Fabrication and characterization of a novel carbon fiber-reinforced calcium phosphate silicate bone cement with potential osteo-inductivity
Bibliographic record
Abstract
The repair of bone defects is still a pressing challenge in clinics. Injectable bone cement is regarded as a promising material to solve this problem because of its special self-setting property. Unfortunately, its poor mechanical conformability, unfavorable osteo-genesis ability and insufficient osteo-inductivity seriously limit its clinical application. In this study, novel experimental calcium phosphate silicate bone cement reinforced by carbon fibers (CCPSC) was fabricated and characterized. First, a compressive strength test and cell culture study were carried out. Then, the material was implanted into the femoral epiphysis of beagle dogs to further assess its osteo-conductivity using a micro-computed tomography scan and histological analysis. In addition, we implanted CCPSC into the beagles' intramuscular pouches to perform an elementary investigation of its osteo-inductivity. The results showed that incorporation of carbon fibers significantly improved its mechanical properties. Meanwhile, CCPSC had better biocompatibility to activate cell adhesion as well as proliferation than poly-methyl methacrylate bone cement based on the cell culture study. Moreover, pronounced biodegradability and improved osteo-conductivity of CCPSC could be observed through the in vivo animal study. Finally, a small amount of osteoid was found at the heterotopic site one month after implantation which indicated potential osteo-inductivity of CCPSC. In conclusion, the novel CCPSC shows promise as a bioactive bone substitute in certain load-bearing circumstances.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".