Nanoparticle-Functionalized Acrylic Bone Cement for Local Therapeutic Delivery to Spinal Metastases
Bibliographic record
Abstract
Abstract Polymethylmethacrylate bone cement is often used to reconstruct critical-sized defects generated by surgical resection of spinal metastases. Residual tumor cells after a resection can drive recurrence and destabilization. Doxorubicin (DOX) is a common chemotherapeutic drug with unwanted side-effects when administered systemically. Mesoporous silica nanoparticles are gaining attention for targeted drug delivery to bypass the negative side effects associated with systemic drug administration. We developed a nanoparticle-functionalized cement for the local release of DOX and tested its ability to suppress cancer cells. DOX was loaded onto nanoparticles which were then mixed into the cement. Drug release profiles were obtained over a period of 4 weeks. Cement constructs were incubated with 2D and 3D cultures of breast and prostate cancer cell lines, and cell metabolic activity and viability were evaluated. Cell migration and spheroid growth were assessed in collagen-coated spheroid cultures. Nanoparticles were homogenously dispersed and did not alter cement mechanical strength. A sustained DOX release profile was achieved with the addition of nanoparticles to the bone cement. The release profile of DOX from nanoparticle cement may be modified by varying the amount of the drug loaded onto the nanoparticles and the proportion of nanoparticles in the cement. Cells treated with the cement constructs showed a dose- and time-dependent inhibition. Cell migration and spheroid growth were impaired in 3D culture. We show that nanoparticles are essential for sustained DOX release from bone cement. DOX-loaded nanoparticle cement can inhibit cancer cells and impair their migration, with strong potential for in vivo translation studies.
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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) | 0.001 | 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".