Aβ43 aggregates exhibit enhanced prion-like seeding activity in mice
Bibliographic record
Abstract
Abstract When injected into genetically modified mice, aggregates of the amyloid-β (Aβ) peptide from the brains of Alzheimer’s disease (AD) patients or transgenic AD mouse models seed cerebral Aβ deposition in a prion-like fashion. Within the brain, Aβ exists as a pool of distinct C-terminal variants with lengths ranging from 37 to 43 amino acids, yet the relative contribution of individual C-terminal Aβ variants to the seeding behavior of Aβ aggregates remains unknown. Here, we have investigated the relative seeding activities of Aβ aggregates composed exclusively of recombinant Aβ38, Aβ40, Aβ42, or Aβ43. Cerebral Aβ42 levels were not increased in AppNL−F knock-in mice injected with Aβ38 or Aβ40 aggregates and were only increased in a subset of mice injected with Aβ42 aggregates. In contrast, significant accumulation of Aβ42 was observed in the brains of all mice inoculated with Aβ43 aggregates, and the extent of Aβ42 induction was comparable to that in mice injected with brain-derived Aβ seeds. Mice inoculated with Aβ43 aggregates exhibited a distinct pattern of cerebral Aβ pathology compared to mice injected with brain-derived Aβ aggregates, suggesting that recombinant Aβ43 may polymerize into a unique strain. Our results indicate that aggregates containing longer Aβ C-terminal variants are more potent inducers of cerebral Aβ deposition and highlight the potential role of Aβ43 seeds as a crucial factor in the initial stages of Aβ pathology in 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".