Alterations in the gut microbiome of children with severe ulcerative colitis
Bibliographic record
Abstract
BACKGROUND: Although the role of microbes in disease pathogenesis is well established, data describing the variability of the vast microbiome in children diagnosed with ulcerative colitis (UC) are lacking. This study characterizes the gut microbiome in hospitalized children with severe UC and determines the relationship between microbiota and response to steroid therapy. METHODS: Fecal samples were collected from 26 healthy controls and 27 children hospitalized with severe UC as part of a prospective multicenter study. DNA extraction, polymerase chain reaction (PCR) amplification of bacterial 16S rRNA, and microarray hybridization were performed. Results were analyzed in GeneSpring GX 11.0 comparing healthy controls with children with UC, and steroid responsive (n = 17) with nonresponsive patients (n = 10). RESULTS: Bacterial signal strength and distribution showed differences between UC and healthy controls (adjusted P < 0.05) for Phylum, Class, Order, Family, Genus, and Phylospecies levels with reduction in Clostridia and an increase in Gamma-proteobacteria. The number of microbial phylospecies was reduced in UC (266 ± 69) vs. controls (758 ± 3, P < 0.001), as was the Shannon Diversity Index (6.1 ± 0.23 vs. 6.49 ± 0.04, respectively; P < 0.0001). Steroid nonresponders harbored fewer phylospecies than responders (142 ± 49 vs. 338 ± 62, P = 0.013). CONCLUSIONS: Richness, evenness, and biodiversity of the gut microbiome were remarkably reduced in children with UC compared with healthy controls. Children who did not respond to steroids harbored a microbiome that was even less rich than steroid responders. This study is the first to characterize the gut microbiome in a large cohort of pediatric patients with severe UC and describes changes in the gut microbiome as a potential prognostic feature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".