808 Combination of Adipose Micro Fragments and Liquid Scaffold Improve Wound Healing Outcomes
Bibliographic record
Abstract
Abstract Introduction In full-thickness wounds, high levels of inflammation, lack of matrix deposition and paucity of progenitor cells delays normal healing processes. One major problem with commercially available solid (sheet) scaffolds is their inability to conform to wounds of varying shapes and sizes. To overcome this, we previously generated a liquid, injectable skin substitute which can fill wounds of any shape and depth from bottom up and has all the necessary ingredients for skin cells to be nourished, proliferate, and migrate in. In combination with adipose micro fragments as a viable source of progenitor cells, a composite, in situ forming skin substitute was tested for treatment of silicon ring splinted wounds in rats. Methods The in vitro survival and migratory capacity of adipocytes derived from rat micro-fragmented fat when cultured in our 3D nutritional scaffold was examined with a Live/Dead assay. The efficacy of our combined liquid scaffold alone (MF) or with adipose micro fragments (MFA) in treating full thickness splinted wounds in rats was compared to a standard dressing protocol (NT). The healing process was monitored for 10 days. Following wound measurements, histological and immunofluorescent analyses were performed and compared. Results Adipose-derived cells migrated within the 3D nutritional liquid scaffold after 7 and 14 days. The number of red (dead) cells was negligent, indicating cell viability. In vivo, both MFA and MF showed both accelerated and ameliorated wound healing, including complete epithelialization and less immune cell infiltration, compared to the NT control. No significant differences were observed between the MF and MFA groups for any outcome. Conclusions Our findings show that a 3D nutritional liquid skin scaffold is a rich environment for adipocyte viability and migration and that addition of adipose micro fragments to this scaffold can be used as a rich source of cells.
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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) | 0.002 | 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".