Softening bioactive glass for bone regeneration: sol–gel hybrid materials
Bibliographic record
Abstract
There is a clinical need for materials that can stimulate repair of bone by kindling the body's own healing mechanisms. Bone can heal itself if the defect is small, but needs assistance if the defect is over a critical size. It is widely accepted that a temporary template (scaffold) is needed that can act as a guide and stimulus for vascularised bone growth. However, no material exists which fulfils all of the criteria for a bone regeneration scaffold. Although ceramics and glasses have been developed that have excellent biological properties, including pore structures that mimic porous bone, tailored degradation rates, the ability to bond to bone and stimulate new bone growth (bioactive), they are inherently brittle materials and cannot be used in applications that experience cyclic loads. These current bioactive materials must be softened to introduce toughness and plasticity. The obvious way to improve toughness is to make a composite material, using a bioactive ceramic or glass as the inorganic phase within a biodegradable polymer as the organic matrix. Unfortunately, the bioactive particles or fibres are initially covered and are only exposed as the polymer degrades, since the particles degrade at a slower rate. Cells will preferentially attach to these exposed bioactive particles, but further degradation of the polymer can cause inflammation as the particles are released. Commonly US Food and Drug Administration (FDA) approved polyesters degrade catastrophically by self-catalytic hydrolysis, causing the rapid loss of mechanical properties. The degradation can be made more congruent by careful polymer selection and manipulation or by developing specialised types of nanocomposites. This article focuses on the shift in emphasis from hard, brittle matter to durable, tough materials for bone scaffolds, specifically the development of a particular type of nanocomposite: inorganic/organic hybrids synthesised through the sol–gel process. Selected chemical and process challenges are described that must be overcome if they are to become a clinical success.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".