Emergence of distinct and heterogeneous strains of amyloid beta as Alzheimer’s disease progresses in Down syndrome
Bibliographic record
Abstract
0. ABSTRACT Amyloid beta (Aβ) is thought to play a critical role in the pathogenesis of Alzheimer’s disease (AD). Prion-like Aβ polymorphs, or “strains”, can have varying pathogenicity and may underlie the phenotypic heterogeneity of the disease. In order to develop effective AD therapies, it is critical to identify the strains of Aβ that might arise prior to the onset of clinical symptoms and understand how they may change with progressing disease. Down syndrome (DS), as the most common genetic cause of AD, presents promising opportunities to compare such features between early and advanced AD. In this work, we evaluate the neuropathology and Aβ strain profile in the post-mortem brain tissues of 210 DS, AD, and control individuals. We assayed the levels of various Aβ and tau species and used conformation-sensitive fluorescent probes to detect differences in Aβ strains among individuals and populations. We found that these cohorts have some common but also some distinct strains from one another, with the most heterogeneous populations of Aβ emerging in subjects with high levels of AD pathology. The emergence of distinct strains in DS at these later stages of disease suggests that the confluence of aging, pathology, and other DS-linked factors may favor conditions that generate strains that are unique from sAD.
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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 | 0.001 | 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) | 0.001 | 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".