Perspectives on the advanced control of bioreactors for functional vascular tissue engineering<i>in vitro</i>
Bibliographic record
Abstract
Tissue engineering aims to produce tissues using cells and materials. The action of designing tissues involves observing the process of growth to understand its underlying mechanisms. It requires manipulation of the critical parameters for cell growth and remodeling to produce structured tissues and functional organs. Tissue engineers face the challenge of orchestrating the signals in a cell's microenvironment to efficiently grow an anisotropic and hierarchical tissue. It can be performed in vivo through the design of bioactive scaffolds and manipulation of biological signals using growth factors. It can also be performed in vitro in a controlled environment called the bioreactor. This article addresses the matter of finding the optimal dynamic sequence of culture conditions in a bioreactor for the maturation of tissues. Artificial intelligence and optimal control are accelerating technologies towards an understanding of tissue regeneration. The particular example of the functional engineering of small-diameter blood vessels has been chosen to illustrate this idea.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".