Lactobacillus reuteri Maintains a Functional Mucosal Barrier during DSS Treatment Despite Mucus Layer Dysfunction
Bibliographic record
Abstract
Treatment with the probiotic bacterium Lactobacillus reuteri has been shown to prevent dextran sodium sulfate (DSS)-induced colitis in rats. This is partly due to reduced P-selectin-dependent leukocyte- and platelet-endothelial cell interactions, however, the mechanism behind this protective effect is still unknown. In the present study a combination of culture dependent and molecular based T-RFLP profiling was used to investigate the influence of L. reuteri on the colonic mucosal barrier of DSS treated rats. It was first demonstrated that the two colonic mucus layers of control animals had different bacterial community composition and that fewer bacteria resided in the firmly adherent layer. During DSS induced colitis, the number of bacteria in the inner firmly adherent mucus layer increased and bacterial composition of the two layers no longer differed. In addition, induction of colitis dramatically altered the microbial composition in both firmly and loosely adherent mucus layers. Despite protecting against colitis, treatment with L. reuteri did not improve the integrity of the mucus layer or prevent distortion of the mucus microbiota caused by DSS. However, L. reuteri decreased the bacterial translocation from the intestine to mesenteric lymph nodes during DSS treatment, which might be an important part of the mechanisms by which L. reuteri ameliorates DSS induced colitis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".