Stamp-Imprinted Polymer EIS Biosensor for Amyloid-Beta Detection: A Novel Approach Towards Alzheimer’s Screening
Bibliographic record
Abstract
Surface-imprinted polymers (SIPs) represent an exciting and cost-effective alternative to antibodies for electrochemical impedance spectroscopy (EIS)-based biosensing. They can be produced using simple printing techniques and have shown high efficacy in detecting large biomolecules and microorganisms. Stamp imprinting, a novel SIP method, creates the target analyte's imprint using a soft lithography mask of the analyte matrix, thereby reducing material complexities and eliminating the need for cross-linking, which makes the process more scalable than the conventional SIPs. In this work, we demonstrate a stamp-imprinted EIS biosensor using a biocompatible polymer, polycaprolactone (PCL), for quantifying amyloid beta-42 (Aβ-42), a small peptide involved in the pathophysiology of Alzheimer's disease. The evaluated SIP-EIS biosensors showed a detection limit close to 10 fg/mL, and a detection range covering the physiologically relevant concentration range of the analyte in blood serum (from 10 fg/mL to 10 μg/mL). The device sensitivity, which is found to be comparable to antibody-based EIS devices, demonstrates the potential of SIP-EIS biosensors as an exciting alternative to conventional antibody-based diagnostic approaches. We also evaluate the viability of analyzing these proteins in complex media, notably in the presence of serum albumin proteins, which cause biofouling and non-specific interactions. The combination of high sensitivity, selectivity, and ease of fabrication makes SIP-EIS biosensors particularly suited for portable and point-of-care applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".