Conditional expression of Ang‐2 during tumor angiogenesis: tightly balanced Angiopoietin/Tie2 signaling determines the tumor vascular phenotype
Bibliographic record
Abstract
Angiopoietins play important roles in the formation of neovessels and complex vascular networks. Angiopoietin‐1 (Ang‐1) and Angiopoietin‐2 (Ang‐2) are both members of a family of growth factors which display opposing effects on the activation of the tyrosine kinase with Ig und EGF homology domain 2 (Tie2). To more precisely discriminate the antagonizing actions of Ang‐1 and Ang‐2 during tumor angiogenesis, we applied a transgenic animal model which allowed the inducible expression of Ang‐2 in endothelial cells and a mammary carcinoma model designed to express Ang‐1 and Ang‐2. When comparing the vascular morphology of Ang‐2 expressing mammary tumors in nude mice and lewis‐lung tumors in Ang‐2 transgenic mice, small blood vessels with apoptotic endothelial cells and pericyte denudation were evident. In addition, disrupted cell‐cell contacts were often observed. Consequently, we deteced increased vascular permeability by magnetic resonance imaging in Ang‐2 positive tumors, indicative for non‐functional and leaky blood vessels. Notably, high numbers of infiltrating monocytic cells expressing CD11b in Lewis lung tumors of Ang‐2 transgenic animals were also evident. The presence of infiltrating cells suggests a paracrine mechanism and indicates that growth factors secreted by monocytes/macrophages might be important to facilitate/promote angiogenesis in tumors.
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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".