Fibulin-5 functions as in inhibitor of angiogenesis in vivo
Bibliographic record
Abstract
Angiogenesis is the formation of new blood vessels by sprouting from existing vessels. In order for new blood vessels to invade the surrounding tissue, the extracellular matrix must be degraded and subsequently remodeled. Fibulin (fbln)-5 (DANCE, EVEC) is an elastin and integrin binding extracellular matrix protein that is expressed strongly throughout the cardiovascular system during development and is re-expressed during vascular injury. Recently, an increase in the number of cutaneous blood vessels was observed on the posterior surface of the abdominal wall of the fbln5-/- mouse. In the present study, the role of fibulin-5 as an anti-angiogenic factor was investigated in vivo. The increase in vascular sprouting in the fbln5 -/- skin was confirmed to be significant by quantitative analysis. Using a well-established angiogenic model, polyvinyl alcohol (PVA) sponges were implanted subcutaneously in wild-type and fbln5-/- mice to determine the effect of the absence of fibulin-5 on fibrovascular invasion. Results showed no difference in fibroblast migration into the sponges from fbln5-/- and wild-type mice; however, PECAM-1 staining revealed a significant increase in microvascular invasion in sponges from fbln5 -/- mice. Sponges from fbln5-/- mice showed increased expression levels of VEGF and the angiopoietins relative to wild-type counterparts by quantitative RT-PCR. In addition, fibulin-5 inhibited bFGF induced neovascular invasion in Matrigel. In an ex vivo system, a lack of fibulin-5 stimulated vascular sprouting, and this response was blocked by the addition of exogenous fibulin-5. Taken together, these results indicate that fibulin-5 functions as an inhibitor of physiological angiogenesis and the absence of fibulin-5 results in an increase in vascularization.
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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 | 0.000 | 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 | 0.000 |
| 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".