Progressive Microstructural Deterioration Dictates Evolving Biomechanical Dysfunction in the Marfan Aorta
Bibliographic record
Abstract
Medial deterioration leading to thoracic aortic aneurysms arises from multiple causes, chief among them mutations to the gene that encodes fibrillin-1 and leads to Marfan syndrome. Fibrillin-1 microfibrils associate with elastin to form elastic fibers, which are essential structural, functional, and instructional components of the normal aortic wall. Compromised elastic fibers adversely impact overall structural integrity and alter smooth muscle cell phenotype. Despite significant progress in characterizing clinical, histopathological, and mechanical aspects of fibrillin-1 related aortopathies, a direct correlation between the progression of microstructural defects and the associated mechanical properties that dictate aortic functionality remains wanting. In this paper, age-matched wild-type, Fbn1 C 1041 G /+ , and Fbn1 mgR / mgR mouse models were selected to represent three stages of increasing severity of the Marfan aortic phenotype. Ex vivo multiphoton imaging and biaxial mechanical testing of the ascending and descending thoracic aorta under physiological loading conditions demonstrated that elastic fiber defects, collagen fiber remodeling, and cell reorganization increase with increasing dilatation. Three-dimensional microstructural characterization further revealed radial patterns of medial degeneration that become more uniform with increasing dilatation while correlating strongly with increased circumferential material stiffness and decreased elastic energy storage, both of which comprise aortic functionality.
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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 | 0.000 |
| 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".