Large SYBR Green I fluorescence enhancement for label-free aptamer-based detection of estradiol
Bibliographic record
Abstract
Estradiol (E2) and related estrogens are emerging environmental contaminants that may adversely affect the health of humans, animals, and ecosystems. Many aptamers have been reported for the detection of E2, and our lab recently selected a series of high-affinity and short DNA aptamers that showed various binding orientations to E2, leading to different selectivity patterns. In this work, we report that using SYBR Green I (SGI) as a fluorescence probe, up to 200% fluorescence increase was achieved upon titration of E2 to these aptamers. Such enhancement was the highest among all reported small molecule binding aptamers using SGI for signal generation, although some metal-binding DNA can achieve even higher enhancement. By gradually shortening the stem region of an E2 binding aptamer, we concluded that the enhanced fluorescence was from the aptamer binding pocket upon target binding instead of from the duplexed stem region. Comparison was also made with a few other aptamers including those for caffeine, quinine, uric acid and cortisol, and none of them showed more than 20% fluorescence change. Using the SGI method, the detection limit was calculated to be 2.4 nM E2. We attributed the large fluorescence enhancement to the hydrophobic nature of E2 and the high-affinity binding of the aptamers. This study provides insights into the aptamers that can use SGI for their binding assays and biosensor development.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".