In vitro glycation of a tissue‐engineered wound healing model to mimic diabetic ulcers
Bibliographic record
Abstract
Diabetic foot ulcers are a major complication of diabetes that occurs following minor trauma. Diabetes-induced hyperglycemia is a leading factor inducing ulcer formation and manifests notably through the accumulation of advanced glycation end-products (AGEs) such as N-carboxymethyl-lysin. AGEs have a negative impact on angiogenesis, innervation, and reepithelialization causing minor wounds to evolve into chronic ulcers which increases the risks of lower limb amputation. However, the impact of AGEs on wound healing is difficult to model (both in vitro on cells, and in vivo in animals) because it involves a long-term toxic effect. We have developed a tissue-engineered wound healing model made of human keratinocytes, fibroblasts, and endothelial cells cultured in a collagen sponge biomaterial. To mimic the deleterious effects induced by glycation on skin wound healing, the model was treated with 300 µM of glyoxal for 15 days to promote AGEs formation. Glyoxal treatment induced carboxymethyl-lysin accumulation and delayed wound closure in the skin mimicking diabetic ulcers. Moreover, this effect was reversed by the addition of aminoguanidine, an inhibitor of AGEs formation. This in vitro diabetic wound healing model could be a great tool for the screening of new molecules to improve the treatment of diabetic ulcers by preventing glycation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".