Probiotics Are Effective for the Prevention and Treatment of Citrobacter rodentium–Induced Colitis in Mice
Bibliographic record
Abstract
BACKGROUND: Probiotics prevent disease induced by Citrobacter rodentium, a murine-specific enteric pathogen. Whether probiotics can be used to interrupt the infectious process following initiation of infection was determined. METHODS: C57BL/6 adult and neonatal mice were challenged with C. rodentium, and a probiotic mixture containing Lactobacillus helveticus and Lactobacillus rhamnosus was provided 1 week before bacterial challenge, concurrently with infection, or 3 days and 6 days after infection. Mice were sacrificed 10 days after infection, and disease severity was assessed by histological analysis and in vivo intestinal permeability assay. Inflammatory pathways and the composition of the fecal microbiome were assessed in adult mice. RESULTS: Preadministration and coadministration of probiotics ameliorated C. rodentium-induced barrier dysfunction, epithelial hyperplasia, and binding of the pathogen to host colonocytes in adults, with similar findings in neonatal mice. Upregulated tumor necrosis factor α and interferon γ transcripts were suppressed in the pretreated probiotic group, whereas interleukin 17 transcription was suppressed with probiotics given up to 3 days after infection. Probiotics promoted transcription of interleukin 10 and FOXP3, and increased follicular T-regulatory cells in pretreatment mice. C. rodentium infection resulted in an altered fecal microbiome, which was normalized with probiotic intervention. CONCLUSIONS: This study provides evidence that probiotics can prevent illness and treat disease in an animal model of infectious colitis.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".