Heart valve regeneration: the need for systems approaches
Bibliographic record
Abstract
Tissue-engineered heart valves are promising alternatives to address the limitations of current valve replacements, particularly for growing children. Current heart valve tissue engineering strategies involve the selection of biomaterial scaffolds, cell types, and often in vitro culture conditions aimed at regenerating a valve for implantation and subsequent maturation in vivo. However, identifying optimal combinations of cell sources, biomaterials, and/or bioreactor conditions to produce functional, durable valve tissue remains a challenge. Despite some short-term success in animal models, attempts to recapitulate aspects of the native heart valve environment based on 'best guesses' of a limited number of regulatory factors have not proven effective. Better outcomes for valve tissue regeneration will likely require a systems-level understanding of the relationships between multiple interacting regulatory factors and their effects on cell function and tissue formation. Until recently, conventional culture methods have not allowed for multiple design parameters to be considered at once. Emerging microtechnologies are well suited to systematically probe multiple inputs, in combination, in high throughput and with great precision. When combined with statistical and network systems analyses, these microtechnologies have excellent potential to define multivariate signal-response relationships and reveal key regulatory pathways for robust functional tissue regeneration.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".