Cholesterol, a Modulator of Membrane‐Associated Aβ‐Fibrillogenesis
Bibliographic record
Abstract
One of the major pathological features of Alzheimer's disease (AD) is the presence of extracellular amyloid plaques that are predominantly composed of the amyloid-beta peptide (Abeta). Characterization of plaques demonstrated the predominance of two peptides differing at the carboxyl terminus by two hydrophobic amino acids, Abeta40 and Abeta42. Diffuse plaques associated with AD are composed predominantly of Abeta42, whereas senile plaques contain both Abeta40 and Abeta42. Recently, it has been suggested that diffuse plaque formation is initiated as a plasma membrane-bound Abeta species and that Abeta42 is the critical component. In order to investigate this hypothesis, we have examined Abeta40/42-lipid interactions using in situ atomic force microscopy, electron microscopy, and fluorescence anisotropy. While the association of Abeta42 with planar bilayers resulted in peptide aggregation, but no fiber formation, this was not the case for Abeta40, where we observed preferential fiber formation. Cholesterol, a key membrane component and modulating factor in AD, is inversely correlated with the extent of Abeta40/42-bilayer interaction. These results were confirmed using fluorescence anisotropy to evaluate the effect of Abeta on membrane fluidity and fluorimetry to confirm membrane integrity. Our results suggest that the enhanced amyloidogenic properties of Abeta42 are not correlated with fibril formation, but with aggregation on bilayer surfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".