Poly(ester amide)–Bioactive Glass Hybrid Biomaterials for Bone Regeneration and Biomolecule Delivery
Bibliographic record
Abstract
Designing bioactive materials for repairing or regenerating bone defects is an active area of research and discovery. Despite advances made in sol–gel-derived hybrid biomaterials design, three challenges remain: (i) the choice of biodegradable polymers that can form a homogeneous solution in the presence of water is very limited, (ii) low-temperature (below 50 °C) incorporation of calcium into the inorganic matrix while having molecular-level mixing has proven to be a difficult task, and (iii) incorporation of drug-loaded mesoporous nanoparticles into polymer–bioactive glass hybrid scaffolds has not been achieved. In this study, we developed bioactive biomaterials for bone repair/regeneration from an α-amino acid-derived biodegradable poly(ester amide) (PEA) and a tertiary bioglass (SiO 2 –CaO–P 2 O 5 ), where calcium was incorporated into the glass network at ambient temperature. Furthermore, drug-loaded functional mesoporous silica nanoparticles prepared by surfactant templating were successfully incorporated into PEA–bioglass porous scaffolds. The resulting homogenous single-phase materials showed deposition of hydroxyapatite on their surfaces, supported mesenchymal stem cell attachment and proliferation, and showed a sustained and slow release of a model compound. Taken together, these biomaterials have the potential to be used as a bifunctional platform for bone regeneration via ion release and biomolecule delivery.
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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".