Cell Separator Operation within Temperature Ranges To Minimize Effects on Chinese Hamster Ovary Cell Perfusion Culture
Bibliographic record
Abstract
A cell retention device that provides reliable high-separation efficiency with minimal negative effects on the cell culture is essential for robust perfusion culture processes. External separation devices generally expose cells to periodic variations in temperature, most commonly temperatures below 37 degrees C, while the cells are outside the bioreactor. To examine this phenomenon, aliquots of approximately 5% of a CHO cell culture were exposed to 60 s cyclic variations of temperature simulating an acoustic separator environment. It was found that, for average exposure temperatures between 31.5 and 38.5 degrees C, there were no significant impacts on the rates of growth, glucose consumption, or t-PA production, defining an acceptable range of operating temperatures. These results were subsequently confirmed in perfusion culture experiments for average exposure temperatures between 31.6 and 38.1 degrees C. A 2(5-1) central composite factorial design experiment was then performed to systematically evaluate the effects of different operating variables on the inlet and outlet temperatures of a 10L acoustic separator. The power input, ambient temperature, as well as the perfusion and recycle flow rates significantly influenced the temperature, while the cell concentration did not. An empirical model was developed that predicted the temperature changes between the inlet and the outlet of the acoustic separator within +/-0.5 degrees C. A series of perfusion experiments determined the ranges of the significant operational settings that maintained the acoustic separator inlet and outlet temperatures within the acceptable range. For example, these objectives were always met by using the manufacturer-recommended operational settings as long as the recirculation flow rate was maintained above 15 L day(-1) and the ambient temperature was near 22 degrees C.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".