A Spheroid Model of Early and Late‐Stage Osteosarcoma Mimicking the Divergent Relationship between Tumor Elimination and Bone Regeneration
Bibliographic record
Abstract
Osteosarcoma is the most diagnosed bone tumor in children. The use of tissue engineering strategies after malignant tumor resection remains a subject of scientific controversy. As a result, there is limited research that focuses on bone regeneration postresection, which is further compromised following chemotherapy. This study aims to develop the first co-culture spheroid model for osteosarcoma, to understand the divergent relationship between tumor elimination and bone regeneration. By manipulating the ratio of stromal to osteosarcoma cells the modelled cancer state (early/late) is modified, as is evident by the increased tumor growth rates and an upregulation of a panel of well-established osteosarcoma prognostic genes. Validation of the authors' model is conducted by analyzing its ability to mimic the cytotoxic effects of the FDA-approved chemotherapeutic Doxorubicin. Next, the model is used to investigate what effect osteogenic supplements have, if any, on tumor growth. When their model is treated with osteogenic supplements, there is a stimulatory effect on the surrounding stromal cells. However, when treated with chemotherapeutics this stimulatory effect is significantly diminished. Together, the results of this study present a novel multicellular model of osteosarcoma and provide a unique platform for screening potential therapeutic options for osteosarcoma before conducting in vivo experiments.
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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 | 0.001 |
| 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".