P4–024: Alzheimer amyloid peptides aggregation propensity correlates directly with neuronal cell surface binding
Bibliographic record
Abstract
Alzheimer's disease is linked to the formation of amyloid fibrils, which has been shown to be catalyzed by the release of Alzheimer's peptides Abeta40 and Abeta42. These peptides are proteolytic cleavage products from the amyloid precursor protein and are forty and forty–two amino acids in length. The peptides start to deposit in brains as plasma membrane–bound diffuse plaques and have been shown to specifically interact with certain phospholipids and gangliosides. Current methods utilize dyes or antibodies that specifically bind to amyloid plaques in tissues or on synthetic lipid bilayers. However these observation tools might be missing the pre–amyloid structural conformation that has been recently indicated as the cellular toxic form. Temporal images of the Alzheimer amyloid peptides deposition with live cells. Aggregation propensity of Abeta40, Abeta42 and a reported nonaggregating mutant Abeta42 peptide were characterized with dynamic light scattering, thioflavin–T staining and circular dichroism measurements. Utilizing a fluorescent label, direct cell surface association of these peptides was monitored with confocal microscopy. In conjunction, flow cytometry was used for quantitative global measurements of these peptides with model neuronal cell lines. The rate of Alzheimer amyloid peptides association with cells was directly related to their aggregation propensity. The nonaggregating mutant of Abeta42 did not bind to live cells. Therefore, aggregation propensity is essential for cell binding.
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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) | 0.007 | 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".