Development and pilot evaluation of a novel probiotic mixture for the management of seasonal allergic rhinitis
Bibliographic record
Abstract
Microbial exposure may direct the immune system away from allergic-type responses, but until now probiotic interventions have had limited success in the prevention and treatment of allergic diseases. In this study, a novel probiotic mixture was specifically created based on preliminary in vitro investigations on pollen-induced immune responses. A mixture with Lactobacillus rhamnosus GR-1 and a novel fecal Bifidobacterium adolescentis isolate was formulated into a yogurt and tested for its effects in 36 subjects with allergic rhinitis over 2 pollen seasons in a double-blind, placebo-controlled trial. The new formulation was well tolerated, but did not have significant effects on the quality of life scores, use of antihistamines, or eosinophil cationic protein concentration in nasal lavage. However, at the end of the grass pollen season, serum IL-10 and IL-12 levels were increased in the probiotic group compared to the controls. During the ragweed season, the serum TGF-β levels were significantly higher in the probiotic group than in the controls. In conclusion, the novel probiotic formulation had potentially desirable effects on the cytokine profile of patients with allergic rhinitis, but provided few clinical benefits. The study highlights the challenges in designing efficient immunomodulatory probiotic therapies based upon in vitro findings.
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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 | 0.001 | 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 | 0.001 | 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".