Selection of high affinity aptamer-ligand for dexamethasone and its electrochemical biosensor
Bibliographic record
Abstract
Abstract A high specificity aptamer-ligand biorecognition and binding system to monitor of dexamethasone (DXN) was developed. The detection principle was based on a label-free electrochemical aptasensor. The selection of the aptamer was successfully performed by the systematic evolution of ligands through exponential enrichment technique (SELEX). From a random library of 1.08 × 10 15 single-stranded DNA, an aptamer designated as DEX04 showed a highest affinity with a dissociation constant of 18.35 nM. It also showed a good conformational change when binding with DXN. In addition, the aptamer DEX04 did not show any cross-reactivity with other commonly used hormones. An impedimetric aptasensor for DXN was then developed by immobilizing DEX04 on a gold electrode. The binding upon to DXN was monitored by following the change in the charge transfer resistance (Rct) of the [Fe(CN) 6 ] 4−/3− redox couple. The aptasensor exhibited a linear range from 2.5 to 100 nM with a detection limit of 2.12 nM. When applied aptasensor to test in water samples, it showed good recovery percentages. The new DXN aptamer can be employed in other biosensing applications for food control and the diagnosis of some diseases in medicine as a cost-effective, sensitive and rapid detection method.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".