De Novo Elastin Assembly Alleviates Development of Supravalvular Aortic Stenosis—Brief Report
Bibliographic record
Abstract
BACKGROUND: A series of incurable cardiovascular disorders arise due to improper formation of elastin during development. Supravalvular aortic stenosis (SVAS), resulting from a haploinsufficiency of ELN , is caused by improper stress sensing by medial vascular smooth muscle cells, leading to progressive luminal occlusion and heart failure. SVAS remains incurable, as current therapies do not address the root issue of defective elastin. METHODS: We use SVAS here as a model of vascular proliferative disease using both human induced pluripotent stem cell–derived vascular smooth muscle cells and developmental Eln +/– mouse models to establish de novo elastin assembly as a new therapeutic intervention. RESULTS: We demonstrate mitigation of vascular proliferative abnormalities following de novo extracellular elastin assembly through the addition of the polyphenol epigallocatechin gallate to SVAS human induced pluripotent stem cell–derived vascular smooth muscle cells and in utero to Eln +/– mice. CONCLUSIONS: We demonstrate de novo elastin deposition normalizes SVAS human induced pluripotent stem cell–derived vascular smooth muscle cell hyperproliferation and rescues hypertension and aortic mechanics in Eln +/– mice, providing critical preclinical findings for the future application of epigallocatechin gallate treatment in humans.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".