Electrochemical Immunosensors for Effective Evaluation of Amyloid-Beta Modulators on Oligomeric and Fibrillar Aggregation Processes
Bibliographic record
Abstract
A novel electrochemical immunosensor fabricated from gold compact disc electrodes was designed for rapid evaluation of aggregation processes that lead to the formation of oligomeric and fibrillar states of amyloid-beta(1-42) (Aβ(1-42)) during Alzheimer's disease. Conformation-specific antibodies were immobilized on the surface of the gold electrode using a 3,3'-dithiobis (sulfosuccinimidyl) propionate (DTSSP) linker. Surface binding events were analyzed by electrochemical impedance spectroscopy (EIS) in which the formation of an antigen-antibody complex was quantified as a function of charge transfer resistance using a [Fe(CN)6](3-/4-) redox probe. The effectiveness of novel sym-triazine-derived aggregation modulators (TAE-1, TAE-2) to reduce the population of toxic oligomers was evaluated. Aβ fibril formation was validated by thioflavin T (ThT) fluorescence, whereas oligomer formation was investigated by MALDI. Antigen detection by EIS was further supported by immuno dot blot assays for oligomeric and fibrillar components. Docking simulations of the aggregation modulators TAE-1 and TAE-2 with Aβ(1-42) fibrils performed using Autodock Vina suggest a mechanism for the improved aggregation inhibition observed for TAE-2. The results demonstrate the utility and convenience of impedance immunosensing as an analytical tool for rapid and comprehensive evaluation of effective Aβ aggregation modulating agents.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".