Skin wound healing in diabetic β6 integrin‐deficient mice
Bibliographic record
Abstract
Integrin αvβ6 is a heterodimeric cell surface receptor, which is absent from the normal epithelium, but is expressed in wound-edge keratinocytes during re-epithelialization. However, the function of the αvβ6 integrin in wound repair remains unclear. Impaired wound healing in patients with diabetes constitutes a major clinical problem worldwide and has been associated with the accumulation of advanced glycated endproducts (AGEs) in the tissues. AGEs may account for aberrant interactions between integrin receptors and their extracellular matrix ligands such as fibronectin (FN). In this study, we compared healing of experimental excisional skin wounds in wild-type (WT) and β6-knockout (β6(-/-) ) mice with streptozotocin-induced diabetes. Results showed that diabetic β6(-/-) mice had a significant delay in early wound closure rate compared with diabetic WT mice, suggesting that αvβ6 integrin may serve as a protective role in re-epithelialization of diabetic wounds. To mimic the glycosylated wound matrix, we generated a methylglyoxal (MG)-glycated variant of FN. Keratinocytes utilized αvβ6 and β1 integrins for spreading on both non-glycated and FN-MG, but their spreading was reduced on FN-MG. These findings indicated that glycation of FN and possibly other integrin ligands could hamper keratinocyte interactions with the provisional matrix proteins during re-epithelialization of diabetic wounds.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".