Light‐Up Sensing Citrate Using a Capture‐Selected DNA Aptamer
Bibliographic record
Abstract
Abstract Citrate is a key metabolite and nutrient in humans. Its level is associated with many diseases from tumor growth to bone diseases. Detection of citrate has relied on its high negative charge, metal chelating properties and as an enzyme substrate. In this work, the capture‐selection method is used to isolate DNA aptamers for citrate. After 18 rounds of selection, a highly converged library is obtained and the first two sequences reached 99.6% of the library. Using the most abundant sequence named CA1, thioflavin T fluorescence spectroscopy and isothermal titration calorimetry show dissociation constants of 7.4 and 4.4 µm citrate, respectively. CA1 does not require sodium for binding but requires 1.0 mm magnesium. Among the tested carboxylate molecules, only citrate can bind to the aptamer. A light‐up fluorescence strand displacement biosensor is developed and it can detect citrate in simulated urine with a detection limit of 1.1 µm. This short 42‐nucleotide aptamer can be readily adapted to other types of sensing mechanisms for the detection of citrate.
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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) | 0.001 | 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 | 0.001 |
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".