First, Do no Harm: Current Approaches to Assess Tumorigenicity in Stem Cell-derived Therapeutic Products
Bibliographic record
Abstract
Stem cells hold a great promise for regenerative medicine given their ability to proliferate and differentiate into various cell types. However, self-renewal and multipotency also grant a high capacity to form tumor tissues in vivo post-therapeutic administration. Indeed, multiple case reports have revealed the formation of stem cell derived tumors, such as teratoma, in animal models and even in clinical applications. As a result, examination of tumorigenicity becomes one of the major considerations when assessing the safety of stem cell-derived therapeutic products. Ideally, the assessment needs to be performed in a rapid, sensitive, cost-effective, and scalable manner. In this chapter, the current practices of assay development to fulfill this demand are reviewed. Progress in animal models, soft agar culture, PCR, flow cytometry, and microfluidics are introduced and compared comprehensively. Some insights regarding the assay selection and future development are also provided as there is no one-for-all assay at this moment.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".