Plasma functionalization of poly(vinyl alcohol) hydrogel for cell adhesion enhancement
Bibliographic record
Abstract
Tailoring the interface interactions between a biomaterial and the surrounding tissue is a capital aspect to consider for the design of medical devices. Poly(vinyl alcohol) (PVA) hydrogels present suitable mechanical properties for various biological substitutes, however the lack of cell adhesion on their surface is often a problem. The common approach is to incorporate biomolecules, either by blending or coupling. But these modifications disrupt PVA intra- and intermolecular interactions leading therefore to a loss of its original mechanical properties. In this work, surface modification by glow discharge plasma, technique known to modify only the surface without altering the bulk properties, has been investigated to promote cell attachment on PVA substrates. N2/H2 microwave plasma treatment has been performed, and the chemical composition of PVA surface has been investigated. X-ray photoelectron and Fourier transform infrared analyses on the plasma-treated films revealed the presence of carbonyl and nitrogen species, including amine and amide groups, while the main structure of PVA was unchanged. Plasma modification induced an increase in the PVA surface wettability with no significant change in surface roughness. In contrast to untreated PVA, plasma-modified films allowed successful culture of mouse fibroblasts and human endothelial cells. These results evidenced that the grafting was stable after rehydration and that it displayed cell adhesive properties. Thus plasma amination of PVA is a promising approach to improve cell behavior on contact with synthetic hydrogels for tissue engineering.
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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".