An Evaluation of the Treatment of Full-Thickness Wounds Using Adipose Micro-Fragments within a Liquid Dermal Scaffold
Bibliographic record
Abstract
In full-thickness wounds, inflammation, lack of matrix deposition, and paucity of progenitor cells delay healing. As commercially available solid (sheet) scaffolds are unable to conform to wounds of varying shapes and sizes, we previously generated a nutritious, injectable, liquid skin substitute that can conform to wound topography. In combination with adipose micro-fragments as a viable source of progenitor cells, a composite, in situ forming skin substitute was tested for the treatment of silicon ring splinted full-thickness wounds in rats. The in vitro survivability and migratory capacity of adipocytes derived from rat micro-fragmented fat cultured in our scaffold was examined with a Live/Dead assay, showing viability and migration after 7 and 14 days. In vivo, the efficacy of our scaffold alone (LDS) or with adipose micro-fragments (LDS+A) was compared to a standard dressing protocol (NT). LDS and LDS+A showed ameliorated wound healing, including complete epithelialization and less immune cell infiltration, compared to the NT control. Our findings demonstrate that a 3D liquid skin scaffold is a rich environment for adipocyte viability and migration, and that the addition of adipose micro-fragments to this scaffold can be used as a rich source of cells for treating full-thickness 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 | 0.000 | 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".