IL‐33 delivery induces serous cavity macrophage proliferation independent of interleukin‐4 receptor alpha
Bibliographic record
Abstract
IL-33 plays an important role in the initiation of type-2 immune responses, as well as the enhancement of type 2 effector functions. Engagement of the IL-33 receptor on macrophages facilitates polarization to an alternative activation state by amplifying IL-4 and IL-13 signaling to IL-4Rα. IL-4 and IL-13 also induce macrophage proliferation but IL-33 involvement in this process has not been rigorously evaluated. As expected, in vivo delivery of IL-33 induced IL-4Rα-dependent alternative macrophage activation in the serous cavities. IL-33 delivery also induced macrophages to proliferate but, unexpectedly, this was independent of IL-4Rα signaling. In a filarial nematode infection model in which IL-4Rα-dependent alternative activation and proliferation in the pleural cavity is well described, IL-33R was essential for alternative activation but not macrophage proliferation. Similarly, during Alternaria alternata induced airway inflammation, which provokes strong IL-33 responses, we observed that both IL-4Rα and IL-33R were required for alternative activation, while macrophage proliferation in the pleural cavity was still evident in the absence of either receptor alone. Our data show that IL-33R and IL-4Rα promote macrophage proliferation independently of each other, but both are essential for induction of alternative activation.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".