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 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 | 0.001 | 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".