Regulation of Gene Expression by Short Poly‐N‐Acetyl Glucosamine (sNAG) Nanofibers in Endothelial Cells and Keratinocytes
Bibliographic record
Abstract
O‐GlcNAcylation is a catalyzed transfer of N‐acetylglucosamine to protein substrates. This modification aids cells in regulation of many crucial cellular processes. Short poly poly‐N‐acetyl glucosamine nanofibers (sNAG) enhance wound healing, by cell migration, and hemostasis, by promoting fibrin matrix and clot formation. This study investigated the effects of sNAG on global gene expression using the SOLiD 5500xl Next generation sequencing system in both HUVEC (Human Umbilical Vein Endothelial Cells) and HaCaT (Human keratinocytes) focussing on those having roles in angiogenesis and wound healing. In HUVEC cells, 1,705 genes had at least 2‐fold up‐or down‐regulation upon sNAG treatment. Genes which remained up‐regulated at both 1 and 6 hours of treatment were NFKBIZ, defensins and wound heaIing responses. Many of the collagen family of proteins, E‐Selectin, ICAM1, and cytokines, CXCL1 and CCL2, were up‐regulated transiently. In HaCaT cells, 2,329 genes showed 2‐fold up regulation in response to sNAG (1 or 6 hours) and 2,487 genes showed 2‐fold down regulation. Genes that were up‐regulated over the 6 hour treatment include desmogleins and keratins, suggesting regulation of keratinocyte differentiation and re‐epithelialization. Responses to sNAG observed in this study add support to its role in angiogenesis and wound healing pathways at the level of regulation of gene expression.
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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".