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 K d 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 Mg 2+ . 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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 teacher head, 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".