on disease pathophysiology, which, in combination with artificial intelligence (AI), could enable non-invasive assessment of chronic kidney disease (CKD) aetiology. Existing approaches are generally specific for the diagnosis of single aetiologies. We present the development of models able to simultaneously distinguish and spatially visualize multiple CKD aetiologies. METHODS: The urinary peptide data of 1850 healthy control (HC) and CKD (diabetic kidney disease-DKD, IgA nephropathy-IgAN, and vasculitis) participants was extracted from the Human Urinary Proteome Database...
September 11, 2023: Nephrology, Dialysis, Transplantation