A Fluorescent Aptasensor for Sensitive and Selective Determination of Epigenetic Cancer Biomarker N<sup>1</sup>‐Methyladenosine in Urine Samples
Bibliographic record
Abstract
Abstract N1‐methyladenosine (m1A) level in urine increases in the presence of cancer and is associated with the tumor size and stage. In the present study, we aimed to develop a method for rapid, sensitive and accurate determination of m1A in urine samples. The capture systematic evolution of ligands by exponential enrichment (SELEX) method was used to isolate aptamers that could selectively bind to m1A. We successfully isolated two sequences that have high selectivity toward m1A. The affinities against m1A were determined by isothermal titration calorimetry (ITC) and thioflavin T (ThT) assays. The N1MA1a aptamer has a Kd of 1.9±0.1 µm determined by the ThT assay and 0.75±0.04 µm determined by ITC. A strand‐displacement biosensor was designed by labeling the aptamer with a carboxy fluorescein (FAM) and hybridizing it with a quencher‐labeled complementary DNA strand. Using this biosensing system, m1A was detected with a detection limit of 1.9 µm. The system shows high selectivity to m1A and high tolerance to adenosine, cytidine, guanosine, thymidine, uridine and N6‐methyladenosine (m6A) as well as urine constituents at their real levels in urine. The sensor has been applied to five different human urine samples showing quantitative recovery values, which indicates practical potential of this aptamer‐based biosensor.
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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) | 0.001 | 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 | 0.000 |
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".