Alternate Aggregation Pathways of the Alzheimer β-Amyloid Peptide
Bibliographic record
Abstract
Deposition of amyloid-beta (Abeta) aggregates in the brain is a defining characteristic of Alzheimer's disease (AD). Fibrillar amyloid, found in the cores of senile plaques, is surrounded by dystrophic neurites. In contrast, the amorphous Abeta (also called preamyloid) in diffuse plaques is not associated with neurodegeneration. Depending on the conditions, Abeta will also form fibrillar or amorphous aggregates in vitro. In this present study, we sought to characterize the properties of the amorphous aggregate and determine whether we could establish an in vitro model for amorphous Abeta. CD data indicated that Abeta40 assembled to form either a beta-structured aggregate or an unfolded aggregate with the structured aggregate forming at high peptide concentrations and the unstructured aggregate forming at low Abeta40 levels. The critical concentration separating these two pathways was 10 microm. Fluorescence emission and polarization showed the structured aggregate was tightly packed containing peptides that were not accessible to water. Peptides in the unstructured aggregate were loosely packed, mobile, and accessible to water. When examined by electron microscopy, the structured aggregate appeared as protofibrillar structures and formed classic amyloid fibrils over a period of several weeks. The unstructured aggregate was not visible by electron microscopy and did not generate fibrils. These findings suggest that the unstructured aggregate shares many properties with the amorphous Abeta of AD and that conditions can be established to form amorphous Abeta in vitro. This would allow for investigations to better understand the relationship between fibrillar and amorphous Abeta and could have significant impact upon efforts to find therapies for 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 | 0.001 | 0.001 |
| 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".