Fibulin-2 and Fibulin-5 Cooperatively Function to Form the Internal Elastic Lamina and Protect From Vascular Injury
Bibliographic record
Abstract
OBJECTIVE: Recent findings on the role of fibulin-5 (Fbln5) have provided substantial progress in understanding the molecular mechanism of elastic fiber assembly in vitro. However, little is known about differential roles of fibulins in the elastogenesis of blood vessels. Here, we generated double knockout mice for Fbln5 and Fbln2 (termed DKO) and examined the role of fibulins-2 and -5 in development and injury response of the blood vessel wall. METHODS AND RESULTS: Fibulin-2 is distinctly located in the subendothelial matrix, whereas fibulin-5 is observed throughout the vessel wall. All of the elastic laminae, including the internal elastic lamina (IEL), were severely disorganized in DKO mice, which was not observed in single knockout mice for Fbln2 or Fbln5. Furthermore, DKO vessels displayed upregulation of vascular adhesion molecules, tissue factor expression, and thrombus formation with marked dilation and thinning of the vessel wall after carotid artery ligation-injury. CONCLUSIONS: Fibulin-2 and fibulin-5 cooperatively function to form the IEL during postnatal development by directing the assembly of elastic fibers, and are responsible for maintenance of the adult vessel wall after injury. The DKO mouse will serve as a unique animal model to test the effect of vessel integrity during various pathological insults.
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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 | 0.000 |
| 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".