The effect of methacrylic acid in smooth coatings on dTHP1 and HUVEC gene expression
Bibliographic record
Abstract
Without any external additives such as growth factors, polymer beads containing methacrylic acid (MAA) promoted functional vascularization in vivo leading to faster cutaneous wound healing in diabetic mice and improved skin graft integration in Wistar rats. The aim of this work is to understand this material-driven vascularization by investigating the effect of polymer MAA-content, in the absence of surface roughness, on the behaviour of macrophage-like and endothelial cells. Smooth polymer films containing 20, 30 or 40% MAA or methyl methacrylate as a control copolymerized with isodecyl acrylate, were synthesized to study the effect of MAA content in smooth films, without roughness. Macrophage-like cells (dTHP1) incubated on 40% MAA films for 96 hours increased the expression of the angiogenic genes HIF1α and SDF1α, and of the inflammatory genes IL1β, IL6 and TNFα, while decreasing the expression of osteopontin. Endothelial cells (HUVEC) on 40% MAA films for 96 hours increased the expression of the angiogenic genes MMP9 and CXCR4, and of osteopontin. In dTHP1 cells, principal component analysis established a positive correlation between MAA polymer content, HIF1α expression and the expression of IL6, IL1β and TNFα, suggesting that HIF1α and NF-κB pathway may be involved. It was found that MAA chemistry, without topographical differences, promoted changes in gene expression in macrophage-like and endothelial cells. This effect was more significant above a threshold between 30 to 40% MAA. The amount of MAA in the copolymer likely promoted the cell responses, future work will study the effects of varying MAA content. The 40% MAA coatable material developed in this work may also be of interest as a coating to improve the integration of medical devices.
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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".