A Sensitive Aptamer-Based Biosensor for Electrochemical Quantification of PSA as a Specific Diagnostic Marker of Prostate Cancer
Bibliographic record
Abstract
PURPOSE: The current project aimed to design a simple, highly sensitive, and economical label-free electrochemical aptasensor for determination of prostate-specific antigen (PSA), as the gold standard biomarker for prostate cancer diagnosis. The aptasensor was set up using a screen-printed carbon electrode (SPCE) modified by gold nanoparticles (Au NPs) conjugated to thiolated aptamers. METHODS: Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were implemented for electrochemical (EC) characterization of the aptasensor. The determination of PSA was also performed through differential pulse voltammetry (DPV) in [Fe (CN) 6]3-/4- electrolyte solution. RESULTS: The present aptasensor was shown an outstanding linear response in the concentration range of 1 pg/mL - 200 ng/mL with a remarkably lower limit of detection of 0.077 pg/mL. The optimum concentration for PSA separation and the optimum incubation time for antigen-aptamer binding were determined by observing and electing the highest electrochemical responses in a specified time or concentration. CONCLUSION: According to the results of the specificity tests, the designed aptasensor did not show any significant interactions with other analytes in real samples. Clinical functionality of the aptasensor was appraised in serum samples of healthy individuals and patients examining the PSA level through the fabricated aptasensor and the reference methods. Both methods are comparable in sensitivity. The present fabricated PSA aptasensor with substantial characteristics of ultra- sensitivity and cost-effectiveness can be conventionally built and used for the routine check-up of the men for prostate problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".