Integrating melt electrowriting (MEW) PCL scaffolds with fibroblast-laden hydrogel toward vascularized skin tissue engineering
Bibliographic record
Abstract
Three-dimensional (3D) skin equivalents (SEs) are promising platforms for studying skin disease or assessing the safety of skin-relevant products. Vascularization, which improves the functionality of reconstructed skin, is one of the remaining hurdles in SE production that, when successfully introduced, can widen SE applications. Here, combining porous polycaprolactone (PCL) melt electrowritten (MEW) scaffolds with fibroblast-laden methacrylated gelatin hydrogel (GelMA), we developed SEs with cellular vascular structure. The MEW scaffolds were composed of two layers: random fibers for culturing the keratinocytes to fabricate the epidermis; and well-aligned shapes filled with fibroblast-laden GelMA to mimic the dermis. Three dermal designs varying in porosities and pore sizes were compared to optimize the dermis reconstruction. Within one week, the design with bigger pore sizes achieved optimal cell distribution, penetration, and extracellular matrix (ECM) deposition. Additionally, Retinoic acid (RTA) was tested for improving ECM deposition. To mimic vasculature, we incorporated vascular grafts into the optimized SEs. These were fabricated by casting endothelial fibroblast-laden Matrigel onto small-diameter MEW-tubular structures. The versatility and reproducibility of the obtained SEs offer a robust new tool for in vitro testing and exploration of fundamental biological processes of skin tissue. The Figure: Human skin was generated by Biorender, the scientific illustration software (Canada), and the publishing certificate was obtained.
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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".