Human iPSCs-derived mesenchymal stem cells promote skin regeneration and burn wound healing
Bibliographic record
Abstract
The key to surviving severe burns is rapid burn wound excision and closure, yet extensive wounds often surpass natural healing capacity. Alternative treatments, such as synthetic skin substitutes, have not emerged as a standard, optimal solution. Stem cell therapies, especially using allogenic sources, show promise in enhancing wound repair. Induced mesenchymal stem cells (iMSCs) have demonstrated vast possibilities to overcome traditional stem cell therapy limitations. This study utilized Cord tissue-derived iMSCs (CT-iMSCs) incorporated into well-established epidermal-dermal substitutes Integra® Dermal Regeneration Template (DRT) at 5000–20,000 cells/cm 2 in a porcine full-thickness burn model to test their regenerative capabilities. We evaluated healing outcomes, inflammation, neovascularization, collagen levels, and fibrosis markers. Wounds treated with CT-iMSCs showed notable improvements, including faster wound healing, better epithelialization, and marked improvements in healing markers compared to controls. These data support the potential of iMSCs as an ideal cell source for autologous skin regeneration.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".