Computational fluid dynamic characterization of vertical‐wheel bioreactors used for effective scale‐up of human induced pluripotent stem cell aggregate culture
Bibliographic record
Abstract
Abstract Bioreactor‐based processes are the method of choice for efficient expansion of cells in a controlled setting. However, induced pluripotent stem cells (iPSCs) have proven to be extremely sensitive to the bioreactor hydrodynamic environment, making the use of suspension bioreactors to produce quality‐assured cells at clinical and commercial scales very challenging. The PBS vertical‐wheel (VW) bioreactor combines radial and axial flow components to produce uniform hydrodynamic force distributions, making it a promising platform to overcome the scale‐up challenges associated with iPSCs. In this study, hydrodynamic characterization through computational fluid dynamics modelling of VW bioreactors was performed. Analysis of these models proved that important volume average hydrodynamic variables could be maintained throughout scale‐up from the 0.1 L to the 15 L VW bioreactor scale. Each bioreactor scale (0.1, 0.5, 3, and 15 L) was modelled at a variety of agitation rates, leading to the generation of scale‐up correlation equations. These equations allow operators to define a working range of hydrodynamic variables at one scale and calculate the corresponding agitation rates at other modelled scales. A suggested operating range of agitation rates was determined for the successful culture of iPSCs in the VW bioreactor at each scale, corresponding to constant volume average energy dissipation rate. Agitation rates from the 0.1 and 0.5 L VW bioreactor scale were experimentally tested to biologically validate the suggested range. High cell‐fold expansion, healthy aggregate morphology, growth, and uniformity were demonstrated for all conditions tested within the suggested working range.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".