Modular Aptamer Switches for the Continuous Optical Detection of Small‐Molecule Analytes in Complex Media
Bibliographic record
Abstract
Aptamers are a promising class of affinity reagents because signal transduction mechanisms can be built into the reagent, so that they can directly produce a physically measurable output signal upon target binding. However, endowing the signal transduction functionality into an aptamer remains a trial-and-error process that can compromise its affinity or specificity and typically requires knowledge of the ligand binding domain or its structure. In this work, a design architecture that can convert an existing aptamer into a "reversible aptamer switch" whose kinetic and thermodynamic properties can be tuned without a priori knowledge of the ligand binding domain or its structure is described. Finally, by combining these aptamer switches with evanescent-field-based optical detection hardware that minimizes sample autofluorescence, this study demonstrates the first optical biosensor system that can continuously measure multiple biomarkers (dopamine and cortisol) in complex samples (artificial cerebrospinal fluid and undiluted plasma) with second and subsecond-scale time responses at physiologically relevant concentration ranges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".