Cartilage tissue engineering identifies abnormal human induced pluripotent stem cells
Bibliographic record
Abstract
Safety is the foremost issue in all human cell therapies, but human induced pluripotent stem cells (iPSCs) currently lack a useful safety indicator. Studies in chimeric mice have demonstrated that certain lines of iPSCs are tumorigenic; however a similar screen has not been developed for human iPSCs. Here, we show that in vitro cartilage tissue engineering is an excellent tool for screening human iPSC lines for tumorigenic potential. Although all human embryonic stem cells (ESCs) and most iPSC lines tested formed cartilage safely, certain human iPSCs displayed a pro-oncogenic state, as indicated by the presence of secretory tumors during cartilage differentiation in vitro. We observed five abnormal iPSC clones amoungst 21 lines derived from five different reprogramming methods using three cellular origins. We conclude that in vitro cartilage tissue engineering is a useful approach to identify abnormal human iPSC lines.
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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 | 0.001 |
| 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 | 0.001 |
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".