Serpina3n accelerates tissue repair in a diabetic mouse model of delayed wound healing
Bibliographic record
Abstract
Chronic, non-healing wounds are a major complication of diabetes and are characterized by chronic inflammation and excessive protease activity. Although once thought to function primarily as a pro-apoptotic serine protease, granzyme B (GzmB) can also accumulate in the extracellular matrix (ECM) during chronic inflammation and cleave ECM proteins that are essential for proper wound healing, including fibronectin. We hypothesized that GzmB contributes to the pathogenesis of impaired diabetic wound healing through excessive ECM degradation. In the present study, the murine serine protease inhibitor, serpina3n (SA3N), was administered to excisional wounds created on the dorsum of genetically induced type-II diabetic mice. Wound closure was monitored and skin wound samples were collected for analyses. Wound closure, including both re-epithelialization and contraction, were significantly increased in SA3N-treated wounds. Histological and immunohistochemical analyses of SA3N-treated wounds revealed a more mature, proliferative granulation tissue phenotype as indicated by increased cell proliferation, vascularization, fibroblast maturation and differentiation, and collagen deposition. Skin homogenates from SA3N-treated wounds also exhibited greater levels of full-length intact fibronectin compared with that of vehicle wounds. In addition, GzmB-induced detachment of mouse embryonic fibroblasts correlated with a rounded and clustered phenotype that was prevented by SA3N. In summary, topical administration of SA3N accelerated wound healing. Our findings suggest that GzmB contributes to the pathogenesis of diabetic wound healing through the proteolytic cleavage of fibronectin that is essential for normal wound closure, and that SA3N promotes granulation tissue maturation and collagen deposition.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".