IL-9+ IL-10+ T cells link immediate allergic response to late phase reaction
Bibliographic record
Abstract
The mechanism underlying late-phase allergic reactions (LPR) remains incompletely understood. This study aimed to investigate the role of a newly described subset of T cells, interleukin (IL)-9(+) IL-10(+) T cells, in the pathogenesis of LPR. Using a T helper type 2 (Th2) inflammatory mouse model, we examined the frequency of IL-9(+) IL-10(+) T cells in the jejunum by immunohistochemistry. The LPR in the jejunum was observed afterwards. The cytokine profile of IL-9(+) IL-10(+) T cells was characterized and the major cytokine that plays the critical role in the initiation of LPR was investigated. Abundant IL-9(+) IL-10(+) T cells as well as inflammatory cell extravasation in the jejunal sections were observed in sensitized mice 48 h after specific antigen challenge. IL-9(+) IL-10(+) T cells expressed high levels of macrophage inflammatory protein 1 (MIP1) that could be enhanced by T cell receptor activation. MIP1 facilitated macrophage extravasation in local tissue. Macrophage-derived MIP2 contributed to neutrophil infiltration in the intestine in LPR. Pretreatment with anti-MIP antibody inhibited the LPR in the intestine. IL-9(+) IL-10(+) T cells play an important role in LPR. This subset of T cells has the potential to be a novel therapeutic target in the treatment of LPR and LPR-related inflammation.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".