Capture-SELEX of DNA Aptamers for Estradiol Specifically and Estrogenic Compounds Collectively
Bibliographic record
Abstract
Estrogenic compounds such as estrone (E1), 17β-estradiol (E2), and 17α-ethynylestradiol (EE2) are serious environmental contaminants due to their potent biological activities. At least six selections were previously reported to obtain DNA aptamers for E2, highlighting its environmental importance. A careful analysis revealed that the previous aptamers either are too long or do not bind optimally. Herein, a series of new aptamers were obtained from the capture-SELEX method with dissociation constants down to 30 nM as determined by isothermal titration calorimetry (ITC). Two aptamers were converted to structure-switching fluorescent biosensors, which achieved a limit of detection down to 3.3 and 9.1 nM E2, respectively. One aptamer showed similar binding affinities to all the three estrogens, while the other aptamer is more selective for E2. Both aptamers required Mg 2+ for binding. The proposed sensors were successfully applied in the determination of E2 in wastewater. Moreover, comparisons were made with previous aptamers based on primary sequence alignment and secondary structures. Among previously reported truncated aptamers, ITC showed binding only in one of them. The newly selected aptamers have the combined advantages of small size and high affinities.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".