An Ultrahigh Affinity DNA Aptamer for Quinine and Its Intrinsic Fluorescence Based Label‐Free Detection
Bibliographic record
Abstract
Abstract Measuring quinine is critical for the detection of its overdose, understanding its pharmacological and toxicological effects, and monitoring its pollution. While a previously reported aptamer named MN4 can bind quinine, it was not selected for it, leading to compromised binding affinity and specificity. In this work, a new quinine aptamer was isolated using the library immobilization capture‐SELEX technique. The Q1 aptamer has a K d value of 10 nM determined by an isothermal titration calorimetry experiment and 45 nM in a fluorescence binding assay. A 3.5 nM quinine limit of detection was obtained based on the aptamer binding‐induced quenching of the intrinsic fluorescence of quinine. A large blue shift in fluorescence was observed for quinine upon binding to Q1, whereas binding to MN4 led to a very small red shift, indicating different ways of quinine binding by these two aptamers. Q1 did not bind cocaine based on NMR spectroscopy and fluorescence assays also indicated excellent selectivity against other tested molecules. This work has supplied a high affinity aptamer for quinine that can be useful for its detection and fundamental aptamer binding studies. It also highlights the advantages of using capture‐SELEX to isolate aptamers for small molecules.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| 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".