Prebiotic Supplementation Following Ileocecal Resection in a Murine Model is Associated With a Loss of Microbial Diversity and Increased Inflammation
Bibliographic record
Abstract
Background: Individuals with Crohn's disease frequently require ileocecal resection (ICR), and inflammation often recurs in the neoterminal ileum following surgery. Fructooligosaccharide (FOS) is a fermentable prebiotic that stimulates the growth of bifidobacteria and may promote anti-inflammatory activity. The aim of this study was to determine if supplementation of a postICR diet with FOS in a mouse model would be effective in stimulating the growth of bifidobacteria and reducing systemic and local inflammation. Methods: ICR was performed in IL10-/- mice (129S1/SvlmJ) with colitis. Following surgery, nonICR control and ICR mice were fed a chow diet ± 10% FOS for 28 days. Serum, colon, and terminal ileum (TI) were analyzed for cytokine expression by MesoScale discovery platform. DNA extracted from stool was analyzed using 16s rRNA sequencing and qPCR. Expression of occludin and ZO1 was assessed using qPCR. Short-chain fatty acid (SCFA) concentrations were assessed using gas chromatography. Results: ICR led to increased systemic inflammation (P < 0.05) and a significant decline in fecal microbial diversity (P < 0.05). Mice on the FOS diet had a greater reduction in microbial diversity and also had worsened inflammation as evidenced by increased serum IL-6 (P < 0.05) and colonic IFNγ and TNFα (P < 0.05). Expression of occludin and ZO1 were significantly reduced in FOS-supplemented mice. There was a correlation between loss of diversity and the bifidogenic effectiveness of FOS (r = -0.61, P < 0.05). Conclusions: FOS-supplementation of a postICR diet resulted in a decrease in fecal bacterial diversity, reduction in barrier function, and increased gut inflammation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".