Suicide gene‐enabled cell therapy: A novel approach to scalable human pluripotent stem cell quality control
Bibliographic record
Abstract
There are an increasing number of cell therapy approaches being studied and employed world-wide. An emerging area in this field is the use of human pluripotent stem cell (hPSC) products for the treatment of injuries/diseases that cannot be effectively managed through current approaches. However, as with any cell therapy, vast numbers of functional and safe cells are required. Bioreactors provide an attractive avenue to generate clinically relevant cell numbers with decreased labour and decreased batch to batch variation. Yet, current methods of performing quality control are not readily scalable to the cell densities produced during bioreactor scale-up. One potential solution is the application of inducible/controllable suicide genes that can trigger cell death in unwanted cell types. These types of approaches have been demonstrated to increase the quality and safety of the resultant cell products. In this review, we will provide background on these approaches and how they could be used together with bioreactor technology to create effective bioprocesses for the generation of high quality and safe hPSCs for use in regenerative medicine approaches.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".