TgCRND8 Amyloid Precursor Protein Transgenic Mice Exhibit an Altered γ-Secretase Processing and an Aggressive, Additive Amyloid Pathology Subject to Immunotherapeutic Modulation
Bibliographic record
Abstract
We investigated the morphology and biochemistry of the amyloid-beta (Abeta) peptides produced in TgCRND8 Tg mice carrying combined amyloid precursor protein (APP) Swedish (K670M/N671L) and Indiana (V717F) mutations. Histological analyses employing amyloid-specific staining and electron microscopy revealed that the TgCRND8 Tg mice produce an aggressive pathology, evident as early as 3 months of age, that is a composite of core plaques and peculiar floccular diffuse parenchymal deposits. The Abeta peptides were purified using combined FPLC-HPLC, Western blots, and immunoprecipitation methods and characterized by MALDI-TOF/SELDI-TOF mass spectrometry. The C-terminal APP peptides, assessed by Western blot experiments and mass spectrometry, suggested an alteration in the order of secretase processing, yielding a C-terminal fragment pattern that is substantially different from that observed in sporadic Alzheimer's disease (AD). This modified processing pattern generated longer Abeta peptides, as well as those ending at residues 40/42/43, which may partially explain the early onset and destructive nature of familial AD caused by APP mutations. Despite an aggressive pathology that extended to the cerebellum and white matter, these animals tolerated the presence of an imposing amount of Abeta load. Abeta immunization resulted in an impressive 7-fold reduction in the number of amyloid core plaques and, as previously demonstrated, a significant memory recovery. However, given the phylogenetic distance and the differences in APP processing and Abeta chemistry between Tg mice and AD, caution should be applied in projecting mouse therapeutic interventions onto human subjects.
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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 | 0.000 | 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 | 0.001 |
| 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".