In vitro reactivity of Cu doped 45S5 Bioglass® derived scaffolds for bone tissue engineering
Bibliographic record
Abstract
Cu-doped 45S5 bioactive glasses with varying Cu contents were fabricated and used to process 3D porous scaffolds via the foam replica technique. Cu-doping results in the weakening of the glass network and a decrease in its glass transition temperature. Acellular in vitro studies revealed very high bioactivity independent of Cu doping as indicated by the fast formation of a carbonated hydroxyapatite layer (CHA) on scaffold surfaces after immersion in simulated body fluid (SBF). The kinetics of the glass-ceramic scaffold's transition to an amorphous calcium phosphate layer (ACP) and the crystallisation of CHA were explored by FT-IR and SEM analyses. The elemental distribution in the scaffold/fluid interface region was monitored by the advanced micro-PIXE-RBS (particle induced X-ray emission/Rutherford backscattering spectrometry) method. Cu-containing glasses showed slower release of Si, Ca and P from the scaffold periphery, whereas traces of Cu were found incorporated in the CaP layer on the scaffold surface. Cu release kinetics from the scaffolds in SBF were found to depend on culturing conditions while highest Cu concentrations of ∼3.1 ppm and ∼4.6 ppm under static and quasi-dynamic conditions, respectively, were observed. Since Cu exhibits potential angiogenic and osteogenic properties, the Cu-containing scaffolds are suggested as promising materials for bone tissue engineering applications.
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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".