Probiotics prevent death caused by Citrobacter rodentium infection in neonatal mice via T cells
Bibliographic record
Abstract
Intestinal infections cause morbidity and mortality, particularly in young children. C. rodentium challenge of mouse pups is used as a model to study neonatal enteric infections. AIMS Determine if neonatal mice become sick after infection with C. rodentium and if probiotics can ameliorate disease. METHODS Mouse pups were infected with C. rodentium by orogastric gavage at 14d of age and some pups pre‐treated with probiotics daily 1 wk prior to infection and onward. For others, the dam was given probiotics in drinking water to determine if probiotics act indirectly. Immune‐deficient (Rag1 −/− ; J H ) pups were used to study the role of adaptive immunity in mediating the effects of probiotics on infection. RESULTS At 10d post‐infection (PI), infected pups lost weight and displayed colonic epithelial cell hyperplasia, compared to sham‐infected animals. By 12 days PI, death occurred in infected pups ( C. rodentium ; 75% vs sham; 0%). Pre‐treatment with probiotics prevented death (probiotics; 17%), reduced epithelial hyperplasia and weight loss. Probiotics did not prevent death when administered either to the mother or Rag1 −/− pups, but J H , B cell deficient pups survived. CONCLUSION C. rodentium infection causes death in mouse pups. Probiotics prevented these effects, but only in the presence of T cells. Probiotics may serve as an option for therapy in newborns at high risk of developing infections.
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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) | 0.002 | 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".