Small-Scale Bioreactors for the Culture of Embryonic Stem Cells
Bibliographic record
Abstract
Stem cells have great potential for use as a regenerative therapy for degenerative diseases, such as diabetes or Parkinson’s. However, to create a large-scale and reproducible protocol, large homogeneous cell populations are required. For example, it is estimated that to treat one patient with stem cell-derived functional beta-cell equivalents for diabetes would require approximately 1 billion homogeneous beta-cell equivalents (Docherty et al. 2007). To reproducibly expand ESCs to this order remains a major research hurdle. Suspension bioreactors offer major advantages over traditional, static culture methods, including the ability to monitor and control important bioprocess parameters such as dissolved oxygen, pH, and temperature. In addition, for clinical implementation of a stem cell therapy, automation associated with bioreactors will aid in compliance with regulatory protocols (Martin et al. 2009). Other advantages of suspension bioreactors over traditional (static) cell culture methods, include scalability, enhanced oxygen and nutrient transfer, homogeneity, and increased reproducibility. However, the use of larger-scale bioreactors (for example, greater than 100 mL working volume) incurs significant expenses as considerable amounts of media, cells, and other supplies are required. In addition, extensive time and handling is necessary to generate enough cells for inoculation. Small-scale bioreactors (less than 100 mL working volume) require fewer cells, are more economical, and require less labour than larger bioreactors. The use of small-scale bioreactors potentially permits high-throughput experimentation to test operating and growth conditions (media components, agitation rate, cell density) and the resulting interactions. In a suspension bioreactor, cells are suspended in liquid medium which consists of a mixture of water, glucose, amino acids and dissolved oxygen, among other factors. As the suspension bioreactor is agitated, the environment within the bioreactor is more homogeneous than traditional culture environments, such as a T-flask, where gradients occur in the static media. The hydrodynamic environment created by agitation of the suspension bioreactor is known to influence cell survival. Excessive amounts of shear stress can lead to damage to cell membranes (Betts and Baganz 2006) whereas insufficient amounts of shear stress can cause excessive agglomeration (Li et al. 2009). In addition, manipulating
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".