Interaction of bioactive glasses with fibrinogen protein. Effect of porosity and surface modification
Bibliographic record
Abstract
Bioactive glasses are of great interest because of their osteostimulative effects and their intimate bond with bone tissue. Their porosity structuring and surface modification also offer opportunities for controlled adsorption, and further controlled release, of active molecules. These new textural properties and chemical surface modifications undoubtedly have an impact on their interaction with blood proteins. In the present work, we investigate the influence of two parameters, together or separately, mesostructuring porosity and surface modification by silanization, on the interactions of a sol-gel derived bioactive glass (92S6) with the blood protein fibrinogen. Results indicate first that the ordered porosity significantly favors the fibrinogen adsorption at the glass surface. On the other hand, surface silanization induces systematically a delayed adsorption of fibrinogen for at least 8 h. After this period of inhibition of protein adsorption, the protein is better adsorbed on silanized glasses surfaces comparatively to unsilanized surfaces. This effect is particularly noticeable when the glass has no mesostructured porosity. In the case of mesostructured porosity, the surface silanization has a minor impact on the adsorbed quantity of protein after 48 h, the protein adsorption being more influenced by the mesostructured porosity. The study also highlights that a modification of the protein conformation occurs when its approaches the glass surface. This modification is not related to ions release in the medium but really related to surface contact. Additionally, the study shows that the strong fibrinogen adsorption is not associated to the creation of covalent bond between fibrinogen and glass surface but due to a reversible electrostatic interaction. The mesostructured porosity and surface silanization offer new opportunities for tunable properties of the 92S6 bioactive glass.
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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".