Inhibition of PAI-1 Via PAI-039 Improves Dermal Wound Closure in Diabetes
Bibliographic record
Abstract
Diabetes impairs the ability to heal cutaneous wounds, leading to hospitalization, amputations, and death. Patients with diabetes experience elevated levels of plasminogen activator inhibitor 1 (PAI-1), regardless of their glycemic control. It has been demonstrated that PAI-1-deficient mice exhibit improved cutaneous wound healing, and that PAI-1 inhibition improves skeletal muscle repair in mice with type 1 diabetes mellitus, leading us to hypothesize that pharmacologically mediated reductions in PAI-1 using PAI-039 would normalize cutaneous wound healing in streptozotocin (STZ)-induced diabetic (STZ-diabetic) mice. To simulate the human condition of variations in wound care, wounds were aggravated or minimally handled postinjury. Following cutaneous injury, PAI-039 was orally administered twice daily for 10 days. Compared with nondiabetic mice, wounds in STZ-diabetic mice healed more slowly. Wound site aggravation exacerbated this deficit. PAI-1 inhibition had no effect on dermal collagen levels or wound bed size. PAI-039 treatment failed to improve angiogenesis in the wounds of STZ-diabetic mice and blunted angiogenesis in the wounds of nondiabetic mice. Importantly, PAI-039 treatment significantly improved epidermal cellular migration and wound re-epithelialization compared with vehicle-treated STZ-diabetic mice. These findings support the use of PAI-039 as a novel therapeutic agent to improve diabetic wound closure and demonstrate the primary mechanism of its action to be related to epidermal closure.
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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) | 0.000 | 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 | 0.001 |
| 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".