Deposition of organic−inorganic hybrid coatings over 316L surgical stainless steel and evaluation on vascular cells
Bibliographic record
Abstract
Surface coating of metallic materials using the sol-gel technique is a suitable approach to obtain hybrid materials with improved properties for biomedical applications. In this study, an AISI 316L stainless steel surface was coated with ormosils prepared from tetraethylsiloxane and 3-glycidoxypropyltrimethoxysilane or polydimethylsiloxane. The characterization of structural and surface properties was performed by several techniques. Surface microstructure, morphology, and energy are dependent on organosilane type and content. Chemical stability of coatings was investigated by static immersion tests in phosphate buffer solution at 37 °C, and silicon leaching after 21 days was found to be in the range of ∼200−300 μg L−1. Mechanical adhesion was found to be within 1.0 and 3.7 N cm−1. The interaction of the samples and materials in the cardiovascular environment was investigated through cellular behavior. Biological assays were performed with slides to avoid any cytotoxic effects on human endothelial cells (HUVEC) and rabbit arterial smooth muscle cells (RASM). No significant alterations were observed after 24 h in the viability of RASM and HUVEC cells exposed to different coatings. No increase of HUVEC or RASM migration was observed after 24 h as evaluated by transwell migration assay. The hybrid materials showed suitable properties for potential application as biomaterials in cardiovascular environment as well as for incorporation of bioactive species with the aim to prepare drug-eluting stents.
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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".