Development of Electrochemical Aptamer-Based Biosensors for the Detection of Hormonal Contaminants in Water
Bibliographic record
Abstract
Endocrine disrupters (EDs) are compounds that specifically interfere with the normal functions of the body’s endocrine system (system of glands for hormonal secretion) by causing adverse effects in both human and wildlife. Therefore, routine, sensitive, reliable and cost effective detection methods for EDs are highly demanded. In this work we present the selection and characterization of ssDNA aptamers that exhibit high affinity, specificity and sensitivity to progesterone (P4), 17b-estradiol (E2) and, norethisterone (NET). The aptamer selection was conducted by in vitro selections by incubating a highly diverse ssDNA library of about random 60mer sequences with the target analyte. After 15 cycles of selections, the enriched aptamers pool was cloned and sequenced. The dissociation constants (KD) of the selected aptamers determined by fluorometry and by electrochemical impedance spectroscopy (EIS) methods are in the subnanomolar order. Cross-reactivity tests for each aptamer demonstrated the high specificity to their target analyte. In addition, circular dichroism (CD) spectroscopy measurements of the selected aptamers for progesterone showed a marked conformational change upon binding the target analyte. This change in the conformation was exploited to design a biosensing platform to generate a measurable signal after binding the specific analyte by following the redox process of a couple composed for [Fe(CN)6]3-/4-. For first time, an impedimetric aptasensor for progesterone detection was developed with limit of detection of 0.90 ng/mL. We believe, based in our finding, that the continuous selection of high affinity aptamers for other EDs and their integration in a biosensing platform will facilitate the routine monitoring of EDs in environment as well as clinical and medical diagnosis purposes. Figure 1
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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 | 0.000 | 0.001 |
| 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".