Development of a nanofibrous wound dressing with an antifibrogenic properties <i>in vitro</i> and <i>in vivo</i> model
Bibliographic record
Abstract
Dermal fibrosis, characterized by excessive extracellular matrix (ECM), is a pathological condition with limited effective therapeutic modalities. Lack of an antiscarring dressing further impedes the preventive measures for this condition. Here, we develop a new antiscarring dressing and investigate its potential as a slow-releasing vehicle for kynurenic acid (KynA), an antifibrotic agent. KynA was incorporated into polymethyl methacrylate (PMMA) nanofibers, containing increasing concentration of polyethylene glycol (PEG). Fibre morphology, water absorption capacity, surface hydrophilicity, in vitro drug release profile, and in vivo antifibrotic effects were investigated. Increasing concentrations of PEG (1-20%) significantly increased surface hydrophilicity, water absorption capacity, and drug release. Based on the obtained release profiles, PMMA + 10% PEG was the preferred formulation for sustained KynA release up to 120 hours. In vitro studies confirmed the preservation of KynA antifibrotic properties during electrospinning, indicated by fibroblasts proliferation suppression and ECM expression modulation. In vivo application of KynA-incorporated films significantly inhibited collagen (23.89 ± 4.79 vs. 6.99 ± 0.41, collagen-I/β-actin mRNA expression, control vs. treated) and fibronectin expression (7.18 ± 1.09 vs. 2.31 ± 0.05, fibronectin/β-actin mRNA expression, control vs. treated) and enhanced the production of an ECM-degrading enzyme (2.03 ± 0.88 vs. 11.88 ± 1.16 MMP-1/β-actin mRNA expression, control vs. treated). The fabricated KynA-incorporated films can be exploited as antifibrotic wound dressings. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 104A: 2334-2344, 2016.
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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 | 0.000 |
| 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 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".