Stem cells derived from burned skin - The future of burn care
Bibliographic record
Abstract
BACKGROUND: Thermal injuries affect millions of adults and children worldwide and are associated with high morbidity and mortality. The key determinant for the survival of burns is rapid wound healing. Large wounds exceed intrinsic wound-healing capacities, and the currently available coverage materials are insufficient due to lack of cellularity, availability or immunological rejection. METHODS: Using the surgically debrided tissue, we isolated viable cells from burned skin. The isolated cells cultured in tissue culture dishes and characterized. FINDINGS: We report here that debrided burned skin, which is routinely excised from patients and otherwise considered medical waste and unconsciously discarded, contains viable, undamaged cells which show characteristics of mesenchymal skin stem cells. Those cells can be extracted, characterized, expanded, and incorporated into created epidermal-dermal substitutes to promote wound healing in immune-compromised mice and Yorkshire pigs without adverse side effects. INTERPRETATION: These findings are of paramount importance and provide an ideal cell source for autologous skin regeneration. Furthermore, this study highlights that skin contains progenitor cells resistant to thermal stress. FUND: Canadian Institutes of Health Research # 123336. CFI Leader's Opportunity Fund: Project # 25407 National Institutes of Health 2R01GM087285-05A1. EMHSeed: Fund: 500463, A generous donation from Toronto Hydro. Integra© Life Science Company provided the meshed bilayer Integra© for porcine experiments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".