Poly-N-Acetyl Glucosamine Nanofibers
Bibliographic record
Abstract
INTRODUCTION: In several fields of surgery, the treatment of complicated tissue defects is an unsolved clinical problem. In particular, the use of tissue scaffolds has been limited by poor revascularization and integration. In this study, we developed a polymer, poly-N-acetyl-glucosamine (sNAG), with bioactive properties that may be useful to overcome these limitations. OBJECTIVE: To develop a scaffold-like membrane with bioactive properties and test the biologic effects in vitro and in vivo in diabetic wound healing. METHODS: In vitro, cells-nanofibers interactions were tested by cell metabolism and migration assays. In vivo, full thickness wounds in diabetic mice (n = 15 per group) were treated either with sNAG scaffolds, with a cellulosic control material, or were left untreated. Wound healing kinetics, including wound reepithelialization and wound contraction as well as microscopic metrics such as tissue growth, cell proliferation (Ki67), angiogenesis (PECAM-1), cell migration (MAP-Kinase), and keratinocyte migration (p 63) were monitored over a period of 28 days. Messenger RNA levels related to migration (uPAR), angiogenesis (VEGF), inflammatory response (IL-1beta), and extracellular matrix remodeling (MMP3 and 9) were measured in wound tissues. RESULTS: sNAG fibers stimulated cell metabolism and the in vitro migratory activity of endothelial cells and fibroblasts. sNAG membranes profoundly accelerated wound closure mainly by reepithelialization and increased keratinocyte migration (7.5-fold), granulation tissue formation (2.8-fold), cell proliferation (4-fold), and vascularization (2.7-fold) compared with control wounds. Expression of markers of angiogenesis (VEGF), cell migration (uPAR) and ECM remodeling (MMP3, MMP9) were up-regulated in sNAG treated wounds compared with controls. CONCLUSIONS: The key mechanism of the bioactive membranes is the cell-nanofiber stimulatory interaction. Engineering of bioactive materials may represent the clinical solution for a number of complex tissue defects.
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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) | 0.001 | 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".