Influence of mixed organosilane coatings with variable RGD surface densities on the adhesion and proliferation of human osteosarcoma Saos-2 cells to magnesium alloy AZ31
Bibliographic record
Abstract
In the last decade, the use of magnesium and its alloys as biodegradable implant materials has become increasingly accepted. However, surface modification of these materials to control the degradation rate in the early stages of healing and improve their biocompatibility is crucial to the successful implementation of magnesium alloy implants in medicine. Cell adhesion and proliferation at the implant surface is a vital factor for successful integration of a biomaterial within the body. Cells accomplish this task by binding to ligands such as the arginine-glycine-aspartic acid peptide sequence (RGD) commonly found on adhesive proteins present in the extracellular matrix. In this paper, we report a biomimetic surface modification strategy involving deposition of a mixed organosilane layer on Mg AZ31 followed by covalent immobilization of RGD peptides through a heterobifunctional cross-linker molecule. Our results indicate that with optimized deposition conditions uniform organosilane coatings were successfully deposited on the Mg AZ31 substrate. Furthermore, we have demonstrated that the surface density of immobilized RGD can be varied by depositing organosilane layers from solutions containing two different organosilanes in specified ratios. Increases in cell adhesion and cell proliferation were observed on the surface modified substrates.
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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".