Bioactive Glass 45S5 Powders: Effect of Synthesis Route and Resultant Surface Chemistry and Crystallinity on Protein Adsorption from Human Plasma
Bibliographic record
Abstract
Despite its medical applications, the mechanisms responsible for the osseointegration of bioactive glass (45S5) have yet to be fully understood. Evidence suggests that the strongest predictor for osseointegration of bioactive glasses, and ceramics, with bone tissue as the formation of an apatitic calcium phosphate layer atop the implanted material, with osteoblasts being the main mediator for new bone formation. Most have tried to understand the formation of this apatitic calcium phosphate layer, and other bioresponses between the host and bioactive glass 45S5 using Simulated Body Fluid; a solution containing ion concentrations similar to that found in human plasma without the presence of proteins. However, it is likely that cell attachment is probably largely mediated via the adsorbed protein layer. Plasma protein adsorption at the tissue bioactive glass interface has been largely overlooked. Herein, we compare crystalline and amorphous bioactive glass 45S5, in both melt-derived as well as sol-gel forms. Thus, allowing for a detailed understanding of both the role of crystallinity and powder morphology on surface ions, and plasma protein adsorption. It was found that sol-gel 45S5 powders, regardless of crystallinity, adsorbed 3-5 times as much protein as the crystalline melt-derived counterpart, as well as a greater variety of plasma proteins. The devitrification of melt-cast 45S5 resulted in only small differences in the amount and variety of the adsorbed proteome. Surface properties, and not material crystallinity, play a role in directing protein adsorption phenomena for bioactive glasses given the differences found between crystalline melt-cast 45S5 and sol-gel derived 45S5.
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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".