Use of the polycation polyethyleneimine to improve the physical properties of alginate–hyaluronic acid hydrogel during fabrication of tissue repair scaffolds
Bibliographic record
Abstract
Recently alginate-based tissue repair scaffolds fabricated using 3D printing techniques have been extensively examined for use in tissue engineering applications. However, their physical and mechanical properties are unfavorable for many tissue engineering applications because these properties are poorly controlled during the fabrication process. Some improvement of alginate gel properties can be realized by addition of hyaluronic acid (HA), and this may also improve the ability of cells to interact with the gel. Here, we report improvement of the physical properties of alginate-HA gel scaffolds by the addition of the polycation polyethyleneimine (PEI) during the fabrication process in order to stabilize alginate molecular structure through the formation of a polyelectrolyte complex. We find that PEI has a significant beneficial influence on alginate-HA scaffold physical properties, including a reduction in the degree of gel swelling, a reduction in scaffold degradation rate, and an increase in the Young's modulus of the gel. Further study shows that fabrication of alginate-HA gels with PEI increases the encapsulation efficiency of bovine serum albumin, a model protein, and reduces the subsequent initial protein release rate. However, it was also found that survival of Schwann cells or ATDC-5 chondrogenic cells encapsulated during the scaffold fabrication process was modestly reduced with increasing PEI concentration. This study illustrates that the use of PEI during scaffold fabrication by plotting can provide an effective means to control alginate-based scaffold properties for tissue engineering applications, but that the many effects of PEI must be balanced for optimal outcomes in different situations.
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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 | 0.000 |
| 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".