Amelioration of the Effects of<i>Citrobacter rodentium</i>Infection in Mice by Pretreatment with Probiotics
Bibliographic record
Abstract
BACKGROUND: Citrobacter rodentium is a naturally occurring murine pathogen that causes colonic epithelial-cell hyperplasia, disrupts the colonic mucosa, and elicits a predominantly T helper 1 cellular immune response; it thereby serves as a model for the study of mechanisms of disease induced by human attaching-effacing pathogens. We sought to determine whether pretreatment of mice with a mixture of Lactobacillus rhamnosus and L. acidophilus probiotics would attenuate C. rodentium-induced colonic disease in mice. METHODS: Mice were administered sterile drinking water, probiotics (10(9) cfu/mL) in sterile drinking water, maltodextrin in sterile drinking water, orogastric C. rodentium (10(7) cfu in 0.1 mL), or maltodextrin in sterile drinking water for 1 week before C. rodentium infection, or they were pretreated with probiotics (10(9) cfu/mL) for 1 week before challenge with C. rodentium. RESULTS: Mice that received viable probiotics remained healthy. C. rodentium infection elicited mucosal inflammation, epithelial-cell hyperplasia, apoptosis in the colon, and interferon (IFN)- gamma production by splenocytes. Pretreatment with probiotics decreased levels of all but IFN- gamma production. CONCLUSIONS: Pretreatment with probiotics attenuates the effects of C. rodentium infection in mice. Understanding the mechanism of these beneficial effects will aid in determining the efficacy of probiotics in preventing infection with related attaching-effacing enteric pathogens in humans.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".