Human pluripotent stem cell process parameter optimization in a small scale suspension bioreactor
Bibliographic record
Abstract
Background As cell-based therapies begin to enter clinical trials, human pluripotent stem cells (PSCs) present a reliable and robust source of cells for differentiation into all cell types in the human body [1]. PSC may one day provide cells for new treatment options for a wide range of diseases including cardiovascular disease, neurodegenerative diseases, and diabetes. The large numbers of cells required for many of these therapies necessitates the development of scalable and cost efficient technologies for manufacturing PSCs and their derivatives [2]. PSCs are an adherent cell type that is susceptible to phenotypic change in response to environmental stress. Adherent PSCs can be grown in suspension by forming aggregates of undifferentiated cells [3,4], by encapsulating PSCs in beads or hydrogels [5,6], or by adhering PSCs to the surface of microcarriers [7,8]. Most PSC suspension expansion to date has been performed in benchtop stirred-tank bioreactors operating at volumes of 50-200mL. Little bioprocess optimization has been conducted at these scales likely because of the high cost of PSC growth media and labour requirements for bioreactor growth. This study describes the use of a small-scale bioreactor system for Design-of-Experiments based PSC expansion process development. These process developments inform strategies to bring PSC-based processes into clinical production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".