Dissolution and bioactivity of a sol-gel derived borate glass in six different solution media
Bibliographic record
Abstract
Abstract Sol-gel derived bioactive borate glasses (SGBGs) rapidly convert to hydroxycarbonated apatite (HCA) in simulated body fluid (SBF), in vitro. While previous studies have examined the influence of processing and composition on bioactivity, the effect of the in vitro dissolution media has not been well examined for these glasses. In this study, the mineral conversion of a SGBG substituted 45S5 Bioglass® formulation (“B46”, (46.1)B2O3-(26.9)CaO-(24.4)Na2O-(2.6)P2O5, mol%), was examined in six different dissolution media: SBF, tris(hydroxymethyl)aminomethane (TRIS, pH 7.4) buffer, Dulbecco’s phosphate buffered saline (PBS, 1X), Dulbecco’s Modified Eagle Medium (DMEM, 1X), 0.9% Saline (SAL), and deionized water (DIW) at 1.5 mg/mL for 10 min, 2h, and 1d. All media underwent a rapid increase in pH as a result of glass dissolution and ion release. B46 in SBF, TRIS, and PBS converted to HCA while B46 in DMEM, SAL, and DIW converted to calcite according to attenuated total reflectance-Fourier-transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy. The ratio of B46 to SBF was also examined at 3, 6, 12, and 24 mg/mL for 1d. These results help elucidate the dissolution and mineral conversion of borate glasses and help provide insights into optimizing pre-conditioning treatments for both in vitro and in vivo analyses.
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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".