Bioactive Glass Scaffolds with Hierarchical Structure and their 3D Characterization
Bibliographic record
Abstract
Bone tissue has evolved into hierarchical three-dimensional structures with dimensions ranging from nanometres to metres. The structure varies depending on the site in the body, which is dictated by the loading environment. Medically, bone is one of the most replaced body parts (second only to blood) but replicating these complex living hierarchical structures for the purpose of regenerating defective bone is a challenge that has yet to be overcome. A temporary template (scaffold) is needed that matches the hierarchical structure of native bone as closely as possible that is available ‘off the shelf’ for surgeons to use. After implantation the scaffold must bond to bone and stimulate not only three dimensional (3D) bone growth, but also vascularisation to feed the new bone. There are many engineering design criteria for a successful bone scaffold and bioactive glass foam scaffolds have been developed that can fulfil most of them, as they have a hierarchical porous structure, they can bond to bone, and they release soluble silica species and calcium ions that have been found to up-regulate seven families of genes in osteogenic cells. Other ions have also been incorporated to combat infection and to counteract osteoporosis. Their tailorable hierarchical structure consists of highly interconnected open spherical macropores, further, because the glass is sol-gel derived, the entire structure is nanoporous. The macropores are critical for bone and blood vessel growth, the nanopores for tailoring degradation rates and protein adsorption and for cell attachment. This chapter describes the optimised sol-gel foaming process and how bone cells respond to them. Whatever type of scaffold is used for bone regeneration, it is critically important to be able to quantify the hierarchial pore structure. The nanopore size can be quantified using gas sorption, but to obtain full information of the macropore structure, imaging must be done using X-ray microtomography and the resulting images must be quantified via 3D image analysis. These techniques are reviewed.
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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 | 0.001 | 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) | 0.001 | 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".