Injectable Graphene Oxide/Hydrogel-Based Angiogenic Gene Delivery System for Vasculogenesis and Cardiac Repair
Bibliographic record
Abstract
The objective of this study was to develop an injectable and biocompatible hydrogel which can efficiently deliver a nanocomplex of graphene oxide (GO) and vascular endothelial growth factor-165 (VEGF) pro-angiogenic gene for myocardial therapy. For the study, an efficient nonviral gene delivery system using polyethylenimine (PEI) functionalized GO nanosheets (fGO) complexed with DNAVEGF was formulated and incorporated in the low-modulus methacrylated gelatin (GelMA) hydrogel to promote controlled and localized gene therapy. It was hypothesized that the fGOVEGF/GelMA nanocomposite hydrogels can efficiently transfect myocardial tissues and induce favorable therapeutic effects without invoking cytotoxic effects. To evaluate this hypothesis, a rat model with acute myocardial infarction was used, and the therapeutic hydrogels were injected intramyocardially in the peri-infarct regions. The secreted VEGF from in vitro transfected cardiomyocytes demonstrated profound mitotic activities on endothelial cells. A significant increase in myocardial capillary density at the injected peri-infarct region and reduction in scar area were noted in the infarcted hearts with fGOVEGF/GelMA treatment compared to infarcted hearts treated with untreated sham, GelMA and DNAVEGF/GelMA groups. Furthermore, the fGOVEGF/GelMA group showed significantly higher (p < 0.05, n = 7) cardiac performance in echocardiography compared to other groups, 14 days postinjection. In addition, no significant differences were noticed between GO/GelMA and non-GO groups in the serum cytokine levels and quantitative PCR based inflammatory microRNA (miRNA) marker expressions at the injected sites. Collectively, the current findings suggest the feasibility of a combined hydrogel-based gene therapy system for ischemic heart diseases using nonviral hybrid complex of fGO and DNA.
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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".