Invisible trace amounts of RNA in blood become the key to early disease diagnosis
Non-invasive diagnostic system for Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)
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KRW 6 billion
Selected for a national R&D project
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Donga
Distribution agreement With Dong-A ChamMed
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St. Mary’s
Co-development
with St. Mary’s Hospital
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Technology Principle
An innovative diagnostic method that replaces expensive imaging diagnostics and less accurate conventional serum tests.
It analyzes circulating miRNA biomarkers in the patient’s blood by combining them with an ultra-sensitive nanosensor (XENO-Q) -
Outstanding Performance
Overcoming the limitations of conventional methods, analysis can be completed within 1 hour, with a target diagnostic accuracy of up to 95% or higher
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Collaborative Research Network
In collaboration with The Catholic University of Korea St. Mary’s Hospital (cohort clinical studies), Yonsei University (animal models and biomarker validation), and Sungkyunkwan University (nanosensor development), we are developing a multi-integrated (SNP-MDx-NDx) diagnostic system capable of prediction, diagnosis, and prognosis management across different stages of disease progression
Early Diagnosis Kit for Parkinson’s Disease
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KRW 4.6 billion
Selected for a national R&D project
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2026
Product approval scheduled for 2026
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Severance
Co-development
with Severance Hospital
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Technology Principle
To diagnose Parkinson’s disease, which is extremely difficult to detect early, XENOHELIX’s proprietary ultra-sensitive small RNA detection technology has been applied
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Commercialization Status
As the world’s first product to diagnose Parkinson’s disease by analyzing microRNA in blood, it has completed MFDS pivotal clinical trial (IRB) approval and is currently undergoing commercialization procedures
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Scalability
Beyond Parkinson’s disease, we are expanding our neurological diagnostics portfolio by securing patents for methods that provide biomarker information for differential diagnosis of similar conditions such as multiple system atrophy (MSA)
