Engineering diabetic human skin equivalent for in vitro and in vivo applications
Bibliographic record
Abstract
The prevalence of diabetes is increasing worldwide. Diabetes contributes to 70% of all non-traumatic lower-limb amputations, which are directly caused by diabetic foot ulcers (DFU) that are difficult to heal. Non-healing diabetic ulcers represent one of modern society’s most difficult medical challenges. One of the promising initiatives to treat DFU is the grafting of autologous skin or stimulating the skin cells at the edge of the wound to proliferate and close the wound. The present study was to engineer a diabetic human skin equivalent (DHSE) that contains fibroblasts and keratinocytes extracted from the skin collected from diabetic patients. The DHSE was used to investigate whether exposure to low-intensity electrical stimulation (ES) could promote diabetic cell activity. The ES was generated by a direct current (DC) electric field of 20 or 40 mV/mm. We demonstrated that the fibroblasts and keratinocytes could be extracted from older diabetics, cultured, and used to engineer DHSE. Interestingly, the exposure of DHSE to ES led to a structural improvement through tissue stratification, increased Ki-67 expression, and the deposition of basement membrane proteins (laminin and type IV collagen). The DHSE exposed to ES showed a high level of keratin 5 and 14 expressions in the basal and supra-basal layers. The keratinocyte proliferation was supported by an increased secretion of the keratinocyte growth factor (FGF-7). Exposure to ES decreased the activity of metalloproteinases (MMP) 2 and 9. In conclusion, we extracted keratinocytes and fibroblasts from the skin of diabetic-old donors. These cells were used to engineer skin equivalents and demonstrate that ES can promote diabetic wound healing. This DHSE can be a promising tool for various in vitro studies to understand the wound healing mechanisms under chronic inflammatory conditions such as diabetes. The DHSE could also be used as an autologous substrate to cover the DFU permanently.
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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".