Improving Cutaneous Wound Healing in Diabetic Mice Using Naturally Derived Tissue‐Engineered Biological Dressings Produced under Serum‐Free Conditions
Bibliographic record
Abstract
Long‐term diabetes often leads to chronic wounds refractory to treatment. Cell‐based therapies are actively investigated to enhance cutaneous healing. Various cell types are available to produce biological dressings, such as adipose‐derived stem/stromal cells (ASCs), an attractive cell source considering their abundancy, accessibility, and therapeutic secretome. In this study, we produced human ASC‐based dressings under a serum‐free culture system using the self‐assembly approach of tissue engineering. The dressings were applied every 4 days to full‐thickness 8‐mm splinted skin wounds created on the back of polygenic diabetic NONcNZO10/LtJ mice and streptozotocin‐induced diabetic K14‐H2B‐GFP mice. Global wound closure kinetics evaluated macroscopically showed accelerated wound closure in both murine models, especially for NONcNZO10/LtJ; the treated group reaching 98.7% ± 2.3% global closure compared to 76.4% ± 11.8% for the untreated group on day 20 ( p = 0.0002). Histological analyses revealed that treated wounds exhibited healed skin of better quality with a well‐differentiated epidermis and a more organized, homogeneous, and 1.6‐fold thicker granulation tissue. Neovascularization, assessed by CD31 labeling, was 2.5‐fold higher for the NONcNZO10/LtJ treated wounds. We thus describe the beneficial impact on wound healing of biologically active ASC‐based dressings produced under an entirely serum‐free production system facilitating clinical translation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".