Purification of equine IgG using membrane based enhanced hybrid bioseparation technique: A potential method for manufacturing hyperimmune antibody
Bibliographic record
Abstract
Hyperimmune equine IgG is widely used as antivenom and anti-rabies agents. This article discusses a membrane based enhanced hybrid bioseparation technique for efficient and scalable purification of equine immunoglobulin G (IgG) from horse serum. This technique is an improved version of a standard hybrid bioseparation technique developed within our group earlier for fractionation of human plasma proteins (Ghosh. 2004. J Membr Sci 237: 109-117). In the presence of a high antichaotropic salt concentration, equine IgG is selectively and reversibly captured within a stirred cell membrane module from horse serum, partly due to precipitation and microfiltration, and partly due to hydrophobic interaction based membrane adsorption, while the impurities are washed out from the device. The reversibly sequestered IgG is then released by lowering the salt concentration which favor both dissolution of the precipitated IgG and desorption of the membrane bound IgG. The enhanced hybrid bioseparation technique improves the IgG recovery from the membrane module by switching from a stirring to non-stirring mode during the IgG release phase. It also reduces membrane fouling by an appropriate pH switch. The effects of operating conditions on equine IgG capture were first systematically studied. The enhanced hybrid bioseparation technique was followed by an ultrafiltration step to remove ammonium sulfate and low molecular weight impurities. The equine IgG purity obtained under optimized conditions was 88% and its recovery was over 90%, both being significantly higher than corresponding values obtained using currently used purification techniques.
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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 | 0.001 | 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".