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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: BACKGROUND, RESULTS. Meshes: Adolescent, Bayes Theorem, Child, DNA, Bacterial, Feces, Female, Humans, Irritable Bowel Syndrome, Male, Metabolomics, Microbiota.
user
Does simultaneous fecal microbial and metabolite profiling enable accurate classification of pediatric irritable bowel syndrome? Context: We previously showed that stool samples of pre-adolescent and adolescent US children diagnosed with diarrhea-predominant IBS (IBS-D) had different compositions of microbiota and metabolites compared to healthy age-matched controls. Here we explored whether observed fecal microbiota and metabolite differences between these two adolescent populations can be used to discriminate between IBS and health. We constructed individual microbiota- and metabolite-based sample classification models based on the partial least squares multivariate analysis and then applied a Bayesian approach to integrate individual models into a single classifier. The resulting combined classification achieved 84 % accuracy of correct sample group assignment and 86 % prediction for IBS-D in cross-validation tests. The performance of the cumulative classification model was further validated by the de novo analysis of stool samples from a small independent IBS-D cohort.
assistant
High-throughput microbial and metabolite profiling of subject stool samples can be used to facilitate IBS diagnosis. Final Decision: yes