Stability of bioactive bone graft substitutes exposed to different aging and sterilization conditions
Bibliographic record
Abstract
Abstract Bioactive glasses have been used for many years as bone graft substitutes in orthopedic and dental applications as well as an additive in toothpastes, cosmetics, and cosmeceutical products. The interest of using bioactive glass comes from its ability to dissolve and release dissolution products that stimulate bone regeneration. Porous bioactive glass scaffolds that can provide structural support while bone is growing into the structure have generated interest. However, little data is available in the literature on the effect of environmental conditions or sterilization treatments on the structure and properties of these materials. This study presents the evolution of the structure and microstructure of bioactive foams exposed to different accelerated and real‐time aging conditions and sterilization treatments. The results indicate that the material is relatively stable. For example, different sterilization methods (steam, ethylene oxide, hydrogen peroxide, gamma‐rays) have limited effect on the structure and properties of the foams. However, carbonate species may form on the surface of the material when exposed to CO2 and humidity. Some carbonates dissolve rapidly in water and may impact the pH of the solution. Adequate packaging should limit the reaction of the bioactive glass with CO2 and humidity and the formation of carbonate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".