Fluorescence polarization detection for affinity capillary electrophoresis
Bibliographic record
Abstract
Affinity capillary electrophoresis (ACE) with laser-induced fluorescence polarization (LIFP) detection is described, with examples of affinity interaction studies. Because fluorescence polarization is sensitive to changes in the rotational motion arising from molecular association or dissociation, ACE-LIFP is capable of providing information on the formation of affinity complexes prior to or during CE separation. Unbound, small fluorescent probes generally have little fluorescence polarization because of rapid rotation of the molecule in solution. When the small fluorescent probe is bound to a larger affinity agent, such as an antibody, the fluorescence polarization (and anisotropy) increases due to slower motion of the much larger complex molecule in the solution. Fluorescence polarization results are obtained by simultaneously measuring fluorescence intensities of vertical and horizontal polarization planes. Applications of CE-LIFP to both strong and weak binding systems are discussed with antibody-antigen and DNA-protein binding as examples. For strong affinity binding, such as between cyclosporine and its antibody, complexes are formed prior to CE-LIFP analysis. For weaker binding, such as between single-stranded DNA and its binding protein, the single-stranded DNA binding protein is added to the CE separation buffer to enhance dynamic formation of affinity complexes. Both fluorescence polarization (and anisotropy) and mobility shift results are complementary and are useful for immunoassays and binding studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".