Modeling bone marrow: A novel 3-D culture system for characterization of the multiple myeloma stem cell niche
Bibliographic record
Abstract
3052 Bone marrow (BM) is a complex tissue responsible for initial steps of B cell development but is also a site of tumorigenesis. Multiple myeloma (MM), an incurable cancer of the BM plasma cells is responsible for 19% of deaths from hematological malignancies. While considerable progress has been made towards understanding and curing this disease, to date, there is no culture system which can recapitulate the complex interactions between the extracellular matrix (ECM) and the various cells of the BM. Here we describe the design of an in vitro 3-dimensional (3-D) model recapitulating the BM microevironment. The 3-D tissue culture model system presented here mimics in vivo microenvironment of the BM where cells from the BM aspirates are grown in the ECM closely resembling that of the human BM. We show that various cells found in the BM establish individual niches mimicking the in vivo condition. The organization of the reconstructed BM from the MM patients differs from that established by cells from the normal donors. The malignancy of the MM is sustained in the 3-D culture. Thus this is a good model for pre-clinical analysis of novel therapeutics. Finally, this model provides an avenue for the identification and characterization of the microevironmental niche responsible for harboring the drug resistant MM stem cell.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".