A Roadmap for the Design of Bioreactors in Mechanobiological Research and Engineering of Load-Bearing Tissues
Bibliographic record
Abstract
In the field of tissue engineering, a bioreactor is a valuable instrument that mimics a physiological environment to maintain live tissues in vitro. Although bioreactors are conceptually relatively simple, the vast majority of current bioreactors (commercial and custom-built) are not fully adapted to current research needs. Designing the optimal bioreactor requires a very thorough approach to a series of steps in the product development process. These four basic steps are: (1) identifying the needs and technical requirements, (2) defining and evaluating the related concepts, (3) designing the apparatus and drawing up the blueprints, and (4) building and validating the apparatus. Furthermore, the design has to be adapted to the specific purpose of the research and how the tissues will be used. In the emerging field of bioreactor research, roadmaps are needed to assist tissue engineering researchers as they embark on this process. The necessary multidisciplinary expertise covering micromechanical design, mechatronics, viscoelasticity, tissue culture, and human ergonomics is not necessarily available to all research teams. Therefore, the challenge of adapting and conducting each step in the product development process is significant. This paper details our proposal for a roadmap to accompany researchers in identifying their needs and technical requirements: step one in the product development process. Our roadmap proposal is set up in two phases. Phase 1 is based on the analysis of the bioreactor use cycle and phase 2 is based on the analysis of one specific and critical step in the use cycle: conducting stimulation and characterization protocols with the bioreactor. A meticulous approach to these two phases minimizes the risk of forgetting important requirements and strengthens the probability of acquiring or designing a high performance bioreactor.
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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 | 0.035 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".