Using Three-Dimensional Bioprinting to Generate Realistic Models of Wound Healing
Bibliographic record
Abstract
SIGNIFICANCE: The skin serves as the primary defense against external stimuli, making it vulnerable to damage. Injuries can cause a dysregulated environment, resulting in chronic inflammation and inhibition of cell proliferation and migration, which delays recovery. Innovative approaches, such as three-dimensional (3D) bioprinting, can foster a controlled healing environment by promoting synergy between the skin microbiome and cells. RECENT ADVANCES: Traditional approaches to wound healing have focused on fostering an environment conducive to the interplay between cells, extracellular proteins, and growth factors. 3D bioprinting, a manufacturing technology with applications in tissue engineering, deposits biomaterial-based bioink containing living cells to fabricate custom-designed tissue scaffolds in a layer-by-layer fashion. This process controls the architecture and composition of a construct, producing multilayered and complex structures such as skin. CRITICAL ISSUES: The selection of biomaterials for scaffolds has been a challenge when 3D skin tissue engineering. While prioritizing mechanical properties, current biomaterials often lack the ability to interact with environmental stimuli such as pH, temperature, or oxygen levels. Employing smart biomaterials that integrate bioactive molecules and adapt to external conditions could overcome these limitations. This innovation would enable scaffolds to create a sustainable wound-healing environment, fostering microbiome balance, reducing inflammation, and facilitating cellular recovery and tissue restoration, addressing critical gaps in existing wound care solutions. FUTURE DIRECTIONS: Novel bioink formulations for skin injury recovery are focused on improving long-term cell viability, proliferation, vascularization, and immune integration. Efficient recovery of the skin microbiome using bioactive molecules has the potential to create microenriched environments that support the recovery of the skin microbiome and restore immune regulation. This promising direction for future research aims to improve patient outcomes in wound care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".