Fibrin biomatrix-conjugated platelet-derived growth factor AB accelerates wound healing in severe thermal injury
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Bibliographic record
Abstract
Controlled delivery of growth factors from biodegradable biomatrices could accelerate and improve impaired wound healing. The study aim was to determine whether platelet-derived growth factor AB (PDGF.AB) with a transglutaminase (TG) crosslinking substrate site released from a fibrin biomatrix improves wound healing in severe thermal injury. The binding and release kinetics of TG-PDGF.AB were determined in vitro. Third-degree contact burns (dorsum of Yorkshire pigs) underwent epifascial necrosectomy 24 h post-burn. Wound sites were covered with autologous meshed (3:1) split-thickness skin autografts and either secured with staples or attached with sprayed fibrin sealant (FS; n = 8/group). TG-PDGF.AB binds to the fibrin biomatrix using the TG activity of factor XIIIa, and is subsequently released through enzymatic cleavage. Three doses of TG-PDGF.AB in FS (100 ng, 1 µg and 11 µg/ml FS) were tested. TG-PDGF.AB was bound to the fibrin biomatrix as evidenced by western blot analysis and subsequently released by enzymatic cleavage. A significantly accelerated and improved wound healing was achieved using sprayed FS containing TG-PDGF.AB compared to staples alone. Low concentrations (100 ng-1 µg TG-PDGF.AB/ml final FS clot) demonstrated to be sufficient to attain a nearly complete closure of mesh interstices 14 days after grafting. TG-PDGF.AB incorporated in FS via a specific binding technology was shown to be effective in grafted third-degree burn wounds. The adhesive properties of the fibrin matrix in conjunction with the prolonged growth factor stimulus enabled by this binding technology could be favourable in many pathological situations associated with wound-healing disturbances. Copyright © 2013 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it