Studying the <i>in vivo</i> application of a liquid dermal scaffold in promoting wound healing in a mouse model
Bibliographic record
Abstract
Lack of matrix deposition is one of the main factors that complicates the healing process of wounds. The aim of this study was to test the efficacy and safety of a liquid dermal scaffold, referred to as MeshFill (MF) that can fill the complex network of tunnels and cavities which are usually found in chronic wounds and hence improve the healing process. We evaluated in vitro and in vivo properties of a novel liquid dermal scaffold in a delayed murine full-thickness wound model. We also compared this scaffold with two commercially available granular collagen-based products (GCBP). Liquid dermal scaffold accelerated wound closure significantly compared with no-treated control and collagen-based injectable composites in a delayed splinted wound model. When we compared cellular composition and count between MF, no treatment and GCBP at the histology level, it was found that MF was the most analogous and consistent with the normal anatomy of the skin. These findings were matched with the clinical outcome observation. The flowable in situ forming scaffold is liquid at cold temperature and gels after application to the wound site. Therefore, it would conform to the topography of the wound when liquid and provides adequate tensile strength when solidified. This patient-ready gelling dermal scaffold also contains the nutritional ingredients and therefore supports cell growth. Applying an injectable liquid scaffold that can fill wound gaps and generate a matrix to promote keratinocytes and fibroblasts migration, can result in improvement of the healing process of complex wounds.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".