ArticleFrontiers in microbiology2023
A protease activity-based machine-learning approach as a complementary tool for conventional diagnosis of diarrhea-predominant irritable bowel syndrome.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy and quality of artificial intelligence models in irritable bowel syndrome: A systematic review.World journal of gastroenterology · 2025Pooled it
- Protease profiling in fecal samples: a novel non-invasive diagnostic tool for gastrointestinal disorders.Scientific reports · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
Irritable bowel syndrome (IBS) has no clinically accepted biomarkers even though it affects a large number of individuals worldwide. To address this lack of understanding, we evaluated peptidase activity in fecal samples from 35 patients with diarrheal IBS without symptom exacerbation (IBS-n) and 35 healthy subjects using a library of 384 fluorescent enzymatic substrate probes. IBS-n patients had high trypsin-like peptidase activity for cleavage of C-terminal lysine and arginine residues and low elastase-like activity for cleavage of C-terminal serine and glycine residues. These fluorescent probe library data, together with diagnostic machine-learning techniques, were able to accurately predict IBS-n. This approach can be used to diagnose diseases where no clinically accepted biomarkers exist, in which fecal enzyme activity is altered and also suggests that the development of new therapies targeting enzyme activities is possible.
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