IL-2 Modulates Th2 cell Responses to Glucocorticoid: A Cause of Persistent Type 2 Inflammation?
Bibliographic record
Abstract
ABSTRACT Background Inhaled glucocorticosteroids (GCs) are the main treatment for asthma as they reduce type 2 cytokine (IL-4, IL-5 and IL-13) expression and induce apoptosis. Asthma severity is associated with GC insensitivity, increased type 2 inflammation and circulating Th2 cells. Since IL-2 is a T cell survival factor, we assessed whether IL-2 levels associate with the proportion of Th2 cells and/or correlate with clinical features of asthma severity. Methods Peripheral blood from asthma patients (n=18) was obtained and Th2 cell numbers determined by flow cytometry. Peripheral blood cells were activated with mitogen (24hrs) and supernatant levels of IL-2 and IL-13 measured by ELISA. In vitro differentiated Th2 cells were treated with dexamethasone and IL-2 and assessed for apoptosis by flow cytometry staining of Annexin V. Level of mRNA for anti-apoptotic (BCL-2) and pro-apoptotic (BIM) genes as well as IL-13 were determined by qRT-PCR. Results IL-2 produced by activated peripheral blood cells correlated negatively with lung function (FEV 1 ) and positively with daily dose of inhaled GC. When patients were stratified based on IL-2 level, high IL-2 producers made more IL-13 and had more circulating Th2 cells. In vitro , increasing the level of IL-2 in the culture media was associated with resistance to DEX-induced apoptosis, more BCL-2 and less BIM mRNA. Th2 cells cultured with higher IL-2 also had more IL-13 mRNA and required higher concentrations of DEX for cytokine suppression. Conclusions and Clinical Relevance IL-2 modulates Th2 cell responses to GC, supporting both their survival and pro-inflammatory capacity, suggesting that a patient’s potential to produce IL-2 may be a determinant in asthma severity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".