Biocomposite nanofiber matrices to support ECM remodeling by human dermal progenitors and enhanced wound closure
Bibliographic record
Abstract
Cell-based therapies have recently been the focus of much research to enhance skin wound healing. An important challenge will be to develop vehicles for cell delivery that promote survival and uniform distribution of cells across the wound bed. These systems should be stiff enough to facilitate handling, whilst soft enough to limit damage to newly synthesized wound tissue and minimize patient discomfort. Herein, we developed several novel modifiable nanofibre scaffolds comprised of Poly (ε-caprolactone) (PCL) and gelatin (GE). We asked whether they could be used as a functional receptacle for adult human Skin-derived Precursor Cells (hSKPs) and how naked scaffolds impact endogenous skin wound healing. PCL and GE were electrospun in a single facile solvent to create composite scaffolds and displayed unique morphological and mechanical properties. After seeding with adult hSKPs, deposition of extracellular matrix proteins and sulphated glycosaminoglycans was found to be enhanced in composite grafts. Moreover, composite scaffolds exhibited significantly higher cell proliferation, greater cell spreading and integration within the nanofiber mats. Transplantation of acellular scaffolds into wounds revealed scaffolds exhibited improvement in dermal-epidermal thickness, axonal density and collagen deposition. These results demonstrate that PCL-based nanofiber scaffolds show promise as a cell delivery system for wound healing.
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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) | 0.001 | 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".