Collagen I Scaffolds Cross-Linked with Beta-Glycerol Phosphate Induce Osteogenic Differentiation of Embryonic Stem Cells <i>In Vitro</i> and Regulate Their Tumorigenic Potential <i>In Vivo</i>
Bibliographic record
Abstract
Embryonic stem cells (ESCs) have the potential to differentiate into all tissues of the adult organism. This, along with the ability for unlimited self-renewal, positions these cells for regenerative medicine approaches based on tissue engineering strategies. With the objective of developing a treatment regime for skeletal injuries and diseases, this study presents a novel protocol that effectively induces ESC differentiation into osteogenic and chondrogenic lineages while concurrently eliminating observed tumorigenicity during the period of observation after transplantation in vivo. Exposure to a collagen I matrix polymerized with beta-glycerol phosphate (BGP) induced the osteogenic differentiation of the ESCs with an efficiency of >80% without purification and/or lineage-specific cell selection. Furthermore, when the collagen I matrix was supplemented with chondroitin sulfate, chondrogenesis was promoted instead of osteogenesis. Interestingly, without purification of the differentiated cells from the collagen I matrix, these constructs did not lead to the formation of teratomas or tumors when implanted subcutaneously in a severe combined immunodeficiency (SCID). Furthermore, if undifferentiated ESCs were mixed with collagen I and then injected immediately (i.e., without previous in vitro differentiation), again, no teratomas or tumors were observed, whereas undifferentiated ESCs without collagen scaffolds all produced teratomas in this bioassay system. These results suggest that collagen I scaffolds not only induce osteogenic differentiation of ESCs, but also prevent ESCs from producing unwanted tumors when injected 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 | 0.000 | 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".