VEGF and Angiopoietins Promote Inflammatory Cell Recruitment and Mature Blood Vessel Formation in Murine Sponge/Matrigel Model
Bibliographic record
Abstract
A key feature in the induction of pathological angiogenesis is that inflammation precedes and accompanies the formation of neovessels as evidenced by increased vascular permeability and the recruitment of inflammatory cells. Previously, we and other groups have shown that selected growth factors, namely vascular endothelial growth factor (VEGF) and angiopoietins (Ang1 and Ang2) do not only promote angiogenesis, but can also induce inflammatory response. Herein, given a pro-inflammatory environment, we addressed the individual capacity of VEGF and angiopoietins to promote the formation of mature neovessels and to identify the different types of inflammatory cells accompanying the angiogenic process over time. Sterilized polyvinyl alcohol (PVA) sponges soaked in growth factor-depleted Matrigel mixed with PBS, VEGF, Ang1, or Ang2 (200 ng/200 µl) were subcutaneously inserted into anesthetized mice. Sponges were removed at day 4, 7, 14, or 21 post-procedure for histological, immunohistological (IHC), and flow cytometry analyses. As compared to PBS-treated sponges, the three growth factors promoted the recruitment of inflammatory cells, mainly neutrophils and macrophages, and to a lesser extent, T- and B-cells. In addition, they were more potent and more rapid in the recruitment of endothelial cells (ECs) and in the formation and maturation (ensheating of smooth muscle cells around ECs) of neovessels. Thus, the autocrine/paracrine interaction among the different inflammatory cells in combination with VEGF, Ang1, or Ang2 provides a suitable microenvironment for the formation and maturation of blood vessels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".