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Abstract
Inflammatory bowel diseases (IBDs) are increasingly dif-ficult to diagnose and differentiate despite serologicaltesting, endoscopic and histopathological assessments. Nosingle biomarker exists to predict IBD in a timely man-ner. We have developed a spectral library of candidateIBD-biomarkers,establishingcharacteristicmolecularbar-codes, through a combination of multiplex spectroscopicprofiling simultaneously detecting a panel of identifiedbiomarkers with an advanced artificial intelligence (AI)networkandclassifyingpatientsaccordingtodiseasestate.This lays the platform for rapidly and non-invasivelydetecting IBD and discriminating between the subtypesfor timely diagnoses, biomarker discovery, patient strati-fication and further potential significant developments ofdiagnostic methodologies and therapeutic monitoring
Original language | English |
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Article number | e1345 |
Number of pages | 6 |
Journal | Clinical and Translational Medicine |
Volume | 13 |
Issue number | 11 |
DOIs | |
Publication status | Published - 3 Nov 2023 |
Bibliographical note
Acknowledgments:We acknowledge funding from the Wellcome Trust (174ISSFPP) and the EPSRC (EP/W004593/1, EP/V029983/1). We would like to thank Dr Jinglei Yu for assisting with the mass spectrometry data analysis.
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A smart, multi-purpose technology for diagnostics, analytics and drug delivery
Goldberg Oppenheimer, P. & Mendes, P.
Engineering & Physical Science Research Council
1/10/21 → 30/06/23
Project: Research Councils