Hallmarks and metabolic regulation of type 2 activated human lung macrophages
Bibliographic record
Abstract
Abstract Although human lung macrophages are heterogenous and play key roles during health and disease, the mechanisms that govern their activation and function are unclear, particularly in type 2 settings. Our understanding of how human lung macrophages respond to inflammatory signals have predominantly relied on cell lines or peripheral blood derived cells, which have a limited capacity to reflect the complexity of tissue macrophage responses. Therefore, we isolated macrophages from resected human lung tissue and stimulated them ex vivo under type 2 (IL-4, IL-13, or IL-4 + IL-13) or type 1 (IFNγ + LPS) conditions. Human lung macrophages stimulated with IL-4/13, alone or in combination, significantly upregulated expression of the chemokines CCL17 , CCL18 and CCL22, along with the transglutaminase TGM2 and the lipoxygenase ALOX15 . This type 2 activation profile was distinct from LPS + IFNγ activated human lung macrophages, which upregulated IL6 , IL8 , IL1β , TNFα and CHI3L1 (YKL-40). Further, type 2 activated human lung macrophage products showed differential metabolic reliance for their induction, with IL-4/13 induced CCL22 being glycolytically controlled, while ALOX15 was regulated by fatty acid oxidation. These data clarify hallmarks of human lung macrophage activation and polarisation in addition to revealing novel metabolic regulation of type 2 markers.
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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 | 0.000 |
| 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".