3D printed scaffolds for stabilization and local therapeutic delivery in bone metastases
Bibliographic record
Abstract
Bone is the third most common site for cancer metastasis, with breast and prostate tumors often the primary source.The current gold standard of treatment involves surgically removing large sections of the affected bone.An important limitation is that small amounts of residual tumor are often left behind.With patients' survival time increasing, they are faced with high risk of recurrence and re-operation.3D printing is garnering attention as a solution to overcome this limitation, as it is cost-effective and produces unique, patient-specific geometries.In this study, we aim 1) to generate composite implants from two polymer types, a stiff lactide-mineral material (Lactoprene) which supports bone repair, and a sponge-like polymer (Lay FOMM), which can be loaded with therapeutics for local delivery; 2) to test the scaffolds' mechanical integrity and ability to deliver chemotherapeutics to breast and prostate cancer cells in vitro.We hypothesize that composite scaffolds will have comparable mechanical strength to trabecular bone and composite scaffolds with chemotherapeutics will be equally effective at inhibiting tumor cell proliferation and migration in vitro compared to direct chemotherapeutic treatment.Compression testing was performed on Lactoprene, and Lactoprene-Lay FOMM composite scaffolds.The release of doxorubicin was quantified over a period of 1 week using a fluorescence microplate
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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".