A β-Amyloid(1-42) Biosensor Based on Molecularly Imprinted Poly-Pyrrole for Early Diagnosis of Alzheimer’s Disease
Bibliographic record
Abstract
Background: Alzheimer’s disease (AD) is a common form of dementia, characterized by production and deposition of β-amyloid peptide in the brain. Thus, β-amyloid peptide is a potentially promising biomarker used to diagnose and monitor the progression of AD. Objective: The study aims to develop a biosensor based on a molecularly imprinted poly-pyrrole for detection of β-amyloid.Material and Methods: In this experimental study, an imprinted poly-pyrrole was employed as an artificial receptor synthesized by electro-polymerization of pyrrole on screen-printed carbon electrodes in the presence of β-amyloid. β-amyloid acts as a molecular template within the polymer. The biosensor was evaluated by cyclic voltammetry using ferro/ferricyanide marker. The parameters influencing the biosensor performance, including electro-polymerization cycle umbers and β-amyloid binding time were optimized to achieve the best biosensor sensitivity. Results: The β-amyloid binding affinity with the biosensor surface was evaluated by the Freundlich isotherm, and Freundlich constant and exponent were obtained as 0.22 ng mL-1 and 10.60, respectively. The biosensor demonstrated a detection limit of 1.2 pg mL-1. The biosensor was applied for β-amyloid determination in artificial cerebrospinal fluid. Conclusion: The biosensor is applicable for early Alzheimer’s disease detection.
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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) | 0.000 | 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 | 0.000 |
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".