Affinity protection chromatography for efficient labeling of antibodies for use in affinity capillary electrophoresis
Bibliographic record
Abstract
Capillary electrophoresis immunoassay (CEIA) is shown to be substantially more sensitive to the antibody (Ab) reagent quality than are immunosorbent methods such as enzyme-linked immunosorbent assays (ELISA). Cyanine 5 (Cy5)-labeled monoclonal anti-ovalbumin (mAb*) was inactive for CEIA of ovalbumin (Ov), yet was functional in ELISA for Ov. ELISA showed the mAb* was at least ten times less active, accounting for the poor CEIA performance. Labeled polyclonal Ab was inactive for a dye to protein ratio greater than 1.6. An affinity protection chromatography procedure (APC) was developed for Ab labeling, which avoided degradation of the Ab binding site. Ov was covalently bound to cyanogen bromide activated cellulose gel in a column, and used to capture the Ab. The coupling efficiency for Ov to the gel was 74-97%, Ab could then be bound with 95-100% efficiency, and Ab* was recovered in 50% yield following labeling on the column. This procedure was performed successfully in three different laboratories, indicating the robustness of the optimized APC synthetic method. No inactive Ab* could be detected in the APC product. The CEIA detection limit for ovalbumin using APC labeled mAb was 173 nM, when [Ab*] was fixed at 163 nM. The association constants of mAb and mAb* were determined by CEIA.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".