A Three-Dimensional Dense Collagen Hydrogel to Model Cancer Cell/Osteoblast Interactions
Bibliographic record
Abstract
No curative treatment options exist once breast cancer metastasizes to bone. This is due, in part, to an incomplete understanding of how osteolytic cancers interact with bone. Presented here is a novel approach to study the interactions between triple negative breast cancer cells and osteoblasts within a 3D collagenous environment. More specifically, a dense collagen hydrogel was employed to model interactions between MDA-MB-231 breast cancer cells and MC3T3-E1 pre-osteoblasts. Co-cultures with these two cell types, or MDA-MB-231-derived conditioned medium applied to MC3T3-E1 cells, were established in the context of plastically compressed dense collagen gel matrices. Importantly, breast cancer-derived conditioned medium or the establishment of breast cancer/osteoblast co-cultures did not negatively influence MC3T3-E1 cell viability. The inclusion of either conditioned medium or the presence of MDA-MB-231 cells resulted in impaired MC3T3-E1 differentiation into osteoblasts, which coincided with reduced osteoblast-mediated mineralization. The results presented here demonstrate that dense collagen gels provide a model environment to examine the effect of osteolytic breast cancer cells on osteoblast differentiation and subsequent mineralization of the collagen scaffold.
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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".