Elastin, arterial mechanics, and cardiovascular disease
Bibliographic record
Abstract
Large, elastic arteries are composed of cells and a specialized extracellular matrix that provides reversible elasticity and strength. Elastin is the matrix protein responsible for this reversible elasticity that reduces the workload on the heart and dampens pulsatile flow in distal arteries. Here, we summarize the elastin protein biochemistry, self-association behavior, cross-linking process, and multistep elastic fiber assembly that provide large arteries with their unique mechanical properties. We present measures of passive arterial mechanics that depend on elastic fiber amounts and integrity such as the Windkessel effect, structural and material stiffness, and energy storage. We discuss supravalvular aortic stenosis and autosomal dominant cutis laxa-1, which are genetic disorders caused by mutations in the elastin gene. We present mouse models of supravalvular aortic stenosis, autosomal dominant cutis laxa-1, and graded elastin amounts that have been invaluable for understanding the role of elastin in arterial mechanics and cardiovascular disease. We summarize acquired diseases associated with elastic fiber defects, including hypertension and arterial stiffness, diabetes, obesity, atherosclerosis, calcification, and aneurysms and dissections. We mention animal models that have helped delineate the role of elastic fiber defects in these acquired diseases. We briefly summarize challenges and recent advances in generating functional elastic fibers in tissue-engineered arteries. We conclude with suggestions for future research and opportunities for therapeutic intervention in genetic and acquired elastinopathies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".