ArticleThe Journal of clinical investigation2019
Human fecal metabolomic profiling could inform Clostridioides difficile infection diagnosis and treatment.
Article in The Journal of clinical investigation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- UnifiedGreatMod: a new holistic modelling paradigm for studying biological systems on a complete and harmonious scale.Bioinformatics (Oxford, England) · 2025Article
- Article
- Decreased secondary faecal bile acids in children with ulcerative colitis and Clostridioides difficile infection.Alimentary pharmacology & therapeutics · 2021Article
- A Light-Weight Practical Framework for Feces Detection and Trait Recognition.Sensors (Basel, Switzerland) · 2020Article
- Article
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2 authors.
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Abstract
Clostridioides difficile is a significant public health threat, and diagnosis of this infection is challenging due to a lack of sensitivity in current diagnostic testing. In this issue of the JCI, Robinson et al. use a logistic regression model based on the fecal metabolome that is able to distinguish between patients with non-C. difficile diarrhea and C. difficile infection, and to some degree, patients who are asymptomatically colonized with C. difficile. The authors construct a metabolic definition of human C. difficile infection, which could improve diagnostic accuracy and aid in the development of targeted therapeutics against this pathogen.
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