524 A Multi-functional Liquid Skin Substitute in Wound Healing
Bibliographic record
Abstract
The use of autologous meshed grafts saves lives of patients with large burn injuries. However, ungrafted areas remain open with a high chance of infection, fluid and heat loss and formation of devastating fishnet-like irregular scars. To overcome these difficulties, here, we formulated a shelf ready multifunctional powdered re-constituteable liquid skin substitute referred as to MeshFill to fill up void burn areas in a meshed graft. Prior to its use in a clinical setting, in this study, the functionality of filling up non-contractile splinted wounds with MeshFill on wound closer, epithelialization and healing quality was examined. A total of 32 punch wounds (6mm) were generated on the back of 8 mice (4 wounds/ mouse) and they were splinted to prevent contraction. Half of these wounds received nothing (Untreated control) and another half were filled up with MeshFill (MF). Mepitel was used as wound coverage to prevent adhesion and non-adhesive gauze was sutured over the Mepitel dressing. Wounds were daily monitored for healing, dressing and photographed on Day 7 and 14 post application. On day 7 and 14, mice were terminated and wound areas were harvested and stained for infiltrated immune cells (CD45+ cells), tissue histology and cellularity. The results showed a significantly faster epithelialization and wound closure of splinted wounds received MeshFill as compared to those of control. The findings further showed an early appearance and clearance of infiltrated immune cells (CD45+ cells) in treated wounds as compared to control indicating that the inflammation phase was shifted to early time points of healing process. The results demonstrated that the use of in situ forming scaffold accelerates wound closure and shifting the inflammation phase to an earlier time points in a delayed splinted wound model in mice. Proving the functionality of our novel liquid skin substitute in splinted wounds will set the stage for its use in a clinical setting in which void areas of autologous meshed graft can be filled up and improve the healing quality of burn injuries. Proving the functionality of our novel liquid skin substitute in splinted wounds will set the stage for its use in a clinical setting in which void areas of autologous meshed graft can be filled up and improve the healing quality of burn injuries.
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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".