A High-affinity but Low-abundance Kanamycin Aptamer Reveals Hybridization-limited Capture-SELEX
Bibliographic record
Abstract
Kanamycin A, or simply referred to as kanamycin, is an aminoglycoside antibiotic with a narrow therapeutic window. Aptamers are useful recognition molecules for its detection and continuous monitoring. However, a high-quality kanamycin aptamer that works under physiological condition is still lacking. In this work, we revisited a previous aptamer selection done at pH 8, which was abandoned due to poor sequence enrichment. Its top sequence named KAN8-1 shows a Kd of 51 nM at pH 7.5 for kanamycin as measured by isothermal titration calorimetry, and its affinities to kanamycin A and B are similar. Using NMR spectroscopy methods, the KAN8-1 aptamer undergoes ligand-induced folding and likely has a better defined structure compared to the KAN6-1 aptamer, which was highly enriched at the pH 6 selection. Using the KAN8-1 aptamer, a strand-displacement biosensor was developed and it has a limit of detection of 0.9 µM with excellent selectivity. This sensor also has a similar performance in serum. The reason for the poor enrichment of KAN8-1 was attributed to its low hybridization efficiency and poor hybridization stability to the capture strand as demonstrated by a fluorescence titration assay and melting analysis, which indicated a limitation of the capture-SELEX method.
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 | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".