Design and Fabrication of Sub-mm-Sized Modules Containing Encapsulated Cells for Modular Tissue Engineering
Bibliographic record
Abstract
We have proposed modular tissue engineering as a strategy to construct vascularized tissues containing multiple cell types. To create a modular construct, instead of seeding a preformed scaffold, cells were encapsulated within sub-mm modules, and the outer surface of these modules was covered with a layer of endothelial cells. Modules were then added to a larger structure (here by filling a tube) to form the modular construct. Through a systematic process of materials selection, collagen, human umbilical vein endothelial cells (HUVECs), and HepG2 cells, a human hepatoma cell line, were identified as suitable components for module formation, at least for initial studies. A method, which involved cutting and shaping the modules within a tubular mold, was developed to fabricate sub-mm, cylindrical, collagen modules that contained viable, functioning HepG2 cells and that could be seeded with a surface layer of HUVECs. Module dimensions were reproducible and easily altered in a controlled fashion if desired. The module fabrication process developed here not only generated modules suitable for the assembly of a prototype modular construct, but also could potentially be used more generally for other applications for which the goal is to form submm-diameter cylinders from soft hydrogels.
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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) | 0.000 | 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 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".