Immunomodulatory impact of a synbiotic in Th<sub>1</sub> and Th<sub>2</sub> models of infection
Bibliographic record
Abstract
BACKGROUND AND METHODS: The immunomodulatory activity of a synbiotic combination containing three bacterial strains (Lactobacillus helveticus R0052, Bifidobacterium longum subsp. infantis R0033 and Bifidobacterium bifidum R0071) and short-chain fructooligosaccharide was examined in two distinct infectious rat models. In the T(h)1 model, Wistar rats were administered the synbiotic combination for 2 weeks prior to challenge with a single oral dose of enterotoxigenic Escherichia coli or vehicle. In the T(h)2 model, pretreated rats were challenged with a single subcutaneous dose of hook worm, Nippostrongylus brasiliensis. Blood samples were collected 3 hours or 4 days postchallenge and serum levels of pro- and anti-inflammatory cytokines were measured. RESULTS: Significant reductions in pro-inflammatory cytokines interleukin (IL)-1α, IL-1β, IL-6, and tumour necrosis factor (TNF)-α were observed in both models suggesting a single, unifying mode of action on an upstream regulator. The N. brasiliensis study also compared the effect of the individual strains to synbiotic. For most of cytokines the combination appeared to average the effect of the individual strains with the exception of IL-4 and IL-10 where there was apparent synergy for the combination. Furthermore, the cytokine response varied by strain. CONCLUSIONS: It was concluded that this synbiotic combination of these three microbes could be beneficial in both T(h)1 and T(h)2 diseases.
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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 | 0.000 | 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) | 0.001 | 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".