Administration of defined microbiota is protective in a murine Salmonella infection model
Bibliographic record
Abstract
Salmonella typhimurium is a major cause of diarrhea and causes significant morbidity and mortality worldwide, and perturbations of the gut microbiota are known to increase susceptibility to enteric infections. The purpose of this study was to investigate whether a Microbial Ecosystem Therapeutic (MET-1) consisting of 33 bacterial strains, isolated from human stool and previously used to cure patients with recurrent Clostridium difficile infection, could also protect against S. typhimurium disease. C57BL/6 mice were pretreated with streptomycin prior to receiving MET-1 or control, then gavaged with S. typhimurium. Weight loss, serum cytokine levels, and S. typhimurium splenic translocation were measured. NF-κB nuclear staining, neutrophil accumulation, and localization of tight junction proteins (claudin-1, ZO-1) were visualized by immunofluorescence. Infected mice receiving MET-1 lost less weight, had reduced serum cytokines, reduced NF-κB nuclear staining, and decreased neutrophil infiltration in the cecum. MET-1 also preserved cecum tight junction protein expression, and reduced S. typhimurium translocation to the spleen. Notably, MET-1 did not decrease CFUs of Salmonella in the intestine. MET-1 may attenuate systemic infection by preserving tight junctions, thereby inhibiting S. typhimurium from gaining access to the systemic circulation. We conclude that MET-1 may be protective against enteric infections besides C. difficile infection.
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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".