Bibliographic record
Abstract
Sensitive and selective detection assays are essential for the accurate measurement of analytes in both clinical and research laboratories. Immunoassays that rely on nonoverlapping antibodies directed against the same target analyte (e.g., sandwich enzyme-linked immunosorbent assays (ELISAs)) are commonly used molecular detection technologies. Use of split enzyme reporters has simplified the workflow for these traditionally complex assays. However, identifying functional antibody pairs for a given target analyte can be cumbersome, as it generally involves generating and screening panels of antibodies conjugated to reporters. Accordingly, we sought a faster and easier reporter conjugation strategy to streamline antibody screening. We describe here the development of such a method that is based on an optimized ternary NanoLuc luciferase. This bioluminescence complementation system is comprised of a reagent-based thermally stable polypeptide (LgTrip) and two small peptide tags (β9 and β10) with lysine-reactive handles for direct conjugation onto antibodies. These reagents enable fast, single-step, wash-free antibody labeling and sensitive functional screening. Simplicity, speed, and utility of the one-pot labeling technology are demonstrated in screening antibody pairs for the analyte interleukin-4. The screen resulted in the rapid development of a sensitive homogeneous immunoassay for this clinically relevant cytokine.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".