Localized surface plasmon resonance and atomic force microscopy study of model lipid membranes and their interactions with amyloid and melatonin
Bibliographic record
Abstract
Abstract Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of amyloid plaques in the brain. The toxicity of amyloid to neuronal cell surfaces arises from interactions between small intermediate aggregates, namely amyloid oligomers, and the cell membrane. The nature of these interactions changes with age and disease progression. In our previous work, we demonstrated that both membrane composition and nanoscale structure play crucial roles in amyloid toxicity, and that membrane models mimicking healthy neuron were less affected by amyloid than model membranes mimicking AD neuronal membranes. This understanding introduces the possibility of modifying membrane properties with membrane-active molecules, such as melatonin, to protect them from amyloid-induced damage. In this study, we employed atomic force microscopy and localized surface plasmon resonance to investigate the protective effects of melatonin. We utilized synthetic lipid membranes that mimic the neuronal cellular membrane at various stages of AD and explored their interactions with amyloid-β (1–42) in the presence of melatonin. Our findings reveal that the early diseased membrane model is particularly vulnerable to amyloid binding and subsequent damage. However, melatonin exerts its most potent protective effect on this early-stage membrane. These results suggest that melatonin could act at the membrane level to alleviate amyloid toxicity, offering the most protection during the initial stages of AD.
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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".