Modeling the Hydrodynamics in Bioreactors for the Expansion of Embryonic Stem Cells
Bibliographic record
Abstract
Embryonic stem cells have a large potential to help patients with commonly known diseases such as Parkinson’s or diabetes through tissue engineering and regenerative medicine strategies. In order to generate large numbers of cells, efforts have been put into expansion of embryonic stem cells in suspension bioreactors. Usually this means multiple experiments in large volume reactors, with considerable investment of time and resources. For this reason, successful cell expansion in a microbioreactor would facilitate multiple experiments with much lower cost and higher throughout. Previously, embryonic stem cells were successfully expanded in a 100mL bioreactor, but the same expansion was not seen in a prototype 250μL microbioreactor. Using modeling software and simple geometry, it was found that the hydrodynamic environment in the microbioreactor was more heterogeneous and erratic than that of the 100mL bioreactor. In order to design a microbioreactor that will yield successful cell expansion, the hydrodynamic environment of the 100mL had to be studied first in further detail, including more accurate geometry. Modelling software (COMSOL Multiphysics) was used to analyze the velocity, shear stress, streamline, vorticity, and pressure within the 100mL bioreactor while varying multiple parameters to study their effect. Generally, shear stress and vorticity were very sensitive to changes in geometry (i.e. shape of impeller, liquid level) and agitation rate. The pattern of streamlines clearly indicated where the cells would be concentrated within the bioreactor. With the results of this analysis, the next step would be to design a microbioreactor with the most suitable geometrical configuration that will match that seen in the larger 100 mL bioreactor, and ultimately allow successful embryonic stem cell expansion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".