Interleukin‐5 as a pleiotropic cytokine orchestrating airway type 2 inflammation: Effects on and beyond eosinophils
Bibliographic record
Abstract
Interleukin (IL)-5 is the key cytokine in the maturation, activation, proliferation, migration and survival of eosinophils, which are key effector cells in many upper and lower airway diseases. Through its effects on eosinophils, IL-5 indirectly contributes to various pathophysiological processes including tissue damage, repair and remodelling. Understanding the importance of IL-5 in eosinophil-associated diseases led to the development of anti-IL-5 therapies, which provide clinical benefits across a range of conditions. However, recent evidence suggests that eosinophil-depletion alone may not account for all of the therapeutic effects of anti-IL-5 therapy and that IL-5 may also contribute to disease independently of its effects on eosinophils. Indeed, evidence from ex vivo studies and targeted therapy in vivo demonstrates that IL-5 and its inhibition affects a much broader range of cells beyond eosinophils, including epithelial cells, plasma cells, mast cells, basophils, neutrophils, type 2 innate lymphoid cells, T regulatory cells and fibroblasts. This review will provide an update on the evidence supporting the breadth of IL-5 biology relevant to disease pathogenesis beyond eosinophil-associated inflammation, where there is a need for additional insight, and the clinical implications of a more central role of IL-5 in type 2 inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".