Dynamic pH barrage junction focusing of amino acids, peptides, and digested monoclonal antibodies in capillary electrophoresis–mass spectrometry
Bibliographic record
Abstract
Dynamic pH barrage junction focusing in CE enables effective signal enhancement, quantitative capture efficiencies, and straightforward optimization. The method is a technical variant of dynamic pH junction focusing. CE separation with dynamic pH barrage junction focusing is compatible with both optical and mass spectrometric detection. We developed a CE-MS/MS method using hydrophilic polyethyleneimine-coated capillaries and validated it for the qualitative analysis of amino acids, peptides, and tryptic peptides of digested monoclonal antibodies. The S/N of extracted ion electropherograms of zwitterionic analytes were enhanced by approximately two orders of magnitude with a tradeoff of a shortened separation window. Online focusing improved the MS signal intensity of a diluted antibody digest, enabling more precursor ions to be analyzed with subsequent tandem mass spectrometric identification. It also broadened the concentration range of protein digest samples for which adequate sequence coverage data can be obtained. With only 0.9 ng of digested infliximab sample loaded into the capillary, 76% and 100% sequence coverage was realized for antibody heavy and light chains, respectively, after online focusing. Full coverage was achieved with 9 ng of injected digest.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".