Embryonic Stem Cells Incorporate into Newly Formed Bone and do Not Form Tumors in an Immunocompetent Mouse Fracture Model
Bibliographic record
Abstract
Embryonic stem (ES) cells are a uniquely self-renewing, pluripotent population of cells that must be differentiated before being useful for cell therapy. Since most studies utilize subcutaneous implantation to test the in vivo functionality of ES cell-derived cells, the objective of the current study was to develop an appropriate and clinically relevant in vivo implantation system in which the behavior and tumorigenicity of ES cell-derived cells could be effectively tested in a tissue-specific (orthotopic) site. Male ES cells were differentiated either into osteoblasts or chondrocytes using protocols that were previously developed and published by our laboratory. The differentiated cells were implanted into a burr-hole fracture created in the proximal tibiae of immunocompetent female mice, strain matched to the ES cell line. The ability of the differentiated ES cell-derived cells (bearing the Y chromosome) to incorporate into the newly formed bone was assessed by micro-computed tomography imaging and histochemistry. ES cells differentiated with either osteogenic or chondrogenic medium supplementation formed a soft tissue mass that disrupted the normal bone architecture by 4 weeks after implantation in some mice. In contrast, mice receiving osteoblastic cells that were differentiated in a three-dimensional type 1 collagen gel showed evidence of new bone formation at the defect site without evidence of tumor formation for up to 8 weeks after implantation. In this injury model, type 1 collagen is more effective than medium supplementation at driving more complete differentiation of ES cells, as evidenced by reducing their tumorigenicity. Overall, the current study emphasizes the importance of using an appropriate orthotopic implantation system to effectively test the behavior and tumorigenicity of the cells in vivo.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".