<scp>3D</scp> bioprinted osteosarcoma model for experimental boron neutron capture therapy (<scp>BNCT</scp>) applications: Preliminary assessment
Bibliographic record
Abstract
Abstract Osteosarcoma is the most frequently primary malignant bone tumor characterized by infiltrative growth responsible for relapses and metastases. Treatment options are limited, and a new therapeutic option is required. Boron neutron capture therapy (BNCT) is an experimental alternative radiotherapy able to kill infiltrative tumor cells spearing surrounding healthy tissues. BNCT studies are performed on 2D in vitro models that are not able to reproduce pathological tumor tissue organization or on in vivo animal models that are expensive, time‐consuming and must follow the 3R's principles. A 3D in vitro model is a solution to better recapitulate the complexity of solid tumors meanwhile limiting the animal's use. Objective of this study is to optimize the technical assessment for developing a 3D in vitro osteosarcoma model as a platform for BNCT studies: printing protocol, biomaterial selection, cell density, and crosslinking process. The best parameters that allow a fully colonized 3D bioprinted construct by rat osteosarcoma cell line UMR‐106 are 6 × 106 cells/ml of hydrogel and 1% CaCl2 as a crosslinking agent. The proposed model could be an alternative or a parallel approach to 2D in vitro culture and in vivo animal models for BNCT experimental study.
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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".