Biophysical characterization of longer forms of amyloid beta peptides: possible contribution to flocculent plaque formation
Bibliographic record
Abstract
Alzheimer's disease is characterized by amyloid deposits in the parenchyma and vasculature of the brain. The plaques are mainly composed of amyloid beta (Abeta) peptides ending in residues 40 and 42. Novel longer Abeta peptides were found in brain homogenates of mouse models of Alzheimer's disease and human brain tissue of patients carrying the familial amyloid precursor protein V717F mutation. The biophysical characteristics of these longer Abeta peptides and their role in plaque formation are not understood. We chose to focus our studies on Abeta peptides ending in residues Ile45, Val46 and Ile47 as these peptides were identified in human brain tissue. A combination of circular dichroism and electron microscopy was used to characterize the secondary and tertiary structures of these peptides. All three longer Abeta peptides consisted mainly of a beta-sheet secondary structure. Electron microscopy demonstrated that these beta-structured peptides formed predominantly amorphous aggregates, which convert to amyloid fibres over extended time periods. As these longer peptides may act as seeds for the nucleation of fibrils composed predominantly of shorter amyloid peptides, these interactions were studied. All peptides accelerated the random to beta-structural transitions and fibril formation of Abeta40 and 42.
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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) | 0.000 | 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".