Microbial diversity of inflamed and noninflamed gut biopsy tissues in inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Inflammatory bowel disease (IBD) is a chronic gastrointestinal condition without any known cause or cure. An imbalance in normal gut biota has been identified as an important factor in the inflammatory process. METHODS: Fifty-eight biopsies from Crohn's disease (CD, n = 10), ulcerative colitis (UC, n = 15), and healthy controls (n = 16) were taken from a population-based case-control study. Automated ribosomal intergenic spacer analysis (ARISA) and terminal restriction fragment length polymorphisms (T-RFLP) were used as molecular tools to investigate the intestinal microbiota in these biopsies. RESULTS: ARISA and T-RFLP data did not allow a high level of clustering based on disease designation. However, if clustering was done based on the inflammation criteria, the majority of biopsies grouped either into inflamed or noninflamed groups. We conducted statistical analyses using incidence-based species richness and diversity as well as the similarity measures. These indices suggested that the noninflamed tissues form an intermediate population between controls and inflamed tissue for both CD and UC. Of particular interest was that species richness increased from control to noninflamed tissue, and then declined in fully inflamed tissue. CONCLUSIONS: We hypothesize that there is a recruitment phase in which potentially pathogenic bacteria colonize tissue, and once the inflammation sets in, a decline in diversity occurs that may be a byproduct of the inflammatory process. Furthermore, we suspect that a better knowledge of the microbial species in the noninflamed tissue, thus before inflammation sets in, holds the clues to the microbial pathogenesis of IBD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".