Catalyst Awardee

Project Description

URIMON

Dr. Johan de Rooji, PhD, MBI & Mr. Bram de Moor, LLM, MBI | Amsterdam University Medical Center (NL) Technical University of Twente (NL); Mrs. Karin Oudshoorn, PhD; Dr. Marc Warmoes, PhD; Mrs. Maaike Dekker, MSc; Mr. Jeffrey Benistant, MSc; Mrs. Merel Enschot, MSc; Mr. Tintin Wongtana, BSc; Mr. Diemer Harbers, BSc
Competition Sponsor:
EIT Health
Awardee Year:
2025

You2Yourself (Y2Y) develops a personalized early disease warning system called URIMON. More precisely, Y2Y develops methods and algorithms to monitor biomarker profiles in periodic body fluid samples of individuals, to enable the early detection of life-threatening diseases and enhance chances of cure. Y2Y’s ambition is to free the world from late-stage diagnoses, to enable healthy aging at lower costs.
URIMON is a biomarker-based early disease warning system that builds on the fact that every individual is unique and so are his/her biomarker profiles. Y2Y uses microRNAs, an established class of biomarkers for organ problems such as cancer and dysfunction. Quantitative Next Generation Sequencing in blood and urine samples is used to obtain comprehensive small RNA profiles and machine learning is used to develop algorithms for the detection and specification of diseases. Establishing personal baseline values of a large number of RNAs enables the sensitive detection of changes associated with the onset of disease. The specific RNAs that change indicate the nature of the developing disease (e.g. cancer and type).
To validate its technology, Y2Y is building a unique biobank of sample series that capture the onset of disease, by collecting periodic samples from a large cohort of healthy people. Y2Y has analyzed periodic sample series of individuals that developed a top-4 type of cancer or cardiovascular disease to show early disease detection, up to 2 years prior to current diagnosis, at a sensitivity ranging from 85% for prostate cancer to 100% for the future occurrence of stroke. Y2Y is working on improving her detection algorithms for personalization of disease detection and on integration into one multi disease warning algorithm. Furthermore, the laboratory workflow for microRNA profiling is being adapted for automation to enable standardization and upscaling to market requirements.

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