Plasma functionalization of poly(vinyl alcohol) hydrogel for cell adhesion enhancement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 0.001 |
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 it