pH‐Directed Capture‐SELEX for Nanomolar Affinity Aptamers for Kanamycin Detection
Bibliographic record
Abstract
Abstract Kanamycin A is a widely used antibiotic, although it has a narrow therapeutic window demanding highly accurate monitoring. The sensing of kanamycin A using aptamers is of great interest since aptamers can be used for continuous monitoring with a rapid response. While kanamycin has been the target for at least four previous aptamer selections, the binding affinities of the reported DNA aptamers are still sub‐optimal. All the previous aptamer selections were performed at pH 7.5 or higher. Given that kanamycin A has four amino groups with pKa values close to 7, we herein selected DNA aptamers for kanamycin A at both pH 6 and pH 8. The selection at pH 6 enriched aptamers although the pH 8 selection library remained highly diverse in the end. The best aptamer named KAN6‐1 showed a dissociation constant of around 320 nM measured using isothermal titration calorimetry in the selection buffer. In buffers without salt, binding can happen from pH 6 to 8. Specific binding was confirmed using mutation studies. A strand displacement assay was developed with a limit of detection (LOD) of 100 nM in buffer. Similar LOD values were also obtained in lake water and in 10 % human serum. Comparisons were also made with some previously reported DNA aptamers. This study shows the importance of pH value on the selection of aptamers and provides a new aptamer for kanamycin A detection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".