A New Bioreactor Adapts to Materials State and Builds a Growth Model for Vascular Tissue Engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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 it