Improvement in protein separation by pH excursion modulated ion-exchange chromatography
Bibliographic record
Abstract
The transient change in pH during the elution step in ion exchange chromatography is referred to as pH excursion. Acidic pH excursion is commonly observed during salt-based elution in cation exchange chromatography. This could have a detrimental effect on protein stability as well as on protein separation. Acidic pH excursion could delay protein elution and thereby decrease resolution of sequentially eluted proteins. In a recent study, a method for suppressing or modulating pH excursion during salt-induced elution in cation exchange chromatography has been discussed. In the current study, the feasibility of using such pH excursion modulation for increasing resolution in protein separation is examined. Modulation of pH excursion during elution resulted in rapid release of a weakly bound protein from cation exchange media while the release of a strongly bound protein remained largely unaffected. This differential effect was then utilized to increase the resolution in binary protein separation. The resolution obtained in pH excursion modulated cation exchange chromatography was significantly greater than that obtained with unmodulated cation exchange chromatography, i.e., where acidic pH excursion was allowed to happen as usual. This approach for increasing resolution in protein separation could potentially be utilized in analytical as well as preparative protein chromatography applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".