Controlled shear affinity filtration (CSAF): A new technology for integration of cell separation and protein isolation from mammalian cell cultures
Bibliographic record
Abstract
Controlled shear affinity filtration (CSAF) integrates animal cell separation and product isolation in a single unit operation through the use of a specifically designed rotating disk filter with incorporated membrane chromatography column. Because of the decoupling of shear force and pressure generation and the specific hydrodynamics of the system, shear rates can be easily optimized and precisely controlled to maximize filtration performance while viability of the shear sensitive animal cells is maintained. In this study, the general methodology is demonstrated using the integration of Chinese hamster ovary cell separation and isolation of recombinant tissue plasminogen activator (t-PA) as a model example. Direct capture of t-PA from cell culture broth was realized by using custom-made affinity membranes with lysine as a robust, small molecular weight affinity ligand. Small-scale t-PA adsorption experiments, as well as microfiltration experiments, were used to design the integrated CSAF process. A Chinese hamster ovary batch culture was processed with a lab-scale prototype, yielding 86% of the t-PA in the concentrated, particle-free eluate, whereas 95% of the bulk protein was removed. Because the viability of the cells is not significantly affected and high specific flux rates can be achieved, the CSAF technology should also be well suited for continuous perfusion with integrated product isolation. A truly continuous operation could be realized with two systems in tandem configuration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".