The use of nanobead technology to deliver zoledronic acid to prostate cancer cell lines
Bibliographic record
Abstract
Background Up to 80% of patients with primary tumors of the breast, prostate or lung will develop spine metastases.Spine metastases cause pain, functional deficit and severely diminished health related quality of life through fracture, vertebral instability and spinal cord and nerve-root compression.This is associated with high socioeconomic and healthcare costs.With advancements in medical, radiation and surgical oncology, these patients are living longer thereby increasing the disease burden.Surgical bone resection of metastatic tumors leads to large bone defects that cannot self-repair, and treatment with antiresorptive bisphosphonates such as Zoledronate, is being explored for tissue repair.However, bone loss, instability and poor repair following tumor resection remains an unmet clinical need in this population.This leads to the possibility of exploring nanotechnology to delivery drugs locally and at therapeutic to help with the management of spine tumor patients. MethodsWe first investigated the proliferation of various prostate cancer cell lines; LAPC4, 22RV1 and PC3 with different doses of Zoledronate using Alamarblue ® kit and MTT assays.Following this we performed migration assays on similar cell lines using Boyden chambers and scratch assays.Once the action of various Zoledronate doses were established we worked to coat silica nanobeads with this drug and assess its release.The silica nanobeads were first prepared and tested using fluorescently labelled Zoledronate.Once a reproducible protocol was established, we tested the release of nanobeads coated in Zoledronate in a 3D bioprinted in-vitro model using a prostate cancer
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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".