Lactobacillus pentosus S-PT84 and Rubus suavissimus leaf extract suppress lipopolysaccharide-induced gut permeability and egg allergen uptake
Bibliographic record
Abstract
Abstract Increased gut permeability facilitates the uptake of food allergens into the bloodstream and triggers allergenic reactions. The present study aimed to evaluate the effect of Lactobacillus pentosus S-PT84 (S-PT84) and Rubus suavissimus leaf extract (RSLE) against egg ovomucoid (OVM)-uptake in lipopolysaccharide (LPS)-induced increased gut-permeability mice model of food allergy. Six-eight weeks old, female C57BL6 mice were continuously fed with LPS (300 μg/kg BW), for 3 months to increase gut permeability. Reduction in the expression of sealing claudin-4, increase in the expression of pore-forming claudin-2, and increase in D-mannitol absorption into the blood plasma in the LPS treated groups suggested the increase in gut permeability after LPS treatment. The oral administration of major egg allergen, OVM, after LPS intervention, significantly increased the plasma mast cell protease-1 and OVM-specific IgE compared to the negative control group. These results indicated that continuous LPS intervention developed OVM-induced food allergy. However, both the treatment of S-PT84 and RSLE suppressed the claudin-2 expression and the gut permeability induced by LPS. Furthermore, S-PT84 and RSLE treatment also reduced the plasma mast cell protease-1 and OVM-specific IgE, indicating the potential beneficial effect against LPS intervention developed OVM-induced food allergy. These findings suggest that S-PT84 and RSLE ameliorated LPS induced gut permeability and food allergic reactions.
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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) | 0.000 | 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".