COX-2 inhibition represses the generation of M2 macrophages and slows tumor progression (111.31)
Bibliographic record
Abstract
Abstract Macrophages (mΦs) are very heterogeneous and include immune-activating (M1) and immune-suppressive (M2) subsets. In response to cytokine or TLR activation, M1-mΦs (killer mΦs) express iNOS, which produces tumoricidal and bacteriocidal nitric oxide (NO). M2-mΦs (healer mΦs), in contrast, express arginase I, which competes with iNOS for L-arginine, to promote cell proliferation and fibrosis. SHIP (SH2-containing inositol-5’ phosphatase), a hematopoietic-specific negative regulator of the PI3K pathway, prevents M2-mΦ development. Thus, in SHIP-/- C57BL/6 mice, all the mΦs are M2 skewed. As a result, subcutaneously injected Lewis lung carcinoma (LLC) tumors grow faster in SHIP-/- than in +/+ mice, and this rapid tumor growth correlates with high tumor-associated arginase. We therefore hypothesize that high PI3K activity skews bone marrow (bm) progenitors to become tumor-promoting M2-mΦs. Herein we show that PGE2 also skews SHIP-/- , but not +/+, bm progenitors to M2 mΦs and that COX-2 inhibitors repress this skewing. Specifically, the COX-2 inhibitor SC-58125 reduces arginase levels and IL-10 production from in vitro derived SHIP-/- bmmΦs. Furthermore, another COX-2 inhibitor, Celebrex, slows tumor growth in vivo, and this correlates with reduced arginase in tumor-associated mΦs. This is consistent with COX-2 inhibitors slowing tumor growth, at least in part, by preventing M2-skewing of tumor-associated mΦs.
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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) | 0.002 | 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".