Adsorption of Amorphous Silica Nanoparticles onto Hydroxyapatite Surfaces Differentially Alters Surfaces Properties and Adhesion of Human Osteoblast Cells
Bibliographic record
Abstract
Silicon (Si) is suggested to be an important/essential nutrient for bone and connective tissue health. Silicon-substituted hydroxyapatite (Si-HA) has silicate ions incorporated into its lattice structure and was developed to improve attachment to bone and increase new bone formation. Here we investigated the direct adsorption of silicate species onto an HA coated surface as a cost effective method of incorporating silicon on to HA surfaces for improved implant osseointegration, and determined changes in surface characteristics and osteoblast cell adhesion. Plasma-sprayed HA-coated stainless steel discs were incubated in silica dispersions of different concentrations (0-42 mM Si), at neutral pH for 12 h. Adsorbed Si was confirmed by XPS analysis and quantified by ICP-OES analysis following release from the HA surface. Changes in surface characteristics were determined by AFM and measurement of surface wettability. Osteoblast cell adhesion was determined by vinculin plaque staining. Maximum Si adsorption to the HA coated disc occurred after incubation in the 6 mM silica dispersion and decreased progressively with higher silica concentrations, while no adsorption was observed with dispersions below 6 mM Si. Comparison of the Si dispersions that produced the highest and lowest Si adsorption to the HA surface, by TEM-based analysis, revealed an abundance of small amorphous nanosilica species (NSP) of ~1.5 nm in diameter in the 6 mM Si dispersion, with much fewer and larger NSP in the 42 mM Si dispersions. 29Si-NMR confirmed that the NSPs in the 6 mM silica dispersion were polymeric and similar in composition to the larger NSPs in the 42 mM Si dispersion, suggesting that the latter were aggregates of the former. Amorphous NSP adsorbed from the 6 mM dispersion on to a HA-coated disc surface increased the surface's water contact angle by 53°, whereas that adsorbed from the 42 mM dispersion decreased the contact angle by 18°, indicating increased and decreased hydrophobicity, respectively. AFM showed an increase in surface roughness of the 6 mM Si treated surface, which correlated well with an increase in number of vinculin plaques. These findings suggest that NSP of the right size (relative to charge) adsorb readily to the HA surface, changing the surface characteristics and, thus, improving osteoblast cell adhesion. This treatment provides a simple way to modify plasma-coated HA surfaces that may enable improved osseointegration of bone implants.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".