Tyrosinase-doped bioink for 3D bioprinting of living skin constructs
Bibliographic record
Abstract
Three-dimensional bioprinting is an emerging technology for fabricating living 3D constructs, and it has shown great promise in tissue engineering. Bioinks are scaffold materials mixed with cells used by 3D bioprinting to form a required cell-laden structure. In this paper, a novel bioink made of gelatin methacrylamide (GelMA) and collagen (Col) doped with tyrosinase (Ty) is presented for the 3D bioprinting of living skin tissues. Ty has the dual function of being an essential bioactive compound in the skin regeneration process and also as an enzyme to facilitate the crosslink of Col and GelMA. Further, enzyme crosslinking together with photocrosslinking can enhance the mechanical strength of the bioink. The experimental results show that the bioink is able to form stable 3D living constructs using the 3D bioprinting process. The cell culture shows that three major cell lines: human melanocytes (HEM), human keratinocytes (HaCat) and human dermal fibroblasts (HDF) exhibit high cell viabilities. The viability of these three cell lines is above 90%. The proliferation and scratching test show that Ty can enhance the proliferation of HEM, inhibit the growth and migration of HDF and not affect HaCat significantly. Animal tests show that the doped bioinks for 3D bioprinting can help form an epidermis and dermis, and thus have high potential as a skin bioink.
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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".