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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".