Aptamer–Antibody Chimera Sensors for Sensitive, Rapid, and Reversible Molecular Detection in Complex Samples
Why this work is in the frame
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Bibliographic record
Abstract
The development of receptors suitable for the continuous detection of analytes in complex, interferent-rich samples remains challenging. Antibodies are highly sensitive but difficult to engineer in order to introduce signaling functionality, while aptamer switches are easy to construct but often yield only a modest target sensitivity. We present here a programmable antibody and DNA aptamer switch (PANDAS), which combines the desirable properties of both receptors by using a nucleic acid tether to link an analyte-specific antibody to an internal strand-displacement (ISD)-based aptamer switch that recognizes the same target through different epitopes. The antibody increases PANDAS analyte binding due to its high affinity, and the effective concentration between the two receptors further enhances two-epitope binding and fluorescent aptamer signaling. We developed a PANDAS sensor for the clotting protein thrombin and show that a tuned design achieves a greater than 300-fold enhanced sensitivity compared to that of using an aptamer alone. This design also exhibits reversible binding, enabling repeated measurements with a temporal resolution of ∼10 min, and retains excellent sensitivity even in interferent-rich samples. With future development, this PANDAS approach could enable the adaptation of existing protein-binding aptamers with modest affinity to sensors that deliver excellent sensitivity and minute-scale resolution in minimally prepared biological specimens.
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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 | 0.000 |
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 it