Human Wharton’s jelly mesenchymal stem cells promote skin wound healing through paracrine signaling
Bibliographic record
Abstract
INTRODUCTION: The prevalence of nonhealing wounds is predicted to increase due to the growing aging population. Despite the use of novel skin substitutes and wound dressings, poorly vascularized wound niches impair wound repair. Mesenchymal stem cells (MSCs) have been reported to provide paracrine signals to promote wound healing, but the effect of human Wharton's jelly-derived MSCs (WJ-MSCs) has not yet been described in human normal skin. METHODS: Human WJ-MSCs and normal skin fibroblasts were isolated from donated umbilical cords and normal adult human skin. Fibroblasts were treated with WJ-MSC-conditioned medium (WJ-MSC-CM) or nonconditioned medium. RESULTS: Expression of genes involved in re-epithelialization (transforming growth factor-β2), neovascularization (hypoxia-inducible factor-1α) and fibroproliferation (plasminogen activator inhibitor-1) was upregulated in WJ-MSC-CM-treated fibroblasts (P≤0.05). WJ-MSC-CM enhanced normal skin fibroblast proliferation (P≤0.001) and migration (P≤0.05), and promoted wound healing in an excisional full-thickness skin murine model. CONCLUSIONS: Under our experimental conditions, WJ-MSCs enhanced skin wound healing in an in vivo mouse model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".