Amyloid-β Aggregation on Model Lipid Membranes: An Atomic Force Microscopy Study
Bibliographic record
Abstract
Amyloid fibril formation is generally associated with many neurodegenerative disorders, including Alzheimer's disease (AD). Although fibril plaque formation is associated with biological membranes in vivo, the role of the cell surfaces in amyloid fibril formation and the molecular mechanism of amyloid toxicity are not well understood. Understanding the details of amyloid interaction with lipid membrane may shed light on the mechanism of amyloid toxicity. Using atomic force microscopy, we investigated aggregation of amyloid-β1-42 (Aβ1-42) on model phospholipid membranes as a function of time and membrane composition. Neutral, 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), anionic - 1,2-dioleoyl-sn-glycero-3-phospho-(1'-rac-glycerol) (sodium salt) (DOPG), and cationic - 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), were used to study the effect of lipid type on amyloid binding. We showed that both the charge on the lipid head group and lipid phase affect the interaction of amyloid oligomers with the membrane surface changing the rate of adsorption and causing changes in membrane structure and structure of amyloid deposits. We observed that amyloid aggregates progressively accumulate in a similar manner on the surface of neutral DPPC gel phase membrane and on the surface of fluid phase negatively charged DOPG membrane. In contrast to DPPC and DOPG, positively charged fluid DOTAP membrane and neutral fluid phase DOPC membrane contain amyloid deposits with reduced height, which suggests fusing of Aβ1-42 into the lipid membrane surface.
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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 | 0.001 | 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".