A Miniaturized Impedimetric Immunosensor for the Competitive Detection of Adrenocorticotropic Hormone
Bibliographic record
Abstract
Adrenocorticotropic hormone (ACTH) plays an essential role in regulating corticosteroid hormone production, which has important functions in a myriad of critical physiological functions. In this proof-of-concept study, a miniaturized immunosensor was developed for the highly sensitive detection of ACTH using electrochemical impedance spectroscopy (EIS) in connection with disposable screen-printed gold electrodes (SPGEs). A film of 3,3′-dithiobis[sulfosuccinimidylpropionate] (DTSSP) was prepared to immobilize anti-ACTH antibodies covalently on the nanostructured SPGE surface. The surface-immobilized anti-ACTH antibodies captured the biotinylated ACTH (biotin-ACTH) and non-labelled ACTH for the competitive immunoassay. After coupling of a streptavidin-alkaline phosphatase conjugate (Streptavidin-ALP), the bio-catalysed precipitation of an insoluble and insulating product onto the sensing interface changed the charge transfer resistance (Rct) characteristics significantly. The detection limit of 100 fg/mL was determined for ACTH in a 5 μL sample volume, which indicated that this versatile platform can be easily adapted for miniaturized electrochemical immunosensing of cancer marker biomolecules. High selectivity and sensitivity of our immunoassay to detect ACTH in real samples demonstrated its promising potential for future development and applications using clinical samples.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".