Aptamer-Based Label-Free Impedimetric Biosensor for Detection of Progesterone
Bibliographic record
Abstract
Rising progesterone (P4) levels in humans due to its overconsumption through hormonal therapy, food products, or drinking water can lead to many negative health effects. Thus, the simple and accurate assessment of P4 in both environmental and clinical samples is highly important to protect public health. In this work, we present the selection, identification, and characterization of ssDNA aptamers with high binding affinity to P4. The aptamers were selected in vitro from a single-stranded DNA library of 1.8 × 10(15) oligonucleotides showing dissociation constants (KD) in the low nanomolar range. The dissociation constant of the best aptamer, designated as P4G13, was estimated to be 17 nM by electrochemical impedance spectroscopy (EIS) as well as fluorometric assay. Moreover, the aptamer P4G13 did not show cross-reactivity to analogues similar to progesterone such as 17β-estradiol (E2) and norethisterone (NET). An impedimetric aptasensor for progesterone was then fabricated based on the conformational change of P4G13 aptamer, immobilized on the gold electrode by self-assembly, upon binding to P4, which results in an increase in electron transfer resistance. Aptamer-complementary DNA (cDNA) oligonucleotides were tested to maximize the signal gain of the aptasensor after binding with progesterone. Significant signal enhancement was observed when the aptamer hybridized with a short complementary sequence at specific site was used instead of pure aptamer. This signal gain is likely due to the more significant conformational change of the aptamer-cDNA than the pure aptamer upon binding with P4, as confirmed by circular dichroism (CD) spectroscopy. The developed aptasensor exhibited a linear range for concentrations of P4 from 10 to 60 ng/mL with a detection limit of 0.90 ng/mL. Moreover, the aptasensor was applied in spiked tap water samples and showed good recovery percentages. The new selected progesterone aptamers can be exploited in further biosensing applications for environmental, clinical, and medical diagnostic purposes.
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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) | 0.001 | 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".