Graphene functionalized decellularized scaffold promotes skin cell proliferation
Bibliographic record
Abstract
An increasing number of new strategies for skin tissue engineering have been developed with the potential to mimic the biological properties of native tissue with a high degree of complexity, flexibility, and reproducibility. In this study, decellularized tissue (DT) was prepared from the bovine heart by using chemical treatments. However, the mechanical properties of the DT constructs were poorer than the extra cellular matrix of the skin tissue. To overcome this challenge, hybrid scaffolds of DT and graphene oxide (GO) were developed and the effects of the GO concentration on the morphology, pore size, porosity, mechanical strength, and water uptake capacity of the samples were evaluated. Moreover, the biocompatibility of hybrid scaffolds was studied by Live/Dead staining. The results show that a hybrid scaffold incorporating 3 % graphene oxide improved the mechanical strength and cell viability by ~25 % in comparison to the DT scaffolds. Cell viability results confirmed that the porous scaffolds could support cell adhesion, proliferation, and cell activity for 7 days. This study provides new insight into and opportunities for using graphene‐based materials to develop biomimetic constructs for clinical 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) | 0.001 | 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".