Biomimetic biaxial mechanical properties enhance hemodynamic performance and prevent adverse tissue remodelling in tissue-engineered heart valves
Bibliographic record
Abstract
Abstract Heart valve tissue engineering holds the promise of providing an unlimited supply of heart valve replacements for patients with valvular heart disease. While tissue-engineered heart valves show promising performance in the short term, regurgitation due to fibrotic leaflet shortening remains a persistent limitation in most cases. Here we tested the hypothesis that engineering heart valves with native biaxial mechanical properties would mitigate adverse tissue remodeling in vitro. Melt electrowriting and hydrogel casting were used to generate four types of tissue-engineered heart valves with combinatorial biomimetic or non-biomimetic mechanical properties in the radial and circumferential direction of the heart valves. The heart valves were subject to pulmonary hemodynamic conditions in a pulse duplicator bioreactor, and their tissue remodelling, including cell phenotype and orientation and extracellular matrix properties, was investigated. The results showed that tissue-engineered heart valves with native mechanical properties, particularly in the radial direction, outperformed those with non-physiological mechanics in hemodynamic function, maintaining quiescent cell phenotype and cell orientation, and avoiding fibrosis. This study, for the first time, elucidates the impact of native mechanical properties on the performance of tissue- engineered heart valves and their cell and extracellular matrix homeostasis.
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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".