Intranasal administration of abatacept enhances IL-35+ and IL-10+ producing Bregs in lung tissues of ovalbumin-sensitized asthmatic mice model
Bibliographic record
Abstract
BACKGROUNDS: Treating asthmatic rheumatoid arthritis patients with abatacept has been shown to associate with better control of asthma symptoms. However, the mechanism behind that is not investigated. METHODS: Ovalbumin (OVA)- sensitized BALB/c female mice were treated intranasally (IN) or intraperitoneally (IP) with abatacept 4 hrs before the OVA challenge. The effects of abatacept IN or IP on the lungs and blood levels of Tregs and Bregs and their production of immunosuppressive cytokines, were determined using FACS analysis and ELISA assay. RESULTS: Treating OVA- sensitized asthmatic mice model with abatacept, IN or IP, reduced lung inflammation. IN treatment with abatacept increased the frequency of IL-35 and IL-10 producing Bregs in the lung tissues to a higher level compared to IP treatment. Moreover, the frequency of lungs LAG3+ Tregs was significantly increased following treatment. This was also associated with a reduction in lung tissue and serum IL-17 levels of treated mice. CONCLUSIONS: These results suggest that abatacept by enhancing IL-35+IL-10+ Bregs and LAG3+ Tregs might reverse IL-17 induced lung inflammation during asthma.
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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) | 0.002 | 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".