Polyelectrolyte Multilayer Coating of 3D Scaffolds Enhances Tissue Growth and Gene Delivery: Non‐Invasive and Label‐Free Assessment
Bibliographic record
Abstract
Layer-by-layer (LbL) deposition is a versatile technique which is beginning to be be explored for inductive tissue engineering applications. Here, it is demonstrated that a polyelectrolyte multilayer film system composed of glycol-chitosan (Glyc-CHI) and hyaluronic acid (HA) can be used to coat 3D micro-fabricated polymeric tissue engineering scaffolds. In order to overcome many of the limitations associated with conventional techniques for assessing cell growth and viability within 3D scaffolds, two novel, real-time, label-free techniques are introduced: impedance monitoring and optical coherence phase microscopy. Using these methods, it is shown that LbL-coated scaffolds support in vitro cell growth and viability for a period of at least two weeks at levels higher than uncoated controls. These polyelectrolyte multilayer coatings are then further adapted for non-viral gene delivery applications via incorporation of DNA carrier lipoplexes. Scaffold-based delivery of the enhanced green fluorescent protein (EGFP) marker gene from these coatings is successfully demonstrated in vitro, achieving a two-fold increase in transfection efficiency compared with control scaffolds. These results show the great potential of Glyc-CHI/HA polyelectrolyte multilayer films for a variety of gene delivery and inductive tissue engineering applications.
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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".