Bioplotting Alginate/Hyaluronic Acid Hydrogel Scaffolds with Structural Integrity and Preserved Schwann Cell Viability
Bibliographic record
Abstract
Bioplotting is an emerging freeform scaffold fabrication technique useful for creating artificial tissue scaffolds containing living cells. Simultaneous maintenance of scaffold structural integrity and cell viability is a challenging task. In this article, we present strategies developed to bioplot alginate-based three-dimensional tissue scaffolds containing hyaluronic acid and living Schwann cells for potential use in peripheral nerve tissue engineering. The fabrication platform, upon which the scaffold is created, was coated with the polycation polyethylenimine to immobilize the first layer of the scaffold on the platform. Each layer was then dispensed into a bath containing calcium chloride to cross-link the alginate, polyvinyl alcohol, and low concentrations of polyethylenimine to improve the structural integrity of the resulting scaffolds while retaining biocompatibility. The present study illustrated that with these strategies, porous alginate/hyaluronic acid scaffolds can be fabricated with good structural integrity and long-term cell viability. Plotting method and speed were also important factors determining the printability of single-layered scaffolds. The methods developed and the results obtained from this study form the basis for a general bioplotting method by which more sophisticated alginate/hyaluronic acid scaffolds can be fabricated with the inclusion of living cells into desired structures.
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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".