Fragmented Dermo-Epidermal Units (FdeU) as an Emerging Strategy to Improve Wound Healing Process: An In Vitro Evaluation and a Pilot Clinical Study
Bibliographic record
Abstract
Innovative strategies have shown beneficial effects in healing wound management involving, however, a time-consuming and arduous process in clinical contexts. Micro-fragmented skin tissue acts as a slow-released natural scaffold and continuously delivers growth factors, and much other modulatory information, into the microenvironment surrounding damaged wounds by a paracrine function on the resident cells which supports the regenerative process. In this study, in vitro and in vivo investigations were conducted to ascertain improved effectiveness and velocity of the wound healing process with the application of fragmented dermo-epidermal units (FdeU), acquired via a novel medical device (Hy-Tissue® Micrograft Technology). MTT test; LDH test; ELISA for growth factor investigation (IL) IL-2, IL-6, IL-7 IL-8, IL-10; IGF-1; adiponectin; Fibroblast Growth Factor (FGF); Vascular Endothelial Growth Factor (VEGF); and Tumor Necrosis Factor (TNF) were assessed. Therefore, clinical evaluation in 11 patients affected by Chronic Wounds (CW) and treated with FdeU were investigated. Functional outcome was assessed pre-operatory, 2 months after treatment (T0), and 6 months after treatment (T1) using the Wound Bed Score (WBS) and Vancouver Scar Scale (VSS). In this current study, we demonstrate the potential of resident cells to proliferate from the clusters of FdeU seeded in a monolayer that efficiently propagate the chronic wound. Furthermore, in this study we report how the discharge of trophic/reparative proteins are able to mediate the in vitro paracrine function of proliferation, migration, and contraction rate in fibroblasts and keratinocytes. Our investigations recommend FdeU as a favorable tool in wound healing, displaying in vitro growth-promoting potential to enhance current therapeutic mechanisms.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".