Osteogenic and anti-osteoporotic effects of risedronate-added calcium phosphate silicate cement
Bibliographic record
Abstract
Osteoporosis greatly impairs bone fracture restoration with bone cement because the accelerated resorption decreases the osseointegration between bone and implants. In this study, we designed a new drug delivery system based on the third generation bisphosphonate risedronate (RA) and the osteogenic calcium phosphate silicate cement (CPSC). The impact of RA on CPSC's material properties and microstructure was evaluated by different characterization methods (μCT, XRD, FTIR, SEM and gas sorption). In addition, in vitro biocompatibility of RA-added CPSC was evaluated (MTT assay, flow cytometry, real-time PCR). In an in vivo study of osteoporotic rabbits, osteoporosis- and bone resorption-related biomarkers were measured over time (ELISA) and local osteogenic and anti-osteoporotic effects investigated (x-ray, CT, histology, PCR arrays). RA decreased the setting rate and compressive strength of CPSC by impeding the hydration of calcium silicate. The overall porosity of CPSC was also decreased with RA. The RA-added CPSC was biocompatible and improved osteoblast proliferation and differentiation. The slow release of RA from CPSC reduced the prevalence of osteoporosis in rabbits and improved peri-implant bone formation and osseointegration. In conclusion, RA-containing CPSC demonstrates its potentials to improve fractural restoration under osteoporotic conditions and should be further engineered to increase its effectiveness in fractural restoration.
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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) | 0.000 | 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".