CD8+ Tregs ameliorate inflammatory reactions in a murine model of allergic rhinitis
Bibliographic record
Abstract
Abstract Background CD8 + CD25 + fork-head box transcription factor (Foxp3) + regulatory T cells (CD8 + Tregs) play a role in immune tolerance. However, the role of these cells in allergic rhinitis (AR) has not been elucidated. The study aimed to evaluate influences of CD8 + Tregs on inflammatory conditions in a murine model of AR. Methods A murine model of AR was established. CD8 + Tregs were isolated from mice nasal mucosa and cultured in vitro. We examined interleukin (IL)-10 and transforming growth factor (TGF)-β in cell cultures. Then, we administered CD8 + Tregs into mice nasal mucosal cultures, and examined eosinophil cation protein (ECP), IL-4, IL-5 and IL-13 in these cultures. Finally, we adoptively transferred CD8 + Tregs into mice models, and evaluated percentages of CD8 + Tregs, numbers of sneezing and nasal rubbing, and counts of eosinophils and contents of ECP, IL-4, IL-5, IL-13, IL-10 and TGF-β in nasal lavage fluid (NLF) in mice. Results The percentage of CD8 + Tregs from AR mice was reduced. IL-10 and TGF-β were increased in cell cultures from AR mice. ECP, IL-4, IL-5 and IL-13 were decreased after the AR mice CD8 + Tregs administration in mucosal cultures. However, their contents were not changed after normal CD8 + Tregs treatment. Additionally, the adoptive transfer of AR CD8 + Tregs enhanced the percentage of CD8 + Tregs and levels of IL-10 and TGF-β in NLF, reduced numbers of sneezing and nasal rubbing, and counts of eosinophils and concentrations of ECP, IL-4, IL-5 and IL-13 in NLF. However, normal CD8 + Tregs could not change above parameters. Conclusion These findings show that CD8 + Tregs may inhibit inflammatory responses in the AR condition.
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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 | 0.002 |
| 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".