Liquid Dermal Scaffold With Adipose-Derived Stem Cells Improve Tissue Quality in a Murine Model of Impaired Wound Healing
Bibliographic record
Abstract
Wound repair and regeneration is a multidisciplinary field of research with considerable potential value to the management of deep and large burn injuries. These injuries lack an appropriate tissue scaffold and pro-healing cells making them difficult to heal. An alternative to the often limited autologous skin is a therapy that would restore the essential matrix and cellular components for rapid healing. In this study, they use a novel liquid dermal scaffold capable of gelation in vivo to show that it is biocompatible with adipose-derived stem cells. Using a validated method of wound splinting in a delayed-healing murine model, we show that wounds treated with the scaffold and stem cells had a significant reduction in wound size and had accelerated healing compared with control. The wounds treated with stem cells had increased capillary formation, collagen content, epidermal thickness, and essential growth factor expression in the healed tissue compared with control and liquid scaffold alone. This liquid dermal scaffold combined with cells is a feasible treatment strategy for complex or large burn wounds that are otherwise lacking the appropriate cellular matrix necessary for healing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".