A Generalizable Screening Platform for Developing Functional Aptasensors
Bibliographic record
Abstract
Aptamers are versatile sensing elements for the construction of biosensors. A common approach for signal generation in "aptasensors" involves the displacement of short complementary "probes" resulting from conformational changes upon aptamer-target binding. However, designing strands that rapidly and completely displace when the target binds is nontrivial. Typically, probes are discovered through a lengthy process of screening several potential sequences. Here, we explored properties governing probe displacement efficiency using a well-characterized aptamer for the agricultural contaminant ochratoxin A (OTA). Surprisingly, the length, probe affinity, and melting temperature did not correlate with probe displacement efficiency. We therefore developed a novel surface plasmon resonance (SPR) assay to rapidly measure target-induced displacement of probes from aptamers. Fitted displacement results from the SPR assay were correlated with fast proportional fluorescence recovery from quencher-labeled probe displacement. This new method allows for the rapid distinction of efficient probes, resulting in sensitive biosensing of OTA. Finally, we demonstrated our new method is adaptable to diverse aptamers, offering a generally applicable method to improve probe design and accelerate aptasensor development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".