Atomic force microscopy to study molecular mechanisms of amyloid fibril formation and toxicity in Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is a devastating neurodegenerative disease characterized by dementia and memory loss for which no cure or effective prevention is currently available. Neurodegeneration in AD is linked to formation of amyloid plaques found in brain tissues of Alzheimer's patients during post-mortem examination. Amyloid plaques are composed of amyloid fibrils and small oligomers - insoluble protein aggregates. Although amyloid plaques are found on the neuronal cell surfaces, the mechanism of amyloid toxicity is still not well understood. Currently, it is believed that the cytotoxicity is a result of the nonspecific interaction of small soluble amyloid oligomers (rather than longer fibrils) with the plasma membrane. In recent years, nanotechnology has contributed significantly to understanding the structure and function of lipid membranes and to the study of the molecular mechanisms of membrane-associated diseases. We review the current state of research, including applications of the latest nanotechnology approaches, on the interaction of lipid membranes with the amyloid-β (Aβ) peptide in relation to amyloid toxicity. We discuss the interactions of Aβ with model lipid membranes with a focus to demonstrate that composition, charge and phase of the lipid membrane, as well as lipid domains and rafts, affect the binding of Aβ to the membrane and contribute to toxicity. Understanding the role of the lipid membrane in AD at the nanoscale and molecular level will contribute to the understanding of the molecular mechanism of amyloid toxicity and may aid into the development of novel preventive strategies to combat 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".