Abstract 18657: Fibulin-4 Deficiency Alters Collagen Regulation in the Aortic Wall
Bibliographic record
Abstract
Thoracic aortic aneurysms/dissections (TAAD) are a major cause of mortality each year in the U.S. Disruption of fibulin-4 (fbln4), an extracellular matrix component, leads to TAAD in human and mouse models. Fbln4 plays an essential role in elastic fiber assembly: our recent data suggest a role for proliferation in the aortic wall that may be independent of elastic fiber defects. Roles for Fbln4 beyond elastic fiber assembly have been suggested but not previously investigated. We hypothesize that Fbln4 loss alters the aortic wall microenvironment to promote localized cell proliferation, in part through altered collagen synthesis and maturation. Smooth muscle-specific Fbln4 deficient (SMKO) ascending aortas show increased BrdU+ cells at P30 (p<.05) localized throughout the aortic wall but mainly near the medial/adventitial border, irrespective of elastic fiber disruption. Smooth muscle actin inducible Cre mice show intact elastic fibers but a similar pattern of BrdU+ cells. Fbln4 loss also alters synthesis of collagen isoforms, leading to increased Type VIII mRNA (p<.05) and Type I protein expression. Electron microscopy, second harmonic generation images, and molecular analysis of collagen cross-linking revealed highly irregular collagen fiber bundle size, collagen fiber disorganization, and decreased mature cross-links (p<.05). Our data demonstrate a proliferative phenotype and previously uncharacterized collagen defect in Fbln4 deficient mice. Altered collagen isoforms and maturity are known to influence cell proliferation: future work will determine the precise identity of the proliferating cells and specific mechanisms by which collagen defects drive the proliferative phenotype. Increased understanding of the molecular mechanisms driving TAAD will ultimately lead to more effective treatments to delay or prevent onset of disease.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".