Surfactant-Assisted Label-Free Fluorescent Aptamer Biosensors and Binding Assays
Bibliographic record
Abstract
Using DNA staining dyes such as SYBR Green I (SGI) and thioflavin T (ThT) to perform label-free detection of aptamer binding has been performed for a long time for both binding assays and biosensor development. Since these dyes are cationic, they can also adsorb to the wall of reaction vessels leading to unstable signals and even false interpretations of the results. In this work, the stability of the signal was first evaluated using ThT and the classic adenosine aptamer. In a polystyrene microplate, a drop in fluorescence was observed even when non-binding targets or water were added, whereas a more stable signal was achieved in a quartz cuvette. Equilibrating the system can also improve signal stability. In addition, a few polymers and surfactants were also screened, and 0.01% Triton X-100 was found to have the best protection effect against fluorescence signal decrease due to dye adsorption. Three aptamers for Hg2+, adenosine, and cortisol were tested for their sensitivity and signal stability in the absence and presence of Triton X-100. In each case, the sensitivity was similar, whereas the signal stability was better for the surfactant. This study indicates that careful control experiments need to be designed to ensure reliable results and that the reliability can be improved by using Triton X-100 and a long equilibration time.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".