Development of Biosensors to Monitor the Interaction of Small Molecules with Amyloidogenic Proteins using Optical and Electrochemical Methods
Bibliographic record
Abstract
Amyloidogenic protein fibrils are well known pathological hallmark of neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD). The Amyloid Cascade Hypothesis attributes the onset and progression of AD to an imbalance in amyloid-beta (Abeta). In PD, a definitive diagnosis of Parkinson's disease can be confirmed only by post mortem examination of the patient's substantia nigra for the presence of Lewy bodies, mainly comprised of alpha-synuclein (a-S). As the toxicity in these neurodegenerative diseases is highly correlated with the formation of soluble oligomers from their corresponding proteins, a strategy to inhibit the aggregation of Abeta and a-S may help to ameliorate AD and PD respectively (Chapter 1). Herein, we review the fundamentals of electrochemistry (Chapter 2) before demonstrating the use of electrochemical techniques, acoustic wave sensor and BiacoreX surface plasmon resonance (SPR) to characterize the aggregation of Abeta (Chapter 3). We have shown that amyloid aggregation could be monitored through these label-free methods and clioquinol (CQ) inhibits the progression of aggregation. We further increased the throughput of monitored small molecules and Abeta interactions through the use of SPR imaging (SPRi) (Chapter 4) and LED-interferometric reflective imaging sensor (LED-IRIS) (Chapter 5). These studies showed that epigallocatechin gallate (EGCG) modulates the Abeta aggregation pathway to form beta-sheet absent aggregates while certain metal ions generally accelerate the Abeta aggregation process to form thick mature fibrils. These results are supported by Thioflavin T (ThT) and transmission electron microscopy (TEM) studies.We then studied the effects of CQ on a-S (Chapter 6) using electrochemical techniques and spectroscopic dyes such as ThT and Congo Red. Both electrochemical and spectroscopic studies showed that Cu(II) accelerated the fibril formation of a-S, while CQ inhibited such activity. This electrochemical analysis was further modified to include an optical screening test on the same transduction platform by utilizing nanosphere lithography (NSL) (Chapter 7). Complemented by Localized-SPR, SPRi, TEM and ThT studies, it was found that dense and unstructured amorphous a-S aggregates were induced by EGCG, while beta-sheet-rich and compact a-S mesh-networks were promoted by Cu(II) ions, in agreement with previous results.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".