Delivery of Bioactive Gene Particles via Gelatin-Collagen-PEG-Based Electrospun Matrices
Bibliographic record
Abstract
The fabrication of fiber mats via electrospinning has been adopted in the last decades to produce high quality scaffolds for tissue engineering. However, an effective combination of electrospinning methods with gene delivery therapies remains a challenge. In this study, we describe how the delivery of gene complexes via electrospun mats that contain different volumes of gelatin (Gel), collagen (Col), and polyethylene glycol (PEG) can affect gene expression by transfected cells. Non-viral complexes were formulated by using lipid modified polyethylenimine (PEI) polymer and plasmid DNAs (pDNA) like the reporter Green Fluorescent Protein (GFP) and the therapeutically relevant Bone Morphogenetic Protein-2 (BMP-2) and electrospuned after being mixed with different volumes of Gel-Col-PEG mats and delivered to human myoblast (C2C12) and mouse osteoblast cells (MC3T3). The entrapment of GFP complexes via different homogeneous electrospun fiber mats revealed that a high fraction of collagen in the mats affected the quality of the fibers and led to reduced transfection efficiency on target cells. On the other hand, the fabrication of double-layered mats that contained collagen without complexes as a first layer and gelatin-collagen-PEG with complexes as a second layer successfully induced GFP expression and ALP activity in C2C12 cells. We conclude that this study has established the advantage of formulating multilayered bioactive collagen-based mats for gene delivery 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) | 0.000 | 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".