Reduced Graphene Oxide‐GelMA Hybrid Hydrogels as Scaffolds for Cardiac Tissue Engineering
Bibliographic record
Abstract
Biomaterials currently used in cardiac tissue engineering have certain limitations, such as lack of electrical conductivity and appropriate mechanical properties, which are two parameters playing a key role in regulating cardiac cell behavior. Here, the myocardial tissue constructs are engineered based on reduced graphene oxide (rGO)-incorporated gelatin methacryloyl (GelMA) hybrid hydrogels. The incorporation of rGO into the GelMA matrix significantly enhances the electrical conductivity and mechanical properties of the material. Moreover, cells cultured on composite rGO-GelMA scaffolds exhibit better biological activities such as cell viability, proliferation, and maturation compared to ones cultured on GelMA hydrogels. Cardiomyocytes show stronger contractility and faster spontaneous beating rate on rGO-GelMA hydrogel sheets compared to those on pristine GelMA hydrogels, as well as GO-GelMA hydrogel sheets with similar mechanical property and particle concentration. Our strategy of integrating rGO within a biocompatible hydrogel is expected to be broadly applicable for future biomaterial designs to improve tissue engineering outcomes. The engineered cardiac tissue constructs using rGO incorporated hybrid hydrogels can potentially provide high-fidelity tissue models for drug studies and the investigations of cardiac tissue development and/or disease processes in vitro.
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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".