Effects of low doses of allergen administration and/or probiotics supplementation on cow’s milk protein allergy in a mouse model. (120.10)
Bibliographic record
Abstract
Abstract Cow’s milk allergy (CMA) is one of the most common food allergies among infants and children of developed countries. Accumulating evidence indicates that probiotic supplementation and oral tolerance (OT) induction have food allergy reducing properties. However, there is no report regarding the effects of small doses of allergen and/or multi-strains probiotics (VSL#3) supplementation on CMA. In this study, we examined the effects of OT induction by low doses of allergen and/or VSL#3 supplementation on CMA in a mouse model. Mice were intraperitoneally sensitized and then orally challenged with β-lactoglobulin (BLG), one of the major milk proteins. Allergic responses, including hypersensitivity scores, sera immunoglobulins, fecal IgA and cytokines from spleen lysates, were monitored. Compared to BLG-sensitized control mice, mice supplemented with VSL#3 and/or administered with low doses of allergen showed significantly lowered hypersensitivity scores and reduced BLG-specific serum IgE levels. Allergy reducing effects of VSL#3 supplementation was associated with significantly higher levels of fecal IgA while suppression of both Th-1 and Th-2 responses were observed in OT-induced mice. However, mice that received both low doses of allergen and VSL#3 exhibited allergen-specific protection at the systemic level and the potential to protect non-specific challenges at the mucosal level through increased total intestinal IgA.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".