A Novel High‐Speed Production Process to Create Modular Components for the Bottom‐Up Assembly of Large‐Scale Tissue‐Engineered Constructs
Bibliographic record
Abstract
To replace damaged or diseased tissues, large tissue-engineered constructs can be prepared by assembling modular components in a bottom-up approach. However, a high-speed method is needed to produce sufficient numbers of these modules for full-sized tissue substitutes. To this end, a novel production technique is devised, combining air shearing and a plug flow reactor-style design to rapidly produce large quantities of hydrogel-based (here type I collagen) cylindrical modular components with tunable diameters and length. Using this technique, modules containing NIH 3T3 cells show greater than 95% viability while endothelial cell surface attachment and confluent monolayer formation are demonstrated. Additionally, the rapidly produced modules are used to assemble large tissue constructs (>1 cm(3) ) in vitro. Module building blocks containing luciferase-expressing L929 cells are packed in full size adult rat-liver-shaped bioreactors and perfused with cell medium, to demonstrate the capacity to build organ-shaped constructs; bioluminescence demonstrates sustained viability over 3 d. Cardiomyocyte-embedded modules are also used to assemble electrically stimulatable contractile tissue.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".