Nanoporous 3D-Printed Scaffolds for Local Doxorubicin Delivery in Bone Metastases Secondary to Prostate Cancer
Bibliographic record
Abstract
The spine is the most common site of bone metastasis, often originating from prostate, lung, and breast cancers. High systemic doses of chemotherapeutics such as doxorubicin (DOX), cisplatin, or paclitaxel often have severe side effects. Surgical removal of spine metastases also leaves large defects which cannot spontaneously heal and require bone grafting. To circumvent these issues, we designed an approach for local chemotherapeutic delivery within 3D-printed scaffolds which could also potentially serve as a bone substitute. Direct treatment of prostate cancer cell line LAPC4 and patient derived spine metastases cells with 0.01 µM DOX significantly reduced metabolic activity, proliferation, migration, and spheroid growth. We then assessed uptake and release of DOX in a series of porous 3D-printed scaffolds on LAPC4 cells as well as patient-derived spine metastases cells. Over seven days, 60⁻75% of DOX loaded onto scaffolds could be released, which significantly reduced metabolic activity and proliferation of both LAPC4 and patient derived cells, while unloaded scaffolds had no effect. Porous 3D-printed scaffolds may provide a novel and inexpensive approach to locally deliver chemotherapeutics in a patient-specific manner at tumor resection sites. With a composite design to enhance strength and promote sustained drug release, the scaffolds could reduce systemic negative effects, enhance bone repair, and improve patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".