pGlcNAc Nanofiber Treatment of Cutaneous Wounds Stimulate Increased Tensile Strength and Reduced Scarring via Activation of Akt1
Bibliographic record
Abstract
Treatment of cutaneous wounds with poly-N-acetyl-glucosamine containing nanofibers (pGlcNAc), a novel polysaccharide material derived from a marine diatom, results in increased wound closure, antibacterial activities and innate immune responses. We have shown that Akt1 plays a central role in the regulation of these activities. Here, we show that pGlcNAc treatment of cutaneous wounds results in a smaller scar that has increased tensile strength and elasticity. pGlcNAc treated wounds exhibit decreased collagen content, increased collagen organization and decreased myofibroblast content. A fibrin gel assay was used to assess the regulation of fibroblast alignment in vitro. In this assay, fibrin lattice is formed with two pins that provide focal points upon which the gel can exert force as the cells align from pole to pole. pGlcNAc stimulation of embedded fibroblasts results in cellular alignment as compared to untreated controls, by a process that is Akt1 dependent. We show that Akt1 is required in vivo for the pGlcNAc-induced increased tensile strength and elasticity. Taken together, our findings suggest that pGlcNAc nanofibers stimulate an Akt1 dependent pathway that results in the proper alignment of fibroblasts, decreased scarring, and increased tensile strength during cutaneous wound healing.
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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) | 0.002 | 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".