N‐1 semi‐continuous transient perfusion in shake flask for ultra‐high density seeding of <scp>CHO</scp> cell cultures in benchtop bioreactors
Bibliographic record
Abstract
viable cells/mL). This very high seeding density improves cell culture performance by shortening the timeline and/or achieving higher final product concentrations. Typically, an N-1 seed train employs bioreactors with alternating tangential flow filtration (ATF) or tangential flow filtration (TFF) perfusion systems or Wave cell bag bioreactor with integrated filtration membrane, which have costs and technical complexity. Here, we propose an alternative method using semi-continuous transient perfusion through media exchange in shake flasks, which is suitable for benchtop-scale intensification process development. Daily media exchange was necessary to prevent nutrient limitations. The observed limitation of maximum viable cell densities (VCD) in various flask sizes was demonstrated to be due to oxygen limitations through the measurements of maximum oxygen transfer rates (OTR) using the sulfite system. By increasing agitation frequency from 200 to 300 RPM, maximum OTR in 500-mL shake flasks was increased by 62.3%, allowing an increase in maximum VCD of 29.6%. However, in 1000-mL shake flasks, an increase in agitation rate resulted in early cell death. After demonstrating that media exchange in shake flasks by centrifugation had no significant impact on cell growth rates, metabolism, and productivity, a benchtop bioreactor was seeded from semi-continuous transient perfusion cell expansion. The ultra-high cell density seeding resulted in a 49.3% increase in space-time-yield (STY) when compared to a standard low seeding density culture.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".