A New Bioreactor Adapts to Materials State and Builds a Growth Model for Vascular Tissue Engineering
Bibliographic record
Abstract
Bioreactors are a promising enabling technology for vascular tissue engineering. Beyond their value for the scale-up and manufacturing of tissue-engineered blood vessels, bioreactors represent a potential path toward the understanding of the regeneration process of tissues in vitro, toward the development of mathematical models for growth and remodeling in tissue engineering, and toward the study of pathological conditions. To achieve these promises, bioreactors must overcome the paradigm of a black box for the growth of tissues and become a tool for the study of growth in tissue engineering. An advanced control strategy was developed to study and maximize growth in bioreactors. The aim of this paper is to validate experimentally the ability of this controller to build knowledge during the culture of a tissue-engineered blood vessel. During the experiments, the controller proposed linear regression models, therefore making hypotheses on the parameters that influence growth; then, it chose experiments to refine these models, therefore verifying these hypotheses. These results show that tissue maturation in bioreactors can become more efficient by acquiring information about the process, and by dynamically adapting culture conditions according to this information input.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".