Hydoxyapatite/beta‐tricalcium phosphate biphasic ceramics as regenerative material for the repair of complex bone defects
Bibliographic record
Abstract
Bone is a composite material composed of collagen and calcium phosphate (CaP) mineral. The collagen gives bone its flexibility while the inorganic material gives bone its resilience. The CaP in bone is similar in composition and structure to the mineral hydroxyapatite (HA) and is bioactive, osteoinductive and osteoconductive. Therefore synthetic versions of bone apatite (BA) have been developed to address the demand for autologous bone graft substitutes. Synthetic HA (s-HA) are stiff and strong, but brittle. These lack of physical attributes limit the use of synthetic apatites in situations where no physical loading of the apatite occurs. s-HA chemical properties differ from BA and thus change the physical and mechanical properties of the material. Consequently, s-HA is more chemically stable than BA and thus its resorption rate is slower than the rate of bone regeneration. One solution to this problem is to introduce a faster resorbing CaP, such as β-tricalcium phosphate (β-TCP), when synthesizing the material creating a biphasic (s-HA and β-TCP) formulation of calcium phosphate (BCP). The focus of this review is to introduce the major differences between BCP and biological apatites and how material scientists have overcome the inadequacies of the synthetic counterparts. Examples of BCP performance in vitro and in vivo following structural and chemical modifications are provided as well as novel ultrastructural data. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 2493-2512, 2018.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".