Surface Plasmon Resonance Imaging of Amyloid-β Aggregation Kinetics in the Presence of Epigallocatechin Gallate and Metals
Bibliographic record
Abstract
A number of human protein misfolding disorders, including Alzheimer's disease (AD), are closely related to the accumulation of β-sheet-rich amyloid fibrils or aggregates. Neuronal toxicity in AD has been linked to the interactions of amyloid-β (Aβ) with metals, especially Zn(2+), Cu(2+), and Fe(3+), which leads to the production of reactive oxygen species. Nucleation-dependent Aβ aggregation, or "seeding", is thought to propagate fibril formation. In this surface plasmon resonance imaging (SPRi) study, we have shown that the fibril seeds formed with the incubation of Aβ in the presence of metals are better at promoting monomer elongation compared to Aβ alone or in the presence of a well-described polyphenol, (-)-epigallocatechin-3-gallate (EGCG). This is a novel attempt to simultaneously monitor the effects of multiple modulators on fibril elongation using a single chip. EGCG was shown in transmission electron microscopy (TEM) and thioflavin T (ThT) studies to promote the formation of off-pathway, highly stable unstructured oligomers, supporting the SPRi results. These findings suggest that SPRi provides a promising platform as a screening tool for small molecules that can affect the aggregation pathways in neurodegenerative diseases.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".