Microbiome Survey of the Inflamed and Noninflamed Gut at Different Compartments Within the Gastrointestinal Tract of Inflammatory Bowel Disease Patients
Bibliographic record
Abstract
BACKGROUND: We aimed to contrast the mucosal microbiota in Crohn's disease (CD) and ulcerative colitis (UC). METHODS: We assessed the concept of localized dysbiosis by comparing the bacterial communities of inflamed and noninflamed mucosa of patients with inflammatory bowel disease (IBD) and by analysis of the microbiota composition at distinct gut compartments (ileum, cecum, mid-colon, and rectum). We performed 16S rDNA sequencing to analyze population structures. Quality control and operational taxonomic unit classification of reads were performed using mothur with statistical analyses executed in the R package, phyloseq. RESULTS: There was no variation in any phyla or genera comparing inflamed to noninflamed mucosa within CD (or UC) or when comparing different gut compartments within CD (or UC). There were differences between the inflamed and noninflamed mucosa between CD and UC: analysis of the inflamed IBD gut at the phylum level indicated that Bacteroidetes (P = 0.002) and Fusobacteria (P < 0.05) were detected more frequently in inflamed CD mucosa than in inflamed UC mucosa. Conversely, Proteobacteria and Firmicutes (P < 0.05) were more frequently observed in the inflamed UC mucosa. At the genus level, the abundance of Faecalibacterium (P ≤ 0.05), Bacteroides (P = 0.003), and Pseudomonas (P < 0.001) were significantly different between the inflamed CD and UC and the abundance of 13 genera were significantly different within the noninflamed mucosa. The noninflamed UC mucosa was the most different from non-IBD mucosa. CONCLUSIONS: Dramatic shifts of microbial communities were not observed between the noninflamed and inflamed mucosa within CD (or UC) although both the inflamed (and noninflamed) mucosa was different between CD and UC.
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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) | 0.001 | 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".