Separate roles of <scp>IL</scp>‐6 and oncostatin M in mouse macrophage polarization <i>in vitro</i> and <i>in vivo</i>
Bibliographic record
Abstract
Arginase-1 (Arg-1)-expressing M2-like macrophages are associated with Th2-skewed immune responses, allergic airway pathology, ectopic B16 melanoma cancer growth in murine models, and can be induced by Oncostatin M (OSM) transient overexpression in vivo. Here, we compare OSM to the gp130-cytokine IL-6 in mediating macrophage polarization, and find that IL-6 overexpression alone (Ad vector, AdIL-6) did not induce Arg-1 protein in mouse lungs at day 7, nor ectopic melanoma tumor growth at day 14, in contrast to overexpression of OSM (AdOSM). AdOSM elevated levels of IL-4, IL-5 and IL-13 in bronchoalveolar lavage fluid, whereas AdIL-6 did not. Bone marrow-derived macrophages respond with Arg-1 enzymatic activity to M2 stimuli (IL-4/IL-13), which was further elevated in combination with IL-6 stimulation; however, OSM or LIF had no detectable activity in vitro. Arg-1 mRNA expression induced by AdOSM was attenuated in IL-6-/- and STAT6-/- mice, suggesting requirements for both IL-6 and IL-4/IL-13 signaling in vivo. Ectopic B16 tumor burden was also reduced in IL-6-/- mice. Thus, OSM induces Arg-1+ macrophage accumulation indirectly through elevation of Th2 cytokines and IL-6 in vivo, whereas IL-6 acts directly on macrophages but requires a Th2 microenvironment, demonstrating distinct roles for OSM and IL-6 in M2 macrophage polarization.
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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 | 0.001 |
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".