Selection of DNA Aptamers for Sensing Uric Acid in Simulated Tears
Bibliographic record
Abstract
Abstract Uric acid is a biomarker for a range of diseases and hyperuricemia is the cause of many diseases including gout. While most biosensors for detecting uric acid relied on enzymatic reactions, in this work a library‐immobilization method was used to obtain DNA aptamers for uric acid. After 18 rounds of selection, two representative aptamers were obtained with a Kd around 1.2 μM measured by isothermal titration calorimetry (ITC). Based on their difference in binding to xanthine, which differs from uric acid by only one oxygen atom, these two aptamers have different binding orientations to uric acid. ITC also indicated that the UA‐1 aptamer specifically required a high concentration of Na+ for binding, which cannot be replaced by Li+, K+ or Mg2+. Combined ITC and fluorescence spectroscopy data indicated the need of three Na+ ions, which explained the requirement of a high Na+ concentration. The UA‐1 aptamer was engineered into a fluorescent biosensor based on the strand‐displacement reaction, resulting in a limit of detection of 90 nM uric acid. This sensor was also tested in simulated tears with a limit of detection of 350 nM uric acid.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".