Specific probiotic therapy attenuates antibiotic induced visceral hypersensitivity in mice
Bibliographic record
Abstract
BACKGROUND AND AIMS: Abdominal pain and discomfort are common symptoms in functional disorders and are attributed to visceral hypersensitivity. These symptoms fluctuate over time but the basis for this is unknown. Here we examine the impact of changes in gut flora and gut inflammatory cell activity on visceral sensitivity. METHODS: Visceral sensitivity to colorectal distension (CRD) was assessed at intervals in healthy mice for up to 12 weeks, and in mice before and after administration of dexamethasone or non-absorbable antibiotics with or without supplementation with Lactobacillus paracasei (NCC2461). Tissue was obtained for measurement of myeloperoxidase activity (MPO), histology, microbiota analysis, and substance P (SP) immunolabelling. RESULTS: Visceral hypersensitivity developed over time in healthy mice maintained without sterile precautions. This was accompanied by a small increase in MPO activity. Dexamethasone treatment normalised MPO and CRD responses. Antibiotic treatment perturbed gut flora, increased MPO and SP immunoreactivity in the colon, and produced visceral hypersensitivity. Administration of Lactobacillus paracasei in spent culture medium normalised visceral sensitivity and SP immunolabelling, but not intestinal microbiota counts. CONCLUSION: Perturbations in gut flora and in inflammatory cell activity alter sensory neurotransmitter content in the colon, and result in altered visceral perception. Changes in gut flora may be a basis for the variability of abdominal symptoms observed in functional gastrointestinal disorders and may be prevented by specific probiotic administration.
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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 | 0.002 |
| 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".