Probiotics Prevent Death Caused by<i>Citrobacter rodentium</i>Infection in Neonatal Mice
Bibliographic record
Abstract
BACKGROUND: Intestinal bacterial infections are a major cause of morbidity and mortality, especially in neonates. Therefore, the aims of this study were to characterize Citrobacter rodentium infection in neonatal mice and determine the role played by specific probiotics in ameliorating disease severity. METHODS: Infection of C57BL/6 mice with C. rodentium was performed at postnatal day 14. A subset of mice was pretreated orally with either a mixture of Lactobacillus rhamnosus and Lactobacillus helveticus or placebo, starting at 7 days. To study the effects of B and T cells, rag1(-/-) and JH(-/-) mice were used, with survival, colonic crypt depth, and barrier function evaluated as outcome measures. RESULTS: C. rodentium infection caused weight loss and death. In contrast, survival was maintained by daily treatment with Lactobacilli. Weight loss, colonic epithelial cell hyperplasia, mucosal barrier dysfunction, and elevated serum corticosterone levels in C. rodentium-infected wild-type mice were ameliorated by probiotics, but not in rag1(-/-) animals. Beneficial effects of probiotics were observed in B cell-deficient (JH(-/-)) mice, indicating the requirement of T cells in reducing the adverse sequelae of neonatal enteric infection. CONCLUSIONS: These findings demonstrate that C. rodentium infection in newborn mice causes death and that probiotics promote survival, but only in the presence of T cells.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".