Specific immunotherapy in combination with Clostridium butyricum inhibits allergic inflammation in the mouse intestine
Bibliographic record
Abstract
The current therapy on allergic inflammation is unsatisfactory. Probiotics improve the immunity in the body. This study aims to test a hypothesis that administration with Clostridium butyricum (C. butyricum) enforces the effect of specific immunotherapy (SIT) on intestinal allergic inflammation. In this study, an ovalbumin (OVA) specific allergic inflammation mouse model was created. The mice were treated with SIT or/and C. butyricum. The results showed that the intestinal allergic inflammation was only moderately alleviated by SIT, which was significantly enforced by a combination with C. butyricum; treating with C. butyricum alone did not show much inhibitory efficacy. The increase in the frequency of the interleukin (IL)-10-producing OVA-specific B cell (OVAsBC) was observed in mice in parallel to the inhibitory effect on the intestinal allergic inflammation. The in vitro treatment of the OVAsBCs with OVA increased the histone deacetylase-1 (HDAC1) phosphorylation, modulated the transcription of the Bcl6 gene, and triggered the OVAsBCs to differentiate to the IgE-producing plasma cells. Exposure to both OVA and butyrate sodium in the culture increased the expression of IL-10 in OVAsBCs. In conclusion, administration with C. butyricum enforces the inhibitory effect of SIT on allergic inflammation in the mouse intestine.
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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".