<scp>3D</scp> Bioprinting a Novel Skin Co‐Culture Model Using Human Keratinocytes and Fibroblasts
Bibliographic record
Abstract
3D bioprinting can generate the organized structures found in human skin for a variety of biological, medical, and pharmaceutical applications. Challenges in bioprinting skin include printing different types of cells in the same construct while maintaining their viability, which depends on the type of bioprinter and bioinks used. This study evaluated a novel 3D bioprinted skin model containing human keratinocytes (HEKa) and human dermal fibroblasts (HDF) in co-culture (CC) using a high-viscosity fibrin-based bioink produced using the BioX extrusion-based bioprinter. The constructs containing HEKa or HDF cells alone (control groups) and in CC were evaluated at 1, 10, and 20 days after bioprinting for viability, immunocytochemistry for specific markers (K5 and K10 for keratinocytes; vimentin and fibroblast specific protein [FSP] for fibroblasts). The storage, loss modulus, and viscosity properties of the constructs were also assessed to compare the effects of keratinocytes and fibroblasts individually and combined, providing important insights when bioprinting skin. Our findings revealed significantly higher cell viability in the CC group compared to individual keratinocyte and fibroblast groups, suggesting the combined cell presence enhanced survival rates. Additionally, proliferation rates of both cell types remained consistent over time, indicating non-competitive growth within the construct. Interestingly, keratinocytes exhibited a greater impact on the viscoelastic properties of the construct compared to fibroblasts, likely due to their larger size and arrangement. These insights contribute to optimizing bioprinting strategies for skin tissue engineering and emphasize the important role of different cell types in 3D skin models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".