Evaluation of PBS Treatment and PEI Coating Effects on Surface Morphology and Cellular Response of 3D-Printed Alginate Scaffolds
Bibliographic record
Abstract
Three-dimensional (3D) printing is an emerging technology for the fabrication of scaffolds to repair/replace damaged tissue/organs in tissue engineering. This paper presents our study on 3D printed alginate scaffolds treated with phosphate buffered saline (PBS) and polyethyleneimine (PEI) coating and their impacts on the surface morphology and cellular response of the printed scaffolds. In our study, sterile alginate was prepared by means of the freeze-drying method and then, used to prepare the hydrogel for 3D printing into calcium chloride, forming 3D scaffolds. Scaffolds were treated with PBS for a time period of two days and seven days, respectively, and PEI coating; then they were seeded with Schwann cells (RSC96) for the examination of cellular response (proliferation and differentiation). In addition, swelling and stiffness (Young's modulus) of the treated scaffolds was evaluated, while their surface morphology was assessed using scanning electron microscopy (SEM). SEM images revealed significant changes in scaffold surface morphology due to degradation caused by the PBS treatment over time. Our cell proliferation assessment over seven days showed that a two-day PBS treatment could be more effective than seven-day PBS treatment for improving cell attachment and elongation. While PEI coating of alginate scaffolds seemed to contribute to cell growth, Schwann cells stayed round on the surface of alginate over the period of cell culture. In conclusion, PBS-treatment may offer the potential to induce surface physical cues due to degradation of alginate, which could improve cell attachment post cell-seeding of 3D-printed alginate scaffolds.
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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) | 0.001 | 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".