515 A Novel Hand-Held Bioprinter Enhances Skin Regenration and Wound Healing in a Burn Porcine Model
Bibliographic record
Abstract
Skin regeneration after a severe burn injury is crucial for survival. Current treatment options are limited. Mesenchymal Stem Cells (MSCs) are known to promote wound healing and tissue regeneration but delivery of these cells remains a challenge. Bioprinting represents a promising delivery strategy. We have designed, validated in vitro as well as in a large animal wound healing model a novel intraoperatively usable hand-held bio-printer, that delivers MSCs directly and conformally onto wounds, embedded within an extracellular matrix layer. Aim: Determine postburn skin wound regeneration after MSC deposition with a novel intraoperatively usable bioprinter. We conducted a study in a full-thickness porcine burn wound model where we in situ delivered umbilical cord mesenchymal stem cells (UC-MSC) conformally via our hand-held bio-printer. An acellular collagen-based dressing that is the clinical gold standard was seeded with MSC and used as a control. Wound healing and skin regeneration were determined at day 28 (remodeling) and followed until day 49 (maturation) post-intervention for full tissue remodeling assessment. Our data (day 28) show accelerated wound healing after stem cell application, with significantly increased collagen regeneration (<p=0.05, ANOVA) compared to controls, as well as increased neovascularization (<p=0.05, T-Test) and less contracture formation (
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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) | 0.002 | 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".