2002 W.A.E. McBryde Award Lecture Affinity recognition, capillary electrophoresis, and laser-induced fluorescence polarization for ultrasensitive bioanalysis
Bibliographic record
Abstract
The combination of affinity recognition, capillary electrophoresis (CE), laser-induced fluorescence (LIF), and fluorescence polarization for the ultrasensitive determination of compounds of biological interest is described. Competitive immunoassays using CELIF eliminate the need for fluorescently labeling trace analytes of interest and are particularly useful for determination of small molecules, such as cyclosporine, gentamicin, vancomycin, and digoxin. Fluorescence polarization allows for differentiation of the antibody-bound from the unbound small molecules. Noncompetitive affinity CELIF assays are shown to be highly effective in the determination of biomarkers for DNA damage and HIV-1 infection. An antibody (or aptamer) is used as a fluorescent probe to bind with a target DNA adduct (or the reverse transcriptase of the HIV-1 virus), with the fluorescent reaction products being separated by CE and detected by LIF. Aptamers are attractive affinity probes for protein analysis because of high affinity, high specificity, and the potential for a wide range of target proteins. Fluorescence polarization provides unique information for studying molecular interactions. Innovative integrations of these technologies will have broad applications ranging from cancer research, to biomedical diagnosis, to pharmaceutical and environmental analyses.Key words: capillary electrophoresis, laser-induced fluorescence, fluorescence polarization, immunoassay, affinity probes, antibodies, aptamers, DNA damage, toxins, therapeutic drugs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.035 |
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".