CEACAM1+ myeloid cells control angiogenesis in inflammation
Bibliographic record
Abstract
Local inflammation during cutaneous leishmaniasis is accompanied by accumulation of CD11b(+) cells at the site of the infection. A functional role for these monocytic cells in local angiogenesis in leishmaniasis has not been described so far. Here, we show that CD11b(+) cells express high levels of the myeloid differentiation antigen carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1). In experimental cutaneous leishmaniasis in C57BL/6 wild-type (B6.WT) and B6.Ceacam1(-/-) mice, we found that only B6.Ceacam1(-/-) mice develop edemas and exhibit impairment of both hemangiogenesis and lymphangiogenesis. Because CEACAM1 expression correlates with functional angiogenesis, we further analyzed the role of the CD11b(+) population. In B6.Ceacam1(-/-) mice, we found systemic reduction of Ly-6C(high)/CD11b(high) monocyte precursors. To investigate whether CEACAM1(+) myeloid cells are causally related to efficient angiogenesis, we used reverse bone marrow transplants (BMTs) to restore CEACAM1(+) or CEACAM1(-) bone marrow in B6.Ceacam1(-/-) or B6.WT recipients, respectively. We found that angiogenesis was restored by CEACAM1(+) BMT only. In addition, we observed reduced morphogenic potential of inflammatory cells in Matrigel implants in CEACAM1(-) backgrounds or after systemic depletion of CD11b(high) macrophages. Taken together, we show for the first time that CEACAM1(+) myeloid cells are crucial for angiogenesis in inflammation.
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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".