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Join us on our journey to advance truly curative therapies

Mass cytometry and machine learning delineate tumor-defining cells in Sézary syndrome to allow discrimination from benign erythroderma (European Journal of Cancer)
Sezary syndrome is a cutaneous (skin) lymphoma which can be hard to distinguish from atopic dermatitis, a common skin condition. Our platform was able to identify rare tumor-related cells in blood from patients with early stages of Sezary syndrome enabling the rapid and sensitive diagnosis of this life-threatening condition.
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DLBCL white paper (potential)
While cell therapies have driven a paradigm shift in cancer treatments, they are also associated with dangerous side effects. ScaiVision helps guide treatment decisions by predicting the likelihood of neurotoxicity in patients receiving CAR-T cells to treat their lymphoma.
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GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis (Nature Medicine)
Using the core machine learning algorithm underlying ScaiVision, our co-founder Prof. Claassen and coworkers were able to identify a novel T-cell population involved in multiple sclerosis expressing specific markers which allows them to attack the central nervous system. This discovery could help drive faster diagnosis and future targeted therapies.
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Comparative immune profiling of acute respiratory distress syndrome patients with or without SARS-CoV-2 infection (Cell Reports Medicine)
In this study, exploiting ScaiVision’s unique sensitivity, we were able to identify an immune population strongly linked to the development of complications from COVID associated with a high mortality rate.
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Helping you speed up your therapy development and increase your success rate