Regulatory ILC2 – Role of IL-10 Producing ILC2 in Asthma
Bibliographic record
Abstract
Over the past two decades, a growing body of evidence observations have shown group two innate lymphoid cells (ILC2) to be critical drivers of Type 2 (T2) inflammatory responses associated with allergic inflammatory conditions such as asthma. ILC2 release copious amounts of pro-inflammatory T2 cytokines – interleukin (IL)-4, IL-5, IL-9, and IL-13. This review provides a comprehensive overview of the newly discovered regulatory subtype of ILC2 described in murine and human mucosal tissue and blood. These KLRG1+ILC2 have the capacity to produce the anti-inflammatory cytokine, IL-10. Papers compiled in this review were based on query of PubMed and Google Scholar for articles published from 2000-2023 using keywords “IL-10” and “ILC2”. Studies with topical relevance to IL-10 production by ILC2 were included. ILC2 respond to microenvironmental cues including retinoic acid (RA), IL-2, IL-4, IL-10, IL-33, as well as neuropeptide mediators such as neuromedin-U (NMU), prompting a shift towards IL-10 and away from T2 cytokine production. In contrast, TGF-β attenuates IL-10 production by ILC2. Immune regulation provided by IL-10+ILC2s holds potential significance for management of T2 inflammatory conditions. The observation of context-specific cues which alter phenotype of ILC warrants examining characteristics of ILC subsets to determine the extent of plasticity or whether the current classification of ILCs requires refinement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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; both teacher heads agree on what is shown here.
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".