Probiotic lactobacillus ameliorates heightened colonic inflammatory responses in infected stressor-exposed C57BL/6 mice and inhibits dysbiosis (MPF1P.771)
Bibliographic record
Abstract
Abstract The mammalian colon is a primary niche for the microbiota, a community of bacteria in part involved in immune maintenance. Alterations in these microbial populations, referred to as dysbiosis, can have deleterious effects on host health and previous studies show psychological stress can lead to dysbiosis. We investigated if stressor exposure could increase the severity of colonic infection with the enteric pathogen, Citrobacter rodentium. Mice that were subjected to social stressor exposure had increases in pathogen burden and the inflammation-related gene transcripts TNF-alpha, iNOS, and CCL2. The stressor-exposed mice also had increased colitic pathology. When mice were treated with a probiotic strain of Lactobacillus reuteri, the colitic pathology and resultant inflammatory markers were abrogated. The mechanism by which L. reuteri reduces colitis is unknown. Probiotic treatment of germ-free mice mono-associated with C. rodentium did not reduce inflammation, indicating that direct immune intervention is unlikely. However, quantitative PCR of conventional mice infected with C. rodentium revealed that stressor-exposed mice had altered levels of commensal microbes, and probiotic treatment caused these populations to rebound. Thus, stressor exposure exacerbated infection-induced dysbiosis and colonic inflammation. It is proposed that probiotic treatment prevents stressor-induced exacerbation of colonic inflammation by preventing dysbiosis.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".