Temporal Characterization of the Amyloidogenic APPswe/PS1dE9;hAPOE4 Mouse Model of Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a devastating disorder with a global prevalence estimated at 55 million people. In clinical studies administering certain anti-beta-amyloid (Aβ) antibodies, amyloid-related imaging abnormalities (ARIAs) have emerged as major adverse events. The frequency of these events is higher among apolipoprotein ε4 allele carriers (APOE4) compared to non-carriers. To reflect patients most at risk for vascular complications of anti-Aβ immunotherapy, we selected an APPswe/PS1dE9 transgenic mouse model bearing the human APOE4 gene (APPPS1:E4) and compared it with the same APP/PS1 mouse model bearing the human APOE3 gene (APOE ε3 allele; APPPS1:E3). Using histological and biochemical analyses, we characterized mice at three ages: 8, 12, and 16 months. Female and male mice were assayed for general cerebral fibrillar and pyroglutamate (pGlu-3) Aβ deposition, cerebral amyloid angiopathy (CAA), microhemorrhages, apoE and cholesterol composition, astrocytes, microglia, inflammation, lysosomal dysfunction, and neuritic dystrophy. Amyloidosis, lipid deposition, and astrogliosis increased with age in APPPS1:E4 mice, while inflammation did not reveal significant changes with age. In general, APOE4 carriers showed elevated Aβ, apoE, reactive astrocytes, pro-inflammatory cytokines, microglial response, and neuritic dystrophy compared to APOE3 carriers at different ages. These results highlight the potential of the APPPS1:E4 mouse model as a valuable tool in investigating the vascular side effects associated with anti-amyloid immunotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".