Fetal development, mechanobiology and optimal control processes can improve vascular tissue regeneration in bioreactors: An integrative review
Bibliographic record
Abstract
Vascular tissue engineering aims to regenerate blood vessels to replace diseased arteries for cardiovascular patients. With the scaffold-based approach, cells are seeded on a scaffold showing specific properties and are expected to proliferate and self-organize into a functional vascular tissue. Bioreactors can significantly contribute to this objective by providing a suitable environment for the maturation of the tissue engineered blood vessel. It is recognized from the mechanotransduction principles that mechanical stimuli can influence the protein synthesis of the extra-cellular matrix thus leading to maturation and organization of the tissues. Up to date, no bioreactor is especially conceived to take advantage of the mechanobiology and optimize the construct maturation through an advanced control strategy. In this review, experimental strategies in the field of vascular tissue engineering are detailed, and a new approach inspired by fetal development, mechanobiology and optimal control paradigms is proposed. In this new approach, the culture conditions (i.e. flow, circumferential strain, pressure frequency, and others) are supposed to dynamically evolve to match the maturity of vascular constructs and maximize the efficiency of the regeneration process. Moreover, this approach allows the investigation of the mechanisms of growth, remodeling and mechanotransduction during the culture.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".