CHO cells adapted to hypothermic growth produce high yields of recombinant β‐interferon
Bibliographic record
Abstract
Mild hypothermic conditions (30-33 degrees C) have previously been shown to increase cell-specific productivity (Q(p)) of recombinant proteins from mammalian cells. However, this is often associated with a lower growth rate which off-sets any potential advantage of higher product titers. We report the isolation of a population of Chinese Hamster Ovary (CHO) cells that have been adapted to low-temperature growth by continuous subculture at low temperature for up to 300 days. This adapted cell population achieved a growth rate twofold greater than nonadapted cells under low-temperature conditions (32 degrees C) while maintaining an elevated level of cell-specific expression of recombinant beta-interferon. The volumetric titer of beta-interferon was enhanced by 70% in stationary cultures and by more than twofold by application of a temperature-shift strategy involving a growth to production phase. However, the low-temperature-adapted cells were fragile and demonstrated an increased sensitivity to hydrodynamic stress in agitated cultures. This problem was resolved by the use of macroporous microcarriers which protected the cells and allowed growth of high-density cultures under hypothermic conditions. This eventually resulted in a threefold enhancement of volumetric titer of monomeric beta-interferon compared to the original control culture at 37 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".