Enzyme-Amplified Aequorin-Based Bioluminometric Hybridization Assays
Bibliographic record
Abstract
The sensitivity of aequorin-based bioluminometric hybridization assays was enhanced by introducing, enzymically, multiple aequorin labels per DNA hybrid. The target DNA was hybridized in microtiter wells with an immobilized capture probe and a digoxigenin-labeled detection probe. The hybrids were reacted with an anti-digoxigenin antibody conjugated to horseradish peroxidase. Peroxidase catalyzed the oxidation of digoxigenin-tyramine by hydrogen peroxide, resulting in the attachment of multiple digoxigenin moieties to the solid phase. Aequorin-labeled anti-digoxigenin antibody was then allowed to bind to the immobilized digoxigenins. The bound aequorin was determined by its characteristic Ca2+-triggered bioluminescence. As low as 20 fmol/L (1 amol/ well) target DNA was detected with a signal-to-background ratio of 2.7. A hybridization assay that used only aequorin-labeled anti-digoxigenin antibody without the peroxidase amplification step gave a signal-to-background ratio of 2 for 160 fmol/L target DNA. The signal enhancement of the amplified assay was in the range of 14-38 times. The analytical range of the amplified assay extended up to 2600 fmol/L. The CVs were in the range of 5.5-7.3%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".