Integrated fragmentation of human IgG and purification of Fab using a reactant adsorptive membrane bioreactor separator system
Bibliographic record
Abstract
This article discusses an integrated separation-reaction-separation scheme for producing Fab fragment directly from human immunoglobulin G (hIgG) present in serum feed. The novel reactant adsorptive membrane bioreactor separator (or RAMBS) system used in the current study consisted of a stack of microporous adsorptive membranes held within a temperature controlled module. The membrane stack, in the presence of salt, selectively and reversibly adsorbed hIgG by hydrophobic interaction while allowing most other serum proteins to flow through. The bound hIgG was then fragmented by pumping a solution of papain through the reactor at controlled temperature and flow rate. The salt concentration and pH for reaction and separation were systematically optimized using pure hIgG as reactant. The Fab fragment was separated from undigested hIgG and other byproducts such as Fc fragment based on their differences in hydrophobicity. Under optimal conditions, Fab was obtained in the reaction flow through while the other proteins remained bound to the membrane, these being subsequently eluted by lowering the salt concentration. The RAMBS system in addition to being convenient from process integration and intensification points of view also showed higher catalytic efficiency of papain in comparison to that in liquid phase reactions.
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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".