Amyloid-β seeding effects are dependent on the presence of knock-in genes in the App <sup>NL-G-F</sup> mice
Bibliographic record
Abstract
Abstract Alzheimer’s disease (AD) is characterized by the prion-like propagation of amyloid-β (Aβ). However, the role of Aβ in cognitive impairment is still unclear. To determine the causal role of Aβ in AD, we intracerebrally seeded the entorhinal cortex of two-month-old App NL-G-F mouse model with an Aβ peptide derived from patients who died from rapidly progressing AD. When the mice were three and six months of age, or one- and four-months following seeding, respectively, spatial learning and memory were tested using the Morris water task. Immunohistochemical labeling showed seeding with the Aβ seed increased plaque size one month following seeding, but reduced plaque counts four months following injection compared to the control seeded mice. A significant increase in microgliosis was found. However, we found no correlation between pathology and spatial performance. The results of the present study show that seeding human tissue with or without Aβ alters learning and memory ability, Aβ plaque deposition, plaque size, and microgliosis in the App NL-G-F knock-in model, and these effects are dependent on the presence of a humanized App gene and the presence of Aβ in the seed. But these pathological changes were not initially causal in memory impairment.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".