SerpinA3N accelerates wound closure in a murine model of impaired wound healing (413.1)
Bibliographic record
Abstract
Granzyme B (GzmB) is a serine protease that accumulates in the extracellular milieu during chronic inflammation and cleaves many extracellular matrix (ECM) proteins that are essential for proper wound healing to ensue. We hypothesized that GzmB contributes to the pathogenesis of impaired wound healing observed in the diabetic population through excessive degradation of the ECM. On the dorsal back of genetically‐induced type II diabetic db/db mice, 10 mm excisional wounds were created and covered with semi‐transparent dressing. SerpinA3N, a GzmB inhibitor, was administered topically and/or subcutaneously on the wounds every 3 days. GzmB was found to co‐localize with mast cells in wound sites at the dermal‐epidermal junction. In addition, preliminary results suggest that administration of serpinA3N accelerates wound closure as by day 35, 100% of serpinA3N‐treated compared to 55% of vehicle‐treated mice have fully re‐reepithelialized. In serpinA3N‐treated animals, an increase in full‐length ECM proteins involved in collagen fibrillogenesis was observed along with fewer cleavage fragments by immunostaining. These findings suggest that GzmB contributes to the pathogenesis of diabetic wound healing through the proteolytic cleavage of ECM proteins that are essential for normal wound closure. Grant Funding Source : Supported by Canadian Diabetes Association and Canadian Institutes of Health Research
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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 | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".